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The relationship between learning communities and student interaction and retention in general biology courses

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The relationship between learning communities and student interaction and retention in general biology courses
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Wardle, Karen
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English
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105 leaves : ; 28 cm

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Team learning approach in education ( lcsh )
Biology -- Study and teaching (Higher) ( lcsh )
Interaction analysis in education ( lcsh )
Dropouts -- Prevention ( lcsh )
Prediction of scholastic success ( lcsh )
Biology -- Study and teaching (Higher) ( fast )
Dropouts -- Prevention ( fast )
Interaction analysis in education ( fast )
Prediction of scholastic success ( fast )
Team learning approach in education ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Includes bibliographical references (leaves 100-105).
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School of Education and Human Development
Statement of Responsibility:
by Karen Wardle.

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|University of Colorado Denver
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ocm54531076
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Full Text
THE RELATIONSHIP BETWEEN LEARNING COMMUNITIES AND
STUDENT INTERACTION AND RETENTION IN GENERAL
BIOLOGY COURSES
by
Karen Wardle
B. A., University of Colorado, 1990
M. A., University of Colorado, 1994
A thesis submitted to the
University of Colorado at Denver
in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Educational Leadership and Innovation


This thesis for the Doctor of Philosophy
Degree by
Karen Wardle
has been approved
by
Ellen Stevens
Michael Marlow
Alan Davis
/& <=2 o&2>
Date


Wardle, K. (Ph.D., Educational Leadership and Innovation)
The Relationship between Learning Communities and Student Interaction and
Retention in General Biology Courses
Thesis directed by Associate Professor Ellen Stevens
ABSTRACT
The relationship between learning communities and student interaction and
retention in community college general biology courses was investigated in this
study.
The purposes of the study were to discover the students perceptions of factors
influencing their desire to study science, and to examine the use of learning
communities as a method of enculturation into the field of science. The learning
community in the CCD science courses involved an entry-level science course
that was linked with a tutorial enrichment of the underlying principles in
scientific research. The coordination between the class and the learning
community involved an extensive research project that incorporated important
scientific principles. The project goals for student research included an
understanding of the scientific method, and an increased engagement in scientific
inquiry. Collaboration and communication among students was an additional
goal of the leaning communities.
A quasi-experiment with pre- and post-measures of student attitudes and
perceptions of success in first and second semester biology courses. A pre-
measure was followed by a quasi experiment in which entry level biology
courses were conducted using either learning communities or traditional lecture.
Results show the factors students perceived as important to their success in entry-
level science courses included their professors and peers. Discriminant results
revealed that the factors predicted completion of the courses 75% of the time.
Qualitative tests reveal that students in learning communities show a slight
increase in community interactions and willingness to explore the content
material beyond the material needed for the class, however these results were not
significantly higher than the control courses.
111


Future studies include collecting data on the learning communities for longer
than a one-year period. The incorporation of the research projects into the
courses has lasting value in terms of encouraging new professors to expand their
pedagogy beyond simply reading notes from the textbook, in terms of engaging
students in the scientific process and in terms of encouraging students to
collaborate on conducting a scientific experiment. Research into the
measurement of that value needs to be continued.
This abstract accurately represents the content of the candidates thesis. I
recommend its publication.
Signed
Ellen Stevens
IV


CONTENTS
Figures .................................................. vii
Tables ...................................................viii
CHAPTER
1. INTRODUCTION .......................................... 1
Background of the Problem............................. 1
Statement of the Problem ............................... 2
Specific Problem Statement............................... 5
About the Community College of Denver .............. 5
CCD Biology Students .................................. 6
Defining the Research Problem ......................... 7
Learning Communities .................................. 7
Learning .............................................. 9
Community ............................................ 11
Nature of Goals ...................................... 12
Summary ................................................ 13
2. REVIEW OF THE LITERATURE................................ 14
Introduction ........................................... 14
Community College Entry Level Science Pedagogy ......... 14
The Physiological Process of Learning ................ 18
The Map .............................................. 19
Putting the Map to Work .............................. 21
Sociological Process of Learning ....................... 22
Learning Communities ................................... 27
Summary ................................................ 34
3. METHODOLOGY ............................................ 35
Research Design ........................................ 36
Sample ................................................. 38
Setting ................................................ 39
Treatment .............................................. 40
Learning Communities ................................. 40
Control Traditional Courses .......................... 42
Dependent Variables .................................... 42
Instruments .......................................... 43
v


Validity............................................. 44
Reliability .......................................... 45
Procedure ............................................. 52
Data Analysis ........................................ 52
Summary .............................................. 55
4. RESULTS ................................................57
Research Question Number One............................58
Phase I Survey ..................................... 58
Research Question Number Two ......................... 59
Phase II Questionnaire .............................. 59
Discriminant .........................................61
Focus Groups ........................................ 64
Open-Ended Questionnaire Questions ...................70
Research Question Number Three ....................... 74
Chi-Square Analysis .................................. 75
Assumptions for a Chi-Square Test ....................75
Random Sampling ................................... 75
Independence of Observations ....................... 75
Research Question Number Four ........................ 79
Observations ......................................... 79
Summary ............................................... 83
5. DISCUSSION AND CONCLUSIONS ............................. 85
Research Question Number One ........................ 86
Research Question Number Two ...........................87
Research Question Number Three..................... ..88
Research Question Number Four ......................... 90
Summary ............................................... 91
Limitations of Study ............................... 92
Future Studies ........................................93
APPENDIX
A. SCIENCE COURSE QUESTIONNAIRE ....................... 94
B. SEMESTER PROJECT ................................ ...95
C. OBSERVATION FORM .................................. 97
D. INTERVIEW FORM...................................... 98
REFERENCES................................................ 100
vi


FIGURES
Figure 1.1 Framework of Learning Community Factors ................... 9
Figure 3.1 Phase Two Quasi-Experimental Design ....................... 37
Figure 4.1a Student Responses to Retention Strategy Questions ....... 68
Figure 4.1b Student Responses to Interaction Questions ............... 69
Figure 4.1c Student Responses to Questions about Course Logistics .... 70
Figure 4.2a Student Responses to Retention Strategy Questions .........72
Figure 4.2b Student Responses to Interaction Questions ................73
Figure 4.2c Student Responses to Course Logistics Questions .......... 74
Figure 4.3 a Student Responses Regarding Basic Content Material .......81
Figure 4.3b Student Responses Identifying Community Interactions ......82
Figure 4.3c Student Responses Regarding Course Logistic Material ......83
vii


TABLES
Table 1.1 Risk Factors and Percentages ....................................... 4
Table 3.1 Reliability Analysis Scale .;...................... ............ 47
Table 3.2 Calendar of Events for Phase Two ................................... 52
Table4.1 Discriminant Analysis: Group Statistics ............................. 61
Table 4.2 Discriminant Analysis: Tests of Equality of Group Means ............ 62
*
Table 4.3 Discriminant Analysis: Boxs Test of Equality of Covariance
Matrices............................................................ 62
Table 4.4 Discriminant Analysis: Classification Results for Prediction ....... 63
Table 4.5 Discriminant Analysis: Pooled Within-Groups Matrices ............... 63
Table 4.6 Discriminant Classification for Professor and Peer Variables .......64
Table 4.7 Discriminant Analysis: Boxs Test of Equality of Covariance
Matrices for Professor and Peers............................................ 64
Table 4.8 Retention Strategies Coding Categories ............................. 68
Table 4.9 Community Interaction Coding Categories ............. .............. 69
Table 4.10 Course Logistics Coding Categories ............................. 70
Table 4.11 Chi-Square Table for BIO 111 Courses: Passing Rate .............. 77
Table 4.12 Chi-Square Table for BIO 111 Courses: Retention ................... 77
Table 4,13 Chi-Square Table for BIO 112 Courses: Passing Rates ............... 78
Table 4.14 Coding Categories for Observations: Basic Content ................. 81
Table 4.15 Coding Categories for Observations: Community Interactions ........ 82
Table 4.16 Coding Categories for Observations: Course Logistics .............. 83
vm


CHAPTER 1
INTRODUCTION
Background of the Problem
If, as the historical literature suggests, learning occurs within the context of
societal traditions, then we can not ignore the knowledge base and contributions of
the community and culture in which the learning takes place (Gould 1985; Lave and
Wenger, 1995; Roschelle and Clancy, 1992; Vygotsky, 1978). We as a culture have
not focused enough on the communitys involvement in the learning process. We
might benefit more from the linking of community and education. Walls (2000)
suggests that we teach our students to think using the multiple perspectives they need
to function in society by linking subjects such as English and science together.
Maybe we would increase the number of students establishing careers in the
sciences, if we welcomed them into a community of science practitioners early in
their studies. That community of science practitioners might look like many of our
work places today.
Teams of workers viewing problems from multiple perspectives and applying
complex critical thinking skills are becoming increasingly important in the work
place today. Employers often use the ability to work as part of a team as a screening
tool in job interviews. Employers expect workers to emerge from school with these
1


teamwork and critical thinking skills already in place. As a result of these needs,
schools are being challenged to add these skills in their instructional design (Atran
and Sperber, 1991; Flick, 1995; Fritschner, 2000; Hamberg, 1991; McGilly, 1994;
Nelson, 1996).
Statement of the Problem
The purposes of the study were to discover the students perceptions of factors
influencing their desire to study science, and to examine the use of learning
communities as a method of student retention. The goal was to see if learning
communities encouraged students to make time to study collaboratively with each
other and place more priority on their science studies.
In the United States, enculturation into a community of learners traditionally
begins in graduate school. In the sciences, graduate students begin to practice the
research they will eventually conduct. Graduate students in the sciences begin to
prepare for their careers by immersing themselves in reading primary literature,
attending seminars and conferences, participating in established research projects
and presenting their work. Immersion into the culture for a medical student also
begins in the first year. In addition to their studies, students in the second quarter of
classes begin following physicians on their hospital rounds.
Preparation for enculturation into the scientific community is to take place during
the students undergraduate work. For example, research projects are encouraged for
2


honors students as part of independent studies in established labs. Often though, the
entire undergraduate career for science students consists of memorizing content
material in a variety of courses. There is very little encouragement to include
undergraduate students into the scientific community.
The undergraduate model for college life is designed for students to spend four
years broadening their horizons, maturing, and learning content. Unfortunately, that
model no longer captures the reality of todays student. Students entering colleges,
especially community colleges are often unprepared for college life in many ways.
Many students are first generation college students and unfamiliar with all aspects of
college life. Many students attend college part time and have very busy schedules
that often interfere with a successful college completion. These students are at risk
of failure from the very onset of their enrollment. Testing Center results complied
by the U.S. Department of Education list seven factors that put students at risk of not
completing a degree. Twenty-four percent of community college students have four
or more of the seven risk factors, compared to only four percent of students in four
year institutions (ETS, 2000). The seven risk factors and the percentage of students
in two and four year public colleges exhibiting the risk factors in the academic year
1995-1996 are shown in Table 1.1.
3


Table 1.1. Risk Factors and Percentages (ETS, 2000)
Risk Factor Community College Percentage Public Four Year Percentage
Delayed Entry 48% 17%
Enrolled Part Time 46% 11%
Worked Full Time 35% 11%
Financially Independent 35% 9%
Had Dependents 21% 5%
Single Parent 11% 3%
No High School Diploma 11% 3%
Adding to the risk factors above is that students who attend community colleges
include a large number of under-represented groups that may also not be prepared for
college courses.
Entry-level science courses are challenging courses even for the academically
prepared student. Students entering introductory college science courses should have
good reading and study skills in place. Students are expected to immediately apply
these skills to learn science. The courses require a large time commitment both in
and out of the classroom. Students enrolled in science courses are required to not
only memorize the vocabulary and content, but use the information in inquiry based
models and problem solving methods (Goodman & Bemston, 2000). These are
difficult tasks for any entry-level science student. Also difficult for science students
4


in our busy society today is the collaboration between students studying the same
material. This formation of study groups is an invaluable asset, but it is often
difficult to coordinate due to community college students busy schedules.
Most of the risk factors mentioned above are beyond the aid of the college faculty,
but there are ways in which faculty can stimulate the learning process.
The focus of this research was to explore 1) the factors first year science students
perceive as important to their success in entry level science courses, and 2) if the
addition of learning communities would encourage students to collaboratively work
with each and place more emphasis on their studies.
Specific Problem Statement
About the Community College of Denver
The Community College of Denver (CCD) is the only community college serving
the city and county of Denver. The mission of the College is to provide transfer
programs that lead to baccalaureate degrees, occupational programs for job-entry
skills or upgrading, first and second year education courses, developmental/remedial
instruction and GED preparation, continuing education and community services and
cooperative inter-institutional programs.
The Colleges educational services help students persist in their academic
program until they have completed their goals. Currently, 50.9% of CCDs minority
5


students graduate with an associate degree/certificate and/or they transfer to a four-
year institution of higher education.
CCD Biology Students
Students that take CCD introductory biology courses have limited goals in mind.
The majority of students that take the first year general biology courses are pursuing
associate of science degrees with emphases in either pre-med or pre-pharmacy. The
students that are working towards a pre-med or pre-pharmacy degree require both
semesters of the general biology courses. The biology students that are seeking a
degree in nursing or are not a science major but need the science course to fulfill the
science part of their core curriculum do not need to take both general biology
courses.
Base line statistics from the first year general biology courses at CCD for the
academic years Fall 1998 through Summer 2000 show the average of students
passing with a C or better to be approximately 60%. Summary statistics from the
academic years of2000 and 2001 show a range of between 56% and 72% in these
classes.
Some of these students complete the semester course and end up with a failing
grade of D or F, but most of the students are what we call non-completers.
These non-completers register and attend class, but withdraw during various times
6


during the semester. If the student officially withdraws from the class, they receive
a W, but if they do not officially withdraw, they receive an F for the class.
One encouraging outcome from this information is that the passing rate for
science courses matches that of the overall passing rate for students taking classes at
CCD. One disturbing statistic is that the second semester science courser passing
rate is not much higher than the first semester passing rate. This result is puzzling,
as students that pass the first semester science course should have incorporated
appropriate study skills into their routines. Understanding theses puzzles deserves
further attention.
Defining the Research Problem
Question #1: What factors are perceived by first semester students as
important to their success in science courses?
Question #2: Will those perceived variables be accurate predictors of
completion of science courses?
Question #3: Will more students who experience the first semester sequence
in a learning community be more likely to complete the course and enroll in
sequential courses than students in the traditional first semester courses?
Question #4: Do students believe the learning community factors
were important to their success in entry-level science courses?
7


Learning Communities
The framework for learning communities focuses on the learning process, and the
importance of community in the learning process. Learning communities were
introduced into the curriculum at CCD as an instructional tool; the goal was to
increase student engagement in the scientific process. The definition of a learning
community is very broad or general in nature. The term learning community is an
educational buzzword, as well as an educational goal. The definition often depends
on the audience and the groups defining the term. The definition of a learning
community can be as general as a group of individuals working together toward a
common goal, or as specific as linking the curriculum of two particular courses. The
goals however general or specific, involve some type of learning.
Learning communities in this setting support entry-level novices by allowing
them to be included in the social culture of the scientific world, thus increasing then-
engagement in the scientific process in the early stages of their education. The
learning communities at CCD were designed to provide the environment with the
proper context for collaborative science learning to take place.
There are many factors influencing learning communities. Some of the most
obvious are outlined in Figure 1.1 below. The aspects of a learning community in
the diagram is described in more detail in the following sections.
8


Community
i
Learning -
Learning Communities
<-
Nature of Goals
Figure 1.1. Framework of Learning Community Factors
Learning
Questions about the process of learning are many and include: Is the learning
different if it is practiced with others in a community, as opposed to that of an
individual student pursuing learning goals while listening to a lecture or working
assigned problems? How do students learn to learn? How can educators adjust their
pedagogy to better enable students to retain their learning? These are questions
educators wrestle with constantly. Brown (1992), describes two stumbling blocks to
lasting learning associated with traditional pedagogy 1) inert knowledge students
memorize facts that they do not use, and 2) passive learning students are not
pursing their knowledge building in ways that are intentional and self directed (p.
144). She refers to these two problems as diseases of schooling(p. 144).
Bereiter and Scaramalia (1989), believe learning itself is a form of problem
solving. They refer to this type of learning as intentional learning. They propose that
there are different forms of learning, one form of learning occurs by rote
9


memorization, examples include playing a memorized piece of music, performing
everyday chores that are memorized to achieve a certain goal or solve a particular
problem and learning about different customs or countries by enjoying a movie.
Another form of learning occurs when that the pursuit of the goal itself requires the
use of problem solving techniques. In this latter form of learning, it is expected that
by using problem solving skills to reach a certain goal, individuals will acquire a
deeper understanding of the goal and the process required to achieve their goal
(Bereiter, 1989). Resnick and Neches (1984), also refer to this form of learning as
intentional learning. Intentional learning requires that the students seek more
long-term goals such as competency and understanding. According to Brown
(1992), in the intentional learning classroom, the students act as researchers to
explore a concept in depth rather than breadth and then teach what they learn to their
classmates. In educational terms, intentional learning means going beyond simply
learning the subject material. This form of learning if done in a community, gives
students the opportunity to collaboratively construct knowledge about the learning
process that goes above and beyond learning the subject matter (Lord, 2001). In a
sense, the students become the like spirited individuals Eldredge (1999) feels is
crucial to the acquisition and analysis of new knowledge. Together a community
might analyze new knowledge with an increase in critical thinking skills and a
deeper sense of meaning. This focus on communities of learners is becoming
10


important in the workplace and in education (Castle and Estes, 1995, Clark, 1996,
Gabelnick, et al, 1990), and may address Browns (1992) critique of schooling.
Community
The process of learning has an important social or human culture component.
How we come to know certain things depends on the view of the community in
which we live. Popper (1965), wrote about the importance of tradition in the
acquisition and analysis of new knowledge. He stated that Most things we know we
have learned by example, by being told, by reading books, by learning how to
criticize, how to take and to accept criticism, how to respect the truth (p. 27, 28).
This learning occurs through our interactions with our community members, and
prepares us for our life in the workplace.
Apprenticeship into the scientific community often takes many years of study and
includes interactions with the scientific community at all levels. Undergraduates
focus on content material and communication skills within a small community of
professors and peers. Graduate students are expected to use the skills they learned as
an undergraduate to assist professors in more specific types of learning, often
involving research. Initiation into the scientific community continues for years after
graduate school, often in the form of post doctoral work. Postdocs as they are called
conduct their own research, but under the supervision of a senior faculty.
Throughout this lengthy process, the researchers gain the experience needed to work
11


as part of the scientific community (Lee, 2000). Even scientists working within the
scientific community work in communication with their peers. Lee (2000) states that
Science is a social institution p. 56. La Farge states it even better when he says
Thus at the vital point in his life work [doing research] the scientist
is cut off from communication with his fellow-men. Instead, he has
the society of two, six, or twenty men and women who are working
in his specialty, with whom he corresponds, whose letters he receives
like a lover, with whom when he meets them he wallows in an orgy
of talk, in the keen pleasure of conclusions and findings compared,
matched, checked against one another the pure joy of being
understood (657).
Nature of Goals
The goals of the educational learning community parallel the educational goals of
the professional community. The expectations and goals for all students entering the
job market or leaving college are to demonstrate higher order cognitive skills. These
skills include critical and analytical thinking, problem solving, clear writing and
communication and the ability to think on the job (Bruer, 1993; McGilly, 1994;
Resnick, 1984). In 1983, A Nation at Risk describes an educational system that
reports poor standardized test scores and poor student performance on international
competitions. The report describes a system that has a poor match between students
abilities and the needs of society (National Commission on Excellence in Education,
1983). Walls (2000), believes that by using authentic, real world problems in
college courses, we can teach students to think using the multiple perspectives they
must use in society.
12


Summary
Learning communities at CCD have two purposes, 1) they provide under
prepared students with instruction on study strategies, and 2) they introduce science
students to a community of practitioners in the fields of science. The study will
examine the degree to which introducing science students into the community of
practitioners at such an early stage in their studies would help them keep their
science career goal in mind through the difficult course work.
Chapter 2 presents the physiological and sociological models of learning and the
framework for the learning communities as it is used in the science courses at CCD.
Chapter 3 describes the research methodology, instrument and data analysis
techniques. Chapter 4 presents the findings and analysis, and Chapter 5 describes the
interpretation of the findings.
13


CHAPTER 2
REVIEW OF THE LITERATURE
Introduction
The chapter begins by providing an overview of the literature on entry level
college science course pedagogy at community colleges. It is followed by an
examination of the physiological and sociological aspects of the learning process.
Next, it provides a discussion of the attributes of learning communities and then-
ability to provide a link between the learning process and entry-level college science
course pedagogy. A summary of the research questions and hypotheses concludes
the chapter.
Community College Entry Level Science Pedagogy
Striking changes in the way college students are taught science curriculum are
underway. These changes are being initiated not only at the entry-level science
courses (Gabelnick, et al., 1990; Nelson, 1996; OBanion, 1996, Tinto and Russo,
1994), but also to the way medical students are taught. Medical schools are being
pushed to move away from the traditional nine hours of lecture, and better connect
14


the science content they learn in their first two years to the students practice in the
clinic setting (Scanlon, 2002).
Different approaches to teaching college science courses include discussion
(Nelson, 1996), inquiry based learning (Cavallo and Laubach, 2001), learning
apprenticeships (Lave, 1988; Scardamalia, 1996) and learning communities
(OBanion, 1996, Tinto and Russo, 1994). Critical thinking skills are emphasized in
all curricula, not just the sciences. The House Committee on Science stated in a
1998 report to Congress that in addition to preparing students for science careers, the
education system must ... provide scientific and technical understanding so that
citizens may make informed decisions as consumers and as citizens. To achieve
these goals, schools must be able to develop curricula that are rigorous, develop
critical thinking, and impart an appreciation of the excitement and utility of science
(House Committee on Science, 1998). In order to increase their scientific
knowledge, students need to develop an appreciation or an interest in scientific
issues. They need sufficient knowledge of scientific concepts to understand the
purpose of public policies and practices aimed at increasing and maintaining the
health and welfare of the earth and all its inhabitants. Eldredge (1999), describes the
relationship between our understanding of the world in which we live and the
material world as one of matching patterns through models of how we believe the
world operates. In order to create the models, we need to be students of nature and
recognize the intricate patterns between our mental pictures of the world and the
15


physical reality of the world. As instructors, placing an emphasis on scientific
patterns in the curriculum requires teaching students to recognize patterns between
their lives and scientific concepts. This matching of patterns requires more than just
a simple identification of a resemblance between a part of your life and a scientific
phenomenon; it requires an examination to be of any lasting value (Moriarity, 1997).
Examination of the scientific world can take many forms from traditional lectures
and labs to inquiry based research projects. Inquiry based research projects allow
students to use their own pictures of the scientific world to discover patterns in
science and construct their own meaning. In addition, inquiry based research also
introduces students to the world of the scientific community. Collaboration and
inquiry make up the backbone of the scientific community. Introducing these
components early in the scientific educational process greatly expands the students
scientific horizons and enhances the excitement of learning science on any level
(Dixon-Krauss, 1996). Introducing collaborative scientific inquiry early in the
educational process builds connections that move beyond the purely cognitive
training and creates a social and more realistic picture of the scientific community
for the student.
Vygotsky (1978) describes the social origin of cognitive ideas as the
interrelationship of the outside and the inside, and used this interrelationship to coin
the term zone of proximal development. Vygotsky states, All higher mental
functions are internalized social relationships (1981, 164). Rogoff (1990) proposes
16


that cognitive development must be studied within the context of activities, and
every cognitive act needs to be analyzed in the circumstances in which it occurs.
Included in the term circumstances is the arrangement of community activities in
which the individuals live. As people listen to what those around them have to say,
they begin to recognize and internalize the opinions of the people in their
community.
Conceptually, community can be described broadly as being an idealized
concept whose representation is based on trust, respect and service (Bushnell,
2001). There is an increasing call for community to be put back into the school
systems from kindergarten to higher education. What does the addition of
community look like within the school systems?
Many K-12 schools use the phrase community to elicit parental involvement in
their childs education, for example school activities, sports and academics.
Bushnell (2001) describes this community involvement as a Bed of Roses with
Thoms. Bushnell describes the positive aspects of building a school community as
a strong, supportive relationship between teachers, parents and students. A strong
community is built through parental involvement and participation. She describes
the negative aspects of school community as coercive, oppositional and gendered.
She goes on to describe the need to recognize community as an active, changing
creature that takes work and time and requires change (p. 160, 161).
17


Community building can also occur within the college classroom through
collaborative, inquiry based research projects, or through the formation of learning
groups. Learning groups consist of combinations of students working on projects,
presentations or papers. The learning groups can be composed according to interest
or ability. The learning groups can also be composed of students working on the
same science course requirement. Research projects involve the student in the
scientific process and encourage them to work together as a community of learners.
Learning science or any other subject requires a physiological change in brain
function and a sociological participation in the community of practice. Before
linking the learning process to education, I would like to explore the learning process
by examining the physiological and sociological aspects of learning in more detail.
The Physiological Process of Learning
Humans are capable of acquiring and performing many different tasks within
short periods of time. Some of the tasks learned last a lifetime, some are forgotten in
a matter of hours. Surviving and benefiting from a college education requires that
students remember large amounts of information for periods of a few weeks, years,
or at least until the test is over. Many student responses to the demands on their
memory include those beginning with the question is this task possible given the
restraints on the human brain? What do scientists know about learning and
memory? The study of neuroscience examines the brain and its functions including
18


memory and learning. Neuroscience is a relatively new science and the brain is a
complicated organ.
The Map
The brain is a very complex organ, and it is the organ that scientists know the
least about. Scientists have been tracing the neurons involved in perception, memory
and motor functioning since they learned such cells existed. In humans, these
experiments involve examining what functions are disrupted when the brain is
injured or diseased. Scientists have traced many of the brains pathways based on
these examinations and others involving animals from the worm to primates. Motor
pathways involved in movement can be simple to trace, for example, a muscle reflex,
or as complicated as throwing a baseball. Complicated learned motor tasks not only
are difficult to trace; the learned motor task can be transferred to other muscle groups
making the task even more difficult (Nicholls, et al. 1992). Can you write your name
with your toes? Visual pathways can be followed step by step from the time light
hits the retina as it passes through the anatomical pathways to the perception of an
image in the motor areas of the brain, but we still do not know much about the
specialized circuitry of recognizing your grandmothers face (Nicholls, et al. 1992,
Wandell, 1995).
The pathways involved in learning and memory are even more difficult to
elucidate. Nevertheless, great advances in the progress in the formation of motor,
19


visual and memory maps have been made (Grafton et al. 2001). Simultaneous
studies on the functions of the neurons making up the maps are encouraging in the
study of how the brain functions. Studies on pathway function include work
involving neuron communication and pathway formation and elimination. It is not
only crucial to identify the pathway, but also how it developed and how is it
maintained.
Pathway development and maintenance studies are still in their infancy. Added to
the complexity of the studies are the complications moving from in vitro (cell and
tissue cultures) to in vivo (whole brain) studies. The firing activity of neurons can
change from in vitro to in vivo due to the environment (Steriade, 2001). It is
difficult to replicate the environment in the intact brain in a petri dish. Steriade
(2001) states, While behavioral and system neuroscience stands to gain from
achievements of biophysics and molecular biology in simplified preparations, the
logic of life requires orchestration of the different parts composing the whole (p. 1).
Mapping the brain and tracing its functions is an ongoing process that will keep
neuroscientists busy for a long time. The tracing of pathways involved in memory
will also take time due to the pathways being dispersed throughout the entire brain,
but we can make some observations of the memory maps based on the information
gathered thus far.
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Putting the Map to Work
So, what advise do the experts suggest for students attempting to use their
learning and memory skills to survive their college requirements? Practice, practice,
practice! There is no substitution for repetition and practice. Valerie Strauss (2001)
a writer for the Washington Post, sums it up nicely. She states forget herbs; focus
on focus (Strauss, 2001). The use of all modalities can facilitate this process.
Many students need to do more than simply read the book or go over their notes.
Most students also need to write the information as some form of practice. The use
of all the modalities including kinesthetic involves more connections in the making
of the map. Experiments examining the contributions of motor areas and visually
directed motor areas in visually guided motor tasks show that some subjects could
only carry out a visual movement pattern if they actually did the movements
(Grafton, 2001).
Learning communities foster the increased use of modalities by their very
nature. Learning communities that encourage students to work together in teams
encourage students to use their social skills, their communication skills and their
cognitive skills. Working together in a community also brings emotions into the
learning process.
The use of emotion is a surprising connection in the map schema that should
not be neglected. Most students can recall information that is linked to some strong
emotion. Recent studies show that the areas of the brain involved in emotion lit up
21


implying activity when a person is struggling to work out a moral dilemma
(Helmuth, 2001). Researchers studied peoples brains while they reasoned their way
through a set of scenarios. They found that scenarios involving moral dilemmas lit
up the emotional regions of the brain instead of the judgment parts of the brain,
implying that emotional responses are important in guiding peoples solutions
(Farah, 2001, and Helmuth, 2001). The bottom line when learning those math
tables? Practice, practice, practice laughing all the while!
Sociological Process of Learning
Humans are unique in the animal kingdom in that with our large, complex brains
we have amassed a knowledge base throughout our history, and we have the ability
to pass on this knowledge through cultural evolution. Each culture has it moment in
history, its vision, yet we easily forget how much of our lives are based on the
continuity from past communities (Bronowski, 1973). The knowledge base has
increased the availability of more facts and inferences to learn, remember and reason
our way through when making decisions. We can learn from the knowledge of
others and this enables us to build bridges and cure diseases, however our large
database of knowledge also increases the necessity to perform abstract reasoning.
This need to manipulate such a large database means our evidence is often fuzzy and
our reasoning is often full of errors (Ruchlis & Oddo, 1990).
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Today as in the past, we can cause disastrous results with our incorrect reasoning
and decisions. In fact, mistakes in reasoning while using our increased knowledge
base, especially our increase in technological knowledge can lead to disastrous and
costly results. We only need to look at recent disasters to confirm this, for instance
Three Mile Island and the Exxon Valdez oil spill. We have increased our need for
problem-solving skills, and this is a skill that needs to be taught. Reasoning is a skill
many parents begin to teach their children at a young age. We encourage young
childrens curiosity about the world, and when they make errors in their reasoning
we provide them with alternate experiences and observations.
As parents, we build on the childs previous experiences in life. Beginning in
elementary school, we teach children the facts from our storehouse of knowledge in
math, science, and language. As teachers, we expect our students to incorporate the
facts they are given and put them together in many different ways and draw
conclusions from them. It may be that the need to teach critical thinking skills is an
important part of the education process.
In schools today, more effort has been placed on collaboration and critical
thinking skills as an important part of the education experience. These latter
concepts are not new ones. As mentioned earlier, Vygotsky (1978) believed that the
social context in which a child develops plays a pivotal role in the cognitive growth
of the child and therefore, collaborative learning experiences should be part of the
childs learning experience. Dewey (1959) also believed that the cognitive
23


development of children and their cultural life is inseparable and should be part of
the educational experience.
How do we make sense of the increasing complexity and accumulation of
knowledge? One means is to become immersed in one particular part of the overall
community. By forming separate learning communities within our larger community
we can focus our energy on learning and working with a smaller database. Each
learning community group, for example scientists and computer technicians have
different language usage, different thought patterns, and different observational
patterns. Within these small groups we can comfortably learn and work. These
differing patterns of thought and language in smaller communities do create
problems among the larger community as a whole, but they allow us to cope with
large database of knowledge we accumulate. Throughout history, humans have had
to use their problem solving skills to co-exist with other communities, often through
the establishment of rules.
The process of learning has an important social or human culture
component. How we come to know certain things depends on the view of the
community in which we live. The importance of community can be traced back to
Platos time and the differing paradigms for the quest of obtaining certain
knowledge. Plato believed that in the quest for certain knowledge the processes
of the mind used certain stable operating principles that did not change with time. In
contrast, Herodotus believed that in order to understand past events, one needed to
24


also understand the societal background of the researcher, because the researchers
background shaped how they viewed the events of the past (Cole, 1996, Shweder,
1991).
Herodotuss view of learning can be described as a creation of a cultural
literacy there is a certain amount of relevant information that can be taken for
granted in the communication between members of a community. A sense of
familiarity exists in communities. In fact, communities create their own reality.
Ackerman (1998), calls this shared meaning or meaning common to a group. He
believes this shared meaning is the foundation of culture. Nietzsche states that
. .men and women are the makers of the reality before which they bow down as its
slave (Shweder, 1996, p. 41). Shweder (1996), uses the term intentional worlds
to describe the reality each society creates around its artifacts and artifactual
symbolism in the society. Shweder further states that without the symbolism, the
artifacts would not mean anything. Vygotsky (1978), agreed with this idea when he
stated that social interactions were behind all higher functions and their relationships
(Wertsch, 1998). Wertsch also describes all human actions as socioculturally
situated (p. 109). Before the advent of formal schooling, and in many societies,
both human and primate, learning is often a byproduct of interacting with society
members, rather than direct, formal education (Atran and Sperber, 1991, Kitahara,
1991).
25


Learning any discipline requires becoming involved in a process of enculturation,
or as Lave (1991) stated: learning is situated in a social character. Lave and Wenger
(1991) propose that learning is a process that occurs within a framework of social
participation rather than in a single individuals mind. They believe that learning
occurs within the proper social engagement. Roschelle and Clancy (1992) agree:
... learning science (or any other discipline) requires crossing
a large gap in perspective and practice; it requires becoming
a member of a community of observers that sees and acts in
ways that are at first incomprehensible or imperceptible to a
newcomer. Put succinctly, learning science is a process of
enculturation (p 437).
What is the social culture of todays workforce? How do we place learning within
the context of the community? Brown (1992), stresses the importance of students
practicing the skills they are learning within the context in which they will use them.
Not all learning can be transmitted from teacher to student, experience with the
content material plays a necessary role (Atran and Sperber, 1991; Vygotsky, 1978;
Wertch, 1998). Apprenticeships and internships that are designed to accomplish this
learning experience are playing a large role in the ever-changing technological fields
in todays culture. Internships allow an individual to practice the knowledge base
within the context of the communitys needs.
In summary, learning science involves learning scientific concepts and practicing
science in the world through a set of patterns and observations that are practiced in
the scientific community (Rochelle and Clancey, 1992). Scardamalia (1996), defines
26


a learning community as a group of individuals whose goal is the shared pursuit of
knowledge. Lave and Wenger (1991), take the idea of learning communities further
describing those participating in a learning community as apprentices within
communities of practice.
Learning Communities
Learning communities are not a new concept. Historically, before science was a
profession, well-educated men with time and money formed scientific communities
to explore the curiosities about the natural world. Eldridge (1999) believes that a
community of critical thinkers is crucial to the understanding of science. We use
each other to bounce ideas off of one another, fill in knowledge gaps, orient thought
processes and confirm belief systems (Rockeach, 1960; Roschelle and Clancey,
1992).
In traditional classrooms from elementary school to college, teachers understand
the importance of creating learning communities in their classrooms. The creation of
a community does not have to happen on a large scale. Sometimes it is enough just
to be in the same classroom to give students a common purpose, even if the goal is to
make it through the particular class. Given the social nature of our species, it would
make sense that an attempt to establish a learning community would be a necessary
part of the success of understanding science. We observe, practice and eventually
take over a occupation. Physiologically, we train the brain with repetition.
27


Culturally, we first leam the concepts and then begin to utilize that knowledge -
using problem solving techniques. Observation practice problem solving.
Critical thinking, pattern matching and problem solving skills are viewed by many
scientists as essential to the growth and progress of understanding science (Eldredge,
1999; Feder, 1999; Fotun and Bernstein, 1998; Hatton and Plouffe, 1997; Hazen,
1990; Moriarty, 1997; Rutherford and Ahlgren, 1990). Carl Sagan (1979) agrees, he
states in his book Brocas Brain: Science is a way of thinking much more than it is
a body of knowledge.
The framework for learning communities encompasses both the physiological and
cultural processes involved in learning. Karl Popper (1965) asks and attempts to
answer difficult questions about the learning process. For example, how do we come
to know what we know? And, how do we define the sources of our knowledge? His
well-defined theory on the answers to these questions includes nine theses, which are
listed below.
1. An ultimate source of knowledge does not exist.
2. We must continually test or examine our assertions about a knowledge source.
3. We must examine all types of arguments about an assertion.
4. The most important source of learned knowledge, both quantitative and
qualitative is tradition.
5. Knowledge can not exist without tradition, however all knowledge is open to
criticism.
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6. New knowledge generally occurs through the building on previous knowledge.
7. We need to continually reach beyond the surface level appearances of our
theories and seek to examine potentials for error.
8. Processes such as reason, observation, imagination and intuition can help us
critically analyze our theories.
9. Solutions to problems and new theories lead to new and unsolved problems.
Poppers epistemology on the sources of our knowledge and the process of
gaining new knowledge are still applied today. Eldredge (1999) discusses the
acquisition of new knowledge in the area of science in his book The Pattern of
Evolution. He states that ideas about the material world in which we live come from
pre-existing possibilities, which appear as either patterns in the natural world or
patterns that are perceived in our minds. Eldredge believes that humans have
practiced the process of critical examination of these patterns only since the times of
Plato, which is in evolutionary times a fairly recent human accomplishment. He and
several other scientists believe that this critical examination of our acquisition of new
knowledge, or critical thinking process is a crucial piece to our understanding of
scientific phenomena (Bruer, 1993, Eldredge, 1999, Feder, 1999). If this is so,
Eldredge states ...then surely the development of a community of like-spirited
individuals is crucial to the endeavor (p. 31). Our social structure is how we make
sense of the new knowledge we experience, and perceive the resulting patterns that
determine our existing scientific concepts (Vygotsky, 1978). Roschell and Clancey
29


(1992), believe the organization of the neural pathways involved in the physiological
process of learning are themselves partially the result of an individuals social
involvement. Our individual experiences influence the nature of our synaptic
connections. In other words, we form our own unique patterns of synaptic
connections.
Patterns in learning lie at the center of learning. We each have our own synaptic
patterns leading to our unique learning experiences. The idea of gaining knowledge
through revealing patterns is an important part of learning for Hatton and Plouffe.
They write All good theories contain, at heart, an ordering process that reveals
hidden patterns (Hatton and Plouffe, 1997, p 116). Adding to this thought is Szent-
Gyorgyi who wrote .Discovery consists of seeing what everybody has seen and
thinking what nobody has thought (Hatton and Plouffe, 1997, pi 17). In summary,
acquiring new knowledge involves building on existing knowledge or patterns in our
lives. Both the existing knowledge and the process of gaining this knowledge, for
example the building of new neural pathways in the brain, are dependent on and
shaped by our social interactions.
If indeed, social relationships are a critical part of our communities, how
important are they in our education system? Gabelnick et al (1990) states that
college students today spend less time interacting with other students. If this is so,
then how is this reflected in the learning process? Another way to ask this question
is how important are peer relationships to the process of learning? Van der Bogert
30


(1998) describes the importance of relationships as he established an International
Network of Principals Centers. He believed that the establishment of learning
communities increased relationships by 1) establishing networks, 2) bringing in
resources, 3) keeping the focus, 4) maintaining the momentum, and 5) advancing
learning through competition and emotional support.
A recent report by the House Committee on Science called emphasized the focus
on building a solid relationship between science and society (1998). According to
the House Committee on Science, this solid relationship between a scientifically
literate society and the science community will ...fuel our economy, foster
advances in medical research, and ensure our ability to defend ourselves against ever
more technologically-advanced foes. It can also provide every citizen -not only
the scientists who are engaged in it-with information necessary to make informed
decisions as voters, consumers and policymakers (p. 10). The addition of science
learning communities within the educational system would increase the numbers of
scientifically literate people in a society.
Learning communities are used as a method to increase collaboration among
students and between disciplines. Maybe they can also play a role in increasing
scientific literacy in students. Several two-year colleges across the United States are
experimenting with creating learning communities in efforts to increase student
involvement and retention in their college careers (Hamburg, 1991, OBanion, 1996,
Tinto and Russo, 1994). Tinto, et al. (1994) conducted an in-depth study of a
31


Coordinated Studies Program initiated at Seattle Central Community College. This
community college implemented a number of learning communities within their
curriculum. The focus of these learning communities was to link different
disciplines around a central theme. The goals of the learning communities were to
integrate disciplines and increase faculty and student participation in the learning
process (Tinto and Russo, 1994). The researchers compared records of academic
performance and persistence between the students enrolled in the learning
communities and those in traditional programs at the Community College in Seattle.
They found that students enrolled in the learning communities had more positive
views of their education, saw themselves as acquiring more knowledge, and had a
higher retention rate than those in traditional settings.
The concept of a learning community can involve a broad range of definitions.

Some involve linking different subjects, each pursuing a common goal. Some link
the work place community and the school. Gabelnick et al (1990) describe five types
of learning community models: freshman interest groups in which cohorts of twenty-
five students take three courses that are linked around pre-major topics; linked
courses in which two courses are taken by a cohort of students, some coordination of
content or assignment is conducted; learning clusters in which three or four courses
are linked in the same manner as the linked courses; federated learning communities
in which a cohort of forty students take a cluster of courses in which a faculty
member from another disciple acts as a Master Learner to help the cohort discover
32


and make sense of the coursework; coordinated studies in which course work is fully
integrated, taught by three to five instructors and becomes the students entire load.
Freshman interest groups are one of the more common examples of a learning
community (Gabelnick, et al. 1990). A freshman interest group consists of a group
of students that take two or three courses required for a particular major. The
courses are not necessarily tied together in any way. The idea is to build a
community within the cohort of students taking the courses together. The
community concept is strengthened by providing opportunities for study groups and
social interactions between the students in the cohort. The University of Oregon has
freshman interest groups centered on pre-law, journalism, Art and pre-health
sciences. A peer advisor is the link within each freshman interest group. The
advisor holds monthly meetings to discuss social issues, holds study sessions and
provides information on campus resources (Gabelnick, et al. 1990).
The Shorline Community College in Seattle Washington offers its students
several linked courses. Many of these courses link a natural or social science and an
English composition course. The courses meet daily in two-hour blocks and share
some assignments (Gabelnick, et al. 1990).
While the conceptual definition of a learning community can be very broad in
nature, in the CCD science courses, the learning communities consisted of a course
linked with a tutorial. The tutorial consisted of a one hour per week period of time
33


that the students used to work together as a community of science practitioners on
actual research questions, thus simulating an initiation into the scientific community.
Summary
My research goals focus on the learning environments of students in the
introductory science course, in particular, the first and second semester students in
general biology. First, I wanted to examine the factors important to students in the
first semester biology course in order to structure a class that retained more students.
I also wanted to focus on scientific career choices and treat the students as if they
belong to a larger, scientific community, which I hoped would result in more first
semester biology students will take the second semester biology course. One of my
research questions then became: If we create an atmosphere of enculturation into the
field of science at the introductory level, then more students will continue to pursue
their science degrees?
34


CHAPTER 3
METHODOLOGY
The focus of my research was to examine the impact of learning communities
on student perceptions of success in science courses and the passing and retention
rates in first year science courses. The research questions were
1. What variables are perceived by first semester science students as important
to their success in science courses?
2. Will those perceived variables be accurate predictors of completion of
science courses?
3. Will more students who experience the first semester sequence in a learning
community be more likely to complete the course and enroll in sequential
courses than students in the traditional first semester courses?
4. Do students believe the learning community factors were important to their
success in entry-level science courses?
The design was a quasi-experiment with pre- and post-measures of student
attitudes and perceptions of success in first and second semester biology courses. A
pre-measure was followed by a quasi experiment in which entry level biology
courses were conducted using either learning communities or traditional lecture.
Completion and retention rates between course types were compared at the end of
the semester. The procedure is outlined below.
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Pre-measure
Science attitude survey
Science aptitude and preparation questionnaire
Treatment
Employing intentional learning community development into
introductory science course verses a lecture, textbook, individual
assignments, tests
Post-treatment measure
Observations
Student focus groups, instructor interviews
Science aptitude and preparation questionnaire
Analysis
Statistical measures of successful completion rates
Statistical analysis of students taking more science classes, retention rates
Research Design
A quasi-experiment was used to determine changes in 1) students perceptions
about success in science courses, 2) passing rates and 3) retention rates after
exposure to a learning community science course. The experimental group attended
a section of a first or second semester biology course taught as a learning
community. The control group attended a traditionally taught section of a first or
second semester biology course.
The study was divided into two phases: phase one was a survey consisting of
three questions identifying first year science students perceptions of factors that
influenced their success in their science courses (see Appendix A). The results of the
survey were used to generate the questionnaire subsequently used as the pre- and
post-treatment attitude toward science questionnaire.
36


Phase two of the study, a quasi-experiment, is illustrated in Figure 3.1. The Os
represent the measure, and the Ys represent the experimental treatment period for the
learning community sections and the non-experimental treatment period for the
traditional sections.
Question -naire Course Observation Course Question -naire Inter- view Passing and Retention Rates
BIO 111 FA SP Traditional Section o YTrad 0 ^Trad 0 0 0
Learning Community Section o Ylc o Ylc 0 o 0
BIO 112 FA SP Traditional Section o ^Trad 0 YTrad 0 0 0
Learning Community Section 0 ylc 0 Ylc 0 0 o
Time (Semester) *
Figure 3.1. Phase Two Quasi-Experimental Design
Phase two included eight courses: four experimental groups in which students
attended a learning community course, and four equivalent traditionally taught
courses. Both learning community and traditional courses were offered as day and
evening classes for two semesters: fall and spring. The curriculum was the same for
all groups. In the experimental group, students completed a research project in
addition to their traditional exams, quizzes and laboratory assignments, and met an
37


extra hour per week with the instructor to complete the research project. The control
group received similar exams, quizzes and laboratory assignments. Different
instructors taught all the sections during the fall and spring semesters.
Students completed a pre- and post questionnaire identifying their perceptions of
success in their science course. A series of two observations were conducted in each
class during the course of the semester. Interviews of students and instructors in
both the learning communities and control groups were conducted at the conclusion
of the semester.
Sample
The sample consisted of students enrolled in entry level general biology courses
at CCD, a community college in a metropolitan area. The student population profile
mirrored that of the college profile (see below): 60% female, 60% people of color
and 70% between the ages of 18 and 24 years of age. Eighty percent of students in
the general biology courses were working toward a pre-med, pre-pharmacy, pre-
physician assistant or pre-nursing degree.
Data was collected for two sequential semesters: fall and spring. One learning
community was conducted with the first semester students in the fall and spring; the
other with the second semester students in the fall and spring. The students in the
learning communities represented the experimental group, which was compared with
students enrolled in other sections of the general biology courses. Students that
38


enrolled in the learning community had the option of attending a one hour per week
enrichment tutorial. All the students in the learning community were encouraged to
attend the enrichment tutorial, identified in the schedule as a recitation. The tutorial
was structured to help students complete the semester research projects required in
the class. The students enrolling in the learning community sections also had the
option of formally enrolling in the learning community enrichment tutorial, or
recitation and receiving one elective credit. The control groups did not have the
option of attending an enrichment tutorial. The experimental groups for the year
consisted of 42 students enrolled in two semester General Biology I (BIO 111)
courses and 34 students enrolled in two second semester General Biology II (BIO
112) courses. The control groups for the year consisted of two General Biology I
courses with 42 students enrolled, and two second semester General Biology II
courses with 26 students enrolled.
Setting
The mission of the College is to provide transfer programs that lead to
baccalaureate degrees; occupational programs for job-entry skills or upgrading; first
and second year general education courses; developmental/remedial instruction and
GED preparation; continuing education and community services; and cooperative
inter-institutional programs. The student population profile of the community
39


college consisted of approximately 11,000 students, 60% female, 57% people of
color, 50% between the ages of 18 and 24 years.
The setting is a standard college classroom/laboratory setting. The same
laboratory setting and laboratory assignments were used for both control and
learning community groups. Textbook and curriculum materials were also the same
between control and learning community groups.
Treatment
Learning Communities
The independent variable for the study was the type of instruction with two
levels: a learning community or traditional lecture.
Learning Community: The definition of a learning community for the study involved
a modification of the definition used by Gabelnick et al. (1990): linked courses in
which two courses are taken by a cohort of students, some coordination of content or
assignment is conducted. The learning community in the CCD science courses
involved an entry-level science course that was linked with a one hour per week
tutorial enrichment. The tutorial curriculum concentrated on the underlying
principles in scientific research. The coordination between the class and the learning
community involved an extensive research project that incorporated important
scientific principles. The project goals for student research included an
understanding of the scientific method, an increased engagement in scientific
40


inquiry, and collaboration and communication among students. See Appendix B for
the learning community project outline.
Learning communities were added to two entry-level general biology courses in
the fall and spring semesters. The students that registered for the learning community
enrolled in a recitation in addition to the course that included one additional hour per
week of guidance in a collaborative research project. The students did not receive
any further information about the learning community from the college schedule
other than that they were signing up for a learning community tutorial. Thus, while
the students opted for the additional contact, they did not self select the learning
communities.
Activities in the learning community differed from the control courses in the
following ways. A research component involving a semester project was required in
all the first year science courses at the community college. Students in the control
courses individually conducted a prescribed experiment and wrote a scientific paper
that included a literature search, methodology, results and discussion sections.
The semester project was embellished in the learning communities with the
addition of a choice of four research projects that encompassed several components
learned in the course curriculum, (e.g., genetics, cell division, and genetic
mutations). The chosen projects also encouraged the students to make discoveries on
their own, for example, what happens to plants grown in low levels of antibiotics?
The students in the learning communities were required to collaborate to conduct
41


literature searches, genetic analyses and experiments. Their work resulted in an
individually written research paper and a group poster, (see Appendix A)
Control Traditional Courses
The lecture material, content material, text book, lab exercises and lab book were
identical for the learning community and control groups. The students in the control
courses also conducted research projects in addition to their lecture and laboratory
work, however the research projects of students in the control groups are completed
individually. The students worked individually on a project of their choice and did
not receive instructor guidance. Criteria for the control group projects consisted
solely of the requirement of an experiment. The students received little guidance
regarding the topic, design or procedure.
Dependent Variables
The dependent variables included 1) student perceptions of what was important to
their success in the course, 2) final grade and 3) retention rate.
42


Instruments
The instruments included a phase one pre-measure given to first year science
students to identify variables important to their success in their science courses. The
five most frequently identified variables were then used to create the questionnaire
for phase two. Phase two data sources included a pre- and post measure
questionnaire, a series of observations, and student and faculty interviews.
Phase one survey consisted of three questions given to first year science students:
1) Why are you taking this science course?
2) What do you feel are the most important factors that will influence your success in
this science course?
3) What do you feel are the most important factors that will influence your success in
the future science courses you need to reach your career goal?
The questions were used to identify variables that influenced students success in the
science course they were presently taking and those they would take in the future.
Two independent participants coded the survey. Coding consisted of identifying
important variables in the students answers, for example, professor, peers, study
time, etc. The frequency for each variable was recorded, and the five most frequent
variables were used as themes in the questionnaire. The two coders separately
worked on identification of the variables, then came to agreement as to the
terminology. Agreement between the two coders for the frequency of each variable
averaged 76% for the five variables.
43


Phase two questionnaire: the questionnaire was used to identify the variables
entry level science students perceived were important to their success in both the
class they were enrolled in and in their future science courses. The questionnaire
consisted of 15 questions; three questions from each of the five most frequent factors
based on the survey results (see Appendix B).
The questionnaire was given to students in the experimental and control groups as
a pre- and post measure. The questionnaire was distributed to the students by the
researcher. It was collected by the instructor and returned to the researcher. During
the course of discussions with the professors of the course regarding the research, the
professors showed interest in the overall research goals, but did not appear to be
overly interested in the individual components of the research. In other words, the
professors did not appear to be concerned about the results of the questionnaire,
observations or interviews.
Validity
Validity of the questionnaire has to do with how well the data collected from the
questionnaire measures the variables the students believed were important to their
success in their science courses. The steps taken to enhance valid evidence included:
1) the factors used in the questionnaire were actually factors identified by previous
students as those important in their success (see phase one part of study), 2) an
44


interview and observation process that was structured similarly to the questionnaire
was added to enhance the confidence of the questionnaire results (Hopkins, 1998).
A brief history of the general biology research projects and the nature of the
research being conducted were explained to the students in both the learning
community and control groups. The students were told to answer the questions
honestly and freely. Although the questionnaire was not entirely anonymous, the last
four digits of their social security number identified the students, no concerns or
comments were ever received from students regarding whether it would threaten
their grade or their relationship with the professor.
Reliability
Reliability is a measure of how consistent the pre- and post measure, the
questionnaire, the observations and the interview were at identifying the factors
important to the students success in science courses. Check-coding is often used as
a source of reliability (Miles and Huberman, 1994). The results from the phase one
survey and the interview results were coded by two different participants, after which
the results were discussed and reviewed. The percent agreement for the survey
results for the two coders was 76%; the percent agreement for the interview results
was 90%. Triangulation of results from the pre- and post -measure questionnaire,
observations and interviews also provided reliability in the study. Additional
reliability in qualitative measures also results from eliciting an observer that is
45


familiar with the curriculum, the learning community concept and the setting of
study. In the study, the interviewer/observer was a biology professor at the
community college. Additionally, an internal reliability check on the questionnaire
was run on the questionnaire subscales.
Each of the five subscales: peers, professor, discipline, predisposition and labs, n
= 93 for each subscale, were checked for internal consistency. The reliability
coefficients for the scales were conducted using a Cronbachs a and reported in
Table 3.1. Reliability coefficients for both the pre-measure and post-measure are
reported, see Table 3.1.
46


Table 3.1 Reliability Analysis Scale (Alpha)
Scale Likert Items Cronbachs a
Peers Studying science with other students is more enjoyable than studying on my own. Discussing science material with other students both in and out of class helps me to understand the material better. Challenging material would be best learned in classroom discussions. .6225 beginning of semester; .5727 end of semester
Predisposition * I enjoy reading scientific literature on my own time. I do not expect to pass my science courses without extra help. I enjoy solving problems of a scientific nature. .5262 beginning of semester; .5327 end of semester
Discipline ** In order to be successful in a science course, I need to work harder than in other courses. I expect to change my study habits to study science. My study habits are sufficient for passing my science courses. .4099 beginning of semester; .4879 end of semester
Professor Having a good science professor would help me to understand scientific material. Having a good science professor helps me to pass my class. Students should talk to their science professors about their coursework and grades. .8924 beginning of semester; .7661 end of semester
Lab My labs help me solve scientific problems. Solving problems of a scientific nature are best done in a lab setting. Actually doing science experiments helps me understand science. .1567 beginning of semester; .7720 end of semester
*The second question in the predisposition box was reverse scored.
** The first question in the discipline box was reverse scored.
47


Phase Two Observations: observations of both the learning communities and
control groups occurred twice during the course of the semester. The purpose of the
observations was to identify any effect the learning community addition had in the
classroom. The observations were conducted during the first fifty minutes of the
course because some of the courses were only fifty minutes in length. The structure
of the observations focused on the actions and interactions occurring in lecture
portion of the learning communities and the control courses. The focus included the
collaborative participation of the students, the amount of time the students discussed
the content of the course, the level of confidence the students projected in the
learning of the coursework, the interest level shown for the content. The observer
recorded the interactions between student and professor and between students. The
number of interactions was of interest in identifying if student interaction was higher
in the learning community courses than the controls. The coding categories were
designed to identify the types of interactions that were occurring in the classroom,
for example did students discuss their research projects with each other, and did
students use the project material to expand their discussions and questions?
The coding categories included expansion beyond the material and research
project content to identify the content of the interactions. The coding category
community interactions was used to explore the content of the student-student
interactions. The term scientific community implied that the students would get
involved in their project and collaborate to learn content material and conduct
48


experiments. Due to scheduling difficulties, formal study groups are rare in the
general biology courses at CCD, so the coding category study groups was used to
identify both formal and informal study groups between the students. Comments
made in the class were classified as informal study groups and coded as study groups
if the students made references to getting together before lab to go over the
assignment. Collaboration on the research project required that students meet
outside the class time, therefore the codes were designed to identify this student
collaboration. Coding categories that pertained to the course logistics and basic
content of the course were also recorded in order to collect general information about
the courses, for example, a professors teaching style that may not allow for any
questions or discussion in the classroom.
The observations were spaced so that one occurred at the beginning, and at the
end of each course. The observations were announced to the students as
observations of both instructors and the courses themselves. Instructor observations
were not new to the community college classroom; therefore most returning students
were familiar if not comfortable with instructor observations. Since it was important
that the data collection process be valid (Krathwohl 1998, Miles and Huberman,
1994), the observer was familiar with the content material and the typical
implementation of the type of course she was observing. (A copy of the form used by
the observer can be found in Appendix C.) The observation form was designed to be
used as a measure of triangulation for the pre- and post-measure questionnaire in
49


addition to being a means of identifying effects due to implementation of the
learning community courses.
Phase Two Student Focus Groups and Instructor Interviews: focus groups
consisted of four randomly chosen students per course. Additionally, interviews of
instructors from each of the learning community and the control groups were
conducted. The purpose of the focus groups was to identify themes that were
important to the students success in their general biology course, in particular the
themes that might relate to the learning community goals, for example, interactions
with peers, or references to the research project material. The interview forms were
designed similarly to the observation forms and open-ended questionnaire questions
to provide triangulation for the observations and open-ended questionnaire
responses. (See Appendix D for the interview form)
Faculty from both the learning communities and control sections of each course
were also interviewed. The questions posed to the faculty paralleled those posed to
the students. The focus group facilitators included the primary researcher and
faculty trained by an experienced interviewer. The training included use of the
recorder and protocol for asking the questions.
The coding of the focus group transcriptions involved two different participants.
The participants identified terms and phrases that identified retention strategies, for
example teaching techniques, the research project, the professor and an enjoyment of
the topic. The coding category of community interactions was repeated (from the
50


observation coding categories) to identify the importance of peers and the research
project to the students success. The coding categories included logistics in order to
identify the importance time barriers and content difficulty played in the themes
identified by students as playing a role in their success. (See Appendix D for the
interview questions)
Phase Two Completion and Retention Data: completion data was collected
from grades turned in by instructors at the end of the semester. Retention data was
collected from computerized fall and spring enrollment information.
Percent completion is a measure of those students that completed the course.
Students that do not complete the course included those that received a W, AW,
or I as a grade for the course. A W grade is given to students who withdraw
from the course before the withdrawal date set by the college. The AW grade is
given to students that disappear from the class after a period of two to three weeks.
An I is given to a student that competes 3/4 of the course with a C or better and
can not complete the course due to extenuating circumstances. Percent passing is a
measure of those students that completed the course with a grade of C or higher.
Percent retention is a measure of those students that declare a science major on
their application and take the next science course leading to their degree or certificate
the following semester. Retention was measured as the number of students in the
first semester biology course that took the second semester biology course.
51


Procedure
Data collection consisted of administering the pre-measure, post-measure,
observations, and collecting completion and retention data. The schedule for the pre-
measure, post-measure and observations for both learning communities and control
groups is shown in Table 3.2. The time table was determined by allowing three
weeks for the census date to pass to ensure a stable student population. After the
first three weeks, the remainder of the semester was divided in half.
Table 3.2 Calendar of Events for Phase Two
Weeks 1-3 Explain learning community and selected semester Projects Explain syllabus and Semester Project
Weeks 4-7 Pre-measure Observation 1 Pre-measure Observation 1
Weeks 8- 10 Post-measure Observation 2 Interviews Data Collection on passing rates and retention Post-measure Observation 2 Interviews Data Collection on passing rates and retention
Data Analysis
Descriptive measures in the form of a questionnaire were used to provide a
profile of student perceptions of the factors that were important to their success in
the first year course. The profile provided a case by case analysis of what factors
were important to each student.
52


Qualitative analysis was used to identify emerging patterns from the questionnaire
results that were significant to the first year learning experience in biology courses.
Qualitative research can begin with a target of interest, in this case the hypothesized
factors that play an important role in the first year biology course experience, but
leave the door open for other significant factors (Krathwohl, 1998). Patterns of
variables that were more frequently recorded with the completion or noncompletion
of the course were recorded.
The data gathered from the questionnaires provided an insight as to the
importance of each variable to the student, as well as a comparison of the variables
as they relate to student success in the first semester science courses. The
questionnaire data was used to 1) gain an understanding of the variables important to
the first semester science students experience, and 2) identify whether the variables
(predisposition, peers, discipline, professor, lab) could be used as a predictor of
completion of the general biology courses. The design involved recording the Likert
portion of the questionnaire results and averaging the numbers for each individual on
all the questions attributed to a variable. The scale on the averages for the variables
ranged from 1-5, reflecting the Likert scale the students used to answer the
questionnaire. For example, the variable predisposition included questionnaire
statements: I enjoy reading scientific literature on my own; I do not expect to pass
my science courses without extra help; and I enjoy solving problems of a scientific
nature. The student responses to these statements were averaged and used as one of
53


the five variables in the discriminant analysis. Two items on the questionnaire were
reverse-scored (predisposition and discipline) so that a more positive score would
correspond to a higher position along the continuum of the construct being measured.
Completion of the course was given the score of 1 and noncompletion was given
the score of 0.
Qualitative measures in the form of two observations were conducted to answer
research question number four by identifying any differences in student interactions,
confidence and/or interest between learning community courses and control courses
(see Appendix D for the observation form). According to Krathwol 1998, The
qualitative researcher is concerned with how individuals perceive their world and
sees reality as an interpretation of these perceptions.... (p.23). Observations,
interviews and questionnaires can get a different perspective of the variables that
students perceived were important to their success in their first year biology courses
than quantitative measures between learning communities and control passing and
retention rates. Student interest and enthusiasm are difficult to measure
quantitatively, yet important in any course.
The learning community evaluation involved quantitative measures
comparing the completion rates and retention rates between the learning community
courses and the control groups. Chi-square analyses were used to identify whether
the variables of completion and retention data were independent between learning
communities and control groups. The variables included the learning community
54


courses as the experimental courses, and traditional courses of the same course as
controls.
The dependent variables for the BIO 111 courses were the passing rates of the
courses and the numbers of students enrolling in the sequential science courses. A
chi-square test was used identify any differences between experimental and control
groups for the second semester courses, BIO 112; the dependent variable was the
passing rate of the courses. The passing rate alone was used as a dependent variable
in BIO 112 because there is no subsequent course in the general biology sequence
following BIO 112. A two-tailed test was used with an a level of 0.05 as the level
of significance.
Summary
In summary, the goal of my research was to examine the effects of a learning
community on the entry-level science students perceptions of what factors are
important to their success in their science courses. Initially, a pre-measure identified
the factors students perceived to be important to their success in entry-level science
courses. This pre-measure was followed in two courses with a learning community
treatment that will place a strong emphasis on scientific inquiry, collaboration and
interaction in the classroom. The treatment was followed by a post-measure to
reassess the factors they believe to be important to their success in the class. A
55


quantitative measure in the form of a chi-square analysis was used to compare
completion and passing rates between learning communities and control classes.
Additional qualitative measures in the form of observations and interview were
conducted to assess differences in student participation and interest in the content
and scientific inquiry when compared to the controls.
56


CHAPTER 4
RESULTS
This study used qualitative and quantitative methods to investigate the variables
that students perceived were important in their entry-level biology courses, and to
determine whether strategies designed to increase student engagement in these
courses, in particular leaning communities, increased the passing rates and retention
in these courses.
The learning community treatment added to the general biology courses
focused on increasing student participation in the scientific process and collaboration
with their professors and peers. The students involved in the learning community
worked collaboratively to conduct a research project designed to increase their
exposure to the science they were studying.
The results are reported in two parts: the first part involved the analyses of the
students perceptions of variables important to their success, and the second part
involved the analyses of the learning community treatment.
The data in the results section is presented by research question. The data
analysis that answers the research questions are presented following each research
question.
57


Research Question Number One
What variables are perceived by first semester students as important to their
success in science courses?
Question one was answered by surveying students in first year science courses
regarding the variables they perceived were important to their success in their
science courses. The answers were compiled and the five most frequently mentioned
variables were used as themes for the questionnaire. The questionnaire data was
used to identify whether the variables collectively and separately could be used as
predictors for completion of the course.
Qualitative data from interviews and the open-ended questionnaire questions were
used to identify the students perceptions of what was important to their success in
their science courses. The interviews and open-ended questions gave students the
opportunity to express their opinions more in depth than the Likert portion of the
questionnaire. The data allowed the researcher to identify patterns that supported the
quantitative data.
Phase I Survey
Phase I survey data was collected from 127 students enrolled in seven different
entry-level biology and chemistry courses. The data from phase I results showed that
the five most frequently listed variables were in order of frequency of response:
58


discipline, professor, predisposition, hands on training and study groups. These top
five factors were used as the variables in the questionnaire.
Research Question Number Two
Would the perceived variables be accurate predictors of completion of
the students science courses? A discriminant analysis was used to answer whether
the variables perceived by the students as important to their success in their science
courses were an accurate predictor of completion of their science courses. A pre-
test questionnaire was given to the students in all of the courses at the beginning of
the semester. The purpose of the questionnaire was to expand on the themes the
students felt were important in the phase I survey. The pre-test was given at the
beginning of the semester before the students became familiar with the professor,
class or lab. The goal was to see if the themes were a predictor for the students
success in their science courses.
Phase II Questionnaire
The Likert portion of the questionnaire given as the pre-measure was analyzed
using a discriminant analysis. A discriminant analysis is a series of analyses
designed to compare, in the case of this study, whether the variables students
perceived were important to their success in the entry level science courses could be
used as predictors of completion of the course. A score of 0 on the completion
59


(COMPL) of the course means the student failed to complete the course; a score of 1
means the student completed the course. The group statistics can be seen in Table
4.1. Table 4.2 and Table 4.3 show the Tests of Equality of Group Means and the
Pooled Within Group Matrices, respectively for each factor, n= 93.
The group means for the professor variable in Table 4.1 (PROF) show that there
was a significant difference between the means for the professor of those that
completed the course and those that did not complete the course. Data from the
Tests of Equality of Group Means describe which variable is a significant predictor
of whether a student will complete their science course. Results from Table 4.2
show students that rated having a good science professor (PROF) was a significant
predictor of course completion; the analyses showed that the PROF variable was
significant at the .05 level. The F ratio for the variable was F = 4.701 p = 034. The
LAB variable was significant at the .10 level; the F ratio for the LAB variable was F
= 2.934p = 092. Table 4.3 shows Boxs Test of Equality of Covariance Matrices
results; a F ratio = .104 shows that the results do not violate the assumptions of the
discriminant test. The classification results, (See Table 4.4) show that 67.8% of the
cross-validated groups were correctly classified; therefore the variables predicted
who would complete the course correctly 67.8% of the time.
Results from the discriminant analysis, Pooled Within-Groups Matrices tests
showed a correlation between predisposition and peers (PROF and DISCL)
variables; the correlation coefficient was r = .697. This correlation suggests a
60


correlation exists between those students that believed that a good science professor
was important to their success also believed they had to be self-regulating in order to
be successful in their science courses.
The questionnaire was given to the students at the end of each course. The scores
from the pre-measure and post-measure questionnaires were analyzed using an
ANOVA. The results from this analysis did not reveal any changes in the answers
from the beginning to the end of each course.
Discriminant
Table 4.1. Discriminant Analysis: Group Statistics
Group Statistics
COMPL Mean Std. Deviation Valid N (listwise)
Unweighted Weighted
0 PROF 1.3350 .47376 2 2.000
PEERS 3.3300 1.41421 2 2.000
PREDIS 4.3350 .94045 2 2.000
DISCIP 2.3300 .00000 2 2.000
LAB 2.6700 1.41421 2 2.000
1 PROF 1.8333 .88645 6 6.000
PEERS 2.4450 .83409 6 6.000
PREDIS 2.5000 .75012 6 6.000
DISCIP 2.1117 .54216 6 6.000
LAB 2.7800 .65501 6 6.000
Total PROF 1.7088 .80409 8 8.000
PEERS 2.6662 .97493 8 8.000
PREDIS 2.9588 1.11795 8 8.000
DISCIP 2.1662 .46922 8 8.000
LAB 2.7525 .77121 8 8.000
61


Table 4.2. Discriminant Analysis: Tests of Equality of Group Means
Tests of Equality of Group Means
F df1 df2 Sig.
PROF 4.70 1 57 .034
PEERS .573 1 57 .452
PREDIS .019 1 57 .890
DISCIP .472 1 57 .495
LAB 2.93 1 57 .092
Table 4.3. Discriminant Analysis: Boxs Test of Equality of Covariance Matrices
Test Results
Box's M 26.571
F Approx. 1.480
df1 15
df2 1933.087
Sig. .104
Tests null hypothesis of equal population covariance matrices.
62


Table 4.4. Discriminant Analysis: Classification Results for Prediction
Classification Result#,c,d
COMPL Predicted Group Membership Total
0 1
Cases Selected Original Count 0 7 6 13
1 9 37 46
% 0 53.8 46.2 100.0
1 19.6 80.4 100.0
Cross-validateda Count 0 5 8 13
1 11 35 46
% 0 38.5 61.5 100.0
1 23.9 76.1 100.0
Cases Not Selected Original Count 0 5 5 10
1 7 16 23
Ungrouped cases 0 1 1
% 0 50.0 50.0 100.0
1 30.4 69.6 100.0
Ungrouped cases .0 100.0 100.0
a. Cross validation is done only for those cases in the analysis. In cross validation, each case is
classified by the functions derived from all cases other than that case,
h. 74.6% of selected original grouped cases correctly classified.
c. 63.6% of unselected original grouped cases correctly classified.
d. 67.8% of selected cross-validated grouped cases correctly classified.
Table 4.5. Discriminant Analysis: Pooled Within-Groups Matrices
Pooled Within-Groups Matrices
PROF PEERS PREDIS DISCIP LAB
Correlation PROF 1.000 .465 .246 .697 .589
PEERS .465 1.000 .457 .405 .458
PREDIS .246 .457 1.000 .198 .385
DISCIP .697 .405 .198 1.000 .429
LAB .589 .458 .385 .429 1.000
Because only the reliability coefficients for the professor (.8924) and the peers
(.6225) variables were reliable, therefore the discriminant was run again containing
only those two variables. The classification results for the discriminant are shown in
Table 4.6. The results show that for these two variables, the cross validated cases
63


were correctly classified 74.6% of the time. The professor variable shows
significance at F = 4.701, p = .034. Boxs M test for assumptions shows a result of
F = 2.563, p = .053, see Table 4.7.
Table 4.6. Discriminant Classification for Professor and Peer Variables
Classification Result£>cd
COMPL Predicted Group MembershiD Total
0 1
Cases Selected Original Count 0 5 8 13
1 7 39 46
% 0 38.5 61.5 100.0
1 15.2 84.8 100.0
Cross-validateda Count 0 5 8 13
1 7 39 46
% 0 38.5 61.5 100.0
1 15.2 84.8 100.0
Cases Not Selected Original Count 0 5 5 10 '
1 8 15 23
Ungrouped cases Q 1 1
% 0 50.0 50.0 100.0
1 34.8 65.2 100.0
Ungrouped cases .0 100.0 100.0
a- Cross validation is done only for those cases in the analysis. In cross validation, each case is
classified by the functions derived from all cases other than that case.
b. 74.6% of selected original grouped cases correctly classified.
c. 60.6% of unselected original grouped cases correctly classified.
d. 74.6% of selected cross-validated grouped cases correctly classified.
Table 4.7. Discriminant Analysis: Boxs Test of Equality of Covariance Matrices
For Professor and Peers
Test Results
Box's M 8.213
F Approx. 2.563
df1 3
df2 7009.929
Sig. .053
Tests null hypothesis of equal population covariance matrices.
Focus Groups
The purpose of the focus groups was to give students the chance to express their
views on the variables they felt were important to their success. The focus groups
64


were not facilitated by the professor of the course, and the information was not
shared with the professor. Focus groups from each course consisted of four students
chosen at random from the course roster. The interviews were recorded and
transcribed by a person familiar with the process. (See Appendix 5 for interview
questions) The coding categories are shown in Table 4.8, 4.9 and 4.10. The three
major coding category headings: retention strategies, course logistics and community
interactions reflected the design of the interview questions. Questions regarding
retention strategies were aimed at identifying themes that were important to the
students perceptions of success in their courses. For example, what in particular
helped you to pass the course, and what were the most effective teaching techniques
or assignments that helped you understand the science content in the course? Focus
group questions that pertained to the students career goals and reasons for taking the
science course were coded under the heading course logistics. Since the students
placed an importance on the role their peers played in their success, a separate
coding category was created under the heading community interactions. Student
comments that identified study group importance, either formal or informal, and
comments regarding the collaborative effort the students used to complete then-
research project or learning communities were recorded in the community
interactions category. Examples of informal study group comments included Pretty
much we did not have study groups, but we did talk before class, when we ran into
each other and before the test we would ask each other questions and ask each other
65


for help. Several students made comments regarding the learning community as
important to helping them pass their course. These comments were also recorded in
the community interactions category. For example, when one student was asked
about effective teaching techniques he stated, I guess the learning community part
did help me understand a bit about each part of the class because it brought a lot of it
together in one area where we had to research it.
Some of the coding categories had to be expanded to include the terms negative
and positive due to some of the negative responses in regards to teaching techniques,
research projects and study strategies. For example, there were several negative
responses about the lab portion of the courses. These responses were coded under
retention strategies, teaching techniques because the labs are techniques or
strategies added to most science courses to provide a hands on, collaborative
component to the courses. The negative coding category was added to identify these
negative responses to the category. The frequency represented on the vertical axis
on the graphs in figures 4. la 4. lc refers to the frequency the topic was mentioned
in the group discussion. The topic was counted and recorded if it was made by
different individuals in the focus group, and if it was mentioned multiple times by
the same individual. No topic was mentioned more than twice by the same
individual.
The same coding categories were used to code the faculty interviews because the
questions were similar to the questions used in the student interviews. The purpose
66


of the faculty interviews was to gain a perspective of the themes they perceived were
important to their students success. Since the study did not focus on faculty
perceptions of success, the data was not graphed. When asked about the outcomes
faculty expected their students to take away from their class, there were several
comments that supported the idea of wanting their students to think like a scientist
or use active learning to understand the content. The faculty also described their
enthusiasm for the collaborative research, for example, In BIO 112, can we do an
ecology project? We wrote an NSF grant to do some native plantings around the
Lowry area and the students could all collaborate on one ecology project. Some of
the faculty extended the use of the learning community requirements to include
several in class discussions that went beyond the content material.
Results from the interviews are shown in Figures 4.1a, b and c. Results from the
retention strategies graph parallel the results from the questionnaire data in that they
reveal that students mentioned good professors and positive teaching techniques as
important themes in their success, see Figure 4. la.
67


Table 4.8. Retention Strategies Coding Categories
Retention Strategies______________________
Teaching Techniques_______________________
Positive References to Teaching Techniques
Negative References to Teaching
Techniques________________________________
Research Project__________________________
Negative References to Research Project
Positive References to Research Project
Enjoy Topic_______________________________
Good Professor____________________________
Study Strategies__________________________
Positive References to Study Strategies
Negative References to Study Strategies
Interviews: Retention Strategies
(P) (N)
LC-BI0111
CNT-BI0111
LC- B10112
CNT-BI0112
Coding Categories
Figure 4.la. Student Responses to Retention Strategy Questions
Figure 4.1b describes student responses to questions regarding the interactions of
other students and collaboration between students in both studying for the course
exams and the completion of the research project. Although there were not many
68


responses regarding the research projects in either category 4. la or b, all of the
responses were made by students in the learning communities.
Table 4.9. Community Interaction Coding Categories
Community Interactions
Personal Material______
Study Groups___________
Research Material______
Content Material
Interviews: Community Interactions
i






m ?3r
I g
Personal Material Study Groups Reseach Material Content Material
LC-BI0111
0CNT-BI0111
OLC- B10112
CNT-BI0112
Coding Categories
Figure 4. lb. Student Responses to Interaction Questions
Student responses to questions pertaining to course logistics were also collected
and can be seen in Figure 4. lc. The questions pertaining to course logistics
identified student responses from I need this course to graduate to I need to know
the material to graduate. The student responses do not differ much in any category
between learning community and control groups.
69


Table 4.10. Course Logistics Coding Categories
Course Logistics___________
Course Required____________
Time Barriers______________
Content Difficulty_________
Course Convenience_________
Content Knowledge__________
CL-CK Positive References
CL-CK Negative References
Interviews: Course Logistics
25
Coixse Required Time Banter Content Dtfficuttly Course Content Knowledge Content Knowledge
Convenience (P) (N)
Coding Cat eg ones
Figure 4.1c. Student Responses to Questions about Course Logistics
Open-Ended Questionnaire Questions
The purpose of the open-ended questions was to allow students to expand on their
answers to the questions in the questionnaire. The coding scheme used for the
interviews was also used to code the open ended questions in the questionnaire, see
Tables 4.8, 4.9 and 4.10. The solid bars represent questionnaire responses from the
70


beginning of the semester; the striped bars represent questionnaire responses from
the end of the semester. Figures 4.2a, b and c describe the results of the open-ended
questions in the questionnaires.
The results from the open-ended questionnaires paralleled the results from the
interviews; good teaching techniques were mentioned frequently in all groups as
important to their success in their courses, see Figure 4.2a.
Comparisons between student responses to the open-ended question, what
benefits do you get from your laboratory exposure in your science courses, contradict
their responses to the survey results. The survey results revealed students identified
a hands on variable as important to their success in their science courses. The
negative student responses to the open-ended question above were complaints
regarding the laboratory portion of their course, see RS-TT(N) in Figure 4.2a. It may
be that the labs are not meeting student expectations for a successful experience in
their general biology courses.
71


Questionnaire: Retention Strategies
LC-BI0111-1
SLC-BI0111-2
CNT-BJ0111-1
BCNT-BIO 111-2
LC-BI0112-1
DLC-BI0112-2
CNT-BI0112-1
CNT-BIO 112-2
Coding Categories
Figure 4.2a. Student Responses to Retention Strategy Questions
Although community interaction in the form of study groups were important in all
the courses, only the learning community groups referenced the research project
material as aiding to their success in their science courses, see Figure 4.2b.
72


Questionnaire: Community interactions
Personal Material Study Groups Research Material Content Material
Coding Categories
Figure 4.2b. Student Responses to Interaction Questions
Student responses were collected regarding questions pertaining to course
logistics in order to track responses to questions such as I am taking this class
because it is part of my degree. The responses suggested that the content
knowledge gained from these courses was important to students in both learning
community and control sections, see Figure 4.2c.
73


Questionnaire: Course Logistics
50
LC-BI0111-1
HLC-BI0111-2
CNT-BI0111-1
BCNT-BIO 111-2
LC-BI0112-1
LC-BI0112-2
CNT-BI011M
CNT-BIO 112-2
Course Required Time Barrier Content Difficulty Course Content Content
Convenience Knmfodge(P) Knowledge (N)
Coding Categories
Figure 4.2c. Student Responses to Course Logistics Questions
Research Question Number Three
Will more students who experience the first semester sequence in a learning
community be more likely to complete the course and enroll in sequential courses
than students in the traditional first semester course?
Research question number three was answered using a chi-square analysis to
identify differences between passing rates and retention data between learning
communities and control.
74


Chi-Square Analysis
Quantitative analysis was used to answer the question: will students involved in
learning communities are more inclined to continue to take science courses? Chi-
square analyses were performed to identify any effects the learning communities had
on the passing and retention rates on the first semester general biology courses. The
dependent variables were the passing rates and retention. The experimental and
control groups were classified numerically and identified as JD2; the learning
communities were identified as the number 1 and the control groups as the number
2, see Tables 4.11, 4.12 and 4.13.
Assumptions for a Chi-Square Test
Random Sampling. Chi-square tests assume random sampling of the sample.
This assumption was met in a random-like fashion because there was no systematic
bias in the assignment of the learning community verses the control courses. The
recitations were listed in the schedule as an extra one credit course, which very few
students actually sign up and pay the extra credit hour tuition.
Independence of Observations. The assumption of independence of the
observations required that your observations are independent of each other
(Gravetter and Wallnau, 1988, Norusis, 2002). The independence of observations
could not be assured in this study, since interaction among the students was central to
the topic of investigation, and this is a technical limitation of the study.
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The sample size of 76 students for the learning community (experimental) and 68
students in the traditional (control) group increase the power of the statistical test.
The results show no significant difference between the groups in terms of passing
rates or retention, X2 (2, n = 82) =. 195, p =. 659, see Table 4.11 and X2 (2, n = 82)
= .195, p = .659, respectively, see Table 4.12.
A chi-square test was also conducted on the second semester courses, BIO 112.
A test to measure retention was not conducted because there was no subsequent
course by which to measure retention. The dependent variable was passing rate
between the learning community courses and the traditionally taught courses. The
results show no significant difference between the groups in terms of passing rate, X2
(2, n = 62)= 1.032, p = .310. see Table 4.13.
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Table 4.11. Chi-Square Table for BIO 111 Courses: Passing Rate
Chi-Square Test Frequencies
PASSING COURSE Crosstabulation
Count
COURSE Total
learning community control
PASSING fail 14 8 22
pass 29 31 60
Total 43 39 82
Chi-Square Tests
Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided)
Pearson Chi-Square 1.512 1 .219
Continuity Correction .960 1 .327
Likelihood Ratio Fisher's Exact Test 1.529 1 .216 .318 .164
Linear-by-Linear Association 1.493 1 .222
N of Valid Cases 82
a- Computed only for a 2x2 table
b- 0 cells (.0%) have expected count less than 5. The minimum expected count is
10.46.
Table 4.12. Chi-Square Table for BIO 111 Courses: Retention
Chi-Square Test Frequencies
RETENTIO COURSE Crosstabulation
Count
COURSE- Total
learning community control
RETENTIO stopped 33 24 57
took 112 10 15 25
Total 43 39 82
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Table 4.12. Chi-Square Table for BIO 111 Courses: Retention (continued)
Chi-Square Tests
Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided)
Pearson Chi-Square 2.23115 1 .135
Continuity Correction3 1.571 1 .210
Likelihood Ratio Fisher's Exact Test 2.239 1 .135 .156 .105
Linear-by-Linear Association 2.204 1 .138
N of Valid Cases 82
a- Computed only for a 2x2 table
b. 0 cells (.0%) have expected count less than 5. The minimum expected count is
11.89.
Table 4.13. Chi-Square Table for BIO 112 Courses: Passing Rates
Chi-Square Test Frequencies
PASSING COURSE Crosstabulation
Count
COURSE Total
learning community control
PASSING fail 4 4 8
pass 31 23 54
Total 35 27 62
Chi-Square Tests
Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided)
Pearson Chi-Square .156 1 .693
Continuity Correctiorf .000 1 .990
Likelihood Ratio Fisher's Exact Test .154 1 .694 .719 .490
Linear-by-Linear Association .153 1 .696
N of Valid Cases 62
a- Computed only for a 2x2 table
b. 2 cells (50.0%) have expected count less than 5. The minimum expected count is
3.48.
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Research Question Number Four
Do students believe the learning community variables were important to then-
success in entry-level science courses?
Qualitative methods used to answer question four included analysis of
observation data, and data from the community interaction categories in the
interviews and open-ended questionnaire data. The data described by the tables and
graphs from the observations explore the importance that students placed on the
learning community addition to their science courses. The goals of the learning
community were to increase collaboration between professor and student and also
between students, and to introduce students to scientific inquiry. The purpose of the
observations was to examine the implementation of the learning communities in the
classroom.
Observations
The observation form was divided into two sections: a section in which the
observer recorded the frequency and type of questions or statements between the
students and the professor, and a section in which the observer recorded the
frequency and type of questions or statements between the students. (See Appendix
C for observation form)
Tables 4.14, 4.15 and 4.16 show the coding categories for the observations. The
solid lines represent observations performed at the beginning of the course; the
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striped lines represent observations performed at the end of the course. The coding
categories under the heading basic content describe interchanges between the
professor and the students regarding the content of the course. The category
expansion beyond material under the basic content heading was of particular
importance in identifying implementation of the learning communities. The data in
this category reflected student responses to the link between the research they were
conducting and the lecture content. An example of a question coded expansion
beyond material included the following question asked by a student in a learning
community class when studying the topic of genetics: how can they use an enzyme
for blue pigment that was found in humans to make roses blue? The student had
read an article about scientists finding a gene for blue pigment in the human genome,
and maybe this gene could be inserted into roses to make them blue.
The results of the observation are shown in Figures 4.3a, b and c. Overall, the
frequency of the responses in most of the courses was low. Students in learning
communities showed a slight increase in discussion among themselves as seen in
Figure 4.3a; the coding categories labeled CI-CM shows a slight increase for the
learning community sections. There is also a slight increase in the material
regarding the research projects and content material as can be seen in the coding
category CI-RP in Figure 4.3a.
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Table 4.14. Coding Categories for Observations: Basic Content
Basic Content_____________________
Clarify Content Material__________
Expansion Beyond Content Material
Research Material
Observations: Basic Content
so
45
40
LC-BI0111-1
B LC-BI0111-2
LC-BKD112-1
BLC-BI0112-2
CNT-BI0111-1
BCNT-BI0111-2
CNT-BK)112-1
CNT-BI0112-2
n
Content Mrferiat
Expand Cortfent
Research MMeriai
Codmg Categories
Figure 4.3a. Student Responses Regarding Basic Content Material
The coding categories under the heading Community Interactions describe the
interchanges between students. It was expected that there would be more interaction
between students in the learning community courses, and more references to the
research project material. Although the overall number of responses is low, the
students in the learning communities referred to the research project more than the
control groups, see CI-RP in Figure 4.3b. The coding category CI-CM identified
content material discussions between students that referred to the research project.
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The students in the learning communities discussed the research project material
among themselves more than the students in the control groups, see CI-CM in Figure
4.3b.
Table 4.15. Coding Categories for Observations: Community Interactions
Community Interactions_____________________
Interactions Regarding Personal Material
Interactions Regarding Study Groups
Interactions Regarding Research Projects
Interactions Regarding Content Material
Regarding Research Projects________________
Observations: Community Interactions
Figure 4.3b. Student Responses Identifying Community Interactions
The coding categories under the heading Course Logistics describe interchanges
between professor and student regarding exams, grades or project material logistics.
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The results show less exam clarification is needed at the end of the semester for
almost all courses, see CL-EM in Figure 4.3c.
Table 4.16. Coding Categories for Observations: Course Logistics
Course Logistics________________
Clarify Exam Material___________
Clarify Grade Material__________
Clarify Research Project Material
Observations: Course Logistics
Figure 4.3c. Student Responses Regarding Course Logistic Material
Summary
The analyses of the research questions involved a variety of qualitative and
quantitative analyses.
The variables students perceived as important to their success in entry-level
science courses included their predisposition towards science, their professors and
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peers, their discipline and their hands on training. Discriminant results revealed that
the variables could predict students completion or noncompletion of their science
course 67.8% of the time. The professor variable was significant at the p = .05 level
indicating the students thought having good professors was important in their
success. The lab variable was also an important variable, significant at the p = .10
level. This would indicate that the students felt their lab experience also played a
large role in their success.
Qualitative tests reveal that students in learning communities show a slight
increase in community interactions and willingness to explore the content material
beyond the material needed for the class, however these results were not much
higher than the control courses.
Chi-square tests for the first or second semester courses reveal no significance
between learning communities and controls for passing rates. Chi-square tests also
reveal no significance for retention rates between learning communities and control
groups in the first semester courses.
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CHAPTER 5
DISCUSSION AND CONCLUSIONS
This study examined 1) entry level science students perceptions of factors
important to their success in science courses, 2) whether entry level science students
engagement in their science courses would be increased by the addition of learning
communities, and 3) whether any increased engagement in the learning communities
would show as an increase in passing rates and retention in these courses.
A quasi-experimental design was used to compare learning community courses
and traditionally taught science courses. Qualitative methods were also used to
describe student engagement levels between the learning communities and traditional
courses.
The results, presented in Chapter 4, showed that the students identified the
following factors as important to their success in their science courses: discipline,
good professors, a predisposition toward science, hands on training and discussions
with peers and professors. The professor and peers variables were predictors of
completion for students in general biology courses 74.6 % of the time. Chi-square
results show no significant difference between learning community courses and
controls in terms of passing rates or retention.
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Qualitative results showed a slight increase in the number of students engaged in
the scientific process as seen by the observations, interviews and open-ended
questionnaire statements. This slight increase is reflected in the student statements
and questions pertaining to the content material beyond the course content, and their
interest in the group research projects and study groups.
In the rest of the chapter, I discuss the findings, describe explanations for
alternate views regarding student engagement in the courses, examine the use of
learning communities as a method of student engagement, discuss limitations of the
study, and describe topics for further studies.
Research Question Number One
What variables were perceived by the students as important to their success in
their science courses?
Results from the questionnaire and interview analyses revealed that students
perceived their professors to be important to their success in their general biology
courses, which is not surprising. Students often prefer one teaching style to another,
and in a community college there are several sections of the same course taught by
different professors. It is not unusual for students to withdraw from one class to take
it again with a different professor. News regarding professors teaching styles and
their level of difficulty travels quickly among students. Students will also form a
fierce loyalty to a professor if they do well with their teaching style.
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Research Question Number Two
Would the perceived variables be accurate predictors of completion of the
students science courses?
Results from the discriminant analyses showed that the cross-validated cases were
correctly identified for the professor and peers variables 74.6% of the time, and the
professor variable was significant at the .05 level. The students responses to focus
group questions also indicated that good professors were important to their success
in their current science course. These results supported the findings from the first
research question in which the students listed good professors as an important
variable in their success in their science courses. Additionally, the students
responses to the open-ended questionnaire questions indicated that good teaching
techniques were an important part of their success.
Research Question Number Three
Will more students who experience the first semester sequence in a learning
community be more likely to complete the course and enroll in sequential courses
than students in the traditional first semester course?
The first year of science courses for all entry-level science students forms a solid
base for the rest of their science education. In the year long general biology courses,
the students learn the terminology and the intense content that will form that base for
their careers. This is a heavy load for the first year college student with good study
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skills and habits. Those students without these skills quickly become overwhelmed
and perceive their goals to be unachievable; science may be perceived as boring and
difficult.
Learning communities were added to the first year general biology courses to
increase the first year science students exposure to the scientific process and
increase student interaction in these courses. The goal for the learning communities
was to increase the number of students passing the first semester general biology
course and enrolling in the second semester general biology course.
The chi-square analysis did not reflect any differences in either passing grades or
retention between the learning community sections and the traditionally taught
sections. However, these results may not be an accurate reflection of the success of
learning communities as a method to increase student passing rates and retention.
Factors that need to be considered when reflecting on these results include: 1)
Several students in the course were required to complete the first semester course for
their degree. These students should not be included in retention rate calculations.
2) Not all students take the two general biology courses in sequential semesters.
Some of the students in each class take the second semester course, but not
immediately. Some of the students wait for a particular professor to teach the second
half; some wait because of scheduling difficulties. 3) Many students enter the first
semester general biology course at CCD severely under prepared in reading and
study skills. Many of these students take the course two to three times before
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passing, or drop out completely. All of the factors listed are difficult to control, but
need to be considered.
Research Question Number Four
Did students believe that the learning community variables were important to
their success in entry-level science courses?
The results from observations made in the classrooms of both learning
community and traditionally taught science courses indicated that students interacted
with each other more in the learning community sections. Additionally, students in
the learning community sections mentioned the research project material they were
conducting more often than those students in the traditionally taught sections.
One of the goals of the learning communities was to promote students use of the
research content material to expand their knowledge of biology beyond the subject
matter being taught in the classroom. Although there was an increased dialog
between students in the learning community sections, the use of the research content
to expand on and support the content being taught was not notable in the overall
analysis. One possible explanation for the research content not considerably
expanding on the students knowledge was because there was not enough time for in-
depth discussions of the material in some professors classrooms. There is a constant
pressure on the part of the professor to make sure the content material is told to the
students, unfortunately this may take precedence over inquiry based learning
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strategies in the classroom. Many professors believe they must tell the students the
information in order for the students to be responsible for the information. This
belief is fueled by the understanding of the teachers job; if they do not actually
present the content material, they are not doing their job as a teacher. The need for
some of the professors to tell the content material to the students was a difficult
hurdle to overcome. For some, the research project material that was to be used to
expand students content knowledge was extra material to be presented, rather than a
tool to present the content of the course.
Another factor that may have contributed to the absence of the expansion of
biology content was the research project design. The requirement of a research
project in all sections of the general biology courses was a necessary requirement for
comparison purposes that developed its own set of confounding influences. Several
students in the traditionally taught courses attended the one hour sections of the
learning communities. After conversations with the students, several students in the
control groups exhibited an enthusiasm for the experimental research projects. The
professors in the traditionally taught courses also conversed with professors of the
learning community courses and encouraged their students to join the research
projects that were being done in the learning communities.
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Summary
In summary, students felt that their professors and the teaching strategies used in
their science courses were important variables to their success in these courses. The
professor variable alone was a predictor of success in entry-level science courses.
The learning community addition to these entry-level science courses did not show a
difference in passing rates or retention when compared to traditionally taught
courses, but it may be that those data do not accurately reflect the benefits of
learning community additions at CCD. Qualitative results showed an increase in
communication between students in learning community courses, and an increased
enthusiasm for their science studies.
Limitations of Study
The major limitation of the study was the small sample size, four classes, of
learning community classes. An increased sample size would enhance the reliability
of the tests conducted to identify possible differences in passing rates or retention
rates between the learning community courses and the control courses. It was
difficult to interpret the results because of the intermingling of professors, learning
communities and projects between the learning community and controls. Although
not a bad feature in practice, methodologically there was too much communication
between the learning community groups and the controls making clear distinctions
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impossible to draw. These factors, described above made it was difficult to identify
any one variable as responsible for passing rates and retention rates.
Future Studies
Future studies might be more informative using a longitudinal design. Tracking
students whose programs allow for subsequent science courses would be similarly
informative. Since professors play a large role in students success, tracking students
at the beginning of the entry-level science courses may be informative to CCD.
Students will often make decisions to drop and add a course within the drop/add
period, usually within the first two weeks of a course. It might be interesting to
examine the number of students a professor begins with on day one and the number
they have on census day, two weeks later and to then track those students who
withdrew. The results from question number one and two would point to the hiring
and training of entry level science professors as a crucial component to CCD
students success. Additionally, it might be informative to find out what teaching
styles, or teaching techniques, professors rated as good by students use in their
classrooms. It might also be helpful to know if there is a correlation between
strategies used by these professors and the teaching styles the students rated as
helpful to their success.
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