STUDENT SUCCESS IN AN INQUIRY-BASED
LABORATORY: THE EFFECT OF
PRE-CLASS ACTIVITIES AND
STUDENT PREPARATION
By
MELISSA ROSE GENTRY
Bachelor of Science in Secondary Science Education
Oklahoma State University
Stillwater, OK
2002
Submitted to the Faculty of the
Graduate College of the
Oklahoma State University
in partial fulfillment of
the requirements for
the Degree of
MASTER OF SCIENCE
July, 2005
STUDENT SUCCESS IN AN INQUIRY-BASED
LABORATORY: THE EFFECT OF
PRE-CLASS ACTIVITIES AND
STUDENT PREPARATION
Thesis Approved:
Donald P. French
Thesis Adviser
Margaret S. Ewing
Richard J. Bryant
A. Gordon Emslie
Dean of the Graduate College
ii
ACKNOWLEDGEMENTS
There are many people who assisted me with the production of this thesis and provided
guidance during my graduate studies. First, I would like to thank my committee
members.
Dr. Donald French, my advisor, gave me the opportunity to work both for him
and with him. I can never fully describe the irreplaceable guidance he provided me on
this thesis as well as my coursework, teaching philosophy, and attitude toward research.
I thank him for always keeping his door open and driving me to excel.
Dr. Margaret Ewing was always available for assistance and support. Her
perspectives on my thesis and on being an instructor and graduate student were
invaluable to me. I thank her for being receptive to this project and willing to assist me
in my pursuit to graduate.
Dr. Richard Bryant provided constant encouragement. His positive outlook on
this project (and life in general) made it such a pleasure to work with him. I also would
like to acknowledge him for introducing me to scientific inquiry and being such an
influential instructor in my life.
I would like to recognize Dr. Carla Goad for her assistance with the statistical analyses of
this project. I truly appreciate the time she devoted to coding SAS with me. I would also
like to thank Dr. Tim O’Connell and Dr. Joe Bidwell among the many other zoology
faculty members that either gave me advice or support. For direction and understanding,
I relied a great deal on other graduate students, especially Cathy, Kathleen, Moria,
Tarren, Tiffany, Sara, Brooke, and Mike. I would also like to thank Dr. Connie Russell
for her encouragement, kindness, and unique perspective.
Finally, I would like to express gratitude for my loved ones and their contributions: my
mom, for acting as my life and career counselor; my dad, for his unwavering confidence
in all that I do; Ken and Rhonda, for loving me as their own; my Bobo, James, for being
my source of inspiration; my sisters and their families, for their understanding and
patience; and my friends, especially my girls, Meagan, Jennifer, Melissa, and Amy, for
their constant support and praise. I would not have accomplished any of this without
their love and devotion.
iii
TABLE OF CONTENTS
Chapter
Page
I. INTRODUCTION ......................................................................................................1
The Problem.............................................................................................................1
Previous Research....................................................................................................4
Course Design..........................................................................................................6
Objectives of the Study............................................................................................8
II. METHODS................................................................................................................9
Subjects ....................................................................................................................9
Analyses of Student Grades .....................................................................................9
Regression Analyses ........................................................................................10
Analyses of Covariance ...................................................................................12
Analyses of the Student Survey .............................................................................13
III. RESULTS ..............................................................................................................15
General Regression Models ...................................................................................16
ANCOVA Including Lab Investigation .................................................................24
ANCOVA Including Section .................................................................................34
ANCOVA Including Teaching Assistant...............................................................44
Analyses of the Student Survey .............................................................................54
Summary of Results...............................................................................................58
IV. DISCUSSION........................................................................................................62
Findings..................................................................................................................62
General Regression Models .............................................................................62
Sources of Variation ............................................................................64
ANCOVA Including Lab Investigation ...........................................................67
ANCOVA Including Section ...........................................................................69
ANCOVA Including Teaching Assistant.........................................................71
Analyses of the Student Survey .......................................................................75
iv
Conclusions............................................................................................................76
Recommendations for Future Research .................................................................77
V. LITERATURE CITED ..........................................................................................80
VI. APPENDIXES......................................................................................................85
Appendix A............................................................................................................85
Appendix B ............................................................................................................87
Appendix C ............................................................................................................88
v
LIST OF TABLES
Title
Page
I. Regression Analysis of Each Student’s Average Pre-lab
and Planning Form Scores…………………………………………………….. 17
II. Regression Analysis of Each Group’s Average Pre-lab
and Lab Report Scores…………………………………………………………..17
III. Regression Analysis of Each Group’s Average
Planning Form and Lab Report Scores………………………………………….17
IV. Analysis of Covariance Results for Student Pre-lab and Planning
Form Scores by Lab Investigation……………………………………………….25
V. Analysis of Covariance Results for Group Average Pre-lab
Score and Lab Report Score by Lab Investigation……….……………………...26
VI. Analysis of Covariance Results for Group Average Planning
Form Score and Lab Report Score by Lab Investigation………...………………27
VII. Analysis of Covariance Results for Student Average Pre-lab
Score and Average Planning Form Score by Section……………………….…...35
VIII. Analysis of Covariance Results for Group Average Pre-lab
Score and Average Lab Report Score by Section…….……………………….....36
IX. Analysis of Covariance Results for Group Average Planning
Form Score and Average Lab Report Score by Section.………………………...37
X. Analysis of Covariance Results for Student Average Pre-lab Score
and Average Planning Form Score by Teaching Assistant……………..……..…45
XI. Analysis of Covariance Results for Group Average Pre-lab Score
and Average Lab Report Score by Teaching Assistant……………….…………46
XII. Analysis of Covariance Results for Group Average Planning Form
Score and Average Lab Report Score by Teaching Assistant……….…………..47
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Table
Page
XIII. Student Responses from Selected Questions on a Survey Given
at the End of the Semester……………………………………………………….56
XIV. Sample of Student Comments on a Survey Given at the End of
the Semester……………………………………………………………………...57
XV. Summary of the Results From the Analyses of Student Scores…………………….61
vii
LIST OF FIGURES
Title
Page
1. Regression Analysis of Pre-lab and Planning Form Scores ……………………….....18
2. Regression Analysis of Pre-lab and Lab Report Scores ……………………………...20
3. Regression Analysis of Planning Form and Lab Report Scores………………………22
4. Analysis of Pre-lab and Planning Form Scores by
Lab Investigation………………………………………………………………...28
5. Analysis of Pre-lab and Lab Report Scores by Lab
Investigation……………………………………………………………………..30
6. Analysis of Planning Form and Lab Report Scores
by Lab Investigation……………………………………………………………..32
7. Analysis of Pre-lab and Planning Form Scores by Section…………………………...38
8. Analysis of Pre-lab and Lab Report Scores by Section……………………………….40
9. Analysis of Planning Form and Lab Report by Section………………………………42
10. Analysis of Pre-lab and Planning Form Scores by
Teaching Assistant………………………………………………………………48
11. Analysis of Pre-lab and Lab Report Scores by Teaching
Assistant…………………………………………………………………………50
12. Analysis of Planning Form and Lab Report Scores by
Teaching Assistant……………………………………………………………….52
13. Concept Map of a Summary of the Results from the Analyses
of Student Scores………………………………………………………………...59
viii
CHAPTER I
INTRODUCTION
Following recommendations made in the early 1990s, there has been an effort in
science education to revise how science is taught. The American Association for the
Advancement of Science (1990) suggested that to promote scientific reasoning“…science
should be taught as science is practiced”. The National Research Council (1996)
recommended that students learn science through scientific inquiry. Inquiry-based
classes are preferred over traditional classes because students are engaged in learning
science through an active process (NSTA, 1996). While there are many variations, in an
open-ended inquiry-based laboratory, students formulate their own hypotheses, design a
unique experiment, and conduct an investigation. Courses that include open-ended
scientific investigations enhance students’ skills of observation and discovery, hypothesis
formation, testing, and evaluating (Division of Undergraduate Science, Engineering, and
Mathematics Education, 1990). To optimize learning, students must have the opportunity
to design their own experiments and test their own hypotheses, especially in the
laboratory (Lunsford, 2002).
The Problem
Because of the support for inquiry, educators are now focused on the most
effective ways to implement this type of instructional format in the science classroom,
especially in the laboratory. Two of the major problems reported by instructors of
1
inquiry-based laboratories are that some students do not participate enough and they lack
background knowledge (Lawson, 2000). Other research supports the conclusion that a
challenge to successfully facilitating an inquiry-based investigation is insufficient student
preparation (Oliver-Hoyo et al., 2004 and Mazlo et al., 2002). Logically, if students are
better prepared prior to attending laboratory, then they will be able to understand more of
the rationale behind the processes being tested (Barnes and Thornton, 1998). It is
especially important in inquiry-based laboratories that the students have the conceptual
and procedural background they need to be able to formulate their own hypotheses and
conduct open-ended investigations. Students who do not prepare are unable to fully
engage in the design and completion of the investigation and this may reduce their
opportunity for success in lab.
In response, instructors attempt to encourage students to prepare for laboratory.
The University of Toronto’s Teaching Assistants’ Training Programme (2005) advocates
the use of pre-lab talks. They suggest that the first fifteen minutes of lab should be used
by the instructor to review the background information, generally describe the lab
through the use of a flow chart, and give directions for the proper use of equipment in the
lab. However, the pre-lab talk method creates several problems. First, pre-lab talks are
unlikely to be very effective in motivating students to prepare prior to lab. If the
instructor is providing all of the information that the students need, why would students
make an effort to come to class prepared? Second, it suggests that students will learn
through a passive process as the instructor provides them information. This conflicts
with research on how people learn (Donovan et al., 2000). Furthermore, pre-lab talks do
not provide the students with enough time to carefully reflect on the information
2
provided, especially if it is conceptually challenging. A final drawback to the pre-lab talk
method is that it takes time away from the students; they should be designing and
conducting an experiment rather than being introduced to the lab investigation.
Another method commonly used to encourage student preparation is to quiz the
students at the beginning of class. Mazlo et al. (2002) found that students were more
motivated to study prior to class if they knew that they were going to be quizzed and the
quizzes would affect their grades. Assessment of the physics department at the
University of Arkansas at Little Rock (2003) included a study on the effectiveness of prelab quizzes. Their assessment team analyzed student grades for each of the thirteen labs
and reported that there was a significant positive correlation between pre-lab quiz scores
and post-lab exams for ten of the experiments. From this, they concluded that overall the
pre-lab quiz approach seemed to improve learning.
However, this method causes one of the same problems as the pre-lab talks - it
reduces the amount of time available for students to complete the lab investigation. To
minimize this, instructors typically write quizzes composed of only simple multiple
choice or short answer questions that are unlikely to require high-level conceptual
thinking by the students. Sundberg et al. (2000) found that the traditional laboratory
exams and quizzes are “meaningless” and these activities generally focus on facts rather
than reasoning ability. Encouraging students to prepare by using a quiz will only
motivate them to be able to answer those questions and may distract them from actually
preparing to conduct the investigation.
To avoid these issues, some instructors employ the use of pre-class exercises to
help students prepare for lab. These instructors expect students to complete an activity or
3
series of activities prior to attending the laboratory class. Such activities vary in content
and may include information on procedures, methods, equipment, facts, and concepts. At
a minimum, pre-class activities provide the student with some experience with the
investigation prior to class.
According to the Graduate Student Instructor Teaching and Resource Center at
the University of California in Berkeley (2005), there are several advantages of using prelab assignments over other forms of preparation. The advantages of a pre-lab assignment
are 1) it better prepares students for the lab, 2) it helps the exercises run more smoothly,
3) it increases the student’s level of understanding, and 4) it makes it easier for the
instructor to teach the theory behind the lab. The only disadvantage of using pre-lab
assignments is the time it takes to write and evaluate them.
Educators at other universities have implemented the use of pre-labs. One
instructor of an introductory chemistry course prohibited students from entering the lab
room unless they completed the pre-lab assignment (The University of Michigan, 2005).
In a physics course at Armstrong Atlantic State University, pre-lab exercises comprised
twenty percent of the student’s grade (Mullenax, 2002). Faculty teaching an electronic
design course at the University of South Carolina required a student to repeat a lab if the
pre-lab exercise was not satisfactory (Hudgins, 2002). In a computer science class at
Duke University, the pre-lab assignments were optional in 1999 (Astrachan, 1999) but
later required (Duvall, 2005).
Previous Research
Instructors who promote the use of pre-class activities do so because they think
that it is an effective way to motivate students to prepare for class, which is especially
4
crucial for inquiry-based labs. Although educators may have good reason to feel
confident in this assumption, few researchers have reported the effectiveness of pre-class
activities. Kirk and Layman (1996) conducted research on improving student
understanding in an introductory chemistry course. Of the students interviewed (n=56),
they found that 41% “felt better prepared” when they used a pre-lab guide for lab
preparation.
In another introductory chemistry course, Barnes and Thornton (1998) conducted
research on pre-lab effectiveness using both quantitative and qualitative methods. They
found no significant correlation between performance on pre-laboratory questions and the
lab reports or the mid-semester exams. However, they found that students (n=63) felt
that using the pre-laboratory questions made writing the lab report easier. This group of
students enjoyed lab classes more and felt more confident in their lab work. Similar
results were reported by Wyatt (2003) from Western Kentucky University. He
implemented the use of online pre-labs in his biology course in an attempt to improve the
level of student preparation for the laboratories. The data he collected failed to show any
statistical difference in learning outcomes between students who used the pre-labs and
students that did not. However, through an end of the semester survey, more than 95% of
about 300 students indicated satisfaction with the pre-lab exercises. These students
thought that using the pre-labs probably improved their lab skills and their grade. Wyatt
concluded that student satisfaction with the implementation of pre-lab exercises is an
indication of the importance of using pre-class activities in the laboratory.
Instructors of an undergraduate physics laboratory also wanted to test the
usefulness of pre-lab preparation. Johnstone et al. (1998) studied students (n=95)
5
participating in four laboratories, two with pre-labs and two without pre-labs. First, they
collected student responses on a survey and analyzed student attitudes concerning the
pre-labs. They reported that overall students favored using the pre-labs. These
researchers also compared mean scores on the post-lab assessment of students who used
pre-labs to those that did not. Their results indicated that the students that participated on
the pre-labs achieved significantly higher levels of performance on the post-lab
assessment. They concluded that the students’ understanding of these laboratories was
enhanced by the use of pre-labs.
Generally, the previous research indicates that students perceive that pre-lab
exercises are beneficial. Students who complete pre-lab exercises feel better prepared
and believe that these activities help them perform well during the investigation and on
laboratory assignments (Kirk and Layman, 1996; Barnes and Thornton, 1998, Wyatt,
2003). Moreover, there is some indication that pre-lab exercises may correlate with an
increase in student performance on post-lab assessment (Johnstone, et al., 1998).
However, from such limited research and ambivalent findings, it is difficult to draw
definitive conclusions on the effectiveness of preliminary activities on student
performance and success in lab. The purpose of this study is to extend the research in
this area.
Course Design
The pre-class method of preparation is used in the introductory biology
laboratories at Oklahoma State University. This class is a first-year course offered for
both science majors and non-majors. Students attend one of four or five lecture sections,
6
typically taught by different lecture instructors. Students are also assigned to lab sections
of 21-24 students. During the semester, students conduct fourteen laboratory
investigations. For each laboratory session, the lab manual provides students with
background information and a question for investigation (French, 2004). Based on these,
the students must then propose a hypothesis, design methods to test that hypothesis,
conduct their experiment, and report their results.
During the lab period, the students work in groups of three to four members to
complete the investigation. Students collaborate to write a group hypothesis and conduct
an experiment. To report their results and answer the question, each group writes a lab
report. The lab report includes introduction, methods, results, and discussion sections.
The majority of the points earned by each student in laboratory are from the lab report
scores. A copy of the grading scheme for the lab report can be found in the appendix
(Appendix B).
Students are expected to prepare individually prior to class. To encourage and
guide this, the course employs two activities. The first activity is a required assignment
called a planning form. Here, the individual student must write a hypothesis, describe the
experimental design, and make predictions on possible experimental outcomes. A
smaller portion of the planning form asks questions specific to that lab investigation and
may address either procedural or conceptual information. To help students complete
each planning form, a second activity, the pre-lab exercise, is offered for extra credit.
These activities vary depending on the lab topic and provide the students with either the
procedural or conceptual information specific to the investigation that they should know
7
prior to class. An example of the planning form with the pre-lab activities can be found
in the appendix (Appendix A).
Objectives of the Study
Based on casual observations, it seems that the students who are less engaged in the
investigation and have lower lab report scores are those that are the least prepared. To
test the validity of this association, I designed a study to determine whether a relationship
exists between student performances on pre-class assignments and the laboratory
assessment. I addressed the following questions:
•
Does participation in preliminary exercises lead to higher performance in the
laboratory?
Specifically, I sought to answer the following:
o Do the students that perform well on pre-lab exercises score higher on the
planning forms?
o Do student groups that perform well on the pre-lab exercises score higher on the
lab report scores?
o Do student groups that perform well on planning forms score higher on the lab
reports?
•
What other factors affect student performance in lab?
Specifically, I sought to answer the following:
o Does student performance vary with the lab investigation?
o Does student performance vary with the section?
o Does student performance vary with the teaching assistant?
•
What are the students’ perceptions of the laboratory in our introductory biology class?
8
CHAPTER II
METHODS
Subjects
This study included data on students enrolled in four lecture sections of the
introductory biology course at Oklahoma State University. For the performance analysis,
I used grades from every student who participated in at least one laboratory in the spring
semester of 2004. For the perception analysis, I used student responses on a survey
provided at the end of the course in the spring semester of 2005. This only included
students who remained in the course for the duration of the semester.
Analyses of Student Grades
In the introductory biology course, teaching assistants record student laboratory
grades in WebCT. The course coordinator, Dr. Donald French, provided me with
anonymous student data. For each lab investigation, teaching assistants recorded three
scores for each student: a pre-lab, planning form, and lab report score. The data from
WebCT also included the section in which each student was enrolled, the group to which
each student belonged, and the teaching assistant who taught each student. Thus I
analyzed grades according to the student, group, section, teaching assistant, and lab
investigation.
I analyzed grades of students from only one semester because the sample size
(n=764) was sufficient to provide adequate data while avoiding any confounding effect of
9
semester, which may introduce additional variation in students and teaching assistants. I
measured student performance based on the scores each student earned on pre-labs,
planning forms, and lab reports for each of the fourteen lab investigations during the
semester.
Regression Analyses
To analyze the students’ grades, I used SAS software (SAS Institute, 2002). To
address my questions, I examined the following relationships: pre-labs and planning
forms, pre-labs and lab reports, and planning forms and lab reports. I used regression
analysis to determine the presence of a linear relationship and the predictive abilities of
these models. I used analysis of variance (ANOVA) to evaluate the significance of the
regression models. This also provided the coefficient of determination, or r-square value,
for each model. The r-square values measure the relative strength of the regression
models by reflecting the proportion of the total variation associated with the regression of
the dependent variable on the independent variable.
To determine if pre-lab participation helped students prepare, I analyzed pre-lab
and planning form scores. If pre-labs were beneficial, then students who completed
and/or scored higher on the pre-lab activities should have scored higher on their planning
forms. I performed regression analysis of each student’s average planning form score on
his/her average pre-lab score. I averaged each student’s scores over the fourteen lab
investigations to avoid inflating the sample with multiple pre-lab and planning form
scores from the same student. I applied an ANOVA test to determine the significance of
the regression model. I compared the relationship of pre-lab and planning form scores
10
using the coefficient of determination (r-square) and tested whether the slope of the
regression line was equal to zero.
To determine if preparation helped students perform better in lab, I analyzed
scores on pre-class activities (pre-labs and planning forms) and lab report scores. If prelabs were beneficial, then student groups that completed and/or scored higher on the prelab activities should have scored higher on the lab report. To compare the pre-lab
activities to the lab report score, I averaged the students’ pre-lab scores to provide a
group pre-lab score for each investigation because the lab report is completed by the
group. To calculate the group pre-lab score, I averaged the pre-lab scores of all of the
group members by dividing the total points earned on the pre-lab by all members of the
group who participated in writing the lab report by the number of participants. Thus, the
group pre-lab score reflected both how many students participated and how well each
student performed on the pre-lab activity. For the overall description of the relationship,
I then averaged each group’s pre-lab scores and lab report scores over the fourteen lab
investigations and performed regression analysis. I averaged each group’s scores to
avoid inflating the sample with multiple scores from the same group.
I also analyzed the second pre-class activity, the planning form, in relation to the
lab report. If planning forms were beneficial, then student groups that performed well on
planning forms should have produced higher lab report scores. I repeated the same
process on this regression as I did for the regression of the lab report scores on the pre-lab
scores. I applied ANOVA on each pre-class and lab report regression to determine the
significance of these models. I compared the relationship of pre-lab and planning form
11
scores to lab report scores using the coefficient of determination (r-square) and tested
whether the slopes of the regression lines were equal to zero.
Analyses of Covariance
After I performed these preliminary analyses, I discovered that the regression
models accounted for little of the variation in student grades, demonstrated by low rsquare values. This led me to test for other possible factors contributing to the variation
in student scores. To test these, I performed separate analyses of covariance (ANCOVA)
for three different classification variables: lab investigation, section, and teaching
assistant. I used ANCOVA to maximize the explained variance and determine which
factors, or classification variables, may be increasing the variability in the data. The
ANCOVA test is comparable to multiple regression with qualitative variables
representing the category variables. The ANCOVA tests also provided the r-square
values for the models based on the classification variables. To produce graphs for each
classification group, I used the least square means (LSmeans) of the dependent variable at
the minimum and maximum values of the independent variable. I used this method to
produce the ANCOVA graphs to show the general trends of the data.
To determine if the lab investigation had an effect on student scores, I applied
ANCOVA. For the analysis of pre-lab and planning form scores, I included all of the
students who submitted a planning form for the investigation, regardless of whether they
participated on the pre-lab exercises. To test the relationship of the lab report and the
pre-lab score in relation to the investigation, I used the group pre-lab score. I used the
average pre-lab score for each group because the lab report score is earned by the group
members present for the investigation. I repeated the same procedure to analyze the
12
group average planning form and lab report score for each investigation. Unlike the
general analyses, I did not average the students’ or groups’ scores over the investigations
because the scores needed to be discrete to test for the effect of investigation as a
classification variable. This resulted in very large numbers of sample points included in
these analyses.
To determine if the section had an effect on student scores, I applied ANCOVA.
For the analysis of pre-lab and planning form scores, I used the average of each of the
student’s pre-lab and planning forms scores across the investigations so that the sample
would not be inflated with multiple points from the same student. I repeated the analyses
including the classification variable of section on the group average pre-lab and lab report
scores and on group average planning form and lab report scores.
To determine if the teaching assistant had an effect on student scores, I applied
ANCOVA. For the analysis of pre-lab and planning form scores, I used the student
averages as I did with the section classification. I repeated these analyses on group
average pre-lab and lab report scores and on group average planning form and lab report
scores. With all three ANCOVA analyses, I evaluated the classification effects using the
coefficient of determination (r-square) and tested whether the slopes of the regression
lines were equal to zero.
Analyses of the Student Survey
In addition to grade analyses, I analyzed student perceptions of the biology course
at Oklahoma State University. I included quantitative and qualitative analyses to provide
insight into student opinions concerning their performance and success in laboratory.
13
I reviewed student responses on a survey given to the biology students after their
final examination. They were asked to express their opinions on a five point scale by
how strongly they agreed (1) or disagreed (5) with a set of statements. The students
recorded their responses on a scantron sheet. I gathered the scantron sheets from each of
the four lecture sections and tallied the responses using a scanner. Then I determined the
number of respondents and the percentage of each response on the questions relevant to
laboratory. Some of the questions on the survey referred to inquiry-based investigations,
pre-class activities and preparation for lab, group work, teaching assistants, and the
course website. A copy of the entire survey can be found in the appendix (Appendix C).
The questions of interest can be found in Table XIII.
Students were also given an opportunity to provide open-ended responses
concerning what they did or did not enjoy about the course. I reviewed these written
responses and grouped them by topic. I selected a small subset of these responses to
demonstrate the differences in opinion students hold concerning the same aspect of the
course. I included these data to provide insight into specific student perceptions
concerning the laboratories.
14
CHAPTER III
RESULTS
The purpose of this study was to investigate student success in an inquiry-based
laboratory based on their performance on pre-class activities and the in-class lab report. I
attempted to answer the following questions:
•
Does participation in preliminary exercises lead to higher performance in the
laboratory?
Specifically, I sought to answer the following:
o Do the students that perform well on pre-lab exercises score higher on the
planning forms?
o Do student groups that perform well on the pre-lab exercises score higher on the
lab report scores?
o Do student groups that perform well on planning forms score higher on the lab
reports?
•
What other factors affect student performance in lab?
Specifically, I sought to answer the following:
o Does student performance vary with the lab investigation?
o Does student performance vary with the section?
o Does student performance vary with the teaching assistant?
•
What are the students’ perceptions of the laboratory in our introductory biology class?
15
Question 1: Does participation in preliminary exercises lead to higher success in the
laboratory?
General Regression Models
Each of the regression models (pre-lab and planning form, pre-lab and lab report,
planning form and lab report) was significant. Each model showed a significant
relationship between the independent and dependent variables (each p<0.0001). Each
regression line had a slope significantly different than zero (each p<0.0001). However,
the coefficient of determination was relatively low for each model (Table I, II, and III).
The relationship between pre-lab and planning form scores had the highest r-square value
(0.1576) and the relationship between planning form and lab report scores had the lowest
(0.0965). Therefore, although the linear relationships were significant, this may have
been a result of the large sample sizes. The relatively low r-square values reflect that
although there were significant relationships between the scores, most of the variation
was not accounted for by these models. Graphs of the regression lines are shown in
Figures 1, 2, and 3.
16
TABLE I
REGRESSION ANALYSIS OF EACH STUDENT’S AVERAGE
PRE-LAB AND PLANNING FORM SCORES
Source
n
F-value
p-value
H0: slope = 0
rejection p-value
r2
Model
757
141.25
<0.0001
<0.0001
0.1576
TABLE II
REGRESSION ANALYSIS OF EACH GROUP’S AVERAGE
PRE-LAB AND LAB REPORT SCORES
Source
n
F-value
p-value
H0: slope = 0
rejection p-value
r2
Model
206
31.40
<0.0001
<0.0001
0.1334
TABLE III
REGRESSION ANALYSIS OF EACH GROUP’S AVERAGE
PLANNING FORM AND LAB REPORT SCORES
Source
n
F-value
p-value
H0: slope = 0
rejection p-value
r2
Model
203
21.48
<0.0001
<0.0001
0.0965
17
Figure 1. Regression Analysis of Pre-lab and Planning Form Scores (n=757, r2 = 0.1576).
The model includes each student’s average planning form regressed on his/her average
pre-lab score. The scores are averaged over all fourteen lab investigations.
18
19
Figure 1
Figure 2. Regression Analysis of Pre-lab and Lab Report Scores (n=206, r2 = 0.1334).
The model includes each group’s average lab report score regressed on their average prelab score. The scores are averaged over all fourteen lab investigations.
20
21
Figure 2
Figure 3. Regression Analysis of Planning Form and Lab Report Scores (n=203, r2 =
0.0965). The model includes each group’s average lab report regressed on their average
planning form score. The scores are averaged over all fourteen lab investigations.
22
23
Figure 3
Question 2: What other factors affect student performance in lab?
ANCOVA Including Lab Investigation
Each of the three models (pre-lab and planning form, pre-lab and lab report,
planning form and lab report) was significant according to ANCOVA output (p<0.0001).
The majority of the regression lines based on the classification of lab investigation was
significantly different from zero (p-values <0.05). However, the coefficients of
determination were also relatively low for each model (Table IV, V, and VI). Moreover,
the r-squared values were nearly the same as those from the previous models without the
classification of lab investigation. Similar to those models, the highest r-square value
was with the model of pre-lab and planning form (0.1462) and the lowest r-square value
was with the model of planning forms and lab report (0.0963). These results indicated
that the inclusion of the lab investigation did not reduce the amount of unaccounted for
variation between student scores when compared to the general regression models. The
significance of these analyses of covariance is likely attributed to the large sample sizes.
In other words, there was a relationship between the scores but the relationships did not
vary with the lab investigations. Graphs of the relationships using the LSmeans are
shown in Figures 4, 5, and 6.
24
TABLE IV
ANALYSIS OF COVARIANCE RESULTS FOR
STUDENT (N=757) PRE-LAB AND PLANNING
FORM SCORES BY LAB INVESTIGATION
Sample
Size
H0: slope = 0
rejection p-value
r2
Overall
Model
9370
<0.0001
0.1462
Lab 1
Lab 2
Lab 3
Lab 4
Lab 5
Lab 6
Lab 7
Lab 8
Lab 9
Lab 10
Lab 11
Lab 12
Lab 13
Lab 14
658
692
697
677
685
702
675
671
673
642
667
665
655
611
0.0220
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.0004
<0.0001
<0.0001
<0.0001
0.1211
0.0165
0.0788
0.0678
0.1390
0.0786
0.0391
0.1336
0.1242
0.1087
0.0137
0.0724
0.0430
0.0316
0.0067
Data Set
25
TABLE V
ANALYSIS OF COVARIANCE RESULTS FOR
GROUP (N=206) AVERAGE PRE-LAB SCORE
AND LAB REPORT SCORE BY
LAB INVESTIGATION
Sample
H0: slope = 0
r2
Data Set
Size
rejection p-value
Overall
Model
2834
<0.0001
0.1067
Lab 1
Lab 2
Lab 3
Lab 4
Lab 5
Lab 6
Lab 7
Lab 8
Lab 9
Lab 10
Lab 11
Lab 12
Lab 13
Lab 14
206
204
203
204
203
202
203
203
203
202
202
202
203
194
0.0028
0.0005
0.0007
<0.0001
0.4161
0.0390
0.0412
0.0035
0.0033
0.0033
0.0004
0.2435
0.0028
0.9462
0.0268
0.0521
0.0450
0.1099
0.0044
0.0205
0.0200
0.0559
0.0322
0.0300
0.0663
0.0118
0.0485
0.0000
26
TABLE VI
ANALYSIS OF COVARIANCE RESULTS FOR
GROUP (N=206) AVERAGE PLANNING FORM
SCORE AND LAB REPORT SCORE
BY LAB INVESTIGATION
Sample
Size
H0: slope = 0
rejection p-value
r2
Overall
Model
2834
<0.0001
0.0963
Lab 1
Lab 2
Lab 3
Lab 4
Lab 5
Lab 6
Lab 7
Lab 8
Lab 9
Lab 10
Lab 11
Lab 12
Lab 13
Lab 14
206
204
203
204
203
202
203
203
203
202
202
202
203
194
0.2941
0.0002
0.0087
<0.0001
0.0743
0.0020
0.0051
0.2456
0.0857
0.0205
0.3986
0.0322
0.8230
0.6580
0.0041
0.0585
0.0266
0.1096
0.0214
0.0456
0.0374
0.0088
0.0109
0.0185
0.0038
0.0394
0.0003
0.0012
Data Set
27
Figure 4. Analysis of Pre-lab and Planning Form Scores by Lab Investigation (n=9370,
r2 = 0.1462). The model includes student scores of pre-labs and planning forms with
each line representing a different lab investigation. The scores were not averaged. Lines
were produced using the LSmeans.
28
29
Figure 4
Figure 5. Analysis of Pre-lab and Lab Report Scores by Lab Investigation (n=2834,
r2 = 0.1067). The model includes the group’s average pre-lab score with their lab report
score. Each line represents a different lab investigation. Lines were produced using the
LSmeans.
30
31
Figure 5
Figure 6. Analysis of Planning Form and Lab Report Scores by Lab Investigation
(n=2834, r-square 0.0963). The model includes the group’s average planning form score
and their lab report score. Each line represents a different lab investigation. Lines were
produced using the LSmeans.
32
33
Figure 6
ANCOVA Including Section
The ANCOVA results demonstrated that each of the three models (pre-lab and
planning form, pre-lab and lab report, planning form and lab report) was significant
(p<0.0001). Although some of the slopes were significantly different than zero, many
were not and the results varied among sections (Tables VII, VIII, and IX). The models
with section included had much higher r-square values than the previous models. The
highest r-square was with the model of pre-lab and planning form scores (0.4779) and the
lowest r-square was with the model of pre-lab and lab report (0.4091). These coefficients
of determination demonstrated that the strength of the relationships between the students’
scores varied greatly among sections. Moreover, the ANCOVA analyses with the
inclusion of section were very successful in reducing the amount of unaccounted
variation as compared to the general regression models. Graphs of the relationships using
the LSmeans are shown in Figures 7, 8, and 9.
34
TABLE VII
ANALYSIS OF COVARIANCE RESULTS FOR STUDENT (N=756)
AVERAGE PRE-LAB SCORE AND AVERAGE
PLANNING FORM SCORE BY SECTION
Sample
Ho: slope = 0
r2
Size
rejection p-value
Overall Model
756
<0.0001
0.4779
Section 1
Section 2
Section 3
Section 4
Section 5
Section 6
Section 7
Section 8
Section 9
Section 10
Section 11
Section 12
Section 13
Section 14
Section 15
Section 16
Section 17
Section 18
Section 19
Section 20
Section 21
Section 22
Section 23
Section 24
Section 25
Section 26
Section 27
Section 28
Section 29
Section 30
Section 31
Section 32
Section 33
Section 34
22
22
23
22
20
24
21
24
22
22
22
24
23
24
24
25
22
23
23
21
20
23
23
24
24
24
22
22
19
23
21
23
21
21
0.5034
0.1693
0.0017
0.1638
0.0187
0.0001
0.0482
0.9175
0.3185
0.2202
0.1597
0.1355
0.0008
0.1358
0.0106
0.1876
0.0009
0.1060
0.1679
<0.0001
0.0794
0.0003
0.0002
0.0289
0.0009
<0.0001
<0.0001
0.0311
<0.0001
0.0549
0.0042
0.0233
<0.0001
<0.0001
35
0.0872
0.1623
0.4180
0.2165
0.3014
0.3132
0.1379
0.0022
0.1282
0.0960
0.0608
0.1075
0.3271
0.1176
0.2744
0.0589
0.3118
0.1496
0.2096
0.4822
0.1497
0.4011
0.7558
0.4058
0.2830
0.2821
0.3491
0.3293
0.5914
0.0599
0.4277
0.4065
0.2765
0.3917
TABLE VIII
ANALYSIS OF COVARIANCE RESULTS FOR GROUP (N=203)
AVERAGE PRE-LAB SCORE AND AVERAGE LAB
REPORT SCORE BY SECTION
Overall Model
Section 1
Section 2
Section 3
Section 4
Section 5
Section 6
Section 7
Section 8
Section 9
Section 10
Section 11
Section 12
Section 13
Section 14
Section 15
Section 16
Section 17
Section 18
Section 19
Section 20
Section 21
Section 22
Section 23
Section 24
Section 25
Section 26
Section 27
Section 28
Section 29
Section 30
Section 31
Section 32
Section 33*
Section 34
r2
Ho: slope = 0
rejection p-value
<0.0001
0.4091
0.7657
0.6354
0.1429
0.4298
0.0787
0.9827
0.7419
0.5617
0.3919
0.1584
0.2374
0.2646
0.0021
0.6807
0.1059
0.6816
0.3990
0.1595
0.6416
<0.0001
0.3607
0.0722
0.0673
0.7241
0.0082
0.1003
0.1276
0.4213
<0.0001
0.1586
0.4714
0.3421
0.5408
0.7052
0.0150
0.0408
0.2178
0.0364
0.0815
0.0000
0.0070
0.0205
0.1275
0.2191
0.1658
0.0627
0.1333
0.0091
0.1010
0.0113
0.0301
0.1681
0.0570
0.3174
0.0563
0.0670
0.1920
0.0319
0.2979
0.0842
0.0778
0.0503
0.4061
0.1273
0.1057
0.1265
0.0051
0.0063
*All sections included data from six groups with the exception of
number 33, with only five groups.
36
TABLE IX
ANALYSIS OF COVARIANCE RESULTS FOR GROUP (N=203)
AVERAGE PLANNING FORM SCORE AND AVERAGE
LAB REPORT SCORE BY SECTION
Ho: slope = 0
r2
rejection p-value
Overall Model
<0.0001
0.4607
Section 1
Section 2
Section 3
Section 4
Section 5
Section 6
Section 7
Section 8
Section 9
Section 10
Section 11
Section 12
Section 13
Section 14
Section 15
Section 16
Section 17
Section 18
Section 19
Section 20
Section 21
Section 22
Section 23
Section 24
Section 25
Section 26
Section 27
Section 28
Section 29
Section 30
Section 31
Section 32
Section 33*
Section 34
0.8869
0.4225
0.1758
0.0464
<0.0001
0.4581
0.0386
0.9593
0.6921
0.6631
0.1498
0.5354
<0.0001
0.1386
0.9338
0.0769
0.1161
0.8067
0.3462
<0.0001
0.9155
0.7636
0.9407
0.7023
0.0857
0.0018
0.7324
0.3562
<0.0001
0.0060
0.5010
0.5090
0.0782
0.1775
0.0031
0.1065
0.1697
0.2120
0.4332
0.0474
0.2515
0.0001
0.0249
0.0191
0.2249
0.0176
0.3701
0.1079
0.0002
0.1914
0.0953
0.0046
0.2133
0.5594
0.0007
0.0017
0.0003
0.0341
0.1146
0.2791
0.0036
0.0604
0.3246
0.4431
0.0842
0.0558
0.0385
0.0730
*All sections included data from six groups with the exception of
number 33, with only five groups.
37
Figure 7. Analysis of Pre-lab and Planning Form Scores by Section (n=756, r2 = 0.4779).
The model includes the student’s average pre-lab score and his/her average planning form
score. Each line represents scores from students in different sections. Lines were
produced using the LSmeans.
38
39
Figure 7
Figure 8. Analysis of Pre-lab and Lab Report Scores by Section (n=203, r2 = 0.4091).
The model includes the group’s average pre-lab and average lab report scores. Each line
represents scores from groups in different section. Lines were produced using the
LSmeans.
40
41
Figure 8
Figure 9. Analysis of Planning Form and Lab Report Scores by Section (n=206,
r2 = 0.4607). The model includes the group’s average planning form and average lab
report scores. Each line represents scores from groups in different sections. Lines were
produced using the LSmeans.
42
43
Figure 9
ANCOVA Including Teaching Assistant
Each of the three models (pre-lab and planning form, pre-lab and lab report,
planning form and lab report) was significant according to ANCOVA results (p<0.0001).
At a significance level of p<0.05, the majority of the regression lines of student scores
based on the teaching assistant had slopes not equal to zero. All but one had a nonzero
slope on the model of pre-lab and planning form scores (Table X), but the results from
this test varied greatly for the pre-class and lab report models (Table XI and XII). The rsquare value was highest for the pre-lab and planning form model (0.4319) and lowest for
the pre-lab and lab report model (0.2885). The coefficients of determination demonstrate
that the strength of the relationships between student scores varied greatly among the
teaching assistants. These results suggest that the teaching assistant had a significant
effect on student grades and it was useful to include this factor in an ANCOVA test. This
method reduced the amount of unaccounted variation compared to the general regression
analyses and the analyses including the lab investigation. Graphs of the relationships
using the LSmeans are shown in Figures 10, 11, and 12.
44
TABLE X
ANALYSIS OF COVARIANCE RESULTS FOR STUDENT (N=756)
AVERAGE PRE-LAB SCORE AND AVERAGE PLANNING
FORM SCORE BY TEACHING ASSISTANT (TA)
Sample
Size
Ho: slope = 0
rejection p-value
r2
Overall Model
756
<0.0001
0.4319
TA 1
TA 2
TA 3
TA 4
TA 5
TA 6
TA 7
TA 8
TA 9
TA 10
TA 11
TA 12
TA 13
TA 14
TA 15
TA 16
TA 17
43
42
46
48
47
44
44
43
47
48
44
47
40
44
46
48
45
0.0007
0.0072
0.2741
0.0098
0.0142
0.0466
<0.0001
<0.0001
0.0025
<0.0001
<0.0001
0.0227
<0.0001
0.0003
0.0003
0.0016
<0.0001
0.2284
0.2460
0.0875
0.2281
0.2980
0.0798
0.2618
0.4296
0.1105
0.2709
0.3962
0.1390
0.3384
0.4220
0.2172
0.1575
0.2476
45
TABLE XI
ANALYSIS OF COVARIANCE RESULTS FOR GROUP (N=203)
AVERAGE PRE-LAB SCORE AND AVERAGE LAB REPORT
SCORE BY TEACHING ASSISTANT (TA)
Ho: slope = 0
rejection p-value
r2
Overall Model
<0.0001
0.2885
TA 1
TA 2
TA 3
TA 4
TA 5
TA 6
TA 7
TA 8
TA 9
TA 10
TA 11
TA 12
TA 13*
TA 14
TA 15
TA 16
TA 17
<0.0001
0.4460
0.2753
0.4296
0.2735
0.1167
0.1565
0.0172
0.0457
0.0043
0.1681
0.2631
0.0017
0.0787
0.8760
0.0366
0.8790
0.1690
0.0153
0.0399
0.0451
0.0925
0.1463
0.0420
0.1571
0.1105
0.1659
0.0301
0.0414
0.0923
0.1143
0.0006
0.0518
0.0010
* All teaching assistants included data from twelve groups with the
exception of number 13, with only eleven groups.
46
TABLE XII
ANALYSIS OF COVARIANCE RESULTS FOR GROUP (N=203)
AVERAGE PLANNING FORM SCORE AND AVERAGE
LAB REPORT SCORE BY TEACHING ASSISTANT (TA)
Ho: slope = 0
rejection p-value
r2
Overall Model
<0.0001
0.3667
TA 1
TA 2
TA 3
TA 4
TA 5
TA 6
TA 7
TA 8
TA 9
TA 10
TA 11
TA 12
TA 13*
TA 14
TA 15
TA 16
TA 17
<0.0001
0.0429
0.1375
0.2556
0.1185
0.2328
0.2330
0.4944
0.0255
0.008
0.3090
0.8833
0.0010
0.0867
0.1859
<0.0001
0.0630
0.5933
0.0965
0.0658
0.0831
0.1674
0.0753
0.0265
0.0115
0.1230
0.2023
0.0146
0.0006
0.0898
0.0966
0.0382
0.2680
0.1342
* All teaching assistants included data from twelve groups with the
exception of number 13, with only eleven groups.
47
Figure 10. Analysis of Pre-lab and Planning Form Scores by Teaching Assistant (n=756,
r2 = 0.4319). The model includes the student’s average pre-lab score and his/her average
planning form score. Each line represents scores from students instructed by different
teaching assistants. Lines were produced using the LSmeans.
48
49
Figure 10
Figure 11. Analysis of Pre-lab and Lab Report Scores by Teaching Assistant (n=203,
r2 = 0.2885). The model includes the group’s average pre-lab score and average lab
report score. Each line represents scores from groups instructed by different teaching
assistants. Lines were produced using the LSmeans.
50
51
Figure 11
Figure 12. Analysis of Planning Form and Lab Report Scores by Teaching Assistant
(n=203, r2 = 0.3667). The model includes the group’s average planning form and average
lab report score. Each line represents scores from groups instructed by different teaching
assistants. Lines were produced using the LSmeans.
52
53
Figure 12
Question 3: What are the students’ perceptions of the laboratory in our introductory
biology class?
Analyses of the Student Survey
Student responses (minimum response n= 628) to a selection of questions on the
survey are in Table XIII. Most students (76%) agreed that they felt confident with their
ability to develop a hypothesis and design an experiment, but would prefer (67%) to have
an experiment provided rather than designing their own. Students (69%) generally liked
working with a group and most students (65%) agreed that their group members
explained lab procedures if they did not understand a concept. Only a fourth of the
students agreed, however, that individuals did not have to learn the lab procedures
because the group members would perform the experiment for them. The majority of the
students (66%) felt that they were well prepared for lab each week and most (77%)
indicated that pre-lab exercises helped them prepare. Most students reported that they
regularly prepared for lab by completing pre-labs (72%) and planning forms (91%).
However, some of the students (28%) responded that they rarely understood what each
week’s lab was about even after completing the pre-lab and planning form. Most
students (82%) also said that their teaching assistant explained the lab procedures before
class.
A selection of student responses on the open-ended portion of the survey is found
on Table XIV. Students commented on a variety of topics including inquiry, the preclass activities, working in groups, teaching assistants, and other laboratory resources.
Interestingly, there were students that strongly supported and students that strongly
opposed each of these aspects of the course. These example quotes demonstrate how the
54
students enrolled in the same course at the same time often have very different
experiences.
55
TABLE XIII
STUDENT RESPONSES FROM SELECTED QUESTIONS ON A SURVEY
GIVEN AT THE END OF THE SEMESTER
The number of students is given in parentheses.
n
A – Strongly
B – Agree
Agree
8. I used the WWW site often.
639
35% (223)
42% (268)
C –Neither agree D- Disagree
nor disagree
12% (78)
9% (56)
E – Strongly
Disagree
2% (14)
18. I wish other courses would allow me to work with a group.
638
32% (205)
37% (239)
17% (107)
10% (61)
4% (26)
31. I felt that I was well prepared as I went into lab each week.
628
22% (141)
43% (272)
19% (120)
12% (78)
3% (17)
32. I felt that my group members were well prepared for lab each week.
629
18% (114)
41% (256)
21% (132)
14% (87)
6% (40)
33. When I did not understand a lab procedure, my group members explained it.
629
19% (122)
46% (292)
16% (102)
12% (78)
6% (35)
34. I did not have to learn all the lab procedures because my group members did it.
630
6% (35)
20% (125)
22% (139)
37% (236)
15% (95)
35. The pre-labs helped me prepare for lab.
628
29% (181)
48% (303)
12% (74)
8% (52)
3% (18)
36. I regularly prepared for lab by doing the pre-lab.
630
36% (229)
36% (228)
12% (75)
12% (73)
4% (25)
37. I regularly completed my planning form.
629
61% (384)
30% (190)
5% (29)
3% (16)
2% (10)
38. I regularly received full credit on my planning forms.
627
30% (191)
40% (251)
14% (85)
13% (81)
3% (19)
39. I rarely understood what each week’s lab was about even after I completed my
planning form and pre-labs.
628
7% (44)
21% (129)
19% (121)
39% (248)
14% (86)
40. I feel confident with my ability to develop a hypothesis and design an experiment.
628
29% (182)
47% (293)
17% (108)
6% (37)
1% (8)
41. I would rather be given an experiment to perform than have to design my own.
629
30% (186)
37% (231)
17% (109)
11% (70)
5% (33)
43. My TA explained all the procedures before class.
629
46% (288)
36% (226)
10% (61)
56
6% (40)
2% (14)
TABLE XIV
SAMPLE OF STUDENT COMMENTS ON A SURVEY GIVEN
AT THE END OF THE SEMESTER
Topic
Please list up to three aspects of
the course you liked most and
why:
Please list up to three aspects you
would change and how and why:
Inquirybased
labs
“The lab experiments because they
further my understanding of the
topic.”
“Lab – helped me develop my
experimental skills, developing
hypotheses, etc.”
“Lab – seeing how biology relates
to real life.”
“I really didn’t like conducting our
own experiments and would rather
follow directions.”
“The labs are tedious and are not
helpful to understanding the
material.”
“The labs are often too
complicated”
Pre-class
activities
“Pre-labs – helped you understand
better.”
“Pre-labs prepared me for lab.”
“Pre-lab should be required.”
“I didn’t like the planning forms.”
Group
work
“Working in groups – it helped
when there were many minds over
just one.”
“My lab group – because we were
all cooperative and helped each
other.”
“The group is helpful at times, but
I like individual effort too.”
“Sometimes group members didn’t
come and it made lab longer.”
Teaching
Assistant
“All of the TAs.”
“TAs in lab need to be more
helpful.”
“Some TA grading was not
consistent; some TAs were
extremely lenient in comparison to
others.”
“My lab TA – she did a wonderful
job on helping us understand if we
were confused.”
Resources “The website was very helpful.”
“TA in the LRC is willing to help
when you do not get it.”
57
“Planning forms took a long time
to complete.”
“Sometimes the (on-line) tutorials
would not work.”
“Be willing to help when we come
to the LRC.”
Summary of Results
The purpose of this study was to determine which factors affect student
performance in an inquiry-based laboratory, with specific regard to student preparation.
This project included over seven hundred students’ grades on lab reports and pre-class
activities (pre-labs and planning forms). I tested the following relationships using
regression analyses: pre-lab and planning form scores, pre-lab and lab report scores,
planning form and lab report scores. There were weak, statistically significant
relationships between the independent and dependent variables of student scores,
identified by the relatively low r-square values in these models. I also tested the strength
of the relationships between student scores with the inclusion of the lab investigation,
section, and teaching assistant using analyses of covariance. The coefficients of
determination were higher with the inclusion of either section or teaching assistant,
suggesting that these models provided better descriptions of the relationships. Also, the
r-square values were highest with the relationship of pre-lab to planning form for each of
the ANOVA and ANCOVA models, suggesting an effect of group collaboration on
student scores. A summary of these results can be found in Table XIII. Despite the
relatively low r-square values in the general models, students reported that they felt
prepared after completing the pre-class activities and they were confident with their
abilities to conduct an inquiry-based investigation. A conceptual overview of the factors
affecting student performance can be found on Figure 13. This concept map shows the
connections between pre-lab, planning form, and lab report scores along with the effects
of lab investigation, section, teaching assistant, and group collaboration.
58
Figure 13. Concept Map of a Summary of the Results from the Analyses of Student
Scores.
59
60
Figure 13
TABLE XV
SUMMARY OF THE RESULTS
FROM THE ANALYSES OF
STUDENT SCORES
Model
r- square
value
General Regression
Pre-lab and Planning Form Scores
Pre-lab and Lab Report Scores
Planning Form and Lab Report Scores
0.1576
0.1334
0.0965
Classification 1: Lab Investigation
Pre-lab and Planning Form Scores
Pre-lab and Lab Report Scores
Planning Form and Lab Report Scores
0.1462
0.1067
0.0963
Classification 2: Section
Pre-lab and Planning Form Scores
Pre-lab and Lab Report Scores
Planning Form and Lab Report Scores
0.4779
0.4091
0.4607
Classification 3: Teaching Assistant
Pre-lab and Planning Form Scores
Pre-lab and Lab Report Scores
Planning Form and Lab Report Scores
0.4319
0.2885
0.3667
61
CHAPTER IV
DISCUSSION
Findings
General Regression Models
Based on my regression analyses, it appears that students who performed well on
the pre-lab were more likely to perform well on the planning forms and student groups
who performed well on the pre-labs or the planning forms were more likely to perform
well on the lab reports. Thus, preparation may contribute to student success in the
laboratory.
These results are unlike those reported by Barnes and Thornton (1998). They did
not find any significant correlation between performance on pre-laboratory questions and
laboratory work, or performance on the mid-semester test. Although they found that the
students felt the pre-laboratory exercises were worthwhile, the use of pre-labs did not
affect the students’ grades. The lack of significance found in the grade analysis may have
resulted from the small sample size of the study (n=63). Similarly, Wyatt (2003) was
unable to find any statistical differences in learning outcomes between the students
(n=approximately 300) who completed pre-labs and those who did not. During this
study, students were expected to complete online simulations or tutorials prior to class.
However, Wyatt reported that students who attended lab received an introduction from
62
the instructor, which may have decreased the importance of student preparation and
contributed to the lack of a significant difference between student scores.
The results found here are more comparable to those found by Johnstone, et al.
(1998). These researchers found a significant difference between the mean scores on
post-lab assessments following laboratories before which students (n=95) used pre-lab
exercises and the labs before which they did not. However, the results from their study
may be skewed because the effectiveness of the pre-labs was determined by comparing
student scores from different laboratory investigations. The results may have been
affected by an interaction between the use of pre-lab exercises and the type of
investigation.
Although the r-square values in this study were relatively low, the results were
statistically significant and showed positive relationships between the student scores.
Other studies have drawn similar conclusions on statistical differences with low r-square
values. A study from the University of Arkansas at Little Rock (2003) concluded that
pre-lab quizzes are an effective tool based on the regression of the students’ (n=29 to 74)
post-lab exam scores regressed on their quiz scores. Their analysis showed significant
results for ten of thirteen laboratories with relatively low r-square values, ranging from
0.063 to 0.1584. They based their conclusions on the significance of the regression
analysis, even though low r-square values were present. This suggests that not only are
my conclusions well-founded, but, there may be evidence that pre-lab exercises are more
effective than pre-lab quizzes in increasing student performance in the laboratory.
63
Sources of Variation
Although firm conclusions may be drawn from the results already presented, the
relatively low r-square values demonstrated that the majority of the variation of student
scores was not accounted for by simple regression. Many differences among individuals
may greatly affect student performance in the science classroom (Trowbridge, et al.,
2000). Research demonstrates that students:
1. have preconceptions about natural phenomena
2. vary in their rate of learning
3. have various levels of motivation
4. have differences in their psychomotor skills
5. hold different attitudes, values, and concepts in regard to science
Because the data in the present study was from a large enrollment general education
introductory course taken by students from any major or year, there was high variation
among students in all characteristics. For example, some students, especially the science
majors, might have had different and perhaps more accurate preconceptions about the
content addressed in lab than did others. This suggests that some students may not have
needed as much preparation to perform well on the planning forms and lab reports. Due
to different levels of motivation, some students were more likely to seek additional or
alternative forms of preparation. For example, some students found the learning resource
center or the website useful. These students may have been highly prepared for lab even
though their level of preparation was not necessarily reflected by their pre-lab or planning
form score.
64
Gender (Milne and Ransome, 1993), culture (Madrazo and Hounshell, 1993), and
learning disabilities (Roberts and Bazler, 1993) are all factors that affect student
differences in learning and performance. Such student differences are expected to
increase the variability in student scores in a large indiscriminate sample, like the one
used in this study.
One strategy that has been suggested to help students engage in scientific
investigation, regardless of their individual differences, is the use of cooperative groups.
Groups allow for individual differences while providing students with equal opportunity
for student involvement with science equipment and materials (Trowbridge, et al., 2000).
In support of such research, the effect of student collaboration can be inferred from my
study. In all of the models, the relationships between an individual’s pre-lab and
planning form score exhibited the highest coefficients of determination. Models with lab
report and pre-lab or planning form scores had lower r-square values. Lab reports
resulted from group efforts; therefore, the decrease in the r-square values provides
indirect evidence that group collaboration affected student scores. Perhaps, individuals
participating in a group did not necessarily need to perform well on the pre-lab or
planning form exercises to earn a high score on the lab report. At the beginning of the
laboratory, student groups discuss the lab concepts, originate a group hypothesis, and
design their experiment. Throughout the lab, students must collaborate to successfully
complete the investigation and compose the lab report. Russell and French (2001) found
that students in these labs spent more time on task and had higher levels of active
participation than students in more traditional, or “cookbook”, laboratories. Such active
65
involvement may be a result of the expectation of equal participation among group
members, regardless of individual preparation.
The benefits of collaboration were also reflected by the student responses on the
survey given at the end of the semester. Most students reported that they felt that their
group members were well prepared for lab and that they were able to collaborate well.
Most also agreed that when they did not understand a lab procedure their group members
explained it. However, most did not agree with the statement inferring that they did not
need to prepare individually because their group members did the lab procedures. This
statement contradicts a common hypothesis that a group will perform well because one
student essentially “carries” the group. The data from this research suggest that in these
laboratories, although they prepare individually, the students collaborate their efforts to
compensate for individual weaknesses.
Other research has studied the benefits of group work in the science laboratory.
Howard and Boone (1997) reported that students rated working in groups as the most
enjoyable aspect of their laboratory course. Because their students were assigned seats
alphabetically (similar to our seating arrangements), they concluded the positive response
was not a reflection of friends helping each other. Instead, working in groups provides
the opportunity for cooperative learning. Travis and Lord (2004) demonstrated that
group interaction increased the level of student involvement in the laboratory. They
showed that students working in groups were able to recall and apply the information
learned in laboratory better than students that did not participate in a group.
Both individual differences and the benefits of group interaction are likely sources
of the unexplained variation in these models. Given the data set available, it is difficult to
66
draw firm conclusions on the extent of the effect of these factors. However, including
additional variables with the students’ scores provided additional insight to factors that
affect student performance in the laboratory.
ANCOVA Including Lab Investigation
Each of the fourteen lab investigations addressed different content and most
required students to apply concepts and procedures unique to the investigation. It was
suspected that the student grades might differ depending on the lab investigation because
some labs might require more preparation than others. However, the inclusion of lab
investigation produced nearly the same coefficients of determination as the general
regression models, showing that including lab investigation in the ANCOVA analyses
was not useful in reducing the unexplained variance in the regression models. It appears
that it was equally important for students to prepare for lab, regardless of the type of
investigation being conducted.
One possible explanation for the consistency in grades among investigations lies
in the course expectations. In both the lab and lecture components of this course,
students developed their research skills, including formulating hypotheses, designing
experiments, and interpreting results. These skills were required to complete the
laboratory assignments. The planning form assignment consisted of a series of questions,
most of which were not specific to the lab investigation. Students were expected to write
their own hypotheses, outline their procedures, and make predictions for every lab
investigation. Of the possible score of ten for a planning form, eight of the points were
from these questions. A copy of an example planning form can be found in the appendix
(Appendix A). Perhaps students performed consistently on the planning forms regardless
67
of the investigation because they became familiar with the assignment. According to
responses on the student survey, most of our students felt confident in their ability to
develop a hypothesis and design an experiment. Thus, most students agreed that they
could meet most of the requirements on the planning forms used in this course.
Similarly, the format and expectations for the lab report were identical every week.
Students were expected to include the same type of information in their lab reports,
including detailed introduction, methods, results, and discussion sections. A copy of an
example grading scheme for the lab report can be found in the appendix (Appendix B).
Therefore, it is likely that the student’s scores on planning forms and lab reports did not
vary much with the lab investigation because these assignments required the students to
apply the same skills and address the same questions during every investigation.
Although applying the lab investigation did not improve the covariate model,
there were some differences in the relationships between students’ scores depending on
the investigation. For example, the regression model of planning form on pre-lab scores
was not significant for the final investigation while there was a significant relationship
for all other thirteen investigations (p<0.05) (Table IV). The lack of a significant slope
may be because this was the last lab investigation and either the students were all
performing well regardless of preparation or, perhaps, there were differences in teaching
assistant grading standards. From these analyses, I conclude that although including the
variable of investigation did not reduce the amount of variation in student scores, it may
still be a useful tool to explore student performance in lab and warrants further
investigation.
68
ANCOVA Including Section
This data set included scores of students separated into thirty four laboratory
sections. The regression and ANCOVA analyses demonstrated that including the section
variable was very useful in reducing the amount of the unaccounted for variation of these
models (pre-lab and planning form, pre-lab and lab report, planning form and lab report),
as indicated by the increase in r-square values. In some sections of students, the
relationships between scores were highly correlated while there were no apparent
relationships in other sections.
There are several explanations for why student grades differed by section. The
variable of section was a unique combination of the instructor (both lab and lecture), the
meeting time of the laboratory, and the students enrolled. The effect of teaching assistant
as a classification variable is addressed in the following section of this study.
It is possible that the lecture section was a factor on student grades in the
laboratory. In each of the lectures (these students were enrolled in one of four sections),
instructors used certain scenario software to cover the same material so that the students
were equally prepared for the common exams. However, differences still existed among
the instructors’ teaching styles, depth of content coverage, and pace. Perhaps students
with a particular lecture instructor were able to perform higher on the planning forms or
lab reports regardless of their participation on pre-class activities because their instructor
covered the content information to a greater extent in their lecture. Some lecture sections
may have covered relevant content before some students attended lab and not others
because of differences in the day and time of their section.
69
The day of the lab section may also have influenced student grades, either in
association with the lecture section or as an independent variable. Perhaps students in lab
sections at the end of the week performed well without preparation because their peers
enrolled in earlier labs informed the students of later labs about the procedures or content
that they would address. Perhaps students enrolled in labs earlier in the week or day were
more responsible and more motivated students who were more likely to perform well on
assignments. Perhaps evening labs had students who were less dedicated to the course
than those willing to enroll in an eight-thirty morning lab, or morning lab students did not
perform as well compared to afternoon labs because the students were tired and did not
fully engage in the investigation.
Sampling may also have contributed to the effect of section on student grades.
There were a total of 757 students and 206 groups included in the analyses; however, this
number was greatly reduced when considering only the students from any given section.
Each section included a maximum of 24 students and a maximum of six groups. This
probably resulted in a random distribution of individual student differences across the
sections. It is possible that some sections contained more science majors, or more upperclass students, or more dedicated and highly motivated students, etc. Thus, students in
certain sections may not have needed to prepare as much to perform well on lab
assignments. This would not be a systematic effect (e.g., such as lecture teaching style),
but the effect of uneven samples from the larger population. Although other factors may
have contributed, the significance of the section variable was likely attributed to student
differences across the sections.
70
ANCOVA Including Teaching Assistant
During the spring semester of 2004, students had one of seventeen teaching
assistants instructing their laboratory section. When I included teaching assistant in an
ANCOVA model, it increased the amount of variance accounted for compared to the
general regression models. Thus, the teaching assistant model was better able to predict
the planning form score based on the pre-lab score. It also suggests that grades were
highly influenced by teaching assistant.
It is interesting to note the variability of the relationships between students’ scores
depending on the teaching assistant. To illustrate, the coefficient of determination of the
pre-lab to planning form model was 0.4296 for teaching assistant number eight but was
only 0.0798 for teaching assistant number six (Table X). Several explanations for such
variation among instructors exist. First, it is possible that some teaching assistants had
students who were highly motivated and performed well on assignments. This idea of the
random distribution of students and their abilities by teaching assistant is comparable to
the explanation for the variation of student grades according to section. With teaching
assistant, however, it is less likely that random distribution was a major influence. Each
teaching assistant instructed students from two arbitrarily assigned sections. Also, with
twice the amount of sample data per teaching assistant, it is less likely that a random
distribution of students had a large effect on these analyses. Moreover, although the
coefficients of determination of the models including teaching assistant were higher than
those of the general models, the relationships were not as strong as those including the
section variable. This suggests that even though it is possible that the relationships
71
between students’ scores were affected by chance sampling with the section variable, it is
less likely to have been a factor with the models including the teaching assistants.
Assuming relative equality of students distributed among the teaching assistants,
the instructors had a large influence on student performance in these laboratories. Such
influence could have been a result of one of two factors: the teaching assistant’s method
of instruction or standard of grading. All teaching assistants participated in an
introduction to the course at the beginning of the semester and attended weekly meetings
to explore any issues with instruction. At each meeting, the teaching assistants discussed
the nature of the investigation and expectations of student completion and performance
on the assignments. Teaching assistants, especially those unfamiliar with inquiry-based
learning, were highly encouraged to observe experienced lab instructors prior to teaching
their own sections. Also, all teaching assistants were provided a “TA survival guide” that
detailed how to teach inquiry-based labs, how to evaluate assignments, and what to
expect during each investigation. The teaching assistants were expected to instruct and
grade in a very similar manner.
Unfortunately, it is likely that some teaching assistants did not implement inquirybased instruction as directed because they either did not understand or did not agree with
this instructional style in the laboratory. Inquiry-based labs include open-ended scientific
investigations that require students to observe, form hypotheses, test, and evaluate
(Division of Undergraduate Science, Engineering, and Mathematics Education, 1990).
These explorations are student-centered and call for little if any traditional instruction
from the teacher. However, if the amount of direct instruction is increased, it may
decrease the level of inquiry and subsequently devalue the preparation activities. As
72
emphasized before, students are unlikely to arrive to class prepared if they feel as though
the instructor will provide them with all of the information they require to complete the
laboratory.
Research has demonstrated that instruction from a teaching assistant has a large
impact on student success in the laboratory. Kurdziel (2003) described many of the
issues with graduate teaching assistants (GTAs). She reported that GTAs had either
limited or negative prior experience with inquiry labs. In her study, she found that when
the GTAs were observed in their classrooms, they had restructured the lab and did not
properly implement inquiry-based instruction. Instead, these lab instructors gave explicit
instructions about what the students needed to do, how they needed to analyze their data,
and what results they should find. Glasson and McKenzie (1997) concluded that
laboratory teachers may have difficulty with the shift from teacher-centered to learnercentered instruction. They found that a teaching assistant chosen to pilot the new
instructional method grappled with the problem of how much guidance to offer the
students.
Pratt (2003) researched cooperative learning in the laboratory and concluded that
its success depends on the teacher continually circulating the room to guide students;
otherwise the group time is unproductive. Other education researchers suggest that
constructing truly cooperative groups requires years of teaching experience and many
teaching assistants frequently encounter problems facilitating group work (Jensen, et al.,
2005). Thus, it is possible that the students’ success in lab was also affected by an
interaction between their teaching assistant and their group collaboration.
73
If research such as this consistently found that teaching assistants are challenged
by inquiry-based instruction, it is likely that some of the same problems were experienced
by the instructors of the laboratories in this study. Unexpectedly, 82% of the biology
students strongly agreed or agreed with the statement that their TA explained all the
procedures before class. Therefore, I conclude that some teaching assistants were not
leading their students to fully engage in an inquiry-based lab, which would have affected
their performance on the laboratory assignments. This agrees with French and Russell
(2002), who found that teaching assistants with less experience in inquiry-based
laboratories were less inclined to use that method in teaching than those with more
experience.
Even though the biology lab instructors discussed expectations during the weekly
meetings and had explicit directions in the TA guide, it was likely that the grading
standards were not identical among all of the teaching assistants. Some probably
demanded higher performance levels from their students than others, regardless of the
predetermined expectations. To illustrate, the table and graph of pre-lab and planning
form scores shows that teaching assistant number ten had a slope significantly different
than zero (p<0.0001) but also had scores consistently lower than any other teaching
assistant. This suggests that although this teaching assistant had student scores that
demonstrated a strong relationship with the increase of a pre-lab score predicting the
increase of the planning form score, he/she may have been grading strictly relative to the
other teaching assistants. On the other hand, teaching assistant number three had
consistently high student scores but the slope of the line was not significantly different
than zero (p=0.2741) (Table X, Figure 10). This teaching assistant may have been
74
grading too leniently and those students did not have to participate in the pre-lab in order
to earn a high score on the planning form.
Discrepancies in teaching assistant grading standards have also been expressed by
students, such as this statement on the survey: “Some TA grading was not consistent;
some TAs were extremely lenient in comparison to others”. Also, the majority of these
students (70%) reported that they regularly received full credit on their planning forms.
It is difficult to rely on student opinions on the appropriateness of teaching assistant
grading considering how often students taught by many different teaching assistants are
convinced that they have the harshest teacher. However, it may have been more than
student perception that teaching assistant expectations were not equal.
Analyses of the Student Survey
The student responses corroborate the interpretations of the grade analyses. The
majority of the students reported that they regularly completed the pre-labs and planning
forms and most students felt that they were prepared for lab each week. This suggests
that the students found the pre-class assignments valuable, which is a noteworthy
outcome in itself. This is consistent with other studies (Barnes and Thornton, 1998,
Johnstone, et al., 1998, Kirk and Layman, 1996, Wyatt, 2003).
I also found that most students enjoyed working in groups and agreed that their
group members explained lab procedures if they did not understand them. Other studies
have also found that students enjoy working in groups in the laboratory (Pratt, 2003,
Travis and Lord, 2004). This supports the idea that there were lower coefficients of
determination in the models predicting lab report scores because of student collaboration.
One student eloquently wrote that he/she enjoyed this aspect of the course because “it
75
(working in groups) helped when there were many minds over just one”. The results also
support the proposed variability in teaching assistant instruction. Nearly half the students
reported that they strongly agreed that their TA explained all the procedures before class.
Perhaps the most interesting aspect of the student survey was the variability in the
open-ended responses. When students were asked to comment on their favorite and least
favorite aspects of the course, many reflected on the same topics but evaluated them with
opposing views. For example, with respect to group work, one student responded that
he/she liked this aspect the most stating “…we were all cooperative and helped each
other”. Another student reported a desire to change this aspect of the course because
he/she “…like[s] individual effort”. One student commented that his/her favorite aspect
of the course was “all of the TAs” while another said that the “TAs in lab need to be
more helpful”. These student responses demonstrated that although this course was
designed to consistently provide equal learning opportunities all students, they often had
very different experiences. The wide range of student perceptions may be a reflection of
individual differences, which likely increased the unexplained variation of student grades
in the regression models.
Conclusions
Many factors affect student performance in the laboratory. It is important for
students to prepare prior to attending an inquiry-based lab and the use of pre-labs and
planning form assignments can be effective. These pre-class assignments can influence
the students’ grades and the students themselves found the pre-class assignments
valuable. However, student performance in the laboratory is connected to variables other
than their participation on these preliminary exercises. Although lab investigation may
76
not be a factor, student performance is highly affected by the lab section and teaching
assistant. The section effects may be a result of a random distribution of student
differences, the lecture section, their instructor (either in lecture or laboratory), or the
meeting day and time of the laboratory. The differences resulting from the inclusion of
teaching assistant are likely attributable to differences in instruction style or grading
standard. Also, the results of these analyses also show that student collaboration in
groups is probably an important aspect of student performance in laboratory. Other
factors that likely affect student grades are individual skills and science abilities, personal
attributes such as gender and classification, and student participation and motivation.
Although many variables affect student performance, quantitative and qualitative
methods used in this study demonstrate that pre-class activities are valuable tools for
preparation in the laboratory.
Recommendations for Future Research
More research on pre-class exercises and student performance could be completed
in several areas. The course used in this study did not allow for any comparison with a
control group. It may be beneficial to design a study in which there are some students
who complete pre-labs or planning forms and others who do not. This may provide more
definitive data on how effective these pre-class activities are in helping students to
prepare for and then succeed in the laboratory.
The qualitative portion of this study was limited to a survey given to students at
the end of the course. It would be interesting to evaluate student perceptions at different
times during the semester. Perhaps students value the pre-lab or planning form
77
assignments differently at the beginning of their lab experience as compared to the end
when they are more accustomed to the investigative requirements.
Differences in students’ scores by major, year of study, gender, ethnicity, and
other personal factors could be explored. It may be helpful to collect these data to reduce
the variation by using each quality as a classification variable. Analyzing the grades of a
specifically chosen group of students may provide additional insight into factors affecting
student preparation and performance in the laboratory.
The effect of the lab instructor could also be explored. This research
demonstrated that student grades differ depending on the teaching assistant of the lab
section. As suggested, this may be a result of instructional style. To test this hypothesis,
lab instructors could be observed and categorized by how effectively they implement an
inquiry-based laboratory. Multiple comparison procedures could be performed to
determine if there is a significant difference in student’s grades between teaching
assistants who more fully apply inquiry instruction and those who implement more direct
instruction. If the teaching assistant allows for student-centered investigations during the
laboratory, then student preparation should significantly correlate with student success.
In contrast, an increase in the amount of direct instruction from the teaching assistant
may result in a decline in the importance of student preparation.
It may also be useful to explore student performance in the laboratory based on
the type of investigation. Although these analyses showed that using the investigation as
a classification variable was not useful in reducing the variation in the data, it may be
beneficial to perform additional analyses. Multiple comparison procedures could be
applied to compare the more open-ended investigations with those that call for more
78
specifically defined procedures. It may also be useful to compare investigations that
require the students to know more conceptual background to those that involve more
procedural knowledge. This information could help instructors develop more useful
preparatory exercises for students in an inquiry-based laboratory.
79
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http://gsi.berkeley.edu/resources/labs/pre_assign.html (February 18, 2005).
University of Michigan. Helpful Hints for Success in Chem125.
http://www.umich.edu/~chem125/c125help.html (April 5, 2005).
University of Toronto Teaching Assistants’ Training Programme. April 18, 2005.
TA Training Teaching Tips: Giving Pre-Lab Talks.
http://www.utoronto.ca/tatp/pre-lab_tips.html (April 18, 2005).
Wyatt, Robert. June 1, 2003. Campus Technology: Online Biology Pre-Labs.
http://www.campus-technology.com/article.asp?id=7770 (April 14, 2005).
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APPENDIX A
An example of a planning form (with the pre-lab questions).
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APPENDIX B
An example of the lab report grading scheme.
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APPENDIX C
A copy of the survey given to students at the end of the semester.
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89
VITA
Melissa Rose Gentry
Candidate for the Degree of
Master of Science
Thesis: STUDENT SUCCESS IN AN INQUIRY-BASED LABORATORY: THE
EFFECT OF PRE-CLASS ACTIVITIES AND STUDENT PREPARATION
Major Field: Zoology
Biographical:
Personal Data: Born in Tulsa, Oklahoma on April 23, 1980 to Thomas and
Ruth Willis.
Education: Graduated from Union High School in Tulsa, Oklahoma in May of
1998; received Bachelor of Science in Secondary Science Education
from Oklahoma State University in May of 2002; completed the
requirements for Master of Science in Zoology at Oklahoma State
University in July of 2005.
Experience: I was employed as a Teaching Assistant for the introductory
biology course at Oklahoma State University during the academic years
of 2003-2004 and 2004-2005. During this time I also extended my
experience in biological science coursework and science education
research.
Professional Memberships: National Association of Biology Teachers (NABT),
National Science Teachers Association (NSTA), Society of College
Science Teachers (SCST)
Name: Melissa Gentry
Date of Degree: July, 2005
Institution: Oklahoma State University
Location: Stillwater, Oklahoma
Title of Study: STUDENT SUCCESS IN AN INQUIRY-BASED LABORATORY:
THE EFFECT OF PRE-CLASS ACTIVITIES AND STUDENT
PREPARATION
Pages in Study: 89
Candidate for the Degree of Master of Science
Major Field: Zoology
Scope and Method of Study: Many instructors agree that students need to prepare prior
to attending class, especially for laboratory. While some instructors provide pre-class
activities to help students prepare, there is little research to support the conclusion
that they are effective. This study examined their effectiveness in an introductory
biology course that employed the use of pre-class activities for student preparation.
The laboratory component of the class was designed to enhance students’ scientific
literacy through inquiry. Each week, given background information and a question to
investigate, the students designed and conducted an experiment, analyzed their data,
and wrote a lab report. To help students prepare for the investigation, students were
expected to complete two pre-class assignments, a planning form and a pre-lab. To
test the relationships between student preparation and performance, I performed
regression analyses of the following models: pre-lab and planning form scores, prelab and lab report scores, planning form and lab report scores. To determine the
effects of other possible factors, lab investigation, section of enrollment, and teaching
assistant were included in the models. I also analyzed student perceptions concerning
the preparation activities and other aspects of the laboratory.
Findings and Conclusions: There were statistically significant and positive relationships
between student scores in the three models. Students that performed well on pre-labs
were more likely to perform well on planning forms and student groups that
performed well on pre-labs or planning forms were more likely to perform well on the
lab reports. This indicated that student preparation effected performance in lab.
However, the relatively low variance accounted for by these models alone showed the
presence of other factors affecting student performance. The coefficients of
determination of the models increased substantially when section or teaching assistant
was included in ANCOVA analyses. This supports the conclusion that sources of
variation may be from student differences in skills and abilities or the instructional
style and grading standard of the lab instructor. There was also indirect evidence that
group collaboration affected student scores in these laboratories.
ADVISOR’S APPROVAL: Donald P. French