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CBE—Life Sciences Education
Vol. 11, 402–412, Winter 2012
Article
Peer Learning and Support of Technology in an
Undergraduate Biology Course to Enhance Deep Learning
Masha Tsaushu,*Ta li Tal, *Ornit Sagy,*Yael Kali,†Shimon Gepstein,‡
and Dan Zilberstein‡
*Department of Education in Technology and Science and ‡Faculty of Biology, Technion–Israel Institute of
Technology, Haifa 32000, Israel; †Technologies in Education Graduate Program, Department of Learning,
Instruction and Teacher Education, Faculty of Education, University of Haifa, Mount Carmel, Haifa 31905, Israel
Submitted April 12, 2012; Revised August 20, 2012; Accepted September 1, 2012
Monitoring Editor: Clarissa Ann Dirks
This study offers an innovative and sustainable instructional model for an introductory under-
graduate course. The model was gradually implemented during 3 yr in a research university in a
large-lecture biology course that enrolled biology majors and nonmajors. It gives priority to sources
not used enough to enhance active learning in higher education: technology and the students them-
selves. Most of the lectures were replaced with continuous individual learning and 1-mo group
learning of one topic, both supported by an interactive online tutorial. Assessment included open-
ended complex questions requiring higher-order thinking skills that were added to the traditional
multiple-choice (MC) exam. Analysis of students’ outcomes indicates no significant difference among
the three intervention versions in the MC questions of the exam, while students who took part in
active-learning groups at the advanced version of the model had significantly higher scores in the
more demanding open-ended questions compared with their counterparts. We believe that social-
constructivist learning of one topic during 1 mo has significantly contributed to student deep learning
across topics. It developed a biological discourse, which is more typical to advanced stages of learn-
ing biology, and changed the image of instructors from “knowledge transmitters” to “role model
scientists.”
INTRODUCTION
Criticism of teaching in higher education institutions has been
growing in recent years. Among international scientists, there
is increasing agreement about the need to change the culture
of science education in research universities to promote more
meaningful learning. This change will only happen when we
find the balance between technical interests in science and
human interests in science learners (Gilmer, 2010). Anderson
and his colleagues, biomedical research scientists represent-
DOI: 10.1187/cbe.12-04-0042
Address correspondence to: Masha Tsaushu (tmasha@gmail.com).
c
2012 M. Tsaushu et al. CBE—Life Sciences Education c
2012
The American Society for Cell Biology. This article is distributed
by The American Society for Cell Biology under license from
the author(s). It is available to the public under an Attribution–
Noncommercial–Share Alike 3.0 Unported Creative Commons
License (http://creativecommons.org/licenses/by-nc-sa/3.0).
“ASCB R
” and “The American Society for Cell Biology R
” are regis-
tered trademarks of The American Society for Cell Biology.
ing a diversity of institutions, argued that to maintain the
vitality of research universities requires a culture in which
teaching and research support two equally important enter-
prises: generation of new knowledge and education of stu-
dents (Anderson et al., 2011). Both scientists and science ed-
ucators have called for a program that refrains from merely
providing broad content. They have emphasized the need to
develop students’ analytical skills, while promoting under-
standing of scientific research processes and inspiring curios-
ity (Gilmer, 2010).
In 2009, a conference hosted by the American Associa-
tion for the Advancement of Science, with support from the
National Science Foundation, brought together faculty, ad-
ministrators, students, and other educational stakeholders to
discuss biology teaching at the undergraduate level. The rec-
ommendations from the meeting can be summarized as “the
biology we teach should be the biology we do,” meaning
that in addition to learning the content, students should gain
a better understanding of the nature of science and that as-
sessments should help instructors to figure out how deeply
students understand (or misunderstand) the basics of the
402
Deep Learning in an Undergraduate Course
discipline, rather than test for recall of facts or repetition of
memorized procedures. In addition, Web- and print-based
tools should be available to help students access and interact
with the information, which will enable them to develop tools
to acquire understanding and to become part of a scientific
community (Woodin et al., 2009).
Large enrollments have always been typical of introductory
undergraduate courses, but the growth of universities world-
wide and the global economic crises have further worsened
the faculty–student ratios (Haak et al., 2011). Consequently,
most introductory courses rely on lectures that attempt to
“deliver the content,” a technique that has proven to be in-
effective in fostering conceptual understanding of scientific
reasoning (Handelsman et al., 2004). As the primary means of
informing students, it can hardly inspire curiosity or motivate
learning (Gilmer, 2010). The knowledge introductory courses
attempt to teach is constantly growing, especially in biology,
and the need to change the teaching philosophy is therefore
even more acute. If we want to highlight and discuss main
principles and complex ideas, expose students to biological
research, and encourage “biological thinking,” we cannot do
it with a professor who struggles to teach the entire textbook
to 300 individuals while standing on a distant podium in a
large lecture hall.
The scientific community has not ignored the above-
mentioned challenges, and there are ongoing attempts to sug-
gest other models. Universities and institutions, such as the
U.S. National Institutes of Health (NIH), execute programs
that promote effective pedagogical approaches to undergrad-
uate education in biology (Woodin et al., 2010). Programs
such as the 1-wk NIH Summer Institute, which has taken
place annually since 2004, have shown a positive multidimen-
sional impact on the participants’ teaching methods (Pfund
et al., 2009). Many attempts to supplement or replace science,
technology, engineering, and mathematics (STEM) lectures
with active learning have been made in the past decade,
increasing student conceptual understanding and improv-
ing their attitudes toward these courses (Henderson et al.,
2011).
The teaching effort and research described in this paper
took place in a major research university in Israel. It was a re-
sult of a discussion between two biology professors who were
dissatisfied with their teaching and a group of four science
educators. Together, we developed and implemented this in-
novative instructional model. In line with Handelsman et al.
(2004) and Woodin et al. (2009), we aimed to make teaching
more scientific and student learning more active and mean-
ingful in the large-enrollment introductory course Biology 1.
Our model gives priority to resources not used enough to
enhance active learning in higher education: technology and
the students themselves. Given the constraints of the class
size and a syllabus that we could not change, we substan-
tially reduced the number of lectures and replaced them with
educational technology that supported individual learning
and short-term, small-group learning.
This study is framed with the view of learning as a social-
constructivist activity, as well as a cognitive process, that
can take place face-to-face or through online interactions
(Linn and Hsi, 2000). The social-constructivist approach that
has developed from Vygotsky’s theory and through schol-
ars who followed him emphasizes critical dialogue with
the teacher or among peers to promote meaningful learn-
ing (Driver et al., 1994; Ash, 2004). Vygotsky also argued
for an essential distinction between scientific and common
conceptions. The notion, for example, that mushrooms and
humans share the same basic mechanisms is certainly not a
common-sense assumption (Klymkowsky et al., 2003). This
difficulty in scientific understanding of the natural world is
one of the main factors that inhibit the development of a
deep approach to science learning, especially among young
people.
The distinction between deep and surface learning shows
that students who use a surface approach give “black box”
explanations that do not refer to mechanisms and tend to ask
about more basic, factual, or procedural, information (Chin
and Brown, 2000), while students who use a deep-learning ap-
proach give more elaborate explanations that describe mecha-
nisms and cause–effect relationships; ask questions that focus
on explanations, causes or predictions; and engage in “on-
line theorizing” (Marton and Saljo, 1976). The idea of a deep
versus a surface approach was recently discussed by Gilmer
(2010), who studied her own shift in practice when teach-
ing college biochemistry using technology and small-group
learning.
Marton and Saljo showed that students adapt their way
of learning to their conception of what is required of them.
This phenomenon was more recently documented by Scouller
and Prosser (1994). Deep-learning strategies were employed
by students preparing their assignment essays; these strate-
gies were perceived by the students as application of higher
levels of cognitive processing. In contrast, surface-learning
approaches employed by the students in a multiple-choice
(MC) exam context were apprehended by the students as
merely knowledge-based (Scouller, 1998).
In light of the above, we addressed the calls to change
the style of lecturing in large introductory science classes
in higher education, assuming that a pedagogical change in-
formed by the social-constructivist approach would affect the
depth of student learning.
This study aims to investigate the impact on the students’
learning of the instructional model we developed and imple-
mented.
On the basis of the instructors’ impression with respect
to students’ questioning in class, and in line with the liter-
ature that views student questioning as an opportunity to
express higher-order thinking (Ennis, 1987; Dori and Her-
scovitz, 1999; Marbach-Ad and Sokolove, 2000; Hofstein et
al., 2005), we were also interested in how the change in the
instructors’ focus affected the type and depth of students’
questions in class.
The research question we followed was: How did the in-
structional change affect learning as revealed by:
1. Questions students asked during lectures, and
2. Students’ achievements in tasks requiring various think-
ing skills?
Despite calls to change common assessments in higher ed-
ucation, achievement tests are widespread in undergraduate
studies, particularly in large-enrollment courses. Neverthe-
less, it is important to note that we measured student per-
formances in simple and complex items and in closed and
open-ended ones. The different types of questions reflected
Vol. 11, Winter 2012 403
M. Tsaushu et al.
Table 1 . Design of the instructional change
Preintervention Intervention
Traditional Traditional plus tutorial Adapted teaching Active learning
Tutorial −+ + +
Lectures +++Adapted: focus on chosen
wide-scope issues
Reduced to 30%
Small-group learning −− − +
Learning pattern Passive only Passive and individual–
interactive (with tutorial)
Passive and individual–
interactive (with tutorial)
Active individual and group;
interactive (with tutorial)
Student assessment Final MC exam Final MC exam and O-HOT questions Final MC exam and O-HOT
questions; team’s presentation
different thinking skills, as we elaborate in the following
sections.
METHODS
The Instructional Change
Institutional policy and constraints ruled out changing the
syllabus or the number of students enrolled in the course.
Nor could we affect student attendance in class, as it is not
obligatory.
Moving away from the traditional lecture format of the
course, the instructional change was designed to be imple-
mented in three consecutive phases to allow gradual change
and step-by-step, follow-up student learning. In fact, as will
be explained later, due to the university’s constraints, some
of the versions of the course were carried out simultaneously.
Hereafter, we use the term “versions” to describe the differ-
ent teaching approaches in what was planned to be sequential
phases. The design of the instructional change is presented in
Table 1.
Traditional-plus-Tutorial Version. Regular lectures existed,
as in the traditional format, in which the instructor taught
the entire syllabus. In addition, a tutorial was developed and
placed on the course website to support independent learn-
ing. The tutorial consisted of the videotaped lectures syn-
chronized with PowerPoint presentations and allowed the
students to move back and forth. Interactive visualizations,
self-feedback questions, a glossary, and discussion forums
were incorporated in the online tutorial as well. The purpose
of this version was to improve the tutorial, which was ex-
pected to support learning in the further versions in response
to students’ feedback, and to examine the nature of its usage.
Adapted-Teaching Version. In this version we began chang-
ing the teaching approach, asking the instructor to reduce the
time dedicated to teaching informative topics and to focus
more on complex topics and ideas that have special signif-
icance and implications. The instructor, together with M.T.,
who is a science educator, chose the complex subjects to be
discussed in class. Then, while teaching, the instructor em-
phasized the integration between topics and ideas and high-
lighted connections between theory, contemporary research,
and innovations in biology and biomedicine. Because some
of the informative content was no longer presented in class,
the students were directed to use the online tutorial, which
included the lectures as videotaped before the intervention,
and the textbook to prepare for class.
Active-Learning Version. The number of lectures was re-
duced to 30% of the number before intervention, comprising
mainly an opening and a wrap-up for the course. The stu-
dents were directed to use the time saved for independent
learning, using the online tutorial.
The main innovative component of this version was a
group study of one topic. Each group focused on one of
the main topics from the course syllabus. The groups were
then divided into teams that were guided by teaching assis-
tants (TAs). Each team studied one subtopic in depth for a
month and concluded by preparing and presenting it to the
entire group. Table 2 presents an example of group topics and
subtopics assigned to different teams.
The active-learning version was designed for 300 students
per semester. We planned to divide them into 10 groups, each
of about 30 students focusing on one of the topics from the
course syllabus. Each group would then divide into five teams
of six students mentored by a TA for 1 mo. Overall, five TAs
were needed for the entire cohort. Figure 1 presents a weekly
timetable of group work during one semester (14 wk). Each
group is engaged in peer learning for 4 wk and then presents
in a miniconference. Each vacant space in Figure 1 represents
a week, during which students learn independently, using
the course’s interactive website. The shaded weeks at the be-
ginning and the end of the semester indicate when instructors
lecture to the whole class. The four shaded weeks are the only
time when teaching is in the form of lectures. Other than that,
learning takes places either in teams of six students or in the
larger group that consists of five teams.
Table 2 . Example of group-learning topics and subtopics
Group topics Team subtopics
Eukaryotic cell Chloroplast
Mitochondria
Lysosome
Golgi apparatus
Eukaryotic cell complexity
Cellular membrane Composition and structure
Passive transport
Active transport
Endo- and exocytosis
Signal transduction
404 CBE—Life Sciences Education
Deep Learning in an Undergraduate Course
Figure 1. Timetable for the group learning in the active-learning version.
Team Learning. The team members were expected to be en-
gaged in a comprehensive study of the topic they chose and,
as indicated, present it to the entire group. They had to look
for relevant information, discuss their understanding with
peers and the TA, and collaborate in designing their pre-
sentation and talk. The team learning was supported by a
structured “team space” on the course website.
The team space in the course website included detailed
guidelines for the consecutive working stages: individual
learning of the topic, beginning to work, focusing on the
team subtopic, preparing a draft presentation, revising the
presentation, presenting to the TA and getting feedback, and
presenting and discussing at the miniconference. A timeline
was recommended for each stage, and a place for discussion
and uploaded files was provided. In the second stage, “be-
ginning to work,” we presented an experiment followed by
questions to enhance curiosity and further learning of the
subtopic. An example of such a team question is presented in
Table 3.
TAs met their teams in the opening session of the larger
group. They were guided to facilitate and mediate discus-
sions rather than teach the content. During the following
month, they were available almost daily on the team website,
answering questions, commenting on team discussions and
presentation drafts, and monitoring team progress. Sometime
before their groups’ miniconferences, they met their teams to
give final feedback on the presentations.
Miniconference. The summary and climax of the team learn-
ing was a miniconference for each “larger group” consisting
of all the teams that learned the subtopics of one topic dur-
ing the month (see examples of topics and subtopics in Table
2). In the miniconference, each team presented its subtopic
to the other teams and the teaching staff. Each presentation
was followed by a whole-group discussion led by the instruc-
tor, who added his input and asked challenging questions. He
highlighted the connections between subtopics and provided
information about relevant cutting-edge research as well.
Assessment
The traditional two assessment components of the course
were a midterm quiz (worth 5% of the final score) and a final
exam (worth 95% of the final score), both in the form of MC
questions. The literature on meaningful learning acknowl-
edges the limits of MC tests in developing deep learning.
During our intervention, we added open-ended, higher-order
thinking (O-HOT) questions to the final exam, thus testing
higher-order thinking and reflecting the nature of instruction
more sensitively (Linn et al., 2006; Marx et al., 2004). Open-
ended questions require students to bring forth evidence and
think like scientists by encouraging them to explore a variety
of solutions (Schinske, 2011).
In the active-learning version, in addition to the exams,
the TAs assessed each team’s learning process using a scor-
ing rubric (see Supplemental Material), and the instructors
assessed each team’s final presentation (see Table 1).
Procedure
Biology 1 is taught, in separate classes, to biology majors or
students from affiliated programs, who enroll in the course in
their very first semester, and to other STEM undergraduates,
who are in at least their second semester. Altogether, during
the time of the intervention, more than 2000 students enrolled
to the course.
As previously mentioned, the actual change in the course
was not identical to our initial design. This was because of
the reluctance of the university to obligate students to par-
ticipate is any “treatment group” and to cancel lectures if
students wish to attend them. As we could not assign stu-
dents randomly, the study was quasi-experimental.
In each semester, the students could choose their preferred
version of the course. Because attendance was not mandatory,
many students, especially nonbiology majors, preferred not
to attend class at all. Students who did not attend class were
identified as participating in the traditional-plus-tutorial ver-
sion. The lectures students observed on the website were
videotaped prior to the intervention, and they could also
use all the other resources provided online. A student who
Vol. 11, Winter 2012 405
M. Tsaushu et al.
Table 3 . An example of a driving question for the onset of team work
Protein degradation—Where have the mice brain proteins gone?
It is possible to track body materials in vivo by labeling them using radioactive isotopes. This approach is based on the fact that radioactive
isotopes are processed as are natural substances (e.g., amino acids in proteins). The advantage of this method is that radiolabeled
compounds can be detected using scintillation counters.
A researcher fed mice with [14C]-labeled lysine (note that the half-life time 14C is 5770 yr). He expected that after some time most mice
proteins would contain the radiolabeled lysine and that the level of radioactivity in mice proteins would be proportional to the amount of
[14C]lysine in tissues. The mice mated and reproduced. Females kept on eating radioactive food throughout pregnancy (∼3wk)and
nursing (∼3 wk). Their offspring ate [14C]lysine until maturity (∼60 d), at which time all body proteins became radioactive. When the
offspring were 60-d old, radioactive food was replaced by normal food. Once radioactive food was not available, mice started to
synthesize radioactive-free proteins.
Subsequently, at time intervals after shifting to normal food, mice were scarified, and the level of radioactivity in brain tissues was
monitored. Results are illustrated in the following graph:
0
500
1000
1500
2000
0 50 100 150 200
Radioactivity
(cpm)
Time after shift in diet (days)
When preparing your team presentation, please refer to the following questions:
1. How can you explain the results of the experiment?
2. Why is it reasonable to assume that protein degradation did not happen in lysosomes?
3. What is the link between ubiquitin, a protein discovered by Technion Nobel laureates Hershko and Ciechanover, and the phenomenon
described in the graph?
A question you should consider, but not necessarily include in your presentation: Is it possible that the observed decrease in radioactivity
was the result of natural degradation of 14C?
indicated he/she attended most classes when the adapted
teaching was carried out was tagged as participating in that
version, as were students who enrolled in the active-learning
version. Only in the last semester did the university allow
lectures to be canceled, which enabled students to choose
either the active-learning group or to settle for the individ-
ual learning (traditional plus tutorial). The students who en-
rolled in the active-learning group reported on their motives.
The main motive was hope for better course grade, which
is not based only on the final exam in the active-learning
group. Some students believed this version would force them
to learn throughout the semester, rather than for the exam
only, and a few indicated learning difficulties and preferred
close contact with a TA. Table 4 shows the various course
versions across the 3-yr research, and a data collection sam-
ple that will be explained in the following section. As indi-
cated, although we originally planned the course versions
to be implemented in consecutive versions, the university
constraint eventually became an advantage in the data col-
lection design, since having two or three versions during one
semester allowed us to have comparison groups from the
same class.
Table 4 . Course versions and data collection sample for statistical analysis (n=569)
2009 2010 2011
Approach SpringaWint er aSpring Winter Spring
Traditional plus tutorial — — 61 77 171
Adapted teaching 44 162
Active learning 28 26
aThe consecutive semesters Spring 2009 and Winter 2010 are presented only to indicate the teaching version, although we do not present
student data from these semesters.
406 CBE—Life Sciences Education
Deep Learning in an Undergraduate Course
Data Collection
Data collection included the final achievement tests and class
observations in the form of videotapes and journal entries
made by M.T. In the year prior to the study (2008), we
videotaped all the lectures to follow teaching and learning
patterns before the intervention and to have the videos for
use in the tutorial developed by O.S. (Sagy et al., 2011).
A major reflection of the course instructor that directed
our data collection was that during the implementation of
the adapted-teaching version students asked more thought-
ful questions in class compared with previous years. Conse-
quently, we scrutinized all student questions from the video-
tapes (made in Spring 2008, before intervention) and from the
researchers’ journal (in the adapted-teaching version, Spring
2010). To compare the two, we selected two sessions per
semester that dealt with the same topic and spanned the
same time. These class sessions (lecture periods) dealt with
1) membranes (90 min) and 2) differentiation (45 min) and
were taught by the same instructor in both years. In both
semesters, the course was taught in large auditoriums, and
the number of students who attended class was similar (about
40–50).
Student performance data were collected from Spring 2008
(prior to intervention) and during 2009–2011. Despite the
overall number of students who enrolled in the course dur-
ing the 3 yr, the comparative data sample we present here
is smaller (n=569; see Table 4), as explained later in this
section.
We compared the achievements of students by their learn-
ing patterns. As indicated, students could choose their pre-
ferred learning pattern (i.e., attending/not attending class,
which determined whether they were associated with the
traditional-plus-tutorial or with the adapted-teaching ver-
sions or enrolling in the active-learning version).
To ensure credible comparison, we included in the sample
presented in Table 4 only students who: 1) studied during
the same semester and took the same exam; 2) took the first
term exam1; 3) were classified according to the learning pat-
tern they reported in a voluntary self-reported questionnaire
(attended vs. did not attend class; individual vs. group learn-
ing); and 4) took the (voluntary) precourse test.
To avoid possible bias resulting from students’ unequal as-
signment to “treatments” with respect to prior knowledge,
a pretest was administered prior to the introduction of the
active-learning version. The pretest consisted of questions
from the matriculation exams for high school biology majors,
relevant to topics studied in the Biology 1 course. No sig-
nificant difference was found in pretest scores between the
students’ prior knowledge in the different groups.
Data Analysis
Students’ Questions. Based on Anderson and Krathwohl
(2000) and in line with Shepardson and Pizzini (1991) and
Marx et al. (2004), students’ questions in class were classi-
fied into three cognitive levels. Borrowing Shepardson and
1In Israel, students are allowed to take each final exam twice. They
can take the second term (make-up exam) instead of the first, because
of scheduling constraints or to improve their score. In cases in which
they take the second term exam in addition to the first, the second
exam score will be their final score.
Pizzini’s terminology, input (low-level) questions addressed
merely factual knowledge, for example: “Is Cdk a protein?”
The processing (medium) level required students to draw re-
lationships within and between information, data, and prin-
ciples. The students needed to compare, contrast, and apply
knowledge, for example: “Does the protein regulating the
last stage of the cell cycle act by the same mechanism as the
protein regulating the first stage?” The output (higher) level
questions required students to go beyond the data at hand
and use them to hypothesize, generalize, or predict, for ex-
ample: “If we administer a drug that stops the cell cycle, how
can we prevent it stopping the cycle in all our body cells?”
The primary classification was done by M.T., and then was
further confirmed by T.T, O.S., and another researcher.
The Final Exam. Traditionally, the final exam for the course
consisted of only MC items. In light of the aforementioned
literature, which advocates the use of open-ended questions
that allow higher complexity, we added to the final exam
open-ended questions dealing with either content or scien-
tific processes (Marx et al., 2004). These questions required
four types of thinking skills that we believe students should
have acquired during the course: 1) articulating a biologi-
cal principle drawn from the given data (Ramsden, 1992);
2) describing a mechanism instead of supplying “black box”
answers (Chin and Brown, 2000); 3) using a correct, evidence-
based argument (Kuhn, 1993); and 4) doing near transfer, that
is, using knowledge in a different context within the course
syllabus (Sasson and Dori, 2012). Table 5 presents an open-
ended question and the skills required to respond to each
part.
For scoring these open-ended items, we developed a rubric
that was approved by the two course instructors, D.Z. and
S.G., who are biology professors, and two researchers who
hold PhDs in biology education. The scoring of student re-
sponses according to this rubric was done by M.T., who is
an experienced high school biology teacher with experience
in teaching undergraduate students as well. Statistical analy-
sis was performed on student achievement test scores. Mean
comparisons were done using analysis of variance, and then
multiple comparisons were applied to the pairwise means
comparisons. Their effect with respect to size was evaluated
using effect-size correlation r. Interpretative analysis was car-
ried out on observational data.
RESULTS
With the change the nature of the lectures, the adapted-
teaching version became the first step in diverting the re-
sponsibility for learning informative content to the students.
The instructors’ attempts to refrain from merely deliver-
ing content enabled them to allocate more time to promot-
ing understanding of biological research processes and the
connections between basic and applied science. The instruc-
tors’ reports of a greater quantity of “better questions” asked
by the students led to our attempt to examine the questions
consistently.
Students’ Questions
As indicated, we selected two class sessions: one from the
year prior to the intervention (2008) and another from the
year of the adapted-teaching version (2010) to compare
Vol. 11, Winter 2012 407
M. Tsaushu et al.
Table 5 . An example of open-ended questions added to the final exam and the thinking skills they require
Cyclin-dependent kinase (Cdk1) is a protein that is active in
phase M of the cell cycle. The figure shows Cdk1 activity
levels during several cell cycles and also the concentrations
of Cyclin B and Cdk1.
0
1
2
3
4
5
6
7
8
9
10
Cdk1 activity
Cyclin
concentration
Cdk1
concentration
MMTime
Skill Questions
Evidence-based argumentation 1. Based on the above data and your knowledge of cell cycle regulatory
system, describe the interaction between these two proteins and how it
affects the cell cycle.
Describing a mechanism doing near transfer 2. Other regulatory mechanisms in which protein X can affect protein Y
activity are known in living cells. Explain one.
Evidence-based argumentation 3. Proteins can be regulated at the RNA and at the protein level. Which of
them is the true one for Cdk1 regulation?
Articulating a biological principle doing near transfer 4. Retinoblastoma protein (RB) is a substrate of the cyclin-Cdk complex. The
reaction product is a phosphorylated RB. Separation of RB from the total
cell proteins results in a mixture of phosphorylated and
nonphosphorylated RB molecules. Describe an experiment that measures
phosphorylated RB concentration.
students’ questions about the same subjects. Figure 2 presents
the comparison of students’ questions of various cognitive
levels in two topics: membranes and differentiation. Overall,
in the membranes class session, students asked 27 questions
before intervention and 23 questions in the adapted-teaching
version. In the differentiation lesson, 13 questions were asked
in both versions.
For both topics, membranes and differentiation, the fre-
quency of lower-order cognitive questions was smaller in the
adapted-teaching version (16 and 23%, respectively), while
the frequency of higher-order cognitive level questions was
higher (24 and 31%, respectively). Overall, in the adapted-
teaching version, in which the instructor attempted to go
more deeply into complex ideas and their implications, stu-
dents asked more sophisticated questions in class. Although
this finding might not be statistically rigorous, it supports
the initial impression of the instructor, who has taught the
course over the past decade, that students ask more in-depth
questions in the adapted-teaching version.
Student Performance Following the Adapted-Teaching
Ver s ion
The online tutorial includes, among other components, the
course lectures as videotaped in class before our interven-
tion. These videos show the way the course had been taught
for many years. In the traditional-plus-tutorial version of the
intervention and onward, the vast majority of the students
were already using the tutorial to study for the midterm and
the final exams, and some used it during the semester as well
(Sagy et al., 2011). However, students who attended class
in the adapted-teaching version were taught in a way that
shifted from covering the content to emphasizing complex
ideas and processes. Their counterparts who preferred inde-
pendent learning were actually exposed to the traditional-
plus-tutorial version of the course, although both groups en-
rolled in the course in the same semester. Comparing the
achievements of these two groups in the final exam allowed
us to compare the traditional-plus-tutorial version with the
adapted-teaching version. The final exam scores are pre-
sented in Figure 3, which shows scores on MC items requiring
knowledge recall (MC-K), MC items requiring higher-order
thinking (MC-HOT), and O-HOT items.
There appeared to be no difference between the groups
in the mean score of MC-K. Because MC-K questions rep-
resent 80% of the MC part of the exam, overall there was
no difference in the MC total score between students of the
adapted-teaching version and those of the traditional-plus-
tutorial version. However, performance in questions requir-
ing deeper understanding, MC-HOT and O-HOT, was sig-
nificantly higher (Figure 3, **p<0.01 and 0.03, effect size =
0.47 and 0.21, respectively). This comparison implies that the
students who attended class in the adapted-teaching version
had an advantage over their counterparts in questions that
408 CBE—Life Sciences Education
Deep Learning in an Undergraduate Course
Figure 2. Student questions of various cogni-
tive levels in two topics asked in the adapted-
teaching version (Spring 2010) and before inter-
vention (Spring 2008).
required higher-order thinking (eight out of 40 MC questions
and three open-ended).
Student Performance in the Active-Learning Version
After establishing the foundation for independent learning,
the following step was to further improve the depth of learn-
ing by a substantial decrease in the number of lectures, in
which students are merely passive learners. Instead, students
were expected to study the majority of the topics indepen-
dently with the support of the online tutorial and take part in
active small-group investigations of one topic.
Although we meant to cancel most lectures to allow the
instructors to function as mediators of group discussions, the
Figure 3. Achievements of students studying in
the adapted-teaching version (attended class) vs.
these studying in the traditional-plus-tutorial ver-
sion (did not attend class) in Spring 2010.
Vol. 11, Winter 2012 409
M. Tsaushu et al.
Figure 4. Student performance in the open-ended questions across
the three versions of the course (Winter 2011).
university did not originally permit canceling lectures for
those students who wish to attend class. Therefore, in the
Winter semester of 2011, students could, in fact, take part
in each of the three versions of the course (“treatments,”
see Table 4). Those preferring only individual learning rep-
resented the traditional-plus-tutorial version; students who
attended class represented the adapted-teaching version; and
those who in addition to individual learning took part in
group learning of one topic represented the active-learning
version. The comparison of the three groups is presented in
Figure 4. As indicated, pretest scores of those students show
no significant difference. As in the previous stage, we found
no difference among the three groups with respect to MC
items, which mainly require memorization. Most interesting
is what we found with respect to the most complex, open-
ended items.
As can be seen in Figure 4, O-HOT scores for questions
in the active-learning version group (experimental) were
higher than those of the adapted-teaching version and the
traditional-plus-tutorial version comparison groups (p<0.09
and 0.004; effect size =0.18 and 0.33, respectively).
In the following semester (Spring 2011), most lectures
were canceled, allowing another comparison between stu-
dent performance in the traditional-plus-tutorial version and
the active-learning version. In that semester, a group of 26 stu-
dents enrolled in the experimental 1-mo active learning, while
the others made up the comparison group. Table 6 presents
student scores in the open-ended items in the active-learning
group and the traditional-plus-tutorial group across two con-
secutive semesters. Here again, we found no significant dif-
ference between the groups in the scores of the MC items. Al-
though all the O-HOT questions dealt with topics that were
not taught to the experimental 1-mo learning groups, the
students who enrolled in the active-learning group outper-
formed their counterparts who preferred only independent
learning.
We compared student achievements only within semesters
to avoid interfering variables: different instructors, different
students in different semesters, different exams. As can be
seen in Table 6, gaps between semesters are greater than be-
tween treatments; however, we cannot address gaps between
semesters, for there are too many variables involved.
DISCUSSION
The original course pedagogy, of lectures given in a large
hall, reflects the past requirements, as is evidenced in the fi-
nal test. This test was based mainly on MC items that required
memorization of content. Across all treatments in the three
versions, the results of the overall MC part of the final exam
were similar. Students who learned independently, using the
online tutorial (which included the lectures), had scores com-
parable with those of the students who participated in the
other two treatments (adapted teaching and active learning).
Thus, if learning biology means doing well on an MC knowl-
edge test, then the university and the faculty of biology can
keep lecturing, as this form of teaching is the most effective
in terms of cost. Nevertheless, if one is expecting more than
the ability to recall knowledge, then other forms of teaching
and assessment should be considered. In our study of the
Biology 1 course, we were interested in more sophisticated
learning and in methods that enhance and reflect students’
deep learning.
The adapted-teaching version was the first step in reduc-
ing the instructor’s responsibility for covering all content
and increasing students’ responsibility for learning, using
the course website as a supportive active-learning tool. In
this version, active construction of conceptual knowledge
was enhanced, since some of the content was independently
learned. Moreover, the instructor could make references to
broader issues to a greater extent than before (for example,
the Nobel Prize of that year, awarded for discovering the ri-
bosome structure), or he could expand complex or difficult
topics raised in students’ questions. Such adaptations to the
class level and the context without changing the syllabus
were discussed by Davis and Varma (2008), who emphasized
the importance of designing a curriculum in a way that en-
ables instructors to make adaptations. In line with Handels-
man et al.’s (2004) characterization of scientific teaching, such
adapted teaching offers more understanding of scientific re-
search processes and exposes students to the limitations of
science, as well as to its power. Evidence for how the change
in instruction in the adapted-teaching version promoted deep
learning was expressed by the difference in the nature of stu-
dents’ questions during the lectures, and by their superior
Table 6 . Student O-HOT questions scores in the active-learning (experimental) version vs. the traditional-plus-tutorial (comparison) version
Active learning Traditional plus tutorial
Version Mean score (SD) nMean score (SD) nt-test value pEffect size
Winter 2011 34.6 (22.6) 28 23.5 (21.7) 42 2.05 0.04 0.24
Spring 2011 61.6 (24.3) 26 50.4 (25.8) 171 2.08 0.04 0.22
410 CBE—Life Sciences Education
Deep Learning in an Undergraduate Course
performance in the higher-order thinking parts of the fi-
nal exam compared with the performance of students who
learned independently.
In the active-learning version, social-constructivist learn-
ing was promoted by three factors: 1) peer learning, in
which students shared and negotiated knowledge with their
peers while producing artifacts (the PowerPoint presenta-
tion); 2) TA tutoring, which was encouraging and challeng-
ing, as well as reflective (Wood and Tanner, 2012); and 3)
the miniconference-like lesson, in which students presented
their understanding of a topic to a larger group and the in-
structor and received feedback. At that stage, we found that
student outcomes in the open-ended questions of the final
exam were higher than those of the comparison groups. It
has already been shown that short peer discussions enhance
understanding and consequently improve achievements in
concept questions (Smith et al., 2009). We strongly believe
that the group learning during the 1 mo in which the stu-
dents negotiated their understanding with peers and with
the TAs and worked together to produce an artifact of their
learning (the final presentation) contributed a great deal to
their deep learning. We assert that group learning during 1
mo developed a “biological discourse” as well, which is more
typical of advanced stages of learning biology. Formative as-
sessment given by peers and the TA while working together
on the presentations further developed the students’ learn-
ing outcomes. We suggest that the final miniconference, in
which each team presented its knowledge of a topic and was
subject to summative assessment (by the instructor), had an
additional effect on the students’ outcomes in higher-order
thinking assignments in the final exam.
In-depth coverage of a topic can elaborate incomplete
ideas, provide different views of a phenomenon, enhance
abstract thinking, and elicit abstract reasoning (Eylon and
Linn, 1988). All these happened during the 1-mo group study
while the students constructed their knowledge through com-
prehensive independent learning and maintained a contin-
uous discourse in the team and with the TA and the in-
structor. We further assume that this 1-mo group-learning
experience enhanced patterns of deep learning of the entire
course content. This can be seen from the performance in
the open-ended items that did not address topics discussed
in the groups. The students apparently applied to the rest
of the course topics those biological principles, reasoning
patterns, and ways of thinking and learning they had de-
veloped while learning “their own” topic. The scope of this
paper does not allow us to present qualitative data from stu-
dents’ interviews, but based on the aforementioned data, we
argue that students who experienced the team and group
learning have developed patterns of deep learning across
topics.
An additional component of the instructional model that
promoted deep learning was an assessment process that no
longer required mere rote learning, because it included the
additional open-ended items in the test. The formative and
summative assessment of the team learning encouraged deep
learning as well. This is congruent with the idea that assess-
ment has an impact on the nature of learning (Marton and
Saljo, 1976; Black et al., 2003). The working guidelines posted
on the team website, and especially the ongoing dialogue
with the TA, made it clear to the students that they were ex-
pected to go beyond rote learning, although this part of the
course focused on only one topic. In this regard, Biggs and
Tang (2007) found that assignments focused on one topic had
a positive effect on the nature of learning, while assignments
that required coverage of range of topics encouraged students
to adopt a surface approach.
Our findings also add to what Schwartz et al. (2009) found
regarding the importance of in-depth learning of one topic.
They studied student performance in biology, chemistry, and
physics higher education introductory courses with respect
to the nature of learning these subjects in the high school.
They found that in-depth learning of at least one major sci-
entific topic in high school for a month or more influenced
performance in college more positively than covering all ma-
jor topics in breadth.
Thus, we suggest that at the undergraduate level, studying
even one topic in depth makes a difference and causes learn-
ers to adopt a deep-learning approach. In biology, dealing
with complex representations is common, even in an intro-
ductory course. Understanding complexity requires multiple
opportunities be given to students for constructing their un-
derstanding. Moreover, multiple experiences, in which ideas
and phenomena may be coordinated into richer and more
complex understanding, are needed to establish conceptual
understanding. This process is intensive and time-consuming
(Schwartz et al., 2009). The 1-mo group learning that enhanced
our students’ deep learning of one topic offers such oppor-
tunities. However, the acquired learning habits and deep un-
derstanding constructed in this intensive and multifaceted
learning experience were apparently transferred to the learn-
ing of other course topics.
From a practical point of view, taking into consideration the
current constraints of the university, the instructional model
we implemented is actually sustainable. After the online tu-
torial is developed and established, it only needs mainte-
nance and updating. The scope of this paper does not allow
us to go into the process the TAs went through nor to de-
scribe how they developed as mentors. However, Dolan and
Johnson (2009) have already pointed to the possible merits
of employing TAs. They suggest that the mentorship expe-
rience could help graduate students (scientists-in-training)
to improve their understanding of scientific issues and ac-
quire communication skills, while developing their identities
as scientists.
Finally, we believe that the contribution of the active-
learning instructional model is twofold and goes beyond
merely improving student performance. First, this model,
which implemented a social-constructivist approach in which
learning took place through discourse with peers and staff
and through the carefully designed website, exposed stu-
dents to the essence of biology as a research discipline and
to the ways biologists interpret phenomena and investigate
them. Second, we hope that the students’ perception of the
professor as a researcher and a role model replaced the per-
ception of the professor as a mere deliverer of scientific con-
tent. We attach great importance to both in the future de-
velopment of all students: those who major in life sciences
and those others who get to know biology as engineers or
scientists in other fields.
Vol. 11, Winter 2012 411
M. Tsaushu et al.
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