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Peer Instruction in Computing Higher Education: A Case Study of a Logic in Computer Science Course in Brazilian Context

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  • Federal University of Jataí

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One of the challenges of Computing Education Research is the proposition of new learning methods. Researches indicate active learning methods are more effective than traditional ones. Peer Instruction is one of these learning methods that promotes a student-centered class, enabling (s)he constructs his/her comprehension through a structured approach with questions and peer discussions, used in Computing in the last years. Nevertheless, researches about the use of this method are very scarce in South America. Accordingly, this research aims to discuss the impact of Peer Instruction use on higher education from a Logic in Brazilian Computer Science course. The research context is an undergraduate course in Computer Science in the first term of 2018 at the Federal University of Jataí. Sufficient evidence was found for the veracity of two propositions related to this study: (i) Peer Instruction use guarantees a learning gain of students, and (ii) Peer Instruction is well accepted by students. Therefore, it concluded that Peer Instruction use is suitable for Logic courses in Computing Higher Education in Brazil, with good acceptance from students.
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Revista Brasileira de Informática na Educação – RBIE
Brazilian Journal of Computers in Education
(ISSN online: 2317-6121; print: 1414-5685)
http://br-ie.org/pub/index.php/rbie
Submission: 07/Jul/2021;
Camera ready: 15/Oct/2021;
1st round notif.: 10/Aug/2021;
Edition review: 19/Nov/2021;
New version: 12/Sep/2021;
Available online: 11/Dec/2021;
2nd round notif.: 08/Oct/2021;
Published: 11/Dec/2021;
Peer Instruction in Computing Higher Education: A Case Study
of a Logic in Computer Science Course in Brazilian Context
Esdras L. Bispo Jr.
Federal University of Jataí (UFJ)
bispojr@ufj.edu.br
Rosemara P. Lopes
Federal University of Goiás (UFG)
ORCID: 0000-0002-5498-2025
rosemaralopes@gmail.com
Simone C. Santos
Federal University of Pernambuco (UFPE)
ORCID: 0000-0002-7903-9981
scs@cin.ufpe.br
Abstract
One of the challenges of Computing Education Research is the proposition of new learning methods. Researches
indicate active learning methods are more effective than traditional ones. Peer Instruction is one of these learning
methods that promotes a student-centered class, enabling (s)he constructs his/her comprehension through a structured
approach with questions and peer discussions, used in Computing in the last years. Nevertheless, researches about
the use of this method are very scarce in South America. Accordingly, this research aims to discuss the impact of
Peer Instruction use on higher education from a Logic in Brazilian Computer Science course. The research context is
an undergraduate course in Computer Science in the first term of 2018 at the Federal University of Jataí. Sufficient
evidence was found for the veracity of two propositions related to this study: (i) Peer Instruction use guarantees a
learning gain of students, and (ii) Peer Instruction is well accepted by students. Therefore, it concluded that Peer
Instruction use is suitable for Logic courses in Computing Higher Education in Brazil, with good acceptance from
students.
Keywords: Education; Computing; Logic; Peer Instruction; Brazil.
Cite as: Bispo Jr., E. L., Lopes, R. P., & Santos, S. C. (2021). Peer Instruction in Computing Higher Education: A
Case Study of a Logic in Computer Science Course in Brazilian Context. Revista Brasileira de Informática
na Educação, 29, 1403-1432. DOI: 10.5753/rbie.2021.2127.
Bispo Jr. et al. RBIE v.29 – 2021
1 Introduction
An excellent starting point to describe Computing Education Research (CER) is the junction of
two areas: Education and Computing. Thus, it is possible to define the teaching and learning
process improvement of Computing as a science among the main objectives of CER (Holmboe,
McIver, & George, 2001).
There are many open challenges in CER (Robins, 2015). One of them is the proposition of
new teaching and learning methods (Fincher & Petre, 2004). Several works point out active learn-
ing methods are more effective than traditional ones (Bonwell & Eison, 1991; Grissom, 2013). It
is possible to cite as active learning methods: (i) cooperative learning (Beck & Chizhik, 2013), (ii)
process-oriented guided inquiry learning (Moog & Spencer, 2008), and (iii) peer instruction (PI).
Other active approaches such as case-based learning (Srinivasan, Wilkes, Stevenson, Nguyen, &
Slavin, 2007), project-based learning (Bell, 2010), and problem-based learning (Savery, 2015)
have much more connotation of teaching and learning “models” than active learning methods
applicable to the classroom context. Therefore, these models can incorporate all the previously
mentioned methods ((i)-(iii)).
PI is a teaching method that promotes a student-centered class to (s)he can construct his/her
understanding through a structured approach from questions and peer discussions (Crouch &
Mazur, 2001). Although it was born in Physics courses, other courses like Biology (Smith, Wood,
Krauter, & Knight, 2011) use PI.
Several works discuss the impact of PI use in computing education (Chase & Okie, 2000;
Simon, Kohanfars, Lee, Tamayo, & Cutts, 2010; Zingaro, 2010; Zingaro & Porter, 2014; Lee,
Garcia, & Porter, 2013; Porter, Bailey Lee, & Simon, 2013; Simon, Parris, & Spacco, 2013;
Johnson et al., 2016; Porter et al., 2016). One of PI’s interesting directions in Computing Higher
Education (CHE) is creating metrics to measure the learning quality (Smith et al., 2009; Porter,
Bailey Lee, Simon, & Zingaro, 2011). Questions like (i) “is there effective student learning in
fact?” or (ii) “does student agree passively with your colleagues?” are addressed.
However, research about PI use is scarce in South America (Müller, Araujo, Veit, & Schell,
2017). In this way, this work1aims to discuss the impact of PI use in CHE at Brazil from the
teaching Logic in Computer Science (LCS). Two investigation questions northers this research:
(i) “Why is PI use adequate for LCS courses in CHE in Brazil?”; and (ii) “How are the LCS
students’ impressions concerning PI?”. This last question is crucial because it seems there is a
resistance to active learning approaches by STEM2students (Shekhar et al., 2015). The research
context is the fresh students of the Computer Science program 2018 of the Federal University of
Jataí.
The remains of this paper are divided as follows. Section 2 lists the main related works to
this research. Section 3 presents important concepts about Education. Section 4 describes the PI
methodology. Section 5 details some metrics used to assess the learning gains in PI. Section 6
delineates the research methodology used in this work. Section 7 presents the discussion of the
case study results, listing the found pieces of evidence. And, finally, the final remarks and future
1This work is an extended version of the paper (Bispo Jr. & Lopes, 2021) originally presented in the Brazilian
Symposium of Computing Education (EduComp 2021).
2STEM stands for Science, Technology Engineering, and Mathematics.
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work (Section 8) are presented.
2 Related Works
In Computing Education, it used PI in many contexts. In higher school, Teixeira and Fontenele
(2017) relate a didactic experience during teaching a Linear Algebra course teaching the matrix
content in a Chemical Engineering program. In technical education, Oliveira et al. (2017) assess
the PI efficacy considering fresh students’ performance and engagement in introductory program-
ming in an integrated high school-technical program in Informatics. Still, in technical education,
Nogueira and Nogueira (2018) describe practices applied in an integrated high school-technical
program in Computer Graphics, like tools and techniques used through PI use. Although none of
these works investigates PI use in CHE.
In CHE in Brazil, there are investigations in various courses. In the Formal Languages
course, Schechter and de Mendonça (2017) develop a methodology adaptation using PI and Just-
in-Time Teaching (JiTT) resources. In the Introductory Programming course, Chicon, Quaresma,
and Garcês (2018) stimulate the immediate feedback of students from PI mediated by Socrative
tool. At last, in the Human-Computer Interaction course, Gonçalves, Arpetti, and Baranauskas
(2014) propose a model that aims to facilitate the social construction of meaning structured from
PI, JiTT, and agile methods. However, none of these works investigates PI use in LCS courses.
Some works also investigate PI use in CHE from contexts outside South America. Chase
and Okie (2000) compare a combination between PI and cooperative learning concerning the
traditional approach. Porter et al. (2011) replicate the Smith et al. (2009) study by PI use, finding
that students in upper-division computing courses also learn from peer discussions. Johnson et
al. (2016) present a methodology for developing PI questions systematically for cybersecurity
courses. All these paper investigates PI use from American contexts. However, the cut of this
work is PI use in CHE in Brazil.
3 Fundamentals in Education
In this section, we will present some theoretical foundations in Education. Section 3.1 will de-
lineate the traditional approach. And, in contrast, Section 3.2 will structure the theory of active
learning.
3.1 Traditional Approach
To describe the traditional approach of the teaching and learning process is not an easy task. First,
in most cases, this approach is referred to as an antagonistic (and sometimes negative) perspective
to the other approaches, i.e., it serves as a reference to different approaches about “how does not
conduct” the teaching and learning process.
And, secondly, as a consequence of the first justification, the adepts and the practitioners
of this approach do not use to make this self-denomination (in the negative sense of “traditional”
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term). In the same way that the explicit differentiation about what is sacred and what is not sacred
usually is made by the adepts of a certain religious group, the distinction between the traditional
practice and other practices is established more commonly by discordant groups of traditional one.
Although there are difficulties in characterizing the traditional approach, it is necessary to
do it. If there is a need to rethink the teacher’s practice, it is required to identify which practice
elements are pointing as challenges to be faced and overcome. Mizukami (1986) points out some
important elements in this direction. Three are about the conception of (i) human beings, (ii)
teacher-student relations, and (iii) assessment.
About the conception of human beings, the traditional approach usually assumes that their
understanding of the world is a “clean slate” which to receive knowledge gradually acquired by the
environment. This human being does not have an initial discernment about what is helpful to your
learning, being fundamental the reception of this knowledge from the ancients one. The author
says this human being “is a passive receptor until that, full of necessary pieces of information,
(s)he can repeat them to others that do not possess them yet”3.
About the teacher-student relations, it usually assumes that the teacher has the knowledge
possession which the student needs to learn. The teacher is responsible for establishing method-
ology, content, assessment, and interaction ways in the classroom. These aspects of the teaching-
learning process are of total responsibility of the teacher, being the student disregard in this part of
the process. Once the student is a passive receptor, the teacher’s function is to transmit determined
contents considered adequate to this one. The author says this is a “vertical relation, being that
one of the polos (the teacher) detains the decisory power”4.
About the assessment conception, it typically considers the student’s skill to reproduce the
presented content by the professor in the classroom faithfully. The exam is the primary assessment
instrument used by the professor, being a control instrument from which the students are reproved
(or not) in a course or program. The author says the assessment, in this conception, “measures
[...] the quantity and the accuracy of the information that [the student] can reproduce”. In this
perspective, the assessment is summative, applied at control points at the end of the exposure of
specific content, as opposed to the formative assessment that allows the construction of learning
procedurally, based on continuous feedback (Figuerêdo, dos Santos, Borba, & Alexandre, 2011).
From this characterization, distinct conceptions about how should be the teaching-learning
process were proposed as alternatives to the traditional approach. In opposition to the student
conception as a “passive receptor”, emerges the theory of active learning.
3.2 Theory of Active Learning
The theory of active learning emphasizes the need to engage students during their learning process.
There is no clear definition of active learning, but some authors indirectly mention it as an inherent
student activity in their writings. For instance, Dewey (2004, pp. 342) asserts “[...] learning means
something which the individual does when he studies. It is an active, personally conducted affair”.
3Original text in Portuguese: “é um receptor passivo até que, repleto das informações necessárias, pode repeti-las
a outros que ainda não as possuem”. Os mais antigos também são os que determinam o que deve ser aprendido pelos
seres noviços”.
4Original text in Portuguese: “[relação] vertical, sendo que um dos polos (o professor) detém o poder decisório”.
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Bonwell and Eison (1991, pp. 2) assert that learning is active when “greater emphasis is placed on
students’ exploration of their own attitudes and values”.
It is necessary to highlight it is possible to have active learning even in an expository lesson.
Although the student’s environment significantly determines the conditions to her/his learning, it
does not determine in a general way. In an expository lesson, the student can be active regarding
each information and argument presented, reorganizing and filtering the received new information
from the previous knowledge appropriated by her/him.
However, the role of an expository lesson in the formal education spaces is being rethought,
bearing in mind these kinds of lessons are strongly linked to current conceptions of traditional
teaching (inclusive in higher education). Some methodologies propose more effective student
participation in the classroom to promote an environment that enhances active learning scenarios
more concretely.
The teaching by discovery, for example, refers to a curriculum way that the students are
exposed to specific questions and experiences so that they “discover for themselves the intended
concepts [by the teacher]” (Hammer, 1997, pp. 489). This approach focuses efforts to integrate the
need to attend to curriculum accomplishing expectations through the configuration of a favorable
environment to an investigative inquiry of the students. The teaching by discovery adopts active
learning and is an alternative to the traditional approach.
Despite the effort to present good results (Balım, 2009), there are difficulties with implant-
ing the teaching by discovery. The tension between “to cover the content” and the scientific
investigation is one of these difficulties (Hammer, 1995). Whereas the knowledge of the content
is very required by the traditional approach, the teaching by discovery deposits more emphasis on
developing necessary skills during the experimentation process of that content.
Faced with this scenario, researches in Psychology contributes to a better justification of
active learning. One of these fields is Cognitive Psychology that is “the study of how people per-
ceive, learn, remember, and think about information” (Sternberg & Sternberg, 2012, pp. 3). In
contrast to other Psychology areas, it looks to theorize more strongly about the functioning mech-
anism of cognitive structures of an individual. Other psychology areas can have more interest,
for instance, in the behavioral aspects of individuals instead of the internal process of structuring
assimilated information.
In this area, Ausubel (2000) proposes the assimilation theory of meaningful learning and
retention or, as is known, the theory of meaningful learning (TML). TML asserts the learning
of meaningful reception involves the acquisition of new knowledge from a presented learning
material. However, it is presupposed that a set of essential elements for meaningful learning
occurs beyond the existence of this material.
Some of these elements are better identified by describing the assimilation process in the
meaningful learning phase. Three stages of this phase are important to TML. First is the selective
anchoring of the learning material to existent ideas into the cognitive structure. Second is the
interaction of new ideas introduced with the relevant ones already existing; the meaning of the
new ideas emerges as a product of the interaction with the preexisting ideas. And, at last, third is
the link in the memory (retention) of new emergent meanings with their correspondent anchoring
ideas.
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From the conciliation between the expository lessons and the teaching by discovery as a
purpose, the Peer Instruction methodology was proposed. This methodology looks to operational-
ize a practice that considers the traditional format of the classroom, alternating it with spaces that
engage the students to construct their learning.
4 Peer Instruction Methodology
Prof. Eric Mazur from Harvard University originally proposed the Peer Instruction (PI) method-
ology. In a general way, PI looks to promote the learning with the focus on the inquiry, aiming the
students to dedicate more time reflecting and discussing the content in the classroom instead of
passively watching expository lessons of the teacher (Araujo & Mazur, 2013). This proposal pro-
vides transformations for each element discussed in Section 3.1: the student’s behavior as active
actors in the learning process, the teacher-student relationship, and the assessment process.
PI arises from the found difficulties by Mazur in teaching Introductory Physics courses.
Students, major part consisting of undergraduate ones from distinct courses of Physics, reported
for him their course’s frustrations. Even physics students, which were initially motivated to do the
course, were dissatisfied with the course. Many of them came to transfer themselves to a different
course.
Mazur reports that he was satisfied with your teaching, in a general way, and your students
assessed him positively (Mazur, 1997). However, some papers, which he had read, point out no
significant difference in the students’ common sense concerning some physics concepts. As he
did not believe that this was true in his context, he decided to apply a concept test inventory with
his students.
To his surprise, his students (including physics ones) did not obtain satisfactory performance
in the test. To Mazur, the conceptual test applied was simple and less complex than the exams he
used to do. However, some of his students asked questions for him like: "Professor Mazur, how
should I answer these questions? According to what was taught by you, or like I think?".
He realized that even though students had obtained a good performance in non-conceptual
questions (e.g., formula application), they had not performed well in the conceptual test. Probably
the conventional questions were measuring the student skill to reproduce a specified algorithm
instead of the effective understanding of the presented concept in the classroom. The students
were more focused on learning "recipes" or resolution strategies instead of understanding the
physics concepts. In order to solve this problem, Mazur proposes a methodology that favors the
students to understand the course concepts better. As follows, PI is presented in more detail.
The PI objectives are to explore the student interaction during the class and focus on sub-
jacent concepts. Instead of using the class to present the material to be learned in the detailed
level of the textbook, the classes mainly consist of short presentations of key topics. After these
brief explanations, the students receive one conceptual question to answer and discuss in a group.
This process (i) forces the students to think through their developed arguments and (ii) supplies
for themselves the opportunity to assess their understanding of the concept.
PI consists of two moments: individual and group moments. The personal moment is based
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on the previous study of material available by the teacher to students. This moment is crucial for
the proposed dynamic to reach its efficacy posteriorly. The purpose that is the group moment is
better used with activities to potentialize the learning together with the colleagues and the teacher.
Thus, the previous study is the ideal moment for the student to have the first contact with the
material, recognizing and developing part of the necessary knowledge. It is at this moment that
arises eventual doubts, generating a major need of the student to understand the subject.
The group moment is a format reconfiguration of a classic class. The diagram (see Figure
1) illustrates how the process occurs. Each step of this process will be described in more detail as
follows.
Figure 1: Diagram of the PI implementation process (adapted from ((Araujo & Mazur, 2013, pp. 370)).
Each step of this process will be described in more detail as follows. The process begins
with a dialogued exposition by the teacher. This dialogued exposition should be short with the
only purpose to recap the material available to the students briefly. This step should not last more
than 15 minutes.
The second step presents a conceptual question to the class. This question usually is multiple-
choice. Each student should answer it individually. The conceptual question aims to promote and
assess the student’s understanding of the main important concepts chosen for that moment.
The third step carries out the recording of individual answers. When it chooses multiple-
choice questions, it is possible to generate histograms of given responses by the students. From
the hit percentage of the class referred to that conceptual question, the flux follows:
if it is more than 70%, it assumes the class satisfactorily understood the required concept.
Hence, or it uses a new question involving the same concept, or it follows to the brief
exposition of a new concept;
if it is between 30% and 70%, it assumes the part of the class satisfactorily understands
the concept and can collaborate in the process of knowledge construction of your peers. It
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also supposes the discussion among the peers tests the supposed knowledge obtained by the
part of the class that correctly answered the question. Thus it asks the students to divide
themselves into groups and invest time to convince your peer about the reason to answer in
this way;
if it is less than 30%, it assumes the class does not understand the required concept satisfac-
torily. Thus, the teacher revisits the concept, starting the process again.
At last, the fourth step comprehends the vote recording immediately after the discussion
among the colleagues. This step only occurs if, in the first voting, the hit percentage situates
between 30% and 70%. It generates the new histogram, and the teacher finishes this phase by
explaining the question and conceptual aspects. If still necessary to develop the current concept,
a new question referred to one is presented. On the contrary, the next topic with new concepts is
showed by dialogued exposition.
There are many variants of PI methodology. Some omit the voting in some steps. Others
obligatorily carry out the peer discussions (independently of hit percentage recorded). However,
the original suggestion proposed by Mazur (1997, pp. 10) is enumerated as follows:
1. Question posed (
=1 minute);
2. Students given time to think (
=1 minute);
3. Students record individual answers (optional);
4. Students convince their neighbors - peer instruction (
=1-2 minute);
5. Students record revised answers (optional);
6. Feedback of professor: tally of answers;
7. Explanation of correct answer (
=2+ minutes).
5 PI Metrics
Various educational contexts used PI widely (Vickrey, Rosploch, Rahmanian, Pilarz, & Stains,
2015). Thus arises the need to measure with better accuracy the PI efficacy in student learning.
It will present the concept of isomorphic questions and two metrics used to measure the student’s
learning gain using PI.
5.1 Isomorphic Questions
One of the adopted strategies to answer some of the doubt about the PI efficacy is using iso-
morphic questions presented briefly by Smith et al. (2009). Isomorphic questions have “different
stories” but require applying the same principles or concepts to reach the solution.
The Smith and colleagues’ idea to use isomorphic questions in PI is a concept appropriation
of problem isomorphs (Kotovsky, Hayes, & Simon, 1985, pp. 251). The problem isomorphs can
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not be different in their structure. The differences between them reside in the personages, images,
or models used in the problems. The proposal is to change the problem representation, preserving
the domain structure of the task.
In this work, a pair of isomorphic questions is defined as (q1,q2)where q1and q2are
multiple-choice conceptual questions. q1ad refers to representation of q1after peer discussion.
5.2 Absolute Learning Gain
On purpose to define the absolute learning gain and to provide better conditions to perform a
transferability of this work (Merriam & Tisdell, 2016, pp. 253), it presents the functions 1, 2, and
3:
Hit(x,q) = 1,if xhits q,
0,otherwise.(1)
where xis a student answering to question q;
HitAverage(q) =
xT
Hit(x,q)
n(2)
where Tis a class of nstudents; and
AvgPercentageHit(Q) =
qQ
HitAverage(q)
m(3)
where Qis a set of mquestions.
Let be I={(q1
1,q1
2);(q2
1,q2
2);...;(qm
1,qm
2)}yet as the set of all pairs of isomorphic questions.
Hence, Q1and Q2are defined from I, so that Q1={q1
1,q2
1,...,qm
1}and Q2={q1
2,q2
2,...,qm
2}.
Thus we define Absolute Learning Gain (ALG) as follows:
ALG =AvgPercentageHit(Q2)AvgPercentageHit(Q1)
The subjacent idea of this metric is to measure the learning gain after a given intervention.
Supposes a class obtained 51% of hits in the Q1set and, after the peer discussion, got 72% of hits
in the Q2set. Hence, its ALG will be 21%.
5.3 Normalized Learning Gain
The Normalized Learning Gain (NLG) (Hake, 1998, p. 3) measures how the student performance
grows, comparing to the more extensive possible growth than (s)he can obtain. It computes NLG
as follows:
NLG =AvgPercentageHit(Q2)AvgPercentageHit(Q1)
100% AvgPercentageHit(Q1)
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.
On given example in the previous section, while ALG will be 21%, NLG will be (72%
51%)/(100% 51%)
=42.86%. Thus, of the whole available learning gain, the intervention
obtained approximately 42.86%.
Another example can be more illustrative. Imagine two scenarios where ALG is 10%
for two any class: Aand B. However, the Aclass has AvgPercentageHit(Q1) = 20% and Avg
PercentageHit(Q2) = 30%, while Bclass has AvgPercentageHit(Q1) = 80% and AvgPercentageHit
(Q2) = 90%. Now, it should be more difficult to obtain an ALG of 10% when starts from 80%
than when starts from 20%. NLG has the objective to measure this relative difficult so that
ALGA=12.5% and ALGB=50.0%.
5.4 Weighted Learning Gain
In (Smith et al., 2009), a diagram with the average percentages of correct and incorrect answers
given to conceptual questions was presented (Figure 2). The purpose of this diagram is helping
to measure student learning gains when peer discussion occurs. The diagram is divided into three
levels that match with the percentages referred to Q1,Q1ad, and Q2sets.
Figure 2: Diagram with the average percentages of correct and incorrect answers of Q1,Q1ad and Q2question set (Smith et al., 2009).
Nodes 1 and 2 of Figure 2 are percentages referred to answers of Q1set. Node 1 matches the
average percentage of correct answers, while Node 2 matches the average percentage of incorrect
answers.
Nodes 3 and 4 are percentages referred to answers of Q1ad set, but only with the students
that answered q1correctly (Node 1). Node 3 matches the average percentage of correct answers,
while Node 4 matches the average percentage of incorrect answers. The sum of the percentages
of Node 3 and 4 is 100% and matches the total value referred to Node 1.
This also happens in Nodes 5 and 6. These nodes are percentages referred to answers of
Q1ad set, but only with the students that answered q1incorrectly (Node 2). Node 5 matches the
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average percentage of incorrect answers. The sum of the percentages of Nodes 5 and 6 results
100% and matches the total value referred to Node 2. The same logic follows to other nodes of
the Figure 2.
For Smith et al., some nodes of Figure 2 help to identify the learning gain in PI. For instance,
Node 11 indicates a considerable learning gain. The students matched in this node are those that
answered incorrectly Q1(Node 2) and, after peer discussion, answered correctly Q1(Node 5). Of
these students, 77% responded Q2correctly, possibly indicating a positive effect of peer discussion
in the learning process.
Porter et al. (2011) used PI in two Computer Science courses: Introduction of Theory of
Computation and Computer Architecture. However, some expected results are not found. The
percentage of Node 7 (see Figure 3) was slightly less than Node 3. And Node 11 has a median
absolute value (53%), besides having gain lesser than Node 5.
Figure 3: Diagram with the average percentages of correct and incorrect answers of Q1, Q1ad, and Q2 question set in Theory of Computation
course (Porter et al., 2011).
Thus, they proposed an auxiliary understanding to complement the initial comprehension in
(Smith et al., 2009). They propose two groups: a control group (CG) and a test group (TG) of
potential learners. CG are those that answered both q1and q1ad correctly (Node 3). TG are those
that supposedly learned the concept through peer discussion (Node 5).
The idea is to use the CG skill to answer q2correctly to help in expectation normalization
for TG answers q2correctly. Thus, if CG does not answer Q2satisfactorily, it should weigh the
expectations concerning TG. In this way, it defines Weighted Learning Gain (WLG) as follows:
W LG =AvgPercentageHit(T G)
AvgPercentageHit(CG)
where AvgPercentageHit(T G)is the value of Node 11 and AvgPercentageHit(CG)is the value of
Node 7. Hence, in Figure 2, WLG is 42%/92%
=46%, revealing the learning gain of TG was
approximately 46% of the gain that CG obtained.
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The closer WLG is 100%, the more meaningful is the learning gain obtained by TG. In
principle, WLG should not reach values more than 100%. However, it can not exclude this possi-
bility in practice. Thus, once this occurs, it is necessary a more profound reflection about which
scenarios and why CG obtained a learning gain lower than TG one.
6 Methodology
This work used case study as methodological approach. Yin (2003, pp. 13) defines case study as
[...] an empirical inquiry that investigates a contemporary phenomenon within its
real-life context, especially when the boundaries between phenomenon and context
are not clearly evident”.
Still, Yin (2003, pp. 14) continues characterizing the case study that:
(i) copes with the technically distinctive situation in which there will be many more
variables of interest than data point, and as one result; (ii) relies on multiple sources
of evidence, with data needing to converge in a triangulation fashion, and as another
result; (iii) benefits from the prior development of theoretical propositions to guide
data collection and analysis”.
6.1 General Elements of the Case Study
This is a holistic single-case study using an explanatory strategy. The study justification resides
in the revelatory case of the PI use in CHE in Brazil from the teaching LCS course. The papers
of bibliographical survey concentrate in countries of North America and Europa (Müller et al.,
2017).
Two study questions are as follows. The first one (SQ.1) is “Why is PI use adequate for
LCS courses in CHE in Brazil?”. And the second one (SQ.2) is “How are the LCS students’
impressions concerning PI?”.
It investigates two propositions. The first one (P1) is “The PI use guarantees some learning
gain of the students”. The second one (P2) is “The students well receive PI”. The SQ.1 answer
depends directly on P1 and P2 will be confirmed, and the SQ.2 answer depends directly on the P2
investigation.
The analysis unit of this study is the Logic in CS course of the CS program of the Federal
University of Jataí, situated in the southwest of Goiás. The course occurs in the first semester of
2018, with the participation of 44 students. Figure 4 presents the sociodemographic description of
this analysis unit to provide better conditions to perform the transferability of this work (Merriam
& Tisdell, 2016, pp. 253).
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Figure 4: Pizza charts of the first questionnaire’s percentage results about the sociodemographic data of the unit of analysis.
6.2 Data Collection
The collected data came from three sources: the answer recording performed by the students
during classes (D1); and two questionnaires about the student impressions, being the first one
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with closed-ended questions (D2), and the second one with open-ended questions (D3).
The answers recording of the students occurs during the whole semester. It used QR code
boards that match each student to your respective answers in each question. The Plickers plat-
form5mediated answer collection during the classes, computed the hit percent in real-time, and
organized the responses in a spreadsheet for future analysis. It used 35 questions in total; among
these, 06 pairs of isomorphic questions.
It adapted the first questionnaire about the students’ impressions (Lee et al., 2013, p. 12:18)
through closed-ended questions. Each question required a consent degree of the presented asser-
tion, opting by one of five responses into a Liker scale (Nemoto & Beglar, 2014), ranging from
“Strongly disagree” until “Strongly agree”. The assertions of the first questionnaire as follows as:
[QT1.1] The difficulty level of the course material, used in the previous study, was satisfac-
tory for me.
[QT1.2] Thinking about clicker questions on my own, before discussing with people around
me, helped me learn the course material.
[QT1.3] Discussing course topics with my seatmates in class helped me better understand
the course material.
[QT1.4] The immediate feedback from clickers helped me focus on weaknesses in my un-
derstanding of the course material.
[QT1.5] QR code boards, used during voting, are an easy-to-use class collaboration tool.
[QT1.6] I recommend that other instructors use our approach (reading quizzes, clickers,
in-class discussion) in their courses.
Beyond the assertion translation, there were only two adaptations concerning the original
questionnaire. The first one was the modification of the [QT1.5] question, which instead of “click-
ers” was replaced by “QR code boards”. The second one was the inclusion of the [QT1.1] question
because there was a slight suspicion of the course’s professor about the appropriateness (or not)
of the course reading material.
The second questionnaire about the students’ impressions, through open-ended questions,
was adapted from (Mazur, 1997, p. 21). Its questions are listed as follows.
[QT2.1] What do you love about this class?
[QT2.2] What do you hate about this class?
[QT2.3] If you were teaching this class, what would you do?
[QT2.4] If you could change one thing about this class, what would it be?
[QT2.5] Is there more some commentary or observation to record concerning Peer Instruc-
tion?
5<http://www.plickers.com>
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Beyond the question translation, there was one adaptation concerning the original question-
naire. It included the [QT2.5] question to collect other eventual students’ impressions that could
arise. The students filled all two questionnaires after the ending of the semester, via an online tool.
6.3 Data Analysis
There is a binding between data and proposition were investigated. The D1 data supply support
to the P1 proposition through of the ALG, NLG, and WLG metrics (see Section 5). The D2
and D3 data supply support to P1 and P2 propositions through the answers’ triangulation of two
questionnaires.
The discoveries’ interpretation used three criteria. The first one (C1) has quantitative nature
and is satisfactory if: (I) ALG 10%; (ii) NLG 20%, e (iii) WLG 70%. C1 directly matches
P1.
The second criterion (C2) has qualitative-quantitative nature and contrasts, employing trian-
gulation, the results of the two applied questionnaires. It proposes a representation index for the
collected D2 answers, according defined as follows:
RepInd(P) =
xT
Answer(x,P)
n(4)
where Answer(x,P)has values matches from 0 to 1, in that (i) when xanswers Pwith “Strongly
disagree”, the value is 0, and (ii) when xanswers Pwith “Strongly agree”, the value is 1. The
other answers have values linearly proportional between these two values. The representation
index indicates the average value of the class responses (nstudents) for a given question Pin D2.
The class will tend to “strongly disagree” with the Passertion, if the index tends to value 0; and
will tend to “strongly agree”, if the index tends to value 1.
Each answer in the D2 questionnaire was compared to the expressed impressions in free-text
in D3. It is considered satisfactory if, for all questions in D2, your its representation index will
be more than 70%; and D3 results were coherent with D2 results. C2 directly matches P1 and
P2. The third criterion (C3) has qualitative nature. It considers satisfactory if D3 presents backing
evidence in collected data in D1 and D2. C3 directly matches P1 and P2.
Thus, attending to three criteria assumes the possibility of performing an analytic general-
ization (Kennedy, 1979), extending the study hypothesis to the proposed cut.
7 Results
It presents the discovered evidence from the analysis of collected data during the case study. We
divided the evidence in order to sustain P1 (Section 7.1) and P2 (Section 7.2) propositions. We
also present some methodological aspects as indirect issues to PI (Section 7.3) and threatens to
results validity (Section 7.4).
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7.1 Learning Gain (P1)
P1 asserts the PI use guarantees a students’ learning gain. We will bring investigation results of
this assertion.
Piece of Evidence 1: ALG, NLG, and WLG confirm the learning gain.
From D1’s data analysis, it confirms that the learning gain occurs through ALG, NLG, and
WLG. In Figure 5, the chart presents the average percentages of correct answers of Q1,Q1ad, and
Q2sets. From this chart, it is possible to compute ALG and NLG values.
Figure 5: Chart with the average percentages of correct answers of Q1,Q1ad , and Q2sets of Logic in CS course at the Federal University of Jataí.
According to C1, ALG and NLG are satisfactory. ALG is 62% 49% =13%, being more
than 10%. And NLG is (62% 49%)/(100% 49%)
=25,50%, being more than 20%. Thus
ALG closer to 10% is corresponding to the learning gain of nearly 25%, referent to the whole
possible gain. Hence there was a gain corresponding to more than a quarter of the maximum
possible extent.
Still according to C1, WLG is satisfactory too. Figure 6 summarizes the average percentages
of correct and incorrect answers of the case. WLG is 51%/71%
=71,83%, being more than 70%.
Hence, TG reached a learning gain corresponding to nearly 72% of the obtained gain by CG.
Piece of Evidence 2: The students do not perform a “blind copy” of answers in peer discussion.
Smith et al. (2009) state this proposition from their proposed cut. The average percentages
referring to Q1ad and Q2set (see Piece of Evidence 1) are very close. It leads us to admit that the
concept understanding does not occur simply by “blind copy” of the colleague’s answer in peer
discussion. If it were like that, it is possible the Q2values were closer to Q1(what is not true).
The justification more plausible for these values is that there is an effective learning gain in the
discussion step among the colleagues, given that q1and q2are isomorphic (i.e., are not equals).
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Figure 6: Diagram with the average percentages of correct and incorrect answers of Q1,Q1ad and Q2question set of Logic in CS course at the
Federal University of Jataí .
The fact is, in percentages presented in Piece of Evidence 1, there is a dynamic equilibrium,
such that those who correctly answer Q1ad do not necessarily correctly answer Q2(see Figure 6).
For instance, nearby 23,75% (Nodes 8 and 12) of those who correctly answered Q1ad, incorrectly
answered Q2, occurring a “negative flux”. However, approximately 20,88% (Nodes 9 and 13) of
those who incorrectly answered Q1ad, correctly answered Q2, occurring a “positive flux”. Thus,
the difference 20,88% 23,75%
=2,87% justifies the little difference between the average
percentages of Q2and Q1ad, although the flux from one to another had been considerable.
Piece of Evidence 3: Peer discussion promotes a learning gain.
It verifies, from D3 data analysis, an indication that peer discussion promotes a learning
gain. Socrates6states “[...] that when we were talking about given answers, many times we had
to convince (teach) the other colleagues and, in this way, the knowledge culminates fixing better
in my head7. This feedback kind was present in other students’ answers, like in the Plato words:
The student interaction during the discussion facilitates the learning”.
This interpretation matches with D2 data analysis about the students’ impressions in quanti-
tative terms, presented in Figure 7. The QT1.3 assertion states that “discussing course topics with
my seatmates in class helped me better understand the course material”. This assertion presents
67% of strong agreement with RepInd(QT 1.3) = 88.89%.
Piece of Evidence 4: The immediate feedback guarantees favorable conditions for learning oc-
curs.
6To guarantee anonymity, we use fictitious names for the students.
7The students’ perception was originated written in Portuguese. We translate it to English in this work.
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Figure 7: Chart of the first questionnaire’s percentage results about the PI impressions divided by its six questions (QT1.1 to QT1.6) ranging the
answers on Likert scale.
It verifies, from D3 data analysis, an indication that the immediate feedback guarantees
favorable conditions for learning occurs. Aristotle states as follows:
I can’t see a more effective way to teach the course of Logic in CS. I directly and
fastly see my error through the questions and answers by QR code. It simply was very
important (for me, at least) to subject understanding”.
This feedback kind too was present in other students’ answers, like in Diogenes words: [PI]
stimulates the student to participate better in class, enabling to have better performance and
already to know how your knowledge level is during course”.
It found something interesting in Nichomacus words that state the need to work “[...] ex-
ercises with the students to diagnose the strong and weak points, for remedying them, like was
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made”. The sensation is he realizes, at the same time, that PI performs a diagnosis assessment
(“[...] like was made”) and expresses the desire that this will be more explored in other learning
spaces.
This interpretation matches with D2 data analysis presented in Figure 7. The QT1.4 as-
sertion states that “the immediate feedback from clickers helped me focus on weaknesses in my
understanding of the course material”. This assertion presents 72% of strong agreement with
RepInd(QT 1.4) = 90.28%.
Piece of Evidence 5: The exposition to contradictory enables a validation in real-time of the
supposed occurred learning.
It verifies, from D3 analysis data, an indication that this piece of evidence is true. Sophocles
states that the “[...] fact to have diverse opinions of your classmate” was what he more liked in
this class format.
However, the class presented an inadequate feeling of this PI step. To use the answers in the
classroom as part of necessary punctuation to be approved (or reproved) does not seem to have
been a good decision for this class. Aphrodite says:
Sometimes, a good argumentation of a classmate can lead to a wrong answer, and
assess the answers of given exercises in class can prejudice doubly someone that lost
this class, or that need more time to absorbs the subject”.
Thus, to perform the controlling assessment from PI question voting leads some students to believe
that the exposition to contradictory can have negative consequences for the learning.
Still in this sense, Thales asserts that “the fact to hit in first tentative, but, clarifying the
doubts with the classmates, to miss in second tentative” was what he more hated in PI. The expo-
sition to contradictory should be a natural need of the learner, eager by the knowledge. However,
the sensation still is the loss of punctuation, due to failing (incorrect answers), is received by
students more as a punishment than as an evolution opportunity.
Piece of Evidence 6: The dynamicity of the approach positively contributes to the student’s con-
centration in the classroom.
It verifies, from D3 data analysis, an indication that this assertion is true. Anaximander
asserts that “the dynamicity does not allow the students easily get lost during the class and, conse-
quently, the chance of success in studies and tests tend to increase for each them”. Thus, to him,
dynamicity contributes to one of the favorable elements for good learning.
Still in this direction, to be asked about what would change in this class format, Pythagoras
says that “would look for a dynamic and non-monotonous way. I believe the traditional mode of
the class does not fit for Logic”. To him, the dynamic contributes to the class are not bored, which
certainly favors in a positive way for students’ concentration in the classroom.
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Although some realized the class as dynamic, Anaximenes records the lack of concentration
of some. He says “sometimes, some classmates only joked or talked about random things during
peer discussion”. A way of resolution for this problem is the student rotation among the groups
in each lesson. However, by and large, the strategy of student proximity was the chosen option.
Piece of Evidence 7: The PI structure brings gamification elements that involve the Computing
students in the course.
The CS students, in a general way, like digital games. These lead the students to be more
sympathetic to methodologies that include gamification elements. About what you would teach
this course, Heraclitus asserts that it would be good to have “[...] gymkhanas among the groups
about the Logic subject, works in groups with proposed themes by the professor! Lectures! Exams
and exercises!”.
Still in this sense, Parmenides says that it would be interesting a “competition in the number
of hit questions with a prize for the three first positioned (a week of [free lunch in] University
Restaurant], for example)”. It is possible to extract the implicit association of gamification with
dynamicity (Piece of Evidence 6) and the immersion in the referred activity.
There is the belief that gamification forces, maybe naturally, greater students’ participation
too. Plotinus says “I would try to add more disputes among the students to try forcing the dis-
cussion group to be taken more seriously”. He may want to promote a favorable environment,
according pointed by Anaximenes (Piece of Evidence 6).
It is true that the higher the necessary changes for the professor alters the class dynamic,
the more the onus (s)he will have to pay concerning the class planning. The desire to become the
class more dynamic, to promote a greater engagement with the knowledge discovery, is shared by
professors and students. However, building a favorable academic environment for these changes
can be viable in practice.
7.2 PI Receptivity (P2)
P2 states that the students well receive PI. As follows, we will bring the investigation results of
this assertion.
Piece of Evidence 8: The students would indicate the approach for other professors.
It verifies, from D3 data analysis, an indication that this assertion is true. Democritus asserts
that
The method itself is amazing, and I agree that it should be used in more courses;
therefore, it facilitates learning and leads to the homogeneity of the class knowledge
base. The only part that brings insecurity is the psychological question of the assess-
ment method.”.
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According to the student points, the approach receptivity is positive, although the assessment
method has been an insecure source. Probably this problem associates with the punctuation of the
student answers during peer discussion (according to what is pointed in the Piece of Evidence 5).
This interpretation matches with D2 data analysis presented in Figure 7. The QT1.6 states
that “I recommend that other instructors use our approach (reading quizzes, clickers,in-class dis-
cussion) in their courses”. This assertion presents 67% of strong agreement with RepInd(QT 1.6) =
83.33%.
Piece of Evidence 9: The technology’s use positively favored the approach approval.
The technology’s use positively favors in various directions. For PI, among other reasons,
it is important for (i) to print a greater velocity in the attainment of immediate feedback for the
students (Piece of Evidence 4), (ii) to promote a more favorable environment to students’ concen-
tration (Piece of Evidence 6), and (iii) to bring gamification elements that involve the students in
the classroom (Piece of Evidence 7).
This interpretation matches with D2 data analysis presented in Figure 7. The QT1.5 asser-
tion states that “QR code boards, used during voting, are an easy-to-use class collaboration tool”.
This assertion presents 83% of strong agreement with RepInd(QT 1.5) = 93.75% (the greater in-
dex of the whole questionnaire).
In another direction, it verifies that the “recording of individual answers of the students”, as
a student said, is one of the approach problems. Although there is no clarity in the student report,
it is possible your discomfort associates with the assessment problems cited in Pieces of Evidence
5 and 8.
Still, it registered by some students the lack of agility in QR code board delivery at the
beginning of each class. Euclid says that “thinks the QR code board delivery can be more agile,
but I can have an immediate suggestion”. And Gorgias reinforces that “before the class beginning,
the students should already have the board at hand”.
At the beginning of each class, the delivery of the QR code board takes approximately 15
minutes. The classroom setting, together with board delivery, ranges from 20 to 30 minutes for
each lesson. Each meeting takes one hour and a half. Although to be necessary to rethink this
scenario to mitigate these discomforts, the adaptation costs of a usual classroom for PI compensate
overall.
Piece of Evidence 10: There is a need for CS students to perform group work8.
There is an assertion, assumed as true concerning STEM students, about their sociability.
It used to refer to these as retracted, introspective, and until few sociable. However, in answer
to the question “If you were teaching this class, what would you do?” (QT2.3), some students’
8This piece of evidence was not presented in the original paper (Bispo Jr. & Lopes, 2021) due to space restrictions.
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speech was opposite to this perception. Archimedes says that “[would give] more group activi-
ties, therefore they help in individual development”. It is also interesting the Epicurus’ words, as
follow:
Maybe would experiment with an exercise of group debates, using the logic to defend
or refute arguments, but all in a more relaxed way, to increase the student interest in
the course and to show some ‘different’ usefulness for the course”.
This interpretation is coherent with the evidence about the importance of peer discussion
(Piece of Evidence 3), the exposition to contradictory (Piece of Evidence 5), and the effective
discussion exploitation (Piece of Evidence 2).
Piece of Evidence 11: The choice of the course material for the previous studies was satisfactory.
During the course teaching, It arose a suspect, by part of the professor, if the difficulty level
of the proposed material was adequate (or not). Bearing this inquiry in mind, it included QT1.1
assertion to collect more specific data to investigate it better.
In this way, the QT1.1 assertion states that “the difficulty level of the course material, used
in the previous study, was satisfactory for me”. This assertion obtained near half of the class,
indicating strong agreement (47%, see Figure 7), with RepInd(QT 1.1) = 80.56%. It realizes that
the difficulty of the proposed material was at a satisfactory level for the class.
Still in this direction, the QT1.2 assertion states that “thinking about clicker questions on
my own, before discussing with people around me, helped me learn the course material”. This as-
sertion obtained much more of half the class, pointing strong agreement (67%, see Figure 7), with
RepInd(QT 1.2) = 84.03%. This indication indirectly reinforces that the choice of the proposed
course material was adequate for the class, having a moment of meaningful learning during the
individual voting step.
However, it identified in some students’ reports the objection that “[...] the book is difficult to
understand”. Still, in this sense, Dionysius asserts that, if he would teach the course, “[...] would
use a book with a better language to learn”. The adopted book uses a median math formalism
compared to other Logic books adopted in other institutions.
It is interesting to realize, yet concerning this point, what Athena declared. She says that
usually the solicited texts to be read were very confused. But, after the class, they became sim-
pler to interpret”. This indicates that the learning effectively occurred from the proposed course
material, despite all mentioned reservations by the students.
The difficulty found by the students may arise from their formation’s weaknesses during the
pre-university period. The Leucippus declaration contributes to this reasoning:
To participate in this methodology, the student needs to have a reasonable knowledge
background, but not always everybody has. So it is necessary to pursue it, although
not everybody knows how to do/get this background using the learning instruments are
made, as the texts that we have to read for acquiring some knowledge, sometimes only
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it was possible to understand something when already have been spent 2-3 classes
after the professor explained the given questions”.
Piece of Evidence 12: There was positive feedback from the class concerning all steps of the PI
cycle.
D2 data refers to all steps of the PI cycle. As it is possible to verify in Table 1, the repre-
sentation index of all questions was superior to 80%, such that the general average of these results
was 86.81%. In this way, it is possible to assert that the class positively reacted to this approach
from these indicators.
Question QT1.1 QT1.2 QT1.3 QT1.4 QT1.5 QT1.6 Geral
RepInd(Q)80.56% 84.03% 88.89% 90.28% 93.75% 83.33% 86.81%
Table 1: Representation index of all questions of Questionnaire 1, including their general average.
Still in this direction, when asked for “what you would change, if you could, in the class
format?” (QT2.4), there were various generic feedbacks like “I would do in the way that you do
or “there is no something that I don’t like”. This can reflect an uncritical thought about the process
or maybe a good receptivity about the adopted approach (as points other pieces of evidence), or
perhaps both. In front of other evidence and other students’ reports before mentioned, it is more
probable that there is a favorable disposition to the approach used.
It is important to note some students’ reports about the non-fully adherence of the professor
concerning the approach. The original proposal of PI admits a specific sequence. However, the
professor left out the first explanation and did a direct question exposition to the students. The
explanation was performed subsequently to after all voting moments. About how would teach the
classes, Seneca says
I would do the same, but too would shortly revise the content (5 minutes would be
enough), asking some blatant or generalized doubt that could prejudice the perfor-
mance during the class”.
Augustine says too that would be interesting “a short explanation (resume) about the contents (for
what they serve), raising the interest in the students for they research more deeply about the taught
content [...]”.
The original PI proposal (see Figure 1) expects the initial step of dialogued exposition in
the cycle’s beginning. The professor’s choice concerning performing the repositioning of this
step resided in intent to provoke the student to investigate the presented question more deeply.
Once the voting occurred and the students receive the feedback of correct answers’ percentage,
a natural curiosity in the learner would arise to understand why that question would need to be
better answered in a certain way instead of another one.
It is possible that this concern with the step inversion associates with the students’ perfor-
mance in terms of punctuation in the voting. The need becomes more imperative when (s)he
can’t have doubts before the question because this can prejudice the punctuation in assessment.
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This interpretation is coherent with the exposed about the answers’ evaluation in the classroom in
explaining the Pieces of Evidence 5, 8 and, 9.
Although evidence strongly points to the positive feedback of the class concerning PI, some
students indicate resistance to active learning. Helen asserts that PI should not “[...] occupy the
whole class. We learned all content outside the classroom, reading the texts”. It seems, for He-
len, the professor fulfills your function to teach more adequately, exposing the content instead of
configuring the conditions appropriate for the learning occurs. Confirming this suspicion, Ulysses
asserts that he realizes the “lack of the professor in the classroom” and Oedipus declares that “the
dynamic occupies much time, since the organization. I would teach the content in class and would
conduct this dynamic [in an] online [way]”.
As an active methodology, PI is still seen (by some) as an interesting dynamic, but it is not
the teaching in a strict sense. Although the general perception of PI use was well favorable, still
is present this feeling in some. This is the result, probably, of the substantial paradigm shift that
active methodologies impose on both the learner and the professor.
Apollo also mentions your apprehension about the activities’ overload if other professors
would adopt this strategy. He says that “in the case other professors would require the previous
study of course material to watch the lessons, this would sums to college tasks and homework and
would demand much time”. Before a competitive scenario and much charge, the active learning
can have some rejection, depending on the requirement level put on the students.
7.3 Indirect Issues to PI
Two indirect issues during PI use are important to be mentioned. These issues are presented as
follows.
The first issue concerns the assessment criteria used during the course. As part of the con-
trol’s assessment, 30% of the score obtained by the students was originated from their given
answers in the classroom, collected from PI voting. The voting before and after the discussion
were scored, if correctly answered, with the same weight. If they hit all the PI questions in the
classroom, they received the full score. Otherwise, the score was proportional to the number of
hits.
If he would teach the course, Homer says he would change
[...] the fact of the questions in the classroom to count many scores. Even if
someone defends saying ‘if it doesn’t count point, the students don’t pay attention’,
this is more difficult than until a test. So we have little time to the answers and don’t
have sufficient focus, resulting in the majority of classroom taken low scores in this
area”.
The explanation in Pieces of Evidence 5, 8, 9, and 12 mentions this assessment dissatisfaction of
PI answers. It realizes, subsequently, that this choice does not adequate. As an alternative, a score
could be established as a bonus to the final course grade or simply could remove any PI score like
a component of the course grade.
The assessment issue goes beyond the PI use in the classroom. It evokes frustration and
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(until) rejection feelings, not contributing to a favorable environment to learning. Bacco has a
declaration in this direction:
Some classmates already commented, in talks about the class, that they felt frus-
trated and/or pressed. Even studying and knowing the subject, they didn’t get the
anticipated results due to insecurity or stress. However, this is not really a system
failure, but it is the students’ nature itself. Something that can help, in the first mo-
ment, is to ’comfort’ the student in an environment that he is being exposed to, for he
may stay comfortable with the use of the proposed material, unlike to be pressed by
consequences of its use”.
Another important issue was the need for the course professor to be subject to surgery. Due
to this reality, the class organization during the semester was affected, and the number of weekly
lessons increases from two to three ones.
In this sense, Tobias asserts that “would make available tho lesson by week” only. Cicero
also says that “the teaching time [...] goes by so fast. Not ends to learn one and it has to learn
another”. Maybe the feeling of these students would be different if there is no professor removal
by medical motives.
7.4 Threatens to Results Validity
It is important to note that the flux of isomorphic questions does not identically follow to con-
ducted in (Porter et al., 2011). In the process performed by these authors, the q2individual voting
is immediately performed after the q1ad voting (after peer discussion). One of the purposes of this
approach is to replicate more faithfully the methodological pathway presented in (Smith et al.,
2009), aiming to guarantee a lesser professor influence in relative results to student learning gain.
This work followed the original PI flux, performing only the inversion of the dialogued
expository step (see Piece of Evidence 12). Thus, after q1ad individual voting, it performed the
dialogued expository and presented q2after. The course professor adopted this methodological
pathway by understanding to be more natural in students’ inquiry process, promoting an immedi-
ate curiosity in the real moment that possible doubts are arising.
This register is important in eventual direct comparison among the results of this work and
the obtained ones in (Porter et al., 2011). These reservations need to be made aiming to understand
the involving context in the two projects adequately.
It is also important to mention that the evidence strength presented in this work can be better
explored in future case studies. Causality relations from control groups can supply indications
more solid to serve as support for presented propositions.
8 Conclusions and Future Work
This research aim was to discuss the impact of PI use in CHE in Brazil from the teaching LCS
course. Two investigation questions northers this research. The first question was “Why is PI use
adequate for LCS course in CHE in Brazil?”. And the second one was “How are the LCS students’
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impressions concerning PI?”. The research context was the 2018 entering class of CS program of
the Federal University of Jataí.
It verified enough evidence to veracity of two propositions concerning the investigation
questions. The first proposition is “The PI use guarantees some learning gain of the student”.
The second one is “The students well receive PI”. It concludes that, from an analytic generaliza-
tion perspective (Kennedy, 1979), PI is adequate for LCS course in CHE in Brazil, and students
positively receive the approach in the classroom.
As mentioned in the discussion of the results (Section 7), one of the points of greater dissat-
isfaction by part of the students was issues related to the control’s assessment. The use of voting
answers in the classroom during PI as part of course grade does not well received by the students.
It is necessary to observe these students’ feedbacks since the verified positive effects in PI use
could probably be more expressive.
As future work, the need to extend this study to other revelatory cases in Computing Ed-
ucation in South America arises. It also is interesting to note the percentage presented in Node
13 (Figure 6). Although the students had incorrectly answered both q1and q1ad , 64% of them
correctly answered q2. It would be relevant to investigate it more deeply to identify the reasons
for the occurrence of this fact.
Acknowledgements
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível
Superior – Brasil (CAPES) – Finance Code 001.
Extended Awarded Article
This publication is an extended version of an awarded paper at the Brazilian Symposium on Com-
puting Education (EduComp 2021), entitled “Impacto do Uso da Peer Instruction no Ensino Su-
perior de Lógica para Computação no Brasil”, DOI: 10.5753/educomp.2021.14473.
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... As abordagens ativas de ensino estão bem presentes na realidade de vários professores universitários de computação (e.g. [5]). Elas costumam estar associadas com o que chamamos de ensino pela descoberta em que os estudantes são expostos a questões e experiências específicas de forma que eles "descubram por si mesmos" os conceitos esperados pelo professor [19]. ...
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