Using Student-created Instructional Videos in CS Upper-level
Courses: A Successful Strategy in a Functional Programming Course
Pedro Guillermo Feijóo-García
1,2 a
and Christina Gardner-McCune
1
1
Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL, U.S.A.
2
Program of Systems Engineering, Universidad El Bosque, Bogotá, Colombia
Keywords: Active Learning, Student-created Artifacts, Video Tutorials, Computer Science Education, Strategic Learning,
Instructional Strategies.
Abstract: In this paper we present findings on a pedagogical approach we designed to enhance students' understanding
of Functional Programming, in which they were required to create two video-tutorials. The first video-tutorial
assignment asked the students to develop explanations of Functional Programming concepts. The second
video-tutorial required them to explain their solutions while completing coding exercises using Haskell. We
present a detailed description of the activities, their evaluation, and their impact on students' learning,
motivation, and performance. Our findings suggest that the use of a student-created video-tutorial approach
can be effective for increasing students’ understanding, performance, and engagement on Functional
Programming assessments. This suggests that using student-created video tutorials may be a promising
strategy to implement in other computing courses.
1 INTRODUCTION
The Computer Science Education (CS ED Research
community has been interested in how students learn
to program and use programming as a medium to
achieve computational literacy (Guzdial, 2016).
Many studies have been conducted over the past
twenty years on the first year of Computer Science
(CS) curricula, with most focusing on CS0, CS1 and
CS2. Unfortunately, students struggle to master
advanced CS concepts as well, and upper-level CS
courses remain an under-researched area of CS ED
research. Thus, we lack understanding of the source
of students’ learning challenges in these courses and
on how to best teach upper-level CS courses.
Based on our years of experience instructing a
senior-level Functional Programming (FP) course, we
have observed learning challenges happening as
students transfer skills from other paradigms to the
functional one: these challenges are due to new
restrictions, the strict need of recursion, and the
understanding of functions in higher abstract levels:
passed as parameters, anonymity (i.e., lambdas), and
functions returning functions. Motivated by William
Glasser´s often cited quote that highlights the power
a
https://orcid.org/0000-0002-3024-1257
of teaching to learn: “We learn 95% of what we teach
to others,” we asked students to create video tutorials
as learning tools to help them increase their
conceptual knowledge and fluency in FP and studied
its effectiveness to begin growing the body of CS ED
research in upper-level CS courses.
In this paper, we discuss our implementation of
student-created video tutorials as a course assignment
in a FP course at a university in Colombia. We also
discuss our findings on the effectiveness of using
student-created video tutorials on students' learning,
motivation, and performance on course assessments.
2 RELATED WORK
Student-created video tutorials are part of a collection
of learner-centered classroom techniques designed to
promote student learning and engagement (Guzdial,
2016). Learner-centered design approaches place the
learner at the center of the learning process, and
invites instructors to consider students’ current
knowledge, knowledge boundaries, interests,
motivations, and expectations (Guzdial, 2016; Bain,
412
Feijóo-García, P. and Gardner-McCune, C.
Using Student-created Instructional Videos in CS Upper-level Courses: A Successful Strategy in a Functional Programming Course.
DOI: 10.5220/0009416404120419
In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 1, pages 412-419
ISBN: 978-989-758-417-6
Copyright
c
 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

2014). In this section, we present review several of
these approaches: active learning, peer instruction,
and other types of student-created artifacts.
Studies on active learning have demonstrated
their ability to enhance students’ performance,
motivation, and engagement (Gehringer & Miller,
2009; Kearney & Schuck, 2004). Within Computer
Science, studies have shown that active learning
activities are effective in helping students to learn CS
concepts (e.g., Frank-Bolton & Simba, 2018; Feijóo-
García & Ortíz-Buitrago, 2018). Gehringer & Miller
(2009) studied the use of active learning activities in
introductory CS courses, i.e., CS1 and CS2. Their
findings suggest that use of student designed games,
diagrams, props, and videos, working on topics like
debugging and sorting were effective techniques for
increasing students’ attention. In general, active
learning have been found to be effective in
introductory CS courses across institutions, diverse
demographics, and countries (e.g., Feijóo-García &
Ortíz-Buitrago, 2018; Kearney & Schuck, 2004;
Murray et al., 2017). They have also been shown
effective in upper division courses on algorithms
(Frank-Bolton & Simba, 2018).
Peer-instruction (i.e., peer-review and peer-
tutoring) is a commonly used active learning
technique in the CS ED community (Feijóo-García &
Ortíz-Buitrago, 2018; Porter et al., 2016; Cottam et
al., 2011). Peer-instruction positions the student as
both an instructor and learner allowing them to learn
from and with their peers (Porter et al., 2016). Within
CS1 and CS2 courses, peer-instruction has been
found to increase students’ understanding of topics,
their communication skills, and their motivation
(Feijóo-García & Ortíz-Buitrago, 2018; Porter et al.,
2016; Cottam et al., 2011).
A core feature of peer-instruction is the
requirement for students to explain their
understanding of a topic to a peer (Feijóo-García &
Ortíz-Buitrago, 2018). This is a feature that also
exists in active learning activities that involve
student-created artifacts (e.g. student-created
instructional videos). Studies of student-created
instructional videos in K-12, CS1, and an algorithms
analysis course, report that instructors and students
positively perceived using student-created artifacts to
promote learning (Gehringer & Miller, 2009;
Kearney & Schuck, 2004). Frank-Bolton & Sihma
(2018) reported that student-created videos can
promote students’ understanding of advanced CS
concepts. Additionally, they found that videos’
creators performed better compared to students who
simply watched the videos. In this paper, we refer to
student-created instructional videos as student-
created video tutorials as this is the name we are
accustomed to calling them.
3 THEORETICAL FOUNDATION
The student-created video tutorials described in this
paper were designed to promote significant student
learning and engagement using a learner-centered
design approach built on constructivist notions of
learning and Fink’s Taxonomy on Significant
Learning (Fink, 2013).
Constructivism defines learning as a process in
which knowledge is constructed and adapted by the
learner based on the learner’s assimilation and
accommodation of new knowledge gained in from
new experiences or reinterpreted past experiences
(Bain, 2014). Both, constructivism and learner-
centered design approaches, describe the learner as a
dynamic individual, who learns through active
engagement in their learning process (Guzdial, 2016;
Bain, 2014). As Freire stated, the art of teaching
implies the need for continuous learning (Freire,
2012).
Similarly, Fink’s Taxonomy for significant
learning (Fink, 2013) is based on constructivism and
identifies six interconnected dimensions (Fink,
2013). Coding requires skills from two of these
dimensions. The first is Foundational Knowledge,
which refers to the individual’s understanding of how
a computer or system works according to its
capacities and limitations. The second is the
Application Dimension, which considers the coding
skills needed to use the computer as a medium
(Guzdial, 2016; Fink, 2013). We used these two
dimensions of Fink’s Taxonomy to help us focus
what students focused on in their video tutorials.
Considering knowledge as something not
transferable but constructed, we designed the student-
created video tutorials to provide learners with an
opportunity to consciously reflect on what they
understood about foundational Functional
Programming (FP) concepts, while verbalizing their
understanding as they explained concepts for
someone else to learn.
4 OUR APPROACH
This section describes the FP course in which student-
created video tutorials were used to foster learning of
CS concepts. It explains the video tutorial
Using Student-created Instructional Videos in CS Upper-level Courses: A Successful Strategy in a Functional Programming Course
413

assignment, its evaluation, and how data are analyzed
leading to our findings and results.
4.1 The Course
Our study is based on an undergraduate senior-level
Functional Programming (FP) course at Universidad
El Bosque in Colombia, South America. The course
introduces student to the FP paradigm through
programming in Haskell. Prior to this course, Java is
the main language in which students are proficient.
The course population consisted of sixteen male
senior CS majors enrolled in the course during the
semester this study was conducted. As a result, we
were unable to have female CS students participate in
this study. The university offers two curricular tracks:
(1) traditional where students take courses during the
day and (2) non-traditional where students take
courses in the evening or weekends. The study
population (N=16) included both traditional [N=9]
and non-traditional students [N=7], and all students
were between the ages of 18 to 35 years old.
The semester-long course featured three modules
over sixteen weeks. Module 1 provided an
introduction to Haskell and FP concepts such as lists
and tuples, higher order functions, folds, and
lambdas. Module 1 included a project, pre and post
conceptual and coding assessments. Before our
student-created video tutorial intervention, students
completed the Module 1 project: a two-week
assignment that asked students to conceptually
understand all topics covered in the course so far and
practically apply their knowledge by completing a
programming assignment.
4.2 The Strategy
After the Module 1 project, we assessed students’
conceptual and coding knowledge using two pre-
assessments. The conceptual pre-assessment asked
students to define FP concepts, and the coding pre-
assessment asked students to code the solutions for a
set of exercises.
Then, the students were asked to create two video
tutorials one for the conceptual and one for coding
pre-assessments. Each video tutorial required the
students to address and explain in detail the set of
exercises given in the pre-assessment. In particular,
they were asked to discuss the questions in which they
struggled to complete on the pre-assessments. The
instructor attempted to motivate students to complete
the video tutorials as a grade substitution for their
score on the pre-assessment. They were given one
week to complete these tutorials.
Finally, after the creation of the video tutorials,
the students completed a conceptual and a coding
post-assessments, which kept the same format of the
pre-assessments. Following these post-assessments,
students were given a feedback survey to evaluate the
effectiveness of the activity.
4.3 Data Collection
We evaluated the effectiveness of the student-created
video tutorials from two perspectives: student
performance and perception. We evaluated student
performance from their grades on 1) the pre-
assessments (ST1), 2) video tutorials elaboration and
completion, and 3) the final assessments (ST2). We
evaluated their perceptions of the activities’ structure
and value of the video-tutorials in their learning
processes. The student feedback survey had seven
multiple choice questions, and an open-ended
question for them to comment on their experience
creating the video tutorials. The multiple-choice
questions and their answer options are shown in Table
1.
Table 1: Post-Survey Multiple-choice Questions &
Options.
Questions Option
Q1: How
stressed did you
feel when
completing the
video-tutorial
assignments?
A: Not stressed at all.
B: A little bit stressed.
C: Stressed.
D: Very stressed.
Q2: How much
did you learn
completing the
video tutorials?
A: I learned very much and gained
knowledge of concepts and skills I
did not have before.
B: I learned, but not much given my
prior knowledge.
C: I did not learn at all.
Q3: How much
did you have to
prepare to create
these video
tutorials?
A: I studied and prepared all the
topics asked in it.
B: I barely studied for this activity.
C: I did not study at all.
Q4: How much
time did you
spend creating
the video-
tutorials?
A: More than 10 hours of dedicated
work.
B: Between 8 to 10 hours of
dedicated work.
C: Between 6 to 8 hours of
dedicated work.
D: Between 4 to 6 hours of
dedicated work.
E: Between 2 to 4 hours of
dedicated work.
F: Less than 2 hours of dedicated
work.
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Table 1: Post-Survey Multiple-choice Questions & Options
(cont.).
Questions Option
Q5: How much
did you study
the course
topics, in the
process of
creating the
video tutorials?
A: I had to study very much.
B: I had to study for it, but not too
much.
C: I did not have to study in the
process. I was already ready for it.
Q6: Would you
like to have a
similar activity
in the future?
A: Yes, I would like to.
B: No, I won't.
4.4 Data Analysis
Data was analyzed using quantitative and qualitative
techniques, considering frequencies of responses for
the final survey, students’ grades on both types of
assessments (conceptual and coding), and students’
feedback on the activity.
We analyzed the pre (ST1) and post assessment
(ST2) grades to determine the percentage of
improvement (Im) students made between them,
referring to it as their percentage difference:
  100 ∗ 2.   1. /5.0
(1)
Their improvement score can be positive, neutral,
or negative. We analyze the open-ended data on
students’ perceptions and experiences with the video
tutorials using an inductive categorization method
(Benavides & Restrepo, 2000), identifying and
labeling those categories that come from the students’
voices. These categories are used to find patterns
between participants, and to identify what students
most appreciated, and what they least liked.
5 FINDINGS AND RESULTS
Table 2 presents the students’ grades on the
conceptual pre and post-assessments. We found that
students improved (Im) with the video tutorial
creation, with a peak of 58.3% for one student [S2], a
mean of 26,3%, and a mode of 33.7% [N=2]. Only
two participants reported negative improvement
outcomes [S3 & S11]. S11 did not create the video-
tutorial.
Table 2: Student Outcomes – Conceptual Assessments.
Student ID
Pre-assessment
(ST1)
Scale: 0.0 to
5.0
Post-
assessment
(ST2)
Scale: 0.0 to
5.0
Im
[%]
S1
Video: Yes 1.67 3.8 42.7
S2
Video: Yes 2.08 5.0 58.3
S3
Video: Yes 3.33 2.9 -8.7
S4
Video: Yes 2.92 4.6 33.7
S5
Video: Yes 2.50 5.0 50.0
S6
Video: Yes 4.17 4.6 8.7
S7
Video: Yes 3.33 5.0 33.3
S8
Video: Yes 3.33 4.6 25.3
S9
Video: Yes 2.92 4.6 33.7
S10
Video: Yes 3.33 5.0 33.3
S11
Video: No 3.75 0.0 -75.0
S12
Video: Yes 2.08 4.6 50.3
S13
Video: Yes 2.92 5.0 41.7
S14
Video: Yes 2.50 5.0 50.0
S15
Video: Yes 3.33 4.2 17.3
S16
Video: Yes 2.92 4.2 25.7
Table 3 presents the student grades on the coding
pre and post-assessments. We found that students
improved (Im) with the video tutorial creation, with a
peak of 66.7% for four students [S1, S7, S12 & S15],
a mean of 29.2%, and a mode of 33.3% [N=7]. We
can observe that only one student reported negative
improvement outcomes [S11], being one of the two
participants who did not create the instructional video
as requested [S9 & S11].
As we can observe in both, Tables 2 and 3, the
video-tutorials creation helped to improve their
outcomes in conceptual and coding assessments. This
distribution can be seen in Figure 1, which shows that
students performed above the average for both types
of assessments: coding and conceptual. Additionally,
considering the strategy, students’ performance
improved significantly for those students who created
both video tutorials [N=14, discarding S9 & S11].
Using Student-created Instructional Videos in CS Upper-level Courses: A Successful Strategy in a Functional Programming Course
415

Table 3: Student Outcomes – Coding Assessments.
Student ID
Pre-assessment
(ST1)
Scale: 0.0 to
5.0
Post-
assessment
(ST2)
Scale: 0.0 to
Im
[%]
S1
Video: Yes 0.00 3.3 66.7
S2
Video: Yes 3.33 3.3 0.0
S3
Video: Yes 3.33 3.3 0.0
S4
Video: Yes 3.33 5.0 33.3
S5
Video: Yes 0.00 1.7 33.3
S6
Video: Yes 3.33 3.3 0.0
S7
Video: Yes 0.00 3.3 66.7
S8
Video: Yes 0.00 1.7 33.3
S9
Video: No 1.67 3.3 33.3
S10
Video: Yes 3.33 3.3 0.0
S11
Video: No 1.67 0.0 -33.3
S12
Video: Yes 0.00 3.3 66.7
S13
Video: Yes 1.67 3.3 33.3
S14
Video: Yes 0.00 1.7 33.3
S15
Video: Yes 0.00 3.3 66.7
S16
Video: Yes 0.00 1.7 33.3
Figure 1: Students’ Performance Distribution.
This is based on a paired-samples Student’s t-test
comparing grades between the pre-assessments and
post-assessments 

14

 5.8,   0.01.
Table 4 presents the inductive categories
identified and labelled according to the participants’
voices and their experiences. Complementarily,
Table 5 matches the students with these inductive
Table 4: Inductive Categories.
Category ID Category Label
C1
The student gave positive
feedback about the strategy.
C2
The student explicitly mentioned
they learned.
C3
The student explicitly liked the
strategy.
C4
The student explicitly said the
strategy helped him/her prepare
the topics.
categories, and their corresponding conceptual and
practical (i.e., coding skills) reported improvement.
Fourteen [N=14] out of sixteen students gave
positive feedback about the strategy, with ten out of
14 of these students explicitly expressed liking the
activity. Similarly, seven students explicitly
expressed that they learned from the activity, and six
students expressed that the pedagogic approach
helped them to prepare the topics.
Table 5: Inductive Categories and Students’ Results.
Student
ID
Conceptual
Im [%]
Coding
Im [%] C1 C2 C3 C4
S1 42.7 66.7 1 - 1 -
S2 58.3 0.0 1 1 1 1
S3 -8.7 0.0 1 - - 1
S4 33.7 33.3 1 - 1 -
S5 50.0 33.3 1 - 1 -
S6 8.7 0.0 1 - - 1
S7 33.3 66.7 1 1 1
S8 25.3 33.3 1 1 1
S9 33.7 33.3 1 1 1
S10 33.3 0.0 1 1 1
S11 -75.0 -33.3 - - - -
S12 50.3 66.7 1 1 1 -
S13 41.7 33.3 1 1 - -
S14 50.0 33.3 1 - 1 1
S15 17.3 66.7 1 - 1 1
S16 25.7 33.3 - - - -
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Despite no existing correlation between the
conceptual improvement index (Im1), the coding
improvement index (Im2), and the inductive
categories (see Table 6), students responded
positively to the strategy, with opinions like:
“The video-tutorial schemes eliminate stress and
allow the subjects to be prepared, more learning is
achieved because there is commitment to make
quality material”.
Table 6: Performance and Perceptions Correlation.
Im1
[%]
Im2
[%]
C1 C2 C3 C4
Im1
[%]
1.0 - - - - -
Im2
[%]
0.6 1.0 - - - -
C1 0.6 0.4 1.0 - - -
C2 0.4 0.1 0.3 1.0 - -
C3 0.5 0.5 0.5 0.2 1.0 -
C4 0.0 0.2 0.3 0.2 0.2 1.0
Figure 2 presents the students’ responses for the
question “How much time did you spend creating the
video- tutorials?”. 56% [N=9] of the students
indicated they spent less than six hours creating the
video tutorials, with 25% [N=4] indicating they spent
between two to four hours.
Figure 2: Responses on Time Demanded by the Activity
(see Table 1 – Q4).
Figure 3 presents the students’ responses for the
question “How much did you study the course topics,
in the process of creating the video tutorials?”. As it
shows, 62% of the students indicated they had to
study very much, in order to create the video tutorials.
These responses suggest that the creation of video
tutorials engages students in intentional study of
course materials, helping them reflect on their
learning processes.
Figure 3: Responses on Students’ Preparation.
Figure 4: Responses on Students’ Satisfaction.
Additionally, Figure 4 shows that 86% of students
liked the activity, and that they would like to be
evaluated with similar approaches in the future. This
suggests that the activity engaged the students,
improved their performance conceptually and
practically (i.e., coding skills), and motivated them to
learn.
6 CHALLENGES AND LESSONS
In this section we present the challenges we
experienced while applying this strategy in our
course, in addition to some lessons we learned in the
process. The challenges we can highlight are:
Grading: The videos created by the students had an
average duration of 20 minutes, varying due to the
number of exercises they were asked to explain: it
depended on how many questions they missed or got
wrong in the pre-assessments. Grading the videos was
an interesting pedagogical task that required
significant time and effort from the instructor. We
learned that we could face this challenge using co-
evaluation strategies (e.g., blind peer-review). We
also considered the potential for the activity to
reinforce students’ content knowledge as a result of
reviewing the student-created video tutorials from
Using Student-created Instructional Videos in CS Upper-level Courses: A Successful Strategy in a Functional Programming Course
417

their peers. However, this is a hypothesis we still need
to study.
Time: Based on the questions asked by the students
in the process of creating the instructional videos, and
on the perceptions presented in figure 2, we find that
this strategy was time-consuming for the students,
especially for those who were required to explain
more exercises. From students’ feedback, we learned
that we might improve the strategy by limiting the
number of exercises to explain. This can be achieved
by letting the students pick a 1-2 exercises they
considered were the most challenging from the pre-
assessments.
7 DISCUSSION AND FUTURE
WORK
Our findings suggest that student-created video
tutorials help students to improve their performance,
and that they can be used for conceptual or practical
(i.e., coding) understanding. Likewise, they suggest
that these kinds of approaches are engaging and
meaningful to the students, considering the positive
reactions and feedback students provided about their
experience creating these instructional videos.
Our study provides evidence that using student-
created video tutorials in a Functional Programming
(FP) class, leads to significant learning on
programming concepts and skills. We conclude that it
is possible to use classroom models different from the
traditional ones, with strategies that can empower and
hold students accountable for their learning
processes. More importantly, we experienced that
students learn more when they have to explain or
teach a certain topic. Instruction is a powerful
learning tool that we, as CS Educators, should not
ignore.
The study we report in this paper responds to a
quasi-experimental design, adapted to the classroom
environment described in section 4.1. Future work
may involve using comparing the student-created
video tutorial activity against traditional problem-
solving practice assignments with two different
groups of students in order to validate not only how
using student-created video tutorials impacts
students’ learning processes, but also to measure the
effectiveness of this activity.
Considering the challenges presented in section 5,
we may use peer-reviewing as part of the strategy, not
only to limit the time and effort in regards to grading,
but also to evaluate differences between explaining
and reviewing in terms of educational effectiveness.
Furthermore, we find it interesting to observe how
students who have created content provide feedback
to their peers’, compared to students who are asked to
review without having created any content on the
topic. How does the notional machine (Guzdial,
2016) of a student vary when s/he has been asked to
instruct a certain CS topic? This is a question we find
interesting to explore within the CS ED community
and for contexts regarding CS upper-level courses.
This question is one of the next steps we find for the
current study.
ACKNOWLEDGEMENTS
The authors would like to thank the students who
actively participated in this experience. Their
participation and feedback were essential for this
study to succeed. We also want to thank Carlos Ortiz
Buitrago for his contribution on the curricular
structure of the Functional Programming course used
for the study, and for his feedback on the learning
strategies that have been adapted in it.
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