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\begin{document}

\title{Exemplifying Computational Thinking Scenarios in the Age of COVID-19:
Examining the Pandemic's Effects in a Project-Based MOOC}



\author[1,2]{Juan D. Pinto}%
\author[2]{Chris Quintana}%
\author[2]{Rebecca M. Quintana}%
\affil[1]{University of Illinois at Urbana-Champaign}%
\affil[2]{University of Michigan}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\selectlanguage{english}
\begin{abstract}
The rapid, global spread of COVID-19 has led to an unprecedented rise in
enrollments in online learning experiences among learners of all ages.
In this paper, we explore the impact of the global pandemic on a massive
open online course, \emph{Problem Solving Using Computational Thinking},
with a particular focus on the topics learners chose for their final
projects.~ The \emph{Computational Thinking} MOOC was designed using a
project-based learning approach and aims to provide learners with an
introduction to the ``big ideas'' of computational thinking using a
range of case studies that encompass topics such as airport
surveillance, epidemiology, and human trafficking. Beyond observing a
sharp increase in enrollment and engagement at the time the pandemic
began, we discuss ways in which the course's project-based pedagogy
allowed learners to bring their present experiences and concerns
together with the course's subject matter in order to meet the learning
objectives for the course. Many learners chose to address aspects of the
pandemic in the course's final project and applied ideas about
computational thinking to peer-graded assignments that conveyed an
individualized sense of importance and urgency. We assert that this
approach, along with the inclusion of a timely epidemiology case study,
enabled learners to more deeply internalize the role that computational
thinking can play in their own lives and in society as a whole.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{266961}}

The COVID-19 pandemic has thrust online learning environments into the
global spotlight. The sudden shift to emergency remote teaching
\hyperref[csl:1]{(Hodges et al., 2020)}{~has forced educators and students to adjust their
educational routines using digital platforms and even pedagogical
approaches with which they were previously
unfamiliar~\hyperref[csl:2]{(Quintana \& Quintana, 2020)}. Schools and universities have confronted
this new reality, while lifelong learners have sought new online options
for advancing their education.}

One of the outcomes of these events has been an unprecedented rise in
enrollments in massive open online courses (MOOCs), leading to a series
of publications on the subject \hyperref[csl:3]{(Lohr, 2020)}. In this paper, we
explore the impacts of the pandemic on one MOOC: \emph{Problem Solving
Using Computational Thinking}. We give special attention to the
educational benefits of a pedagogical approach that is rarely used in
courses of this scale: project-based learning (PBL). We identify
particular benefits of this approach that current events have
highlighted, and we offer implications for teaching computational
thinking in online settings.

\section*{Course Description}

{\label{258699}}

\subsection*{Audience and Scope}

{\label{662340}}

The \emph{Computational Thinking} MOOC was developed by an
interdisciplinary team from a large public university in the Midwestern
United States. The course was designed for a target audience of
pre-college learners and early college learners who intend to pursue
STEM careers and would thus need to develop fluency in the computational
tools used in STEM. It aims to equip students with the modes of thinking
needed to set up problems and potential solutions as a foundation for
being able to eventually use computational tools and programming to
address those problems.

Given this perspective, the course is based on one particular definition
of computational thinking (CT). CT has been described and discussed in
some form for decades \hyperref[csl:4]{(Papert, 1996)}, but it has received increased
attention in recent years, with an especially sharp rise in relevant
scholarship since 2015 \hyperref[csl:5]{(Hsu et al., 2018)}. The definition of CT adopted
in this course draws heavily on the work of \hyperref[csl:6]{(Wing, 2006)}, who
presents CT as the practice of conceptualizing problems, complementing
and combining mathematical and engineering thinking. \hyperref[csl:6]{(Wing, 2006)}
argues that CT should not become a synonym for computer programming and
notes that there are a range of skills necessary for CT, including the
ability to define problems, reformulate seemingly intractable problems
into solvable ones, use abstraction and decomposition when approaching a
complex task, and use massive amounts of data and computation for
problem solving.

While the \emph{Computational Thinking} MOOC similarly emphasizes the
problem-solving aspect and many of the skills that Wing highlights, it
does so without requiring learners to make use of actual data or
computer programming. In the course introduction, CT is defined in the
following approachable way:

\begin{quote}
``Before you can think about programming a computer, you need to work
out exactly what it is you want to tell the computer to do. Thinking
through problems this way is Computational Thinking. Computational
Thinking allows us to take complex problems, understand what the problem
is, and develop solutions. We can present these solutions in a way that
both computers and people can understand.''
\end{quote}

The goals of the MOOC, therefore, focus on helping learners to
specifically define and decompose problems through abstraction while
teaching them to use insights from similar problems in other domains to
guide potential solutions.

\subsection*{Project-Based Pedagogy}

{\label{488678}}

This MOOC was designed around a ``project-based learning'' (PBL)
approach, which integrates instructional activities within projects
motivated by students' own interests and contexts~\hyperref[csl:7]{(Krajcik \& Blumenfeld, 2005)}.
PBL is popular in K-12 classrooms, but there are questions of its
utility in other settings, especially in MOOCs, which typically adopt a
didactic, lecture-based format with instructional videos, quizzes,
discussions, and graded assignments. However, some early work has found
positive attitudes among learners in a project-based MOOC and has
emphasized the importance of learner autonomy in these contexts
\hyperref[csl:8]{(Barak \& Watted, 2017)}.~

The \emph{Computational Thinking} MOOC centers much of its pedagogy
around a final project in which learners identify a problem to solve
\emph{computationally}, and then use the knowledge and techniques they
learned throughout the course to iteratively develop an algorithmic
approach towards a solution. They are asked to submit both a graphic
organizer that displays the multiple iterations of their work and a
diagrammed algorithm of their final solution for peer evaluation. This
approach affords learners a level of flexibility rarely found at the
scale of MOOCs since they are able to select the topic that will define
their final projects.

\subsection*{Using Case Studies to Shape Student
Work}

{\label{735348}}

Using case studies is a way to ground CT in real-life scenarios. This
provides a concrete, actionable foundation for CT that can more
effectively lead to learning, retention, and
application~\hyperref[csl:9]{(Weintrop et al., 2015)}. For this purpose,
the~\emph{Computational Thinking} course revolves around a series of
three case studies.

Most pertinent here is the case on epidemiology, which was incidentally
developed before the events surrounding the global pandemic. This case
presents a large, complex problem: how do we prepare for the seasonal
flu and make sure we are ready for the next pandemic? The expert who
presents this case---an associate professor of epidemiology---breaks
this down into a more specific sub-problem and then walks learners
through an algorithm that centers around four categories of people:
vaccinated, susceptible, infected, and recovered. Finally, a series of
computational modeling tools specific to epidemiology are offered to
learners interested in diving more deeply into this problem.

\section*{Enrollment and Engagement
Trends}

{\label{750648}}

As a reflection of the recent spike in attention that online education
has garnered, the~\emph{Computational Thinking} MOOC experienced a sharp
increase in enrollment around the time that many countries began to
experience the full force of the COVID-19 pandemic. We found that most
new learners who reported their educational attainment and employment
status hold a bachelor's degree and are unemployed (see
Figure~{\ref{544146}}). As expected, this suggests that
the rise in overall enrollments is likely due to a combination of more
people working from home, universities shifting to remote teaching, and
rising unemployment rates.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/enrollments-by-education-employment1/enrollments-by-education-employment1}
\caption{{Enrollments by educational attainment (top) and employment status
(bottom).
{\label{544146}}%
}}
\end{center}
\end{figure}

Since MOOCs are notorious for having high numbers of enrollments with
few learners actually engaging in the material or completing the course,
we also explored learner engagement trends, calculated as the number of
learners who completed at least one item (lecture video, quiz,
discussion post, etc.) during the previous week. We discovered an even
sharper rise coinciding with the spread of the virus, suggesting an
impressive surge of learners actively engaged in course activities.

\section*{Student Final Projects}

{\label{704057}}

The course's peer-graded final assignment requires students to identify
a location and natural disaster---either hypothetical or real---and
create a preparation plan to address a specific aspect of this disaster.
The plan must involve the main elements of CT described throughout the
course, namely~\emph{problem
identification},~\emph{decomposition},~\emph{pattern recognition},
and~\emph{abstraction}. Learners' final submissions consist of a
descriptive problem statement, a graphic organizer, and an algorithm
diagram. Examples of the wide range of disasters learners chose include
floods (by far the most common), cyclones, earthquakes, tsunamis,
tornadoes, and droughts.

With the circumstances surrounding the global pandemic influencing
enrollment and engagement in the MOOC, we were interested in whether its
impact would also be realized in topics learners chose for their final
projects. Furthermore, we were interested in the specific problems that
learners identified related to the pandemic which they deemed
``solvable'' using computational thinking approaches.

\subsection*{Methods for Identifying Projects Related to the
Pandemic}

{\label{284041}}

In order to accurately measure the number of final projects submitted
and those related to the COVID-19 pandemic, we first used a series of
Jupyter Notebooks to clean the data. We discarded a total of 61 projects
that were missing attachments, were exact duplicates of previously
submitted projects, or skipped by peer reviewers due to incomplete
submissions. This left us with 164 final projects to analyze, submitted
between November 18, 2019 (course launch date) and August 11, 2020.

To identify projects related to the pandemic, we first extracted those
that contained any of the following words in their title or
description:~\emph{COVID},~\emph{corona},~\emph{virus},~\emph{pandemic},~\emph{epidemic},~\emph{Wuhan},
and~\emph{social distanc(e)}. We then performed the same search through
all PDF, DOCX, and PPTX project attachments. In total, we found 32 final
projects that related to the events surrounding the pandemic.

\subsection*{Findings}

{\label{240931}}

The timing of pandemic-related final projects clearly corresponds with
the rise in coronavirus cases outside of China, with the first related
project submitted on March 24th and quickly increasing from there. Since
that date, 20\% of submitted projects have been related to COVID-19 in
some way.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/final-projects-week/final-projects-stacked}
\caption{{Proportion of pandemic-related final project submissions over time
(stacked).%
}}
\end{center}
\end{figure}

We were interested in exploring whether learners in regions that were
first heavily affected by the pandemic, such as East Asia or Europe,
chose to write about the pandemic before others. However, we found that
the first pandemic-related project was submitted in the United States,
followed by India, Thailand, and then Germany, in that order (based on
self-reported demographic data and tracked IP addresses).

As issues and problems surrounding the pandemic unfolded in real time,
learners articulated specific dimensions that could be addressed using
approaches advanced in the course. Some of the overarching topics in
these projects include being wise about treatment, finding ways to
prevent further spread, dealing with life in quarantine, managing
school, and how to adequately distribute resources to those most
affected. Interestingly, early projects were mostly about deciding to
enforce stay-at-home orders and social distancing measures, while later
projects largely focused on whether and when to begin reopening society.

Some learners who chose to write about the pandemic articulated that
they recognized COVID-19 did not cleanly fit within the category
of~\emph{natural disaster}:

\begin{quote}
``Rather than focusing on a `natural disaster' like too much snow or
flooded streets, I've chosen COVID-19 since it has limited people's
access to proper nutrition and healthy diet.''

``COVID-19 is a pandemic the whole world is talking about right now.
This may not be a natural disaster but is more effective than a natural
disaster.''
\end{quote}

These excerpts---along with learners' decisions to choose a different
path than instructed---highlight the weight of the pandemic in their
personal lives.

\section*{Implications and Conclusion}

{\label{365614}}

Our findings in this case study have highlighted the benefits of a PBL
approach to teaching problem-solving methods and skills such as
computational thinking. Though providing learners with a high degree of
agency in online courses that are offered at scale may not always be
feasible, doing so can empower them and help them establish personal
ties to the subject matter. This heightened level of buy-in from
learners, created through a combination of greater autonomy and personal
interest in the subject, has been shown to lead to more robust learning
\hyperref[csl:10]{(Patall, 2013)}.

In the case of the \emph{Problem Solving Using Computational Thinking}
MOOC, learners were able to choose project topics closely related to
their interests, their experiences, and the large problems currently on
their minds. For those who chose to look at the COVID-19 pandemic
through a CT lens, we expect that the ongoing nature of the problem will
lead to many additional opportunities to reflect on the skills they have
learned. We assert that the PBL approach adopted in this course, along
with the inclusion of a timely epidemiology case study, enabled learners
to more deeply internalize the role that CT can play in their own lives
and in society as a whole.

\section*{Author Biographies}

{\label{727914}}

\textbf{Juan D. Pinto} is currently a learning experience design
research fellow at the University of Michigan's Center for Academic
Innovation and a doctoral student at the University of Illinois at
Urbana-Champaign studying digital environments for learning, teaching,
and agency. His research interests include adaptive learning
environments, educational data science, and remote digital learning. He
received an M.A. in design and technologies for learning from the
University of Michigan. Contact him at
\href{mailto:jdpinto2@illinois.edu}{\nolinkurl{jdpinto2@illinois.edu}}.

\textbf{Chris Quintana} is an Associate Professor in the School of
Education at the University of Michigan, where he applies his background
in human-computer interaction and computer science as he looks at how
different technologies and media can support learning. Much of his work
has focused on software-based scaffolding, including the development of
scaffolded software tools, scaffolding frameworks, and learner-centered
design processes. Quintana received his Ph.D. from the University of
Michigan in Computer Science and Engineering. Contact him at
\href{mailto:quintana@umich.edu}{\nolinkurl{quintana@umich.edu}}.

\textbf{Rebecca M. Quintana} is at the University of Michigan where she
is the Learning Experience Design Lead at the Center for Academic
Innovation and an Intermittent Lecturer at the School of Education. In
these roles she applies her background in learning sciences and
educational technologies to consider how design and technology can
support learning. Much of her work has focused on the design of online
learning environments in higher education contexts. Quintana received
her Ph.D. from the Ontario Institute for Studies in Education at the
University of Toronto in the department of Curriculum, Teaching, and
Learning. Contact her at
\href{mailto:rebeccaq@umich.edu}{\nolinkurl{rebeccaq@umich.edu}}.

\selectlanguage{english}
\FloatBarrier
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