Investigating temporal access in a flipped classroom: procrastination persists
AlJarrah et al. International Journal of Educational Technology in
Higher Education
Investigating temporal access in a flipped classroom: procrastination persists
Abeer AlJarrah
Michael K. Thomas
Mohamed Shehab
This paper reports on a study that examines the learning behaviors and characteristics of students in a mobile applications computer programming class that adopted a “flipped” learning style. By harvesting learning analytics data from a learning management system, we created visualizations of work intensity to explore temporal patterns of students' behavior and then correlate them with the students' performance. Findings indicate that low, medium, and high performing students tend to access learning materials late with work intensity spiking on the lecture day, specifically during the lecture session. While high and low performing students show no difference in temporal access to material, medium performing students demonstrate the greatest degree of vibrancy regarding course content material access. Further a discussion of implications and insights on procrastination in the context of flipped classrooms are included.
Flipped course; Computer science instruction; Learning; Moodle; Visualization; Mobile application development education; Learning analytics; Learning with video; Procrastination
Introduction
Despite a great deal of effort in supporting learning among students in university-based
computer science (CS) courses, many students struggle. In a multi-national study of
failure rates among students in introductory computer programming classes at the university
level, it was found that even small improvements in failure rates could have a great deal
of impact on the field. The authors of this study
(Porter, Guzdial, McDowell, & Simon,
2013)
state “Assuming that the pass rate found in this survey is representative,
approximately 650,000 students every year do not pass CS1. In this light, just a small improvement
of the pass rate of CS1 would cause a gigantic increase in the number of students
passing (and perhaps eventually graduating) – a one percent increase in the pass rate means
20,000 students extra passing CS1” (p. 35). The authors note that even small changes in
the pass rates of students taking entry level computer science courses would impact tens
of thousands of students who might otherwise be discouraged by their struggles and leave
computer science pursuits altogether.
However, there have been laudable efforts in the reform of computer science
instruction. Pair programming, media computation, and peer instruction practices have been
found to greatly enhance students’ success rates and increase student retention in the
field
(Bennedsen & Caspersen, 2007)
. Studies focused on computer programming classes
have identified several key factors related to student success and perseverance at major
universities. These factors include (1) previous computing experience; (2) work style
preference; (3) self-efficacy for computer programming; (4) poor math skills; and (5) poor
advising
(Beaubouef & Mason, 2005)
.
It is clear that many students of computer science struggle. We also know that
it is very important that computer science students learn to regulate their behavior
as they take on challenging course work early in their studies of computer science.
This self-regulation of learning behavior among students is associated with higher
student grades and long-term retention
(Shell, Hazley, Soh, Ingraham, & Ramsay, 2013)
.
Procrastination is a challenging that has both academic and extra-academic dimensions.
Procrastinating behaviors should be addressed early in a student’s career to prevent the
emergence of problems later
(Senécal, Julien, & Guay, 2003)
.
The “flipped classroom” model is a pedagogical approach that offers greater
flexibility, and active student engagement than traditional teacher-centered strategies. It
reverses the typical pattern of a lecture being followed by homework assignments.
Then, class time is devoted to problem solving exercises, hands-on projects, or
indepth discussions. This model has increased in popularity among students particularly
low achievers
(Nouri, 2016)
. Learning Management Systems (LMS) such as Moodle,
Canvas, Blackboard, and Desire2Learn have become key components in implementing
this teaching style.
Fortunately, such systems not only provide an information conduit for students
to use to access course content and complete course assignments, but also to
provide a rich repository of data regarding students’ behavior. An LMS may record
when students access materials and can indicate precisely what materials are accessed.
By creating ways of this data speak to instructors, we may provide them with a tool to
help them make data driven decisions about their pedagogy. It is now possible for
instructors to actually see what materials students access, which students access the materials,
and how much time they spend accessing materials. For e (...truncated)