article · 2015
Snapshots of Student Behavior During Online Learning
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Abstract
Introduction: A significant technology-related challenge in online higher education pertains to students’ abilities to regulate motivation when learning on their own. There is little data on how students structure their online learning experiences, making it difficult for educators to know how best to utilize course technology. In this talk, I discussed an innovative method of collecting “snapshots” of student behavior during online learning sessions. Methods: 73 participants were recruited from an introductory online course in HTML and JavaScript programming. Sampling procedures were programmed into the class software, wherein a pop-up questionnaire appeared at random intervals (experience sampling) or after a key event (event sampling) while the student was logged into the class. Results: Over the course of 928 event-sampled questionnaires and 1,738 experience-sampled questionnaires, significant proportions of students reported that they were performing general multi-tasking behaviors, as opposed to social multi-tasking, Internet multitasking, and learning-directed multi-tasking behaviors. Results from event-sampled and experiencesampled questionnaires mirrored each other. Discussion: These data provide empirical snapshots of how often multi-tasking is occurring during online coursework, while also offering potential for investigations into what types of tasks students are most likely to be doing besides coursework and for whom this multitasking is distracting as opposed to motivating. A major challenge facing the integration of online technology in higher education is the selfregulation of student motivation. That is, online coursework requires students to regulate their motivation, engagement, and interest in the course above and beyond what is required of students in traditional, on-campus courses. For instance, whether or not online students choose to engage at all with their coursework, and if so, for how long, is entirely up to them. Along those lines, students in online courses often have less interaction with the course instructor, teaching assistant(s), and fellow students. Furthermore, students in online learning environments face a multitude of potential distractions, not only from the Internet, but also from their external surroundings. Many online students are learning from home or from work, where they face distractions from co-workers, roommates, and families. Despite all the factors that make online learning environments unique, there is very little data on how students structure their online learning experiences. Online learning experiences are often simulated in laboratory settings, where students complete brief learning sessions within the context of a simulated online course. However, due to the very nature of online coursework being administered through the Internet, there are a variety of opportunities for researchers and educators alike to take both obtrusive (periodic self-report questionnaires) and unobtrusive (e.g. tracing a student’s click-level engagement with online materials) measurements during real online learning sessions (Schraw, 2010). Our lab has capitalized on this notion by collecting “snapshots” of student behavior during online learning, in addition to collecting behavioral data (click-level data indicating which pages and materials students accessed and when they did so), performance data (grades on assignments, quizzes, and tests), and self-report measures (personality measures, interest, and engagement assessments, etc.). Methodology Participants and Procedures Participants were seventy-three students enrolled in an introductory computer science course on HTML and JavaScript programming (27.4% female; 65.8% Caucasian). The study was conducted over two of these semester-long online courses. After providing informed consent, students took an initial questionnaire, assessing personality and demographic characteristics, then proceeded to complete the online coursework at their own pace. Grades on assignments, quizzes, and exams were recorded, as was click-level data, corresponding to what students accessed through the online course page. Most relevant to this research, students also completed short self-report measures that appeared as pop-ups at certain times over the course of the semester while they were logged into the class. These measures included questions about what behaviors students were doing at the time the pop-ups appeared, including behaviors that were not relevant to the course. This occurred in one of two scenarios: experience sampling or event sampling. Experience-sampled questionnaires (see Figure 1) appeared at random intervals while students were logged into the class (on average, once per hour of being logged into the class). As these questionnaires appeared at random intervals, it is unclear whether students were actively engaged with the course materials at the time of sampling; as such, these questionnaires provide us with a very general sense of what most students were doing at any given point in time while merely logged into the online course. Event-sampled questionnaires (see Figure 2), on the other hand, appeared when specific events in the course occurred (e.g. publishing an HTML code example). Thus, as event-sampled questionnaires were keyed to specific course tasks, these questionnaires provide a clear picture of what students were doing while known to be actively engaged with the course materials. Figure 1. Example of an experience-sampled questionnaire Figure 2. Example of an event-sampled questionnaire
Cite this paper
Geerling, D. M. (2015). Snapshots of Student Behavior During Online Learning.
Geerling, Danielle M. “Snapshots of Student Behavior During Online Learning.” 2015.
Geerling, Danielle M. 2015. “Snapshots of Student Behavior During Online Learning.”
Geerling, D.M. (2015) ‘Snapshots of Student Behavior During Online Learning’.
@article{geerling2015snapshots,
title = {{Snapshots of Student Behavior During Online Learning}},
author = {Geerling, Danielle M.},
year = {2015}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.