The role of temporal patterns in students' behavior for predicting course performance

Maarten van de Ven

Courses in higher education increasingly make use of blended learning, ie, the combination of online and face‐to‐face activities to optimize learning. Many studies have tried to establish which student activities predict achievement in blended courses, with the aim of optimizing course design and thereby to avoid dropout and to increase retention rates.

While many studies aim to predict course performance based on students' use of resources or student activity within a blended course, the complication with these predictions is the large variation in the types of learning activities (eg, posted discussion messages or completed online quizzes) students engage in. Building a generalized theoretical understanding of the success of blended learning, therefore, remains difficult. In this paper, the authors investigate two possible explanations for the variation in which specific activities predict course performance.

Two blended courses in higher education were chosen that were designed following two different models of blended learning, and which thus differed in their instructional conditions. The first course concerned a flipped classroom model (FCM), in which delivery of content primarily occurs by means of online materials (rather than face‐to‐face lectures), and the subsequent face‐to‐face meetings with teachers are used for guided practice and processing of knowledge. The online learning activities serve as a preparation for the face‐to‐face learning activities. The second course employed an enhanced hybrid model of blended learning (EHM), in which transmission of information primarily occurred in face‐to‐face lectures and small workgroups, and in which additional and supplementary resources were available online: lecture recordings, PowerPoint presentations and formative assessments.

The results of this study show that in both cases, a regular pattern of activity is more effective than low activity. A difference between the two courses laid in the clusters of students whose study pattern changed during the course. In the FCM, students that started with low activity and then changed to average activity as the weeks progressed, had lower course performance than students with steady above‐average activity. In contrast, in the EHM students that managed to change from low to higher activity performed equally well as the students that showed steady activity. One could say that the strategy of "cramming" or procrastination was thus successful in the EHM, but not in the FCM.

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