Exploring intensive longitudinal measures of student engagement in blended learning

Abstract

In this exploratory study we used an intensive longitudinal approach to measure student engagement in a blended educational technology course, collecting both self-report and observational data. The self-report measure included a simple survey of Likert-scale and openended questions given repeatedly during the semester. Observational data were student activity data extracted from the learning management system. We explored how engagement varied over time, both at the course level and between students, to identify patterns and influences of student engagement in a blended course. We found that clarity of instruction and relevance of activities influenced student satisfaction more than the medium of instruction. Student engagement patterns observed in the log data revealed that exploring learning tools and previewing upcoming assignments and learning activities can be useful indicators of a successful learning experience.Future work will investigate these findings on a larger scale.

Fleiss’s Kappa: Assessing The Concept Of Technology Enabled Active Learning (Teal)

“…The rest of the elements are showing the value of κ approaching the value of 1 which means the assessor reaches the consent of the assessors. This finding in line with the result from Henrie et al, (2015) where they stated that blended learning need to be apply in learning process to make sure students will engaged in class activity. Findings from Zacharis (2015), Okaz (2015) (2016) also showed that the multivariate elements in learning helps student to get involved in classroom activities, especially by using social media technologies.…”

The Impact of Social Media Participation on Academic Performance in Undergraduate and Postgraduate Students

“…As noted by Alhazmi and Rahman (2014), it is important to bear in mind that social networking sites by themselves cannot explain student commitment. Furthermore, as indicated by Henrie, Bodily, Manwaring, and Graham (2015), in order to analyse continuing student participation, it is necessary to consider variables such as the clarity of instruction and the relevance of the proposed activities. It is possible that the proposed Twitter activity on the courses analysed lacked relevance, which could explain this lack of commitment to and participation on social media.…”

“…By themselves, networks alone do not reinforce student commitment; instead, this may be influenced by a multitude of factors (Alhazmi & Rahman, 2014). As Henrie, Bodily, Manwaring, and Graham (2015) have indicated, in an analysis of continued student participation over time in virtual learning settings, it is important to bear in mind that the clarity of the instruction and the relevance of the activities exert more influence on student satisfaction than the medium used for instruction. Forbes (2017) has highlighted the importance in social media-based teaching of offering different types of support according to the students' experience and confidence with social media platforms.…”

Online module login data as a proxy measure of student engagement: the case of myUnisa, MoyaMA, Flipgrid, and Gephi at an ODeL institution in South Africa

“…Most studies conducted on both online student module data and online student module engagement employ mainly quantitative approaches such as surveys and quantitative metrics (e.g., login data and grades) as part of their overall methodology (see, for instance, Bahati, Fors, & Tedre, 2017;Dixson, 2015;Henrie, Bodily, et al, 2015;Henrie et al, 2018;McGarrigle, 2013;Vogt, 2016). For example, Henrie, Bodily, et al's (2015) review study of 113 student engagement studies reports that 39.8% of these studies used qualitative measures.…”