Online tools provide unique access to research students' study habits and problem-solving behavior. In MOOCs, this online data can be used to inform instructors and to provide automatic guidance to students. However, these techniques may not apply in blended courses with face to face and online components. We report on a study of integrated user-system interaction logs from 3 computer science courses using four online systems: LMS, forum, version control, and homework system. Our results show that students rarely work across platforms in a single session, and that final class performance can be predicted from students' system use.
We focus on evaluating students' online tool use in terms of , consecutive sequences of study actions that occur between breaks for food or sleep. Sessions have been previously used to analyze student behavior in MOOCs and in other cohesive online tools such as an LMS (e.g. @cite_6 ). Our work here extends that research by applying to heterogeneous data from blended courses where students can work across platforms and over longer periods of time with the goal of developing early predictors that can be used to identify high- or low-performing students in time for an intervention.
@cite_6
2090861833
All forms of learning take time. There is a large body of research suggesting that the amount of time spent on learning can improve the quality of learning, as represented by academic performance. The wide-spread adoption of learning technologies such as learning management systems (LMSs), has resulted in large amounts of data about student learning being readily accessible to educational researchers. One common use of this data is to measure time that students have spent on different learning tasks (i.e., time-on-task). Given that LMS systems typically only capture times when students executed various actions, time-on-task measures are estimated based on the recorded trace data. LMS trace data has been extensively used in many studies in the field of learning analytics, yet the problem of time-on-task estimation is rarely described in detail and the consequences that it entails are not fully examined. This paper presents the results of a study that examined the effects of different time-on-task estimation methods on the results of commonly adopted analytical models. The primary goal of this paper is to raise awareness of the issue of accuracy and appropriateness surrounding time-estimation within the broader learning analytics community, and to initiate a debate about the challenges of this process. Furthermore, the paper provides an overview of time-on-task estimation methods in educational and related research fields.