![]() ![]() We explore whether those behavioral frames overlap in productive ways with epistemological frames, thus supporting our efforts to interpret rich video data. We argue that by conducting a systematic analysis of behavioral frames using computerized algorithms we can model student frames as a latent class variable. We explore the potential for using a multimodal learning analytic approach to identify whether clusters of observable behaviors can be used to identify and characterize behavioral frames in rich video data of student interviews. It is easier to catalog observable behaviors (e.g., body motions or gaze) without explicitly attempting to identify their social relevance for the participants. One of the challenges many learning scientists face is the laborious task of coding large amounts of video data and consistently identifying social actions, which is time consuming and difficult to accomplish in a systematic and consistent manner. ![]() Finally, the paper provides an overview of commonly adopted time-on-task estimation methods in educational and related research fields. The primary goal of this paper is to raise awareness and initiate a debate on the important issue of time-on-task estimation within a broader learning analytics community. This is particularly true for online setting where the amount of interaction with LMS is typically higher. Based on modeling different student performance measures with popular statistical methods in two datasets (one online and one blended), our findings indicate that time-on-task estimation methods play an important role in shaping the final study results. This paper presents findings from two experiments that looked at the different time-on-task estimation methods and how they influence the final research findings. While time-on-task measures have been extensively used in Learning Analytics research, the details of their estimation are rarely described and the consequences that this process entails are not fully examined. Extracted time-on-task measures are then used to build predictive models of student learning in order to understand and improve learning processes. Among different uses of trace data, it has been extensively used to calculate time that students spent on different learning activities – commonly referred to as student time-on-task. With the widespread adoption of Learning Management Systems (LMS) and other learning technology, large amounts of data – commonly known as trace data – are being recorded and are readily accessible to educational researchers. ![]()
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