A student's capacity to learn a concept is directly related to how much cognitive load is used to comprehend the material. The central problem identified by Cognitive Load Theory is that learning is impaired when the total amount of processing requirements exceeds the limited capacity of working memory. Instruction can impose three different types of cognitive load on a student's working memory: intrinsic load, extraneous load, and germane load. Since working memory is a fixed size, instructional material should be designed to minimize the extraneous and intrinsic loads in order to increase the amount of memory available for the germane load. This will improve learning. To effectively design instruction to minimize cognitive load we must be able to measure the specific load components for any pedagogical intervention. This paper reports on a study that adapts a previously developed instrument to measure cognitive load. We report on the adaptation of the instrument to a new discipline, introductory computer science, and the results of measuring the cognitive load factors of specific lectures. We discuss the implications for the ability to measure specific cognitive load components and use of the tool in future studies.

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This study takes an instructor-centric approach to Learning Analytic (LA) research by analyzing instructor use of the LA within an educational streaming video platform called TrACE. The goal of this study is to understand how instructors naturally interact with analytic dashboards through an empirical analysis. To accomplish this, data of 14 instructors from three institutions that used TrACE from Spring 2015 to Spring 2016 was collected. Data was analyzed to identify frequency of analytic visits, duration of analytic use, differences in analytic use, and differences in use between semesters. Instructors demonstrated preferences for some analytics over others, but the majority of teachers generate short sessions that may not allow for in-depth exploration in analytics. Finally, instructor activity is not always consistent between semesters. Focus groups were conducted to explore motivations behind these findings and future work includes developing LA that address discovered issues.

Video-enabled education is becoming increasingly popular in support of active learning in CS education. Although present work on both video based learning and flipped classrooms emphasize the necessity for students to view the materials, there is a lack of detailed, objective data on student viewing behaviors. This article aims to use fine grain student log data from TrACE, an asynchronous media platform, to understand student viewing behaviors in three sections of a flipped CS1 course taught by the same instructor. We find that students often have low compliance with video viewing expectations in one section, and that re-watching course content does not often occur. Watching course content earlier has a significant correlation to course performance, and other behaviours correlate when compliance is not enforced via course requirements. These findings highlight concerns for flipped classroom researchers and suggest methods instructors can use to improve student viewing behaviors.