Ruf, Alessia
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Impact of learners’ video interactions on learning success and cognitive load
Enhanced video-based learning environments provide new tools (e.g., hyperlinks) – along with the well-known basic video control tools (e.g., play, pause, rewind) – that afford learners‘ enhanced interaction with videos. With these tools, learners can actively transform existing videos into their own hypervideo structures by adding hyperlinks and own materials. Unlike research on basic control tools that has revealed positive impacts on learning, research on enhanced tools is still rare and conflicting. It is thus open, whether the tools support generative interested learning or put too much extrinsic cognitive load onto learners. In the present study, we investigated the effects of video annotation and hyperlinking tools on learning success and cognitive load by analyzing tool-related interaction behavior data of 141 university students. Results indicated that the frequent use of enhanced video tools positively predicted learning success and a decrease in cognitive load. Implications of these results are discussed.
What if the computer crashes? Findings from an exploratory factor analysis on stressors in online exams
The pandemic has forced higher education to shift from onsite to online environments. This novel situation may increase students’ exam stress and induce new stressors. In the present study, we identified stressors in online exams by conducting an exploratory factor analysis of a novel questionnaire. The analysis revealed five factors that categorize students’ experience of stress. Preliminary descriptive results suggest that possible system failures and social pressures cause the highest stress for students.
Logible: Detecting, Analyzing and Visualizing Behavior Sequences for Investigating Learning Behavior
Logible is a highly sensitive web-based interactive tool that automatically detects behavior sequences from raw log files. It further analyzes and visualizes sequences and allows for comparisons of behavior data between different experimental conditions without the use of other software. Logible was developed based on an iterative, exploratory, and rule-based method devised to find meaningful sequences from 92 data sets of students who learned individually or collaboratively with an enhanced video-based environment. The tool is customizable and thus enables researchers to investigate learning behavior with various kinds of sequentially logged interaction data (from web-based video environments, online learning platforms etc.).
Video Data Collection and Video Analyses in CSCL Research
The purpose of this chapter is to examine significant advances in the collection and analysis of video data in Computer Supported Collaborative Learning (CSCL) research. We demonstrate how video-based studies create robust and dynamic research processes. The chapter starts with an overview of how video analysis developed within CSCL by way of its pioneering roots. Linked throughout the chapter are the theoretical, methodological, and technological advances that keep advancing CSCL research. Specific empirical and experimental research examples will illustrate current and future advances in data collection, transformation, coding, and analysis. Research benefits and challenges that include the current state of understanding from observations of single, multiple, or 360° camera recordings will also be featured. In addition, eye-tracking and virtual reality environments for collecting and analyzing video data are discussed as they become new foci for future CSCL research.
Introducing a new approach for investigating learning behavior
The potential of learners’ video interactions to understand learning behavior has been recognized in previous research. However, little research has yet been conducted on enhanced video-based environments using behavior sequence analyses. Hence, we developed Logible, a sensitive, web-based tool to detect and analyze meaningful behavior sequences of learners interacting with such environments. The tool is based on an iterative method. With Logible we were able to visualize learning behavior and emphasize differences in experimental conditions.