Open Access

An increasing amount of data for learning analytics is available due to the spread of digital learning environments. Collecting data on student behavior and action in these environments raises the question of how personal privacy can be addressed, as students are the most important stakeholder in the process. In our study we propose a simple but powerful approach to enhance student privacy: a switch to turn the data collection on and off. We let the students decide for themselves when they want to stop and go on with the collection of their data. In this paper, we take a closer look at how our students make use of this option and when and how this influences the collected data by exemplarily investigating the data of two of our participants in detail. In our analysis we combine system log files, screen recordings and qualitative interviews.

Enforced by the pandemic, the production of videos in educational settings and their availability on learning platforms allow new forms of video-based learning. This has a strong benefit of covering multiple topics with different design styles and facilitating the learning experience. Consequently, research interest in video-based learning has increased remarkably, with many studies focusing on examining the diverse visual properties of videos and their impact on learner engagement and knowledge gain. However, manually analysing educational videos to collect metadata and to classify videos for quality assessment is a time-consuming activity. In this paper, we address the problem of automatic video feature extraction related to video production design. To this end, we introduce a novel use case for object detection models to recognize the human embodiment and the type of teaching media used in the video. The results obtained on a small-scale custom dataset show the potential of deep learning models for visual video analysis. This will allow for future use in developing an automatic video assessment system to reduce the workload for teachers and researchers.