AbstractLearners in Massive Open Online Courses (MOOCs) are reiterating over the provided course material - especially self-tests - to consolidate their knowledge. This is a manual and often cumbersome process as MOOC platforms do not provide personalized revision opportunities. This paper introduces the design and concept of a flashcard-like recap tool based on spaced repetition learning techniques. The recap material is derived from existing self-test questions. The usage rates of the recap tool were observed in three courses and peaked before graded assignments, primarily before the final exam. When choosing the question quantity, learners preferred either the smallest option or wanted to revise all of the available questions, whereas the average number of questions per recap session increases over time. Recap tool users who completed a recap session showed smaller error rates than those who stopped a recap session abruptly, while learners who skipped questions performed worst. Course participants who used the recap tool throughout the course achieved on average more of the available points. Statistically highly significant differences were detected for all observed courses. An additional survey (N=79) gathered qualitative feedback and impressions from the learning community.
From MOOCs to Micro Learning Activities.Bothe, Max; Renz, Jan; Rohloff, Tobias; Meinel, Christoph (2019). 280-288.
AbstractMobile devices are omnipresent in our daily lives. They are utilized for a variety of tasks and used multiple times for short periods throughout the day. MOOC providers optimized their platforms for these devices in order to support ubiquitous learning. While a combination of desktop and mobile learning yields improved course performances, standalone learning on mobile devices does not perform in the same manner. One indicator for this is the mismatch between the average usage pattern of mobile devices and the time to consume one content item in a MOOC. Micro learning builds on bite-sized learning material and focusses on short-term learning sessions. This work examines the potential of micro learning activities in the context of MOOCs. Therefore, a framework for video-based micro learning is presented, which features a personalized curriculum. Videos are suggested to the user in a non-linear order that is determined by content dependencies, users’ preferences and watched videos, as well as explicit and implicit user feedback. A mobile application was implemented to test the approach with restructured MOOC content resulting in 58 connected short videos about engineering education – e.g. web technologies and programming languages. The usage data indicates initial curiosity by the users. To improve retention rates, more user motivation will be required for future studies. A survey gathered additional qualitative feedback. While the content suggestions were seen as a vital feature for such an approach, the results showed good interest and acceptance rates to create a better learning experience for MOOCs on mobile devices.
Applied Mobile-Assisted Seamless Learning Techniques in MOOCs.Bothe, Max; Meinel, Christoph M. Calise, Delgado Kloos, C., Reich, J., Ruiperez-Valiente, J. A., Wirsing, M. (reds.) (2019). 21--30.
AbstractAs Massive Open Online Courses (MOOCs) are nowadays used in an increasingly ubiquitous manner, the learning process gets disrupted every time learners change context. Mobile-Assisted Seamless Learning (MSL) techniques have been identified to reduce unwanted overhead for learners and streamline their learning process. However, technical implementations vary across the industry. This paper examines existing MSL research and applied techniques in the context of MOOCs. Therefore, we discussed related MSL research topics. Afterward, eleven characteristic MSL features were selected and compared their implementations across five major MOOC platforms. While web applications provide a bigger feature set, mobile clients offer advanced offline capabilities. Based on the findings, a concept outlines how MSL features can enhance the learning experience on MOOC platforms while considering the technical feasibility.
Visualizing Content Exploration Traces of MOOC Students.Rohloff, Tobias; Bothe, Max; Meinel, Christoph (2019). 754--758.
AbstractThis workshop paper introduces a novel approach to visualize content exploration traces of students who navigate through the learning material of Massive Open Online Courses (MOOCs). This can help teachers to identify trends and anomalies in their provided learning material in order to improve the learning experience. The difficulty lies in the complexity of data: MOOCs are structured into multiple sections consisting of different learning items and students can navigate freely between them. Therefore, it is challenging to find a meaningful and comprehensible visualization that provides a complete overview for teachers. We utilized a Sankey diagram which shows the students' transitions between course sections by grouping them into different buckets, based on the percentage of visited items in the corresponding section. Three preceding data processing steps are explained as well as the data visualization with an example course. This is followed by pedagogical considerations how MOOC teachers can utilize and interpret the visualization, to gain meaningful insights and execute informed actions. At last, an evaluation concept is outlined.
Towards a Better Understanding of Mobile Learning in MOOCs.Rohloff, Tobias; Bothe, Max; Renz, Jan; Meinel, Christoph (2018).
AbstractThe pervasive presence of mobile devices and growing trends like ubiquitous learning make new demands on Massive Open Online Courses (MOOCs). Users learn increasingly on the go and with multiple devices, instead of being tied to a fixed workstation. However, there is a lack of research how the usage of mobile devices influences the learning behavior and outcome in MOOCs. Thus, this paper presents a first quantitative study to examine this question. To enable a statistical analysis, a proof-of-concept implementation outline is presented, which enhances the Learning Analytics capabilities of the openHPI MOOC platform with contextual data to process various learning behavior metrics. Based on an analysis of four courses, it was found that users who additionally learnt with mobile applications showed a higher engagement with the learning material and completed the course more often. Nevertheless, the reasoning must be addressed with qualitative analyses in future, to better support their learning process and success on mobile and stationary devices.
Supporting Multi-Device E-Learning Patterns with Second Screen Mobile Applications.Rohloff, Tobias; Renz, Jan; Bothe, Max; Meinel, Christoph (2017). 25:1--25:8.
AbstractMany providers of Massive Open Online Course (MOOC) platforms released mobile applications in the recent years to enable learning offline and on the go, for a more ubiquitous learning experience. However, mainly the MOOC content was optimized for small screens, but mobile devices provide the opportunity to enrich the MOOC experience even further by enabling new forms of learning. Based on a previous learning patterns evaluation and a user survey, this paper presents a second screen prototype for the MOOC platform of the Hasso Plattner Institute, whereby the mobile application can be used as a learning companion while using the web platform on a computer. Four different actions were implemented which can be done next to watching a video lecture. The evaluation showed that the prototype was helpful and made learning more efficient, as reported by users, and also ideas for further improvements were proposed.
Recognizing Compound Events in Spatio-Temporal Football Data.Richly, Keven; Bothe, Max; Rohloff, Tobias; Schwarz, Christian (2016). 27-35.
AbstractIn the world of football, performance analytics about a player’s skill level and the overall tactics of a match are supportive for the success of a team. These analytics are based on positional data on the one hand and events about the game on the other hand. The positional data of the ball and players is tracked automatically by cameras or via sensors. However, the events are still captured manually by human, which is time-consuming and error-prone. Therefore, this paper introduces an approach to detect events based on the positional data of football matches. We trained and aggregated the machine learning algorithms Support Vector Machine, K-Nearest Neighbours and Random Forest, based on features, which were calculated on base of the positional data. We evaluated the quality of our approach by comparing the recall and precision of the results. This allows an assessment of how event detection in football matches can be improved by automating this process based on spatio-temporal data. We discovered, that it is possible to detect football events from positional data. Nevertheless, the choice of a specific algorithm has a strong influence on the quality of the predicted results.