Willkommen auf den Seiten des Lehrstuhls "Internet-Technologien und -Systeme" von Prof. Dr. Christoph Meinel und seinem Team. Sie finden hier Informationen über unser Lehrangebot und unsere Forschungsaktivitäten in den Bereichen Security und Knowledge Engineering sowie Innovationsforschung.
Der Lehrstuhl von Prof. Dr. Christoph Meinel bietet Lehrveranstaltungen in folgenden Bereichen an: Internet und Web-Technologien, (Diskrete) Mathematik und Logik, IT-Security und Internet Security, Komplexitätstheorie und Informationssicherheit sowie Design Thinking.
Im Fokus der Forschungen am Lehrstuhl von Prof. Dr. Christoph Meinel zum Thema Knowledge Management und Engineering steht die Frage, wie aus den im Internet und anderen Quellen massenhaft zur Verfügung stehenden digitalen Daten, "Big Data", neues Wissen generiert werden kann.
In Security and Trust Engineering our research and development work is mainly focused on: Network & Internet Security, Cloud and SOA-Security (SOA - Service Oriented Architectures) and Security Awareness.
Aus den IT-technologischen Forschungen des Teams um Prof. Dr. Christoph Meinel im Bereich der Internet-Technologien und -System und der Innovationsforschung werden auch neue Ideen geboren für innovative Anwendungen und Systeme für das Internet der Zukunft.
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 in mLearn 2017 (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.
