1.
Rohloff, T., Sauer, D., Meinel, C.: Student Perception of a Learner Dashboard in MOOCs to Encourage Self-Regulated Learning. IEEE International Conference on Engineering, Technology and Education (TALE 2019). IEEE (2019).
In online learning environments like Massive Open Online Courses (MOOCs), where teachers cannot provide individual support and guidance for thousands of students, self-regulated learning (SRL) is a critical metacognitive skillset for students’ achievement. However, not every student intuitively self-regulates its learning and therefore technical solutions can help to apply SRL strategies. Learner dashboards with visualizations about the learner’s progress and behavior are able to create awareness, encourage self-reflection, and perhaps motivate students to plan and adjust their learning behavior to achieve their learning objectives. Hence, such Learning Analytics tools can support the SRL strategies self-evaluation and strategic planning. To examine this potential, a learner dashboard was integrated into the HPI MOOC platform. This work presents the design process, the concept, and an evaluation of the first dashboard iteration. The perceived usefulness and usability are investigated, and in addition, the question will be considered whether the dashboard encourages students to apply self-regulated learning. The positive results pave the way for future research and a next iteration of the learner dashboard.
2.
Rohloff, T., Sauer, D., Meinel, C.: On the Acceptance and Usefulness of Personalized Learning Objectives in MOOCs. Proceedings of the Sixth ACM Conference on Learning at Scale (L@S 2019). pp. 4:1–4:10. Association for Computing Machinery (2019).
With Massive Open Online Courses (MOOCs) the number of people having access to higher education increased rapidly. The intentions to enroll for a specific course vary significantly and depend on one's professional or personal learning needs and interests. All learners have in common that they pursue their individual learning objectives. However, predominant MOOC platforms follow a one-size-fits-all approach and primarily aim for completion with certification. Specifically, technical support for goal-oriented and self-regulated learning to date is very limited in this context although both learning strategies are proven to be key factors for students' achievement in large-scale online learning environments. In this first investigation, a concept for the application and technical integration of personalized learning objectives in a MOOC platform is realized and assessed. It is evaluated with a mixed-method approach. First, the learners' acceptance is examined with a multivariate A/B test in two courses. Second, a survey was conducted to gather further feedback about the perceived usefulness, next to the acceptance. The results show a positive perception by the learners, which paves the way for future research.
3.
Rohloff, T., Oldag, S., Renz, J., Meinel, C.: Utilizing Web Analytics in the Context of Learning Analytics for Large-Scale Online Learning. IEEE Global Engineering Education Conference (EDUCON 2019). pp. 296–305. IEEE (2019).
Today, Web Analytics (WA) is commonly used to obtain key information about users and their behavior on websites. Besides, with the rise of online learning, Learning Analytics (LA) emerged as a separate research field for collecting and analyzing learners’ interactions on online learning platforms. Although the foundation of both methods is similar, WA has not been profoundly used for LA purposes. However, especially large-scale online learning environments may benefit from WA as it is more sophisticated and well-established in comparison to LA. Therefore, this paper aims to examine to what extent WA can be utilized in this context, without compromising the learners’ data privacy. For this purpose, Google Analytics was integrated into the Massive Open Online Course platform of the Hasso Plattner Institute as a proof of concept. It was tested with two deployments of the platform: openHPI and openSAP, where thousands of learners gain academic and industry knowledge about engineering education. Besides capturing behavioral data, the platforms’ existing LA dashboards were extended by WA metrics. The evaluation of the integration showed that WA covers a large part of the relevant metrics and is particularly suitable for obtaining an overview of the platform’s global activity, but reaches its limitations when it comes to learner-specific metrics.
4.
Bothe, M., Renz, J., Rohloff, T., Meinel, C.: From MOOCs to Micro Learning Activities. 2019 IEEE Global Engineering Education Conference (EDUCON). pp. 280–288 (2019).
Mobile 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.
5.
Rohloff, T., Renz, J., Suarez, G.N., Meinel, C.: A Ubiquitous Learning Analytics Architecture for a Service-Oriented MOOC Platform. In: Calise, M., Delgado Kloos, C., Reich, J., Ruiperez-Valiente, J.A., and Wirsing, M. (eds.) Digital Education: At the MOOC Crossroads Where the Interests of Academia and Business Converge (EMOOCs 2019). pp. 162–171. Springer International Publishing (2019).
As Massive Open Online Courses (MOOCs) generate a huge amount of learning activity data through its thousands of users, great potential is provided to use this data to understand and optimize the learning experience and outcome, which is the goal of Learning Analytics. But first, the data needs to be collected, processed, analyzed and reported in order to gain actionable insights. Technical concepts and implementations are rarely accessible and therefore this work presents an architecture how Learning Analytics can be implemented in a service-oriented MOOC platform. To achieve that, a service based on extensible schema-agnostic processing pipelines is introduced for the HPI MOOC platform. The approach was evaluated regarding its scalability, extensibility, and versatility with real-world use cases. Also, data privacy was taken into account. Based on five years of running the service in production on several platform deployments, six design recommendations are presented which can be utilized as best practices for platform vendors and researchers when implementing Learning Analytics in MOOCs.
6.
Rohloff, T., Bothe, M., Meinel, C.: Visualizing Content Exploration Traces of MOOC Students. Companion Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK 2019). pp. 754–758. SoLAR (2019).
This 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.
