Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2022 IEEE Learning With MOOCS (LWMOOCS) |
| Pages | 114-119 |
| Number of pages | 6 |
| ISBN (electronic) | 9781665424868 |
| Publication status | Published - 2022 |
| Event | IEEE Learning With MOOCS 2022 - Antigua Guatemala, Guatemala Duration: 29 Sept 2022 → 30 Sept 2022 |
Abstract
Massive open online courses (MOOCs) are an indispensable component in university education today. In large introductory courses especially, MOOCs can promote the efficiency of online teaching tremendously, since a large and heterogeneous group of students can be prepared for further courses and learn self-paced and self-directed. However, MOOCs are also characterized by high dropout rates and with a small group of people only completing the course. In this paper, we analyzed the Learning Object Oriented Programming MOOC from Technical University of Munich, an edX course that is dedicated to first-year students in different fields. The course run of 2019 with 2,489 enrolled users is analyzed for this purpose. The dropouts (89 %) were analyzed to better understand future course design. Interaction in the MOOC was considered in this context as a means of detecting behavioral patterns and predicting early dropouts. We found that the interaction with certain MOOC elements such as videos or the problem tool had a major impact on course success. These results may be useful for earlier dropout predictions and the design of future courses to provide an engaging environment with fewer students quitting the course.
Keywords
- MOOC analysis, behavioral patterns, drop-out prediction
ASJC Scopus subject areas
- Engineering(all)
- Media Technology
- Social Sciences(all)
- Education
- Computer Science(all)
- Computer Networks and Communications
Cite this
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Proceedings of 2022 IEEE Learning With MOOCS (LWMOOCS). 2022. p. 114-119.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Analyzing Behavioral Patterns in an Introductory Programming MOOC at University Level
AU - Steinmaurer, Alexander
AU - Schatz, Christoph
AU - Krugel, Johannes
AU - Gütl, Christian
PY - 2022
Y1 - 2022
N2 - Massive open online courses (MOOCs) are an indispensable component in university education today. In large introductory courses especially, MOOCs can promote the efficiency of online teaching tremendously, since a large and heterogeneous group of students can be prepared for further courses and learn self-paced and self-directed. However, MOOCs are also characterized by high dropout rates and with a small group of people only completing the course. In this paper, we analyzed the Learning Object Oriented Programming MOOC from Technical University of Munich, an edX course that is dedicated to first-year students in different fields. The course run of 2019 with 2,489 enrolled users is analyzed for this purpose. The dropouts (89 %) were analyzed to better understand future course design. Interaction in the MOOC was considered in this context as a means of detecting behavioral patterns and predicting early dropouts. We found that the interaction with certain MOOC elements such as videos or the problem tool had a major impact on course success. These results may be useful for earlier dropout predictions and the design of future courses to provide an engaging environment with fewer students quitting the course.
AB - Massive open online courses (MOOCs) are an indispensable component in university education today. In large introductory courses especially, MOOCs can promote the efficiency of online teaching tremendously, since a large and heterogeneous group of students can be prepared for further courses and learn self-paced and self-directed. However, MOOCs are also characterized by high dropout rates and with a small group of people only completing the course. In this paper, we analyzed the Learning Object Oriented Programming MOOC from Technical University of Munich, an edX course that is dedicated to first-year students in different fields. The course run of 2019 with 2,489 enrolled users is analyzed for this purpose. The dropouts (89 %) were analyzed to better understand future course design. Interaction in the MOOC was considered in this context as a means of detecting behavioral patterns and predicting early dropouts. We found that the interaction with certain MOOC elements such as videos or the problem tool had a major impact on course success. These results may be useful for earlier dropout predictions and the design of future courses to provide an engaging environment with fewer students quitting the course.
KW - MOOC analysis
KW - behavioral patterns
KW - drop-out prediction
UR - http://www.scopus.com/inward/record.url?scp=85142620203&partnerID=8YFLogxK
U2 - 10.1109/lwmoocs53067.2022.9927886
DO - 10.1109/lwmoocs53067.2022.9927886
M3 - Conference contribution
SN - 978-1-6654-2486-8
SP - 114
EP - 119
BT - Proceedings of 2022 IEEE Learning With MOOCS (LWMOOCS)
T2 - IEEE Learning With MOOCS 2022
Y2 - 29 September 2022 through 30 September 2022
ER -