Analyzing Behavioral Patterns in an Introductory Programming MOOC at University Level

Alexander Steinmaurer; Christoph Schatz; Johannes Krugel; Christian Gütl

doi:10.1109/LWMOOCS53067.2022.9927886

Analyzing Behavioral Patterns in an Introductory Programming MOOC at University Level

Alexander Steinmaurer, Christoph Schatz, Johannes Krugel, Christian Gütl

Institute of Interactive Systems and Data Science (7060)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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.

Original language	English
Title of host publication	Proceedings of 2022 IEEE Learning with MOOCS, LWMOOCS 2022
Publisher	ACM/IEEE
Pages	114-119
Number of pages	6
ISBN (Electronic)	9781665424868
ISBN (Print)	978-1-6654-2487-5
DOIs	https://doi.org/10.1109/LWMOOCS53067.2022.9927886
Publication status	Published - 30 Sept 2022
Event	2022 IEEE Learning with MOOCS: LWMOOCS 2022 - Antigua Guatemala, Guatemala Duration: 29 Sept 2022 → 30 Sept 2022

Publication series

Name	Proceedings of 2022 IEEE Learning with MOOCS, LWMOOCS 2022

Conference

Conference	2022 IEEE Learning with MOOCS
Country/Territory	Guatemala
City	Antigua Guatemala
Period	29/09/22 → 30/09/22

Keywords

Computer aided instruction
Electronic learning
Distance learning
Education
Behavioral sciences
Object oriented programming
Programming profession
MOOC analysis
drop-out prediction
behavioral patterns

ASJC Scopus subject areas

Education
Computer Networks and Communications
Media Technology

Access to Document

10.1109/LWMOOCS53067.2022.9927886

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9927886

Cite this

Analyzing Behavioral Patterns in an Introductory Programming MOOC at University Level. / Steinmaurer, Alexander; Schatz, Christoph; Krugel, Johannes et al.
Proceedings of 2022 IEEE Learning with MOOCS, LWMOOCS 2022. ACM/IEEE, 2022. p. 114-119 9927886 (Proceedings of 2022 IEEE Learning with MOOCS, LWMOOCS 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Steinmaurer, A, Schatz, C, Krugel, J & Gütl, C 2022, Analyzing Behavioral Patterns in an Introductory Programming MOOC at University Level. in Proceedings of 2022 IEEE Learning with MOOCS, LWMOOCS 2022., 9927886, Proceedings of 2022 IEEE Learning with MOOCS, LWMOOCS 2022, ACM/IEEE, pp. 114-119, 2022 IEEE Learning with MOOCS, Antigua Guatemala, Guatemala, 29/09/22. https://doi.org/10.1109/LWMOOCS53067.2022.9927886

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Analyzing Behavioral Patterns in an Introductory Programming MOOC at University Level

Abstract

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Keywords

ASJC Scopus subject areas

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