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Whats Next? Helping Students to Adapt their Learning Process in University Large-Scale Lectures by Personalized Recommendations
Type
fundamental research project
Start Date
01 January 2018
End Date
30 June 2019
Status
ongoing
Keywords
Self-regulated Learning
Personalized Learning
Personalized Recommendations
Learning Analytics
Large-scale Lecture
Intervention Study
Description
Large-scale lectures at universities with an uneven lecturer-student proportion are still the common default at universities. Moreover, the number of students has steadily risen while simultaneously financial and personal resources stays nearly the same, and there are no signs indicating that this trend is about to stop. Figures from the University of St. Gallen confirm this trend with an increase of students by 14% and a decrease of budget per student by 6% from 2012 to 2016. Thus, large-scale lectures are expected to become increasingly common, which poses challenges to universities, lecturers and students.
A large-scale lecture is characterized by a mainly uninterrupted, highly unidirectional communication method from lecturer to student. Students often take the role of a passive listener with little or no intrastudent communication. This often leads to dissatisfied students, since lecturers can hardly address the individual needs of a single students – e.g., in terms of learning speed or learning style – and thus, the students receive little guidance in how to adapt their learning to ensure good learning outcomes.
Keeping the organizational and economic boundaries in mind, the effective use of IT to improve the quality of large-scale lectures seems to be the most viable approach. More specific, a promising way to improve large-scale lectures is to create personalized recommendations based on students’ data for supporting them in their learning processes. Within our research project we aim to develop and evaluate personalized recommendations that are supposed to help students to adapt their learning process, and aim to answer the following research question:
RQ: How can IT-empowered, personalized recommendations help students in improving their self-regulated learning processes as well as learning outcomes in large-scale lectures?
By answering our research question, we expect to contribute to the literature by providing insights on the effective support of individual learning processes in large-scale scenarios. Furthermore, we contribute to the body of knowledge of personalized learning by providing a deeper understanding of the design and effects of technology-enhanced feedforward systems. To achieve our goal, we plan to conduct intervention studies (one control and one treatment group) in three different lectures (two in HS 18, and one in FS 19). Since we address large-scale lectures (in our case 150+ students), sample size will not be an issue in our study, and by covering both, bachelor and master level, we can account for different learning contexts. In terms of dissemination of our results, our primary publication goal is to submit an article to Journal of Management Information Systems (JMIS, FT 50-ranked MIS journal). To receive feedback from experts in the research process, we plan to submit a Research-in-Progress Paper to the International Conference on Information Systems (ICIS, premier IS conference), and a first draft of the complete journal paper to the Academy of Management Annual Meeting (very good developmental conference that does not publish the presented papers).
A large-scale lecture is characterized by a mainly uninterrupted, highly unidirectional communication method from lecturer to student. Students often take the role of a passive listener with little or no intrastudent communication. This often leads to dissatisfied students, since lecturers can hardly address the individual needs of a single students – e.g., in terms of learning speed or learning style – and thus, the students receive little guidance in how to adapt their learning to ensure good learning outcomes.
Keeping the organizational and economic boundaries in mind, the effective use of IT to improve the quality of large-scale lectures seems to be the most viable approach. More specific, a promising way to improve large-scale lectures is to create personalized recommendations based on students’ data for supporting them in their learning processes. Within our research project we aim to develop and evaluate personalized recommendations that are supposed to help students to adapt their learning process, and aim to answer the following research question:
RQ: How can IT-empowered, personalized recommendations help students in improving their self-regulated learning processes as well as learning outcomes in large-scale lectures?
By answering our research question, we expect to contribute to the literature by providing insights on the effective support of individual learning processes in large-scale scenarios. Furthermore, we contribute to the body of knowledge of personalized learning by providing a deeper understanding of the design and effects of technology-enhanced feedforward systems. To achieve our goal, we plan to conduct intervention studies (one control and one treatment group) in three different lectures (two in HS 18, and one in FS 19). Since we address large-scale lectures (in our case 150+ students), sample size will not be an issue in our study, and by covering both, bachelor and master level, we can account for different learning contexts. In terms of dissemination of our results, our primary publication goal is to submit an article to Journal of Management Information Systems (JMIS, FT 50-ranked MIS journal). To receive feedback from experts in the research process, we plan to submit a Research-in-Progress Paper to the International Conference on Information Systems (ICIS, premier IS conference), and a first draft of the complete journal paper to the Academy of Management Annual Meeting (very good developmental conference that does not publish the presented papers).
Leader contributor(s)
Member contributor(s)
Range
Institute/School
Range (De)
Institut/School
Principal
Projektförderung des Grundlagenforschungsfonds (GFF)
Division(s)
Eprints ID
247624