TY  - JOUR
A1  - Langenhagen, Julian
T1  - Data-driven goal setting: Searching optimal badges in the decision forest
T2  - Telematics and Informatics Reports
N2  - Goal setting is vital in learning sciences, but the scientific evaluation of optimal learning goals is underexplored. This study proposes a novel methodological approach to determine optimal learning goals. The data in this study comes from a gamified learning app implemented in an undergraduate accounting course at a large German university. With a combination of decision trees and regression analyses, the goals connected to the badges implemented in the app are evaluated. The results show that the initial badge set already motivated learning strategies that led to better grades on the exam. However, the results indicate that the levels of the goals could be improved, and additional badges could be implemented. In addition to new goal levels, new goal types are also discussed. The findings show that learning goals initially determined by the instructors need to be evaluated to offer an optimal motivational effect. The new methodological approach used in this study can be easily transferred to other learning data sets to provide further insights.
KW  - Goal setting
KW  - Gamification
KW  - Badges
KW  - Learning analytics
KW  - Educational data mining
KW  - Decision trees
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/79036
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-790362
SN  - 2772-5030
VL  - 11
IS  - 100072
PB  - Elsevier
CY  - Amsterdam
ER  -