Abstract
In computer science, dropout rates are high due to heterogeneous prior knowledge and skills. In this paper, we consider an adaptive remote laboratory as one approach to deal with this challenge at the course level. A remotely controlled laboratory gives each learner the possibility to learn at their own pace and in their individual learning space. As an adaptive learning environment, it is tailored to the needs of the learner. Investigating the underlying causes for dropouts is a precondition for providing suitable forms of adaptivity. Therefore, we analyzed cognitive and motivational differences between students who dropout and students who persist. Additionally we analyzed user behavior, i.e., a pattern of user-system interaction which might goes hand in hand with dropout—the probability of error streaks. Our results indicate that students in the dropout group had a significant higher probability to get stuck than their fellow students. On the one hand, students in the dropout group reported significant higher extraneous cognitive load, indicating difficulties to understand the task and to apply an adequate procedure while solving the task. On the other hand, there were problems in the process of programming. Students in the dropout group had a significant higher probability of falling into error streaks. In the article, we describe practical implications for both types of getting stuck. From our findings, we especially consider the monitoring and analyzing of error streaks as a promising way for the design of adaptive instructional interventions in courses where the students have to program code.
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This work was partially supported by the German Federal Ministry of Education and Research (BMBF, Funding number: 16DHL1033).
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Hawlitschek, A., Krenz, T., Zug, S. (2019). When Students Get Stuck: Adaptive Remote Labs as a Way to Support Students in Practical Engineering Education. In: Ifenthaler, D., Mah, DK., Yau, J.YK. (eds) Utilizing Learning Analytics to Support Study Success. Springer, Cham. https://doi.org/10.1007/978-3-319-64792-0_5
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