Abstract
The modern e-learning environment generates new types of students’ learning records, including their operation records in the learning management system. Recently, wearable devices and a variety of sensors have become common in our daily life. By using these devices, we can access students’ information such as heart rates and facial features. Previous studies [1, 2] have used the bioinformatics data mentioned above to analyze students’ learning effectiveness. However, these approaches only utilize partial information and the diversity of data has not been put into consideration. This paper tries to better address this inefficiency by proposing an Analytic Hierarchy Process (AHP)-based model integrated with professional expertise in education. With this model, lecturers can customize the selection and importance of the criteria according to the used teaching strategy. Then, AHP4Edu can analyzes students’ learning effectiveness scores from the sub-scores of the sub-criteria specified by an expert or a lecturer. We present simulations on assessing students’ learning effectiveness for distance learning. We also demonstrate how AHP4Edu integrates heterogeneous data and provides a reliable learning effectiveness assessment for the lecturer.
This work was supported in part by Ministry of Science and Technology of Taiwan (Grant No. 107-2511-H-009-005-MY3).
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Huang, YL., Wu, YH. (2021). AHP4Edu: An AHP-Based Assessment Model for Learning Effectiveness of Education. In: Huang, YM., Lai, CF., Rocha, T. (eds) Innovative Technologies and Learning. ICITL 2021. Lecture Notes in Computer Science(), vol 13117. Springer, Cham. https://doi.org/10.1007/978-3-030-91540-7_22
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DOI: https://doi.org/10.1007/978-3-030-91540-7_22
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