Challenge Balancing for a Kanji E-Tutoring System
| Autor: | Winkels, M., Roijers, D.M., van Someren, M., Yamamoto, E., Pronk, R., Odijk, E., de Jonge, M., Atzmueller, M., Duivesteijn, W. |
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| Přispěvatelé: | Artificial intelligence, Federated Collaborative Networks (IVI, FNWI) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | ARTIFICIAL INTELLIGENCE: 30th Benelux Conference, BNAIC 2018, Den Bosch November 8-9 2018, Revised Selected Papers, 331-340 STARTPAGE=331;ENDPAGE=340;TITLE=ARTIFICIAL INTELLIGENCE 30th Benelux Conference on Artificial Intelligence: BNAIC 2018 Preproceedings : November 8-9, 2018, Jheronimus Academy of Data Science (JADS), 's-Hertogenbosch, The Netherlands, 331-340 STARTPAGE=331;ENDPAGE=340;TITLE=30th Benelux Conference on Artificial Intelligence |
| Popis: | In this paper, we investigate the potential of direct challenge balancing in e-tutoring, especially in domains where there are many skills to acquire. As a case study, we create an e-tutoring system for kanji. Our system estimates the perceived challenge level using both the correctness of the answers of the students and implicit feedback, and adapts accordingly. In order to make this estimation we train a classifier on labelled data collected via the same system. We show empirically that the perceived challenge can be estimated well using implicit feedback, and that the adaptive system based on challenge balancing is preferred over a system in which the student selects a difficulty setting, indicating that direct challenge balancing is a promising research direction for e-tutoring. |
| Databáze: | OpenAIRE |
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