Affecting off-task behaviour
| Autor: | Nikol Rummel, Manolis Mavrikis, Beate Grawemeyer, Wayne Holmes, Michael Wiedmann, Sergio Gutierrez-Santos |
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| Rok vydání: | 2016 |
| Předmět: |
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Point (typography) business.industry Computer science Learning environment 05 social sciences 050301 education Bayesian network Sample (statistics) 02 engineering and technology Machine learning computer.software_genre Affect (psychology) Task (project management) 020204 information systems Component (UML) 0202 electrical engineering electronic engineering information engineering Artificial intelligence business 0503 education computer Cognitive psychology |
| Zdroj: | LAK |
| DOI: | 10.1145/2883851.2883936 |
| Popis: | This paper describes the development and evaluation of an affect-aware intelligent support component that is part of a learning environment known as iTalk2Learn. The intelligent support component is able to tailor feedback according to a student's affective state, which is deduced both from speech and interaction. The affect prediction is used to determine which type of feedback is provided and how that feedback is presented (interruptive or non-interruptive). The system includes two Bayesian networks that were trained with data gathered in a series of ecologically-valid Wizard-of-Oz studies, where the effect of the type of feedback and the presentation of feedback on students' affective states was investigated. This paper reports results from an experiment that compared a version that provided affect-aware feedback (affect condition) with one that provided feedback based on performance only (non-affect condition). Results show that students who were in the affect condition were less bored and less off-task, with the latter being statically significant. Importantly, students in both conditions made learning gains that were statistically significant, while students in the affect condition had higher learning gains than those in the non-affect condition, although this result was not statistically significant in this study's sample. Taken all together, the results point to the potential and positive impact of affect-aware intelligent support. |
| Databáze: | OpenAIRE |
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