Abstract
Performance prediction models have been proposed countless times due to the benefits that they can provide to educational stakeholders. While many factors have been taken into account when predicting student performance, teachers’ assessment or observation reports have not been commonly used. A teacher’s assessment is a fundamental part of the educational process and has a direct impact on students’ success. In this study, we analyze the topics, and psychological features in teachers’ daily written reports and apply them to the student performance prediction model. Experimental results show the capability of this approach in contributing to the accuracy of performance prediction models.
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Acknowledgements
This work was supported by JST, the establishment of university fellowships towards the creation of science technology innovation, Grant Number JPMJFS2132, in part by e-sia Corporation and by Grant-in-Aid for Scientific Research proposal numbers (JP21H00907, JP20H01728, JP20H04300, JP19KK0257).
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Fateen, M., Mine, T. (2022). Extraction of Useful Observational Features from Teacher Reports for Student Performance Prediction. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_58
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DOI: https://doi.org/10.1007/978-3-031-11644-5_58
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