TY - GEN
T1 - Extraction of Useful Observational Features from Teacher Reports for Student Performance Prediction
AU - Fateen, Menna
AU - Mine, Tsunenori
N1 - Funding Information:
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).
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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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U2 - 10.1007/978-3-031-11644-5_58
DO - 10.1007/978-3-031-11644-5_58
M3 - Conference contribution
AN - SCOPUS:85135887378
SN - 9783031116438
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 620
EP - 625
BT - Artificial Intelligence in Education - 23rd International Conference, AIED 2022, Proceedings
A2 - Rodrigo, Maria Mercedes
A2 - Matsuda, Noburu
A2 - Cristea, Alexandra I.
A2 - Dimitrova, Vania
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Artificial Intelligence in Education, AIED 2022
Y2 - 27 July 2022 through 31 July 2022
ER -