Publication Type : Journal Article
Thematic Areas : Learning-Technologies
Publisher : Education and Information Technologies.
Source : Education and Information Technologies (2020)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082873182&doi=10.1007%2fs10639-020-10144-0&partnerID=40&md5=d70fcc5bd58747aeace551d9d674aae0
Keywords : Intelligent tutoring system, Longitudinal analysis, Reading difficulties, Rural Education, Student performance
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Center : Technologies & Education (AmritaCREATE), Amrita Center For Research in Analytics
Department : Computer Science
Year : 2020
Abstract : In many rural Indian schools, English is a second language for teachers and students. Intelligent tutoring systems have good potential because they enable students to learn at their own pace, in an exploratory manner. This paper describes a 3-year longitudinal study of 2123 Indian students who used the intelligent tutoring system, AmritaITS. The aim of the study was to use the students’ interaction logs with AmritaITS to: (1) predict student performance, in English and Mathematics subjects, via summative and formative assessments, (2) predict students who may be at risk of failing the final examination and (3) screen students who may have reading difficulties. The prediction models for summative assessments were significantly improved by formative assessments scores, along with AmritaITS logs. The receiver operating characteristic (ROC) curve showed that students at risk of failing a class could be identified early, with high sensitivity and specificity. The models also provide recommendations for the amount of time required for students to use the system, and reach the appropriate grade level. Finally, the models demonstrated promise in identifying students who might be at risk of suffering from reading difficulties.
Cite this Research Publication : M. Haridas, Gutjahr, G., Raghu Raman, Ramaraju, R., and Prof. Prema Nedungadi, “Predicting school performance and early risk of failure from an intelligent tutoring system”, Education and Information Technologies, 2020.