Predicting school performance and early risk of failure from an intelligent tutoring system

• Published in: Education and information technologies Vol. 25; no. 5; pp. 3995 - 4013
• Main Authors: Haridas, Mithun, Gutjahr, Georg, Raman, Raghu, Ramaraju, Rudraraju, Nedungadi, Prema
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2020; Springer; Springer Nature B.V
• Subjects: Academic Achievement; Academic failure; Amma; At Risk Students; Computer Appl. in Social and Behavioral Sciences; Computer Science; Computers and Education; Education; Educational Technology; English (Second Language); Foreign Countries; Formative Evaluation; Information Systems Applications (incl.Internet); Intelligent systems; Intelligent Tutoring Systems; Longitudinal Studies; Mathematics Achievement; Predictor Variables; Reading Difficulties; Rural Schools; Scores; Second Language Learning; Summative Evaluation; Tutoring; Tutors and tutoring; User Interfaces and Human Computer Interaction; India; United Kingdom; Rural education; Reading difficulties; Longitudinal analysis; Student performance; Intelligent tutoring system
• DOI: 10.1007/s10639-020-10144-0
• ISSN: 1360-2357

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.