Predicting school performance and early risk of failure from an intelligent tutoring system
- PDF / 1,735,662 Bytes
- 19 Pages / 439.37 x 666.142 pts Page_size
- 27 Downloads / 155 Views
Predicting school performance and early risk of failure from an intelligent tutoring system Mithun Haridas 1 & Georg Gutjahr 1 & Raghu Raman 2 & Rudraraju Ramaraju 3 & Prema Nedungadi 1 Received: 29 March 2019 / Accepted: 24 February 2020/ # Springer Science+Business Media, LLC, part of Springer Nature 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. Keywords Intelligent tutoring system . Student performance . Reading difficulties .
Longitudinal analysis . Rural education
* Mithun Haridas [email protected]
1
Center for Research in Analytics and Technologies for Education (CREATE), Amrita Vishwa Vidyapeetham, Amritapuri, India
2
School of Business, Amrita Vishwa Vidyapeetham, Coimbatore, India
3
UAB School of Medicine, University of Alabama, Birmingham, AL, USA
Education and Information Technologies
1 Introduction The academic performance of students is generally divergent, constrained by the individual learning capabilities and environment. Intelligent tutoring systems (ITS) are designed to address this problem, and provide tailored instructional material based on a student’s cognitive skills, knowledge base, and performance (Conati 2009; Kulik and Fletcher 2016). An ITS simulates a one-on-one interaction with a professional educator and provides instructions and feedback to students without the intervention of human instructors (VanLehn 2006). The meta-analysis of evaluative studies showed that ITS outperformed other comparable modes of instruction (Kulik and Fletcher 2016; Ma et al. 2014). These systems are now being augmented through the incorporation of the advancements in the domain of artificial Intelligence. There is a high student-teacher ratio in many Indian schools; thus, some teachers instruct a large group of students with diverse learning skills. Consequently, teachers may not have the time to personally co
Data Loading...
