Intelligent tutoring system

About: Intelligent tutoring system is a research topic. Over the lifetime, 3472 publications have been published within this topic receiving 58217 citations. The topic is also known as: ITS.

Providing Dynamic Instructional Adaptation in Programming Learning

Abstract: This paper describes an approach to create an Intelligent Tutoring System that provides dynamic personalization and learning activities sequencing adaptation by combining eLearning standards and Artificial Intelligent techniques The work takes advantage of the functionalities provided by an open source Learning Management System, dotLRN, which supports eLearning standards such as IMS-LD, and generates personalized sequences of learning activities Moreover, the user model draws on standards such as IMS-LIP and IMS-AccLIP and the personalized learning path provided to the user is enriched with feedback coming from various Agents In turn, the agents apply Fuzzy Logic to evaluate the students' assignments and to update the user model with their preferences by means of machine learning techniques

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

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.

Implementing an Adaptive Intelligent Tutoring System as an Instructional Supplement.

Abstract: Evidence is emerging that technology-based curricula and adaptive learning systems can personalize students' learning experiences and facilitate development of mathematical skills. Yet, evidence of efficacy in rigorous studies for these blended instructional models is mixed. These studies highlight challenges implementing the systems in classrooms, which may contribute to a lack of consistently positive effects on student learning. This article extends the literature by closely examining implementation models and dosage levels for a supplemental software, two gaps in existing research. It also investigates adherence to the core components of the software, and extent to which the supplement enabled personalized instruction. The study was conducted in 40 algebra I classes in an urban school district. Sixty-two percent of classes implemented models that integrated instructional modalities. There was mixed adherence to core components of the software in classes that used it. In the vast majority of classes (94%), software did not enable personalized instruction. Software and the existing curricula were largely independent and did not inform each other. Only one class implemented an integrated instructional model, adhered to the core design components of the software, and demonstrated high levels of personalized instruction. Findings identify implementation barriers and offer suggestions for future implementations and studies of technology-enabled personalization.