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.

An experience applying reinforcement learning in a web-based adaptive and intelligent educational system

Abstract: The definition of effective pedagogical strate gies for coaching and tutoring students ac- cording to their needs is one of the most important issues in Adaptive and Intelligent Educational Systems (AIES). The use of a Reinforcement Learning (RL) model allows the system to learn au- tomatically how to teach to each student individually, only based on the acquired experience with other learners with similar characteristics, like a human tutor does. The application of this artifi- cial intelligence technique, RL, avoids to define the teaching strategies by learning action policies that define what, when and how to teach. In this paper we study the performance of the RL model in a DataBase Design (DBD) AIES, where this performance is measured on number of students required to acquire efficient teaching strategies.

When Is Assistance Helpful to Learning? Results in Combining Worked Examples and Intelligent Tutoring

Abstract: When should instruction provide or withhold assistance? In three empirical studies, we have investigated whether worked examples, a high-assistance approach, studied in conjunction with tutored problems to be solved, a mid-level assistance approach, can lead to better learning. Contrary to prior results with untutoredproblem solving, a low-assistance approach, we found that worked examples alternating with isomorphic tutored problems did not produce more learning gains than tutored problems alone. However, the examples group across the three studies learned more efficiently than the tutored-alone group. Our studies, in conjunction with past studies, suggest that mid-level assistance leads to better learning than either lower or higher level assistance. However, while our results are illuminating, more work is needed to develop predictive theory for what combinations of assistance yield the most effective and efficient learning.

CALMsystem: A Conversational Agent for Learner Modelling

Abstract: This paper describes a system which incorporates natural language technologies, database manipulation and educational theories in order to offer learners a Negotiated Learner Model, for integration into an Intelligent Tutoring System. The system presents the learner with their learner model, offering them the opportunity to compare their own beliefs regarding their capabilities with those inferred by the system. A conversational agent, or “chatbot” has been developed to allow the learner to negotiate over the representations held about them using natural language. The system aims to support the metacognitive goals of self-assessment and reflection, which are increasingly seen as key to learning and are being incorporated into UK educational policy. The paper describes the design of the system, and reports a user trial, in which the chatbot was found to support users in increasing the accuracy of their self-assessments, and in reducing the number of discrepancies between system and user beliefs in the learner model. Some lessons learned in the development have been highlighted and future research and experimentation directions are outlined.

Scaffolding Meta-Cognitive Skills for Effective Analogical Problem Solving via Tailored Example Selection

Abstract: Although worked-out examples play a key role in cognitive skill acquisition, research demonstrates that students have various levels of meta-cognitive abilities for using examples effectively. The Example Analogy (EA)-Coach is an Intelligent Tutoring System that provides adaptive support to foster meta-cognitive behaviors relevant to a specific type of example-based learning known as analogical problem solving (APS), i.e., using examples to aid problem solving. To encourage the target meta-cognitive behaviors, the EA-Coach provides multiple levels of scaffolding, including an innovative example-selection mechanism that chooses examples with the best potential to trigger learning and enable problem solving for a given student. To find such examples, the mechanism relies on our novel classification of problem/ example differences and associated hypotheses regarding their impact on the APS process. Here, we focus on describing (1) how the overall design of the EA-Coach in general, and the example-selection mechanism in particular, evolved from cognitive science research on APS; (2) our pilot evaluations and the controlled laboratory study we conducted to validate the tutor's pedagogical utility. Our results show that the EA-Coach fosters meta-cognitive behaviors needed for effective learning during APS, while helping students achieve problem-solving success.

MENTOR: A Physiologically Controlled Tutoring System

Abstract: In this paper we present a tutoring system that automatically sequences the learning content according to the learners’ mental states. The system draws on techniques from Brain Computer Interface and educational psychology to automatically adapt to changes in the learners’ mental states such as attention and workload using electroencephalogram (EEG) signals. The objective of this system is to maintain the learner in a positive mental state throughout the tutoring session by selecting the next pedagogical activity that fits the best to his current state. An experimental evaluation of our approach involving two groups of learners showed that the group who interacted with the mental state-based adaptive version of the system obtained higher learning outcomes and had a better learning experience than the group who interacted with a non-adaptive version.