Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System

AutoTutor is an intelligent tutoring system that helps students learn science, technology, and other technical subject matters by holding conversations with the student in natural language. AutoTutor’s dialogues are organized around difficult questions and problems that require reasoning and explanations in the answers. The major components of AutoTutor include an animated conversational agent, dialogue management, speech act classification, a curriculum script, semantic evaluation of student contributions, and electronic documents (e.g., textbook and glossary). This chapter describes the computational components of AutoTutor, the similarity of these components to human tutors, and some challenges in handling smooth dialogue. We describe some ways that AutoTutor has been evaluated with respect to learning gains, conversation quality, and learner impressions. AutoTutor is sufficiently modular that the content and dialogue mechanisms can be modified with authoring tools. AutoTutor has spawned a number of other agent-based learning environments, such as AutoTutor-lite, Operation Aries!, and Guru.

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Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning

The Scientific World JOURNAL ◽

10.1155/2015/352895 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Hsuan-Ta Lin ◽

Po-Ming Lee ◽

Tzu-Chien Hsiao

Keyword(s):

Online Learning ◽

Reinforcement Learning ◽

Real World ◽

Intelligent Tutoring ◽

Intelligent Tutoring System ◽

Rule Discovery ◽

Learning Gains ◽

Tutoring System

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students’ learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.

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Analyzing Learning Gains in a Competition Intelligent Tutoring System

Intelligent Tutoring Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-319-07221-0_98 ◽

2014 ◽

pp. 662-663

Author(s):

Pedro J. Muñoz-Merino ◽

Carlos Delgado Kloos ◽

Manuel Fernández Molina

Keyword(s):

Intelligent Tutoring ◽

Intelligent Tutoring System ◽

Learning Gains ◽

Tutoring System

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Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them

Education Sciences ◽

10.3390/educsci11110719 ◽

2021 ◽

Vol 11 (11) ◽

pp. 719

Author(s):

Oleg Sychev ◽

Nikita Penskoy ◽

Anton Anikin ◽

Mikhail Denisov ◽

Artem Prokudin

Keyword(s):

Learning Strategies ◽

Cognitive Skills ◽

Intelligent Tutoring ◽

Intelligent Tutoring System ◽

Control Flow ◽

Subject Domain ◽

Learning Gains ◽

Tutoring System ◽

Tutoring Systems ◽

Subject Domains

Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system.

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