Mehak Maniktala
College of Engineering
Works (6)
Updated: July 5th, 2023 14:32
2022 article
Enhancing a student productivitymodel for adaptive problem-solving assistance
Maniktala, M., Chi, M., & Barnes, T. (2022, August 3). USER MODELING AND USER-ADAPTED INTERACTION.
author keywords: Adaptive support; Student modeling; Assistance dilemma; Unproductivity; Data-driven tutoring; Propositional logic
TL;DR:
A novel data-driven approach to incorporate students’ hint usage in predicting their need for help that significantly improves the adaptive hint policy’s efficacy in predictingStudents’ HelpNeed, thereby reducing training unproductivity, reducing possible help avoidance, and increasing possible help appropriateness.
(via Semantic Scholar)
open access via link.springer.com (publisher PDF)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Source: Web Of Science
Added: August 15, 2022
2022 article
The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way
Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2022, November 28). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION.
author keywords: Deep reinforcement learning; Pedagogical policy; Explanation
TL;DR:
The results suggest that pairing simple explanations with the Batch DRL policy with explanations can be an important and effective technique for applying RL to real-life, human-centric tasks.
(via Semantic Scholar)
Sources: Web Of Science, ORCID
Added: November 29, 2022
2021 article
Tackling the Credit Assignment Problem in Reinforcement Learning-Induced Pedagogical Policies with Neural Networks
ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I, Vol. 12748, pp. 356–368.
author keywords: Pedagogical agent; Credit assignment problem; Deep reinforcement learning
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
Sources: Web Of Science, ORCID
Added: June 16, 2021
2021 article
The Impact of Looking Further Ahead: A Comparison of Two Data-driven Unsolicited Hint Types on Performance in an Intelligent Data-driven Logic Tutor
Cody, C., Maniktala, M., Lytle, N., Chi, M., & Barnes, T. (2021, May 21). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION.
author keywords: Tutoring system; Hints; Assistance; Data-driven methods
TL;DR:
The influence of data-driven hint type, Waypoints versus Next-Step hints, on student learning in a logic proof tutoring system, Deep Thought, in a discrete mathematics course is investigated to suggest that Waypoint hints could be beneficial, but more scaffolding may be needed to help students follow them.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: June 10, 2021
2020 journal article
Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, 30(4), 637–667.
author keywords: Intelligent tutoring system; Help avoidance; User experience; Unsolicited hints; Aptitude-treatment interaction; Logic proofs; Productive persistence; Clustering; problem solving
TL;DR:
Encouraging evidence is provided that hint presentation can significantly impact how students use them and using Assertions can be an effective way to address help avoidance is provided.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: November 9, 2020
2020 article
Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor (September, 10.1007/s40593-020-00213-3, 2020)
Maniktala, M., Cody, C., Barnes, T., & Chi, M. (2021, March). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 31, pp. 154–155.
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Source: Web Of Science
Added: December 21, 2020
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