Mehak Maniktala

College of Engineering

Works (6)

Updated: July 5th, 2023 14:32

2022 article

Enhancing a student productivity model for adaptive problem-solving assistance

Maniktala, M., Chi, M., & Barnes, T. (2022, August 3). User Modeling and User-Adapted Interaction.

By: M. Maniktala n, M. Chi n & T. Barnes n

author keywords: Adaptive support; Student modeling; Assistance dilemma; Unproductivity; Data-driven tutoring; Propositional logic
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Online Learning and Analytics; Innovative Teaching and Learning Methods
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)
UN Sustainable Development Goals Color Wheel
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.

By: M. Ausin n , M. Maniktala n, T. Barnes n & M. Chi n

author keywords: Deep reinforcement learning; Pedagogical policy; Explanation
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Evolutionary Algorithms and Applications; Online Learning and Analytics
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

Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2021, January 1). Lecture Notes in Computer Science.

By: M. Ausin n , M. Maniktala n, T. Barnes n & M. Chi n

author keywords: Pedagogical agent; Credit assignment problem; Deep reinforcement learning
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Reinforcement Learning in Robotics; Online Learning and Analytics
UN Sustainable Development Goals Color Wheel
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.

By: C. Cody n, M. Maniktala n, N. Lytle n, M. Chi n & T. Barnes n

author keywords: Tutoring system; Hints; Assistance; Data-driven methods
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Visual and Cognitive Learning Processes; Online Learning and Analytics
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 Goals Color Wheel
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science; OpenAlex)
Source: Web Of Science
Added: June 10, 2021

2020 article

Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor

Maniktala, M., Cody, C., Barnes, T., & Chi, M. (2020, October 20). International Journal of Artificial Intelligence in Education.

By: M. Maniktala n, C. Cody n, T. Barnes n & M. Chi n

author keywords: Intelligent tutoring system; Help avoidance; User experience; Unsolicited hints; Aptitude-treatment interaction; Logic proofs; Productive persistence; Clustering; problem solving
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Innovative Teaching and Learning Methods; Online Learning and Analytics
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 Goals Color Wheel
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science)
Source: Web Of Science
Added: November 9, 2020

2020 article

Correction to: Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor

Maniktala, M., Cody, C., Barnes, T., & Chi, M. (2020, December 2). International Journal of Artificial Intelligence in Education.

By: M. Maniktala n, C. Cody n, T. Barnes n & M. Chi n

topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science)
Source: Web Of Science
Added: December 21, 2020

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