Works (9)
Updated: November 8th, 2024 05:01
2023 article
Exploring the Effect of Autoencoder Based Feature Learning for a Deep Reinforcement Learning Policy for Providing Proactive Help
ARTIFICIAL INTELLIGENCE IN EDUCATION. POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS, DOCTORAL CONSORTIUM AND BLUE SKY, AIED 2023, Vol. 1831, pp. 278–283.
author keywords: Intelligent Tutoring Systems; Deep Reinforcement Learning; Autoencoder
Source: Web Of Science
Added: November 4, 2024
2023 article
Impact of Learning a Subgoal-Directed Problem-Solving Strategy Within an Intelligent Logic Tutor
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2023, Vol. 13916, pp. 389–400.
author keywords: Means-ends Analysis; Subgoal; Problem Solving; Intelligent Tutor
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
Source: Web Of Science
Added: November 4, 2024
2023 journal article
Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning Towards Improved Problem Solving
International Journal of Artificial Intelligence in Education, 8.
author keywords: Subgoal; Logic tutor; Intelligent tutor systems; Backward strategy; Forward strategy
open access via link.springer.com (publisher PDF)
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries, Crossref
Added: September 5, 2023
2018 article
Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach
Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach. PROCEEDINGS OF THE 26TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (UMAP'18), pp. 43–51.
author keywords: Constrained Reinforcement Learning; POMDP; Intelligent Tutoring System
TL;DR:
This work constructs a general data-driven framework called Constrained Action-based Partially Observable Markov Decision Process (CAPOMDP) to induce effective pedagogical policies and induces two types of policies: CAPOMDPLG using learning gain as reward with the goal of improving students' learning performance, and CAPomDPTime using time as reward for reducing students' time on task.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions
(OpenAlex)
Source: Web Of Science
Added: April 2, 2019
2016 conference paper
An analysis of feature selection and reward function for model-based reinforcement learning
Intelligent tutoring systems, its 2016, 0684, 504–505.
Source: NC State University Libraries
Added: August 6, 2018
2016 article
Combining Worked Examples and Problem Solving in a Data-Driven Logic Tutor
INTELLIGENT TUTORING SYSTEMS, ITS 2016, Vol. 9684, pp. 347–353.
author keywords: Worked examples; Data-driven tutor; Problem solving
TL;DR:
The results show that worked examples benefits students early in tutoring sessions, but are comparable to hint-based systems for scaffolding domain concepts, which can decrease performance for lower-proficiency students.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: August 6, 2018
2016 article
Data-driven Proficiency Profiling - Proof of Concept
LAK '16 CONFERENCE PROCEEDINGS: THE SIXTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE, pp. 324–328.
author keywords: Data-driven; Tutoring system; Student classification; Clustering
TL;DR:
This study implements the data-driven proficiency profiler (DDPP) into Deep Thought, a logic tutor where students practice constructing deductive logic proofs and shows that the DDPP did improve in performance with additional data and proved to be an effective proof of concept.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: August 6, 2018
2016 journal article
Evolution of an Intelligent Deductive Logic Tutor Using Data-Driven Elements
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, 27(1), 5–36.
author keywords: Deductive logic instruction; Intelligent tutoring systems; Data-driven methods
TL;DR:
This work augmented Deep Thought, an existing computer-based logic tutor, by adding data-driven methods, specifically; intelligent problem selection based on the student’s current proficiency, automatically generated on-demand hints, and determination of student problem solving strategies based on clustering previous students.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: August 6, 2018
2015 conference paper
Data-driven worked examples improve retention and completion in a logic tutor
Artificial intelligence in education, aied 2015, 9112, 726–729.
Source: NC State University Libraries
Added: August 6, 2018
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