Works (44)
Updated: December 3rd, 2024 05:03
2024 conference paper
Engaging Students from Rural Communities in AI Education with Game-Based Learning
Goslen, A., Gupta, A., Muthukrishnan, S., Midgett, R., Min, W., Vandenberg, J., … Mott, B. (2024, March 14).
open access via dl.acm.org (publisher PDF)
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
Source: ORCID
Added: March 22, 2024
2024 conference paper
Investigating the Needs of Middle School Educators in Teaching Artificial Intelligence
Boulden, D., Vandenberg, J., Goslen, A., Cateté, V., Min, W., & Mott, B. (2024, December 5).
Source: ORCID
Added: December 2, 2024
2024 conference paper
Supporting Student Engagement in K-12 AI Education with a Card Game Construction Toolkit
Lim, H., Min, W., Vandenberg, J., Cateté, V., Uchidiuno, J., & Mott, B. (2024, March 14).
Source: ORCID
Added: March 22, 2024
2023 article
Enhancing Stealth Assessment in Collaborative Game-Based Learning with Multi-task Learning
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2023, Vol. 13916, pp. 304–315.
author keywords: Stealth Assessment; Multi-Task Learning; Computer-Supported Collaborative Learning; Collaborative Game-Based Learning; Game-Based Learning Environments
Sources: Web Of Science, NC State University Libraries
Added: November 4, 2024
2023 article
Leveraging Game Design Activities for Middle Grades AI Education in Rural Communities
PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON THE FOUNDATIONS OF DIGITAL GAMES, FDG 2023.
author keywords: artificial intelligence; digital games; middle grades students
TL;DR:
How to introduce rural middle grades students to foundational AI concepts through digital game design activities is explored, finding students’ awareness and understanding of AI varied significantly, whereas teachers had limited knowledge of AI.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: April 11, 2023
2023 conference paper
Multimodal Predictive Student Modeling with Multi-Task Transfer Learning
Emerson, A., Min, W., Rowe, J., Azevedo, R., & Lester, J. (2023, March 13).
TL;DR:
This work investigates a multimodal, multi-task predictive student modeling framework for game-based learning environments and investigates the ability to use information learned from one source dataset to improve models based on another target dataset (i.e., transfer learning using pre-trained models).
(via Semantic Scholar)
Source: ORCID
Added: February 22, 2023
2023 article
Robust Team Communication Analytics with Transformer-Based Dialogue Modeling
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2023, Vol. 13916, pp. 639–650.
author keywords: Team Communication Analytics; Team Training; Dialogue Modeling; Text-to-Text Transformers; Deep Learning; Natural Language Processing
Sources: Web Of Science, NC State University Libraries
Added: November 4, 2024
2023 article
Toward AI-infused Game Design Activities for Rural Middle Grades Students
PROCEEDINGS OF THE 2023 CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE 2023, VOL. 2, pp. 644–644.
TL;DR:
This work designs a set of hands-on activities to elicit deeper feedback from students and educators on their preferences, points of confusion, and interests of AI, and presents its initial AI-infused game design activities.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: June 30, 2023
2022 journal article
Early prediction of student knowledge in game-based learning with distributed representations of assessment questions
BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY, 54(1), 40–57.
author keywords: game-based learning; natural language processing; predictive student modelling
TL;DR:
This work investigates a predictive student modelling approach that leverages the natural language text of the post-gameplay content knowledge questions and theText of the possible answer choices for early prediction of fine-grained individual student performance in game-based learning environments.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: March 6, 2023
2022 article
Promoting AI Education for Rural Middle Grades Students with Digital Game Design
PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 2, SIGCSE 2023, pp. 1388–1388.
TL;DR:
Inspired by prior research that game design holds significant potential for cultivating student interest and knowledge in computer science, this work is designing, developing, and iteratively refining an AI-centered development environment that infuses AI learning into game design activities.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: March 7, 2023
2021 article
Detecting Disruptive Talk in Student Chat-Based Discussion within Collaborative Game-Based Learning Environments
LAK21 CONFERENCE PROCEEDINGS: THE ELEVENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, pp. 405–415.
author keywords: Collaborative Game-Based Learning; Disruptive Talk Detection; Text Analytics
TL;DR:
Findings show that long short-term memory network (LSTM)-based disruptive talk detection models outperform competitive baseline models, indicating that the LSTM-based disruptiveTalk detection framework offers significant potential for supporting effective collaborative game-based learning through the identification of disruptive talk.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: November 28, 2022
2021 article
Enhancing Multimodal Affect Recognition with Multi-Task Affective Dynamics Modeling
2021 9TH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII).
author keywords: multitask learning; affect recognition; multimodal interaction; game-based learning environments
TL;DR:
A multimodal, multitask affect recognition framework that predicts students’ future affective states as auxiliary training tasks and uses prior affectiveStates as input features to capture bi-directional affective dynamics and enhance the training of affect recognition models is presented.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Sources: Web Of Science, NC State University Libraries
Added: June 6, 2022
2021 article
Multidimensional Team Communication Modeling for Adaptive Team Training: A Hybrid Deep Learning and Graphical Modeling Framework
ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I, Vol. 12748, pp. 293–305.
author keywords: Team communication analytics; Probabilistic graphical models; Deep learning; Distributed language representations; Natural language processing
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: November 28, 2022
2021 article
Multimodal Trajectory Analysis of Visitor Engagement with Interactive Science Museum Exhibits
ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT II, Vol. 12749, pp. 151–155.
author keywords: Museum learning; Visitor engagement; Multimodal trajectory; analytics
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Sources: Web Of Science, NC State University Libraries
Added: November 28, 2022
2020 conference paper
A conceptual assessment framework for k-12 computer science rubric design
Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, 1328.
Contributors: B. Akram n, n , E. Wiebe n, A. Navied n, B. Mott n, K. Boyer *, J. Lester n
author keywords: CS Assessment; Evidence Centered Design; K-12 CS Instruction
TL;DR:
A conceptual assessment framework is proposed that guides teachers through designing appropriate assessments for computer science (CS) activities in their classrooms and addresses the critical problem of incorporating CS into K-12 curricula without corresponding assessments.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: ORCID
Added: May 20, 2020
2019 journal article
DeepStealth: Game-Based Learning Stealth Assessment with Deep Neural Networks
IEEE Transactions on Learning Technologies, 13(2), 1–1.
Contributors: n , M. Frankosky, B. Mott n, J. Rowe n, P. Smith n, E. Wiebe n , K. Boyer *, J. Lester n
author keywords: Hidden Markov models; Computational modeling; Games; Predictive models; Task analysis; Adaptation models; Computer science; Computational thinking; deep learning; educational games; game-based learning; stealth assessment
TL;DR:
DeepStealth is presented, a deep learning-based stealth assessment framework that yields significant reductions in the feature engineering labor that has previously been required to create stealth assessments and uses end-to-end trainable deep neural network-based evidence models.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Sources: ORCID, Crossref, NC State University Libraries
Added: February 20, 2020
2019 conference paper
Generating educational game levels with multistep deep convolutional generative adversarial networks
IEEE Conference on Computatonal Intelligence and Games, CIG, 2019-August.
Contributors: K. Park n, B. Mott n, n , K. Boyer *, E. Wiebe n & J. Lester n
TL;DR:
A multistep deep convolutional generative adversarial network for generating new levels within a game for middle school computer science education is proposed and is suggested to significantly enhances the solvability of the generated levels with only minor degradation in the novelty of thegenerated levels.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
Source: ORCID
Added: May 20, 2020
2019 conference paper
Position: IntelliBlox: A Toolkit for Integrating Block-Based Programming into Game-Based Learning Environments
Proceedings - 2019 IEEE Blocks and Beyond Workshop, B and B 2019, 55–58.
Contributors: S. Taylor n, n , B. Mott n, A. Emerson n, A. Smith n , E. Wiebe n, J. Lester n
TL;DR:
IntelliBlox is presented, a Blockly-inspired toolkit for the Unity cross-platform game engine that enables learners to create block-based programs within immersive game-based learning environments.
(via Semantic Scholar)
Source: ORCID
Added: February 19, 2020
2019 article
Predicting Dialogue Breakdown in Conversational Pedagogical Agents with Multimodal LSTMs
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II, Vol. 11626, pp. 195–200.
author keywords: Conversational pedagogical agent; Multimodal; Dialogue breakdown detection; Natural language processing; Gaze
TL;DR:
Results from a study with 92 middle school students demonstrate that multimodal long short-term memory network (LSTM)-based dialogue breakdown detectors incorporating eye gaze features achieve high predictive accuracies and recall rates, suggesting that multi-modal detectors can play an important role in designing conversational pedagogical agents that effectively engage students in dialogue.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: December 2, 2019
2019 article
Take the Initiative: Mixed Initiative Dialogue Policies for Pedagogical Agents in Game-Based Learning Environments
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II, Vol. 11626, pp. 314–318.
Contributors: J. Wiggins*, M. Kulkarni*, n , K. Boyer*, B. Mott n, E. Wiebe n , J. Lester n
author keywords: Pedagogical agents; Game-based learning; Initiative
TL;DR:
A study to investigate two different agent dialogue policies with regard to conversational initiative, a core consideration in dialogue system design found the Mixed Initiative policy better supported the goals of the game-based learning environment by fostering exploration, yielding better performance on in-game assessments, and creating higher student engagement.
(via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: December 2, 2019
2019 conference paper
Toward computational models of team effectiveness with natural language processing
CEUR Workshop Proceedings, 2501, 30–39. http://www.scopus.com/inward/record.url?eid=2-s2.0-85075911853&partnerID=MN8TOARS
Source: ORCID
Added: May 20, 2020
2018 conference paper
Affect-based early prediction of player mental demand and engagement for educational games
Proceedings of the 14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2018, 243–249. http://www.scopus.com/inward/record.url?eid=2-s2.0-85070822616&partnerID=MN8TOARS
Source: ORCID
Added: May 20, 2020
2018 conference paper
High-fidelity simulated players for interactive narrative planning
IJCAI International Joint Conference on Artificial Intelligence, 2018-July, 3884–3890. http://www.scopus.com/inward/record.url?eid=2-s2.0-85055720882&partnerID=MN8TOARS
Source: ORCID
Added: May 20, 2020
2018 conference paper
Improving stealth assessment in game-based learning with LSTM-based analytics
Proceedings of the 11th International Conference on Educational Data Mining, EDM 2018. http://www.scopus.com/inward/record.url?eid=2-s2.0-85080493724&partnerID=MN8TOARS
Source: ORCID
Added: May 20, 2020
2018 conference paper
User Affect and No-Match Dialogue Scenarios: An Analysis of Facial Expression
Proceedings of the 4th International Workshop on Multimodal Analyses Enabling Artificial Agents in Human-Machine Interaction - MA3HMI'18, 6–14.
Contributors: J. Wiggins *, M. Kulkarni*, n , K. Boyer *, B. Mott n, E. Wiebe n , J. Lester n
Event: the 4th International Workshop
author keywords: Dialogue agents; facial expression; no-match dialogue policy
TL;DR:
This work investigates how users' facial expressions exhibited in response to the agent's no-match utterances predict the users' opinion of the agent after the interaction has completed, and indicates that models incorporating users’ facial expressions following no- match utterances are highly predictive of user opinion and significantly outperform baseline models.
(via Semantic Scholar)
Sources: ORCID, Crossref, NC State University Libraries
Added: February 24, 2020
2017 article
"Thanks Alisha, Keep in Touch": Gender Effects and Engagement with Virtual Learning Companions
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2017, Vol. 10331, pp. 299–310.
Contributors: L. Pezzullo*, J. Wiggins *, M. Frankosky n, n , K. Boyer *, B. Mott n, E. Wiebe n , J. Lester n
author keywords: Learning companions; Pedagogical agents; Gender; Engagement; Game-based learning
TL;DR:
Girls were significantly more engaged than boys, particularly with the narrative-integrated agent, while boys reported higher mental demand with that agent, which contributes to the growing understanding that learning companions must adapt to students’ gender in order to facilitate the most effective learning interactions.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018
2017 conference paper
Deep LSTM-based goal recognition models for open-world digital games
AAAI Workshop - Technical Report, WS-17-01 - WS-17-15, 851–858. http://www.scopus.com/inward/record.url?eid=2-s2.0-85046086839&partnerID=MN8TOARS
Source: ORCID
Added: May 20, 2020
2017 article
Inducing Stealth Assessors from Game Interaction Data
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2017, Vol. 10331, pp. 212–223.
Contributors: n , M. Frankosky n, B. Mott n, E. Wiebe n , K. Boyer * & J. Lester n
author keywords: Game-based learning environments; Stealth assessment; Deep learning; Computational thinking; Educational games
TL;DR:
A long short-term memory network (LSTM)-based stealth assessment framework that takes as input an observed sequence of raw game-based learning environment interaction data along with external pre-learning measures to infer students’ post-competencies and indicates that the LSTM-based approach holds significant promise for evidence modeling in stealth assessment.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018
2017 conference paper
Interactive narrative personalization with deep reinforcement learning
IJCAI International Joint Conference on Artificial Intelligence, 3852–3858. http://www.scopus.com/inward/record.url?eid=2-s2.0-85031928990&partnerID=MN8TOARS
Source: ORCID
Added: May 20, 2020
2017 conference paper
Multimodal goal recognition in open-world digital games
Proceedings of the 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2017, 80–86. http://www.scopus.com/inward/record.url?eid=2-s2.0-85051737443&partnerID=MN8TOARS
Source: ORCID
Added: May 20, 2020
2017 conference paper
Simulating player behavior for data-driven interactive narrative personalization
Proceedings of the 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2017, 255–261. http://www.scopus.com/inward/record.url?eid=2-s2.0-85055706729&partnerID=MN8TOARS
Source: ORCID
Added: May 20, 2020
2016 chapter
Integrating Real-Time Drawing and Writing Diagnostic Models: An Evidence-Centered Design Framework for Multimodal Science Assessment
In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Intelligent Tutoring Systems (Vol. 9684, pp. 165–175).
Ed(s): A. Micarelli, J. Stamper & K. Panourgia
author keywords: Assessment; Multimodalilty; Evidence-centered design
TL;DR:
This work utilizes ECD to analyze a corpus of elementary student writings and drawings collected with a digital science notebook and reveals that ECD provides an expressive unified framework for multimodal assessment of science learning with accurate predictions of student learning.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries, Crossref
Added: August 6, 2018
2016 conference paper
Integrating real-time drawing and writing diagnostic models: An evidence-centered design framework for multimodal science assessment
Intelligent tutoring systems, its 2016, 0684, 165–175.
Source: NC State University Libraries
Added: August 6, 2018
2016 conference paper
Player goal recognition in open-world digital games with long short-term memory networks
IJCAI International Joint Conference on Artificial Intelligence, 2016-January, 2590–2596. http://www.scopus.com/inward/record.url?eid=2-s2.0-85006136250&partnerID=MN8TOARS
Source: ORCID
Added: May 20, 2020
2016 conference paper
Predicting dialogue acts for intelligent virtual agents with multimodal student interaction data
Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016, 454–459. http://www.scopus.com/inward/record.url?eid=2-s2.0-85072280923&partnerID=MN8TOARS
Source: ORCID
Added: May 20, 2020
2015 article
DeepStealth: Leveraging Deep Learning Models for Stealth Assessment in Game-Based Learning Environments
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015, Vol. 9112, pp. 277–286.
author keywords: Game-based learning environments; Stealth assessment; Deep learning; Computational thinking; Educational games
TL;DR:
A framework for stealth assessment that leverages deep learning, a family of machine learning methods that utilize deep artificial neural networks, to infer student competencies in a game-based learning environment for middle grade computational thinking, Engage is presented.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018
2015 chapter
Diagrammatic Student Models: Modeling Student Drawing Performance with Deep Learning
In Lecture Notes in Computer Science (Vol. 9146, pp. 216–227).
author keywords: Student modeling; Intelligent tutoring systems; Deep learning
TL;DR:
The diagrammatic student modeling framework utilizes deep learning, a family of machine learning methods based on a deep neural network architecture, to reason about sequences of student drawing actions encoded with temporal and topological features.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Sources: ORCID, Crossref
Added: February 19, 2020
2014 conference paper
Deep learning-based goal recognition in open-ended digital games
Proceedings of the 10th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2014, 37–43. http://www.scopus.com/inward/record.url?eid=2-s2.0-84916877257&partnerID=MN8TOARS
Source: ORCID
Added: May 20, 2020
2014 book
Leveraging semi-supervised learning to predict student problem-solving performance in narrative-centered learning environments
In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 664–665).
TL;DR:
Results suggest the semi-supervised machine-learning approach to predicting whether students will be successful in solving problem-solving tasks within narrative-centered learning environments often outperforms standard supervised learning methods.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
Sources: NC State University Libraries, ORCID, NC State University Libraries
Added: August 6, 2018
2013 book
Personalizing embedded assessment sequences in narrative-centered learning environments: A collaborative filtering approach
In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 369–378).
Source: ORCID
Added: May 20, 2020
2009 book
An interactive-content technique based approach to generating personalized advertisement for privacy protection
In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 185–191).
Contributors: * & Y. Cheong *
author keywords: Privacy; Interactive content; Personalized advertising
TL;DR:
A framework that employs a script-based interactive content technique for privacy protection in conventional approaches where a data server containing customer profiles is employed or the customer profiles are required to be sent over the public network is described.
(via Semantic Scholar)
Source: ORCID
Added: May 20, 2020
2008 conference paper
Planning-integrated story graph for interactive narratives
MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops, 27–32.
Contributors: * , E. Shim*, Y. Kim* & Y. Cheong *
TL;DR:
An interactive story structure incorporating the planning technique into the conditional branch techniques is discussed, expecting that the author can compose well-woven stories which can respond to a wide range of user interaction.
(via Semantic Scholar)
Source: ORCID
Added: May 20, 2020
2006 book
PRISM: A framework for authoring interactive narratives
In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 297–308).
Contributors: Y. Cheong *, Y. Kim*, * , E. Shim* & J. Kim *
TL;DR:
This paper describes a framework for authoring interactive narratives that employs an adapted branching narrative structure that also uses planning formalism to enable automated association between nodes.
(via Semantic Scholar)
Source: ORCID
Added: May 20, 2020
conference paper
Diagrammatic student models: Modeling student drawing performance with deep learning
Smith, A., Min, W., Mott, B. W., & Lester, J. C. User modeling, adaptation and personalization, 9146, 216–227.
Source: NC State University Libraries
Added: August 6, 2018
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