Nathan Henderson

College of Engineering

2022 article

Investigating Student Interest and Engagement in Game-Based Learning Environments

ARTIFICIAL INTELLIGENCE IN EDUCATION, PT I, Vol. 13355, pp. 711–716.

author keywords: Interest; Science learning; Learning technology
TL;DR: It is found that interest is significantly related to performance (both knowledge assessment and game completion), suggesting that students with high interest are likely to perform better academically, but also be more engaged in the in-game objectives. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science)
Sources: Web Of Science, ORCID
Added: November 21, 2022

2022 article

Leveraging Student Goal Setting for Real-Time Plan Recognition in Game-Based Learning

ARTIFICIAL INTELLIGENCE IN EDUCATION, PT I, Vol. 13355, pp. 78–89.

By: A. Goslen n, D. Carpenter n, J. Rowe n, N. Henderson n, R. Azevedo* & J. Lester n

author keywords: Plan recognition; Game-based Learning; Self-regulated learning
TL;DR: A novel plan recognition framework is introduced that leverages trace log data from student interactions within a game-based learning environment called C RYSTAL I SLAND, in which students use a drag-and-drop planning support tool that enables them to externalize their science problem-solving goals and plans prior to enacting them in the learning environment. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science)
Sources: Web Of Science, ORCID
Added: November 21, 2022

2021 article

Enhancing Multimodal Affect Recognition with Multi-Task Affective Dynamics Modeling

2021 9TH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII).

By: N. Henderson n, W. Min n, J. Rowe n & J. Lester n

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, ORCID
Added: June 6, 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.

By: A. Emerson n, N. Henderson n, W. Min n, J. Rowe n, J. Minogue n & J. Lester n

author keywords: Museum learning; Visitor engagement; Multimodal trajectory; analytics
Sources: Web Of Science, ORCID
Added: November 28, 2022

2019 article

4D Affect Detection: Improving Frustration Detection in Game-Based Learning with Posture-Based Temporal Data Fusion

ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2019), PT I, Vol. 11625, pp. 144–156.

By: N. Henderson n, J. Rowe n, B. Mott n, K. Brawner*, R. Baker* & J. Lester n

author keywords: Affect detection; Data fusion; Posture; Frustration; Deep learning
TL;DR: A data-driven framework that leverages spatial and temporal posture data to detect learner frustration using deep neural network-based data fusion techniques and shows significant improvements to frustration detection relative to baseline models. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science)
Source: Web Of Science
Added: December 2, 2019

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