Wookhee Min
Biographic Data
| ID | 9493764 |
|---|---|
| NAME | Wookhee Min |
| GIVEN NAMES | Wookhee |
| FAMILY NAME | Min |
| SIGNATURE | MIN W |
| AFFILIATIONS | North Carolina State University |
| ORCID | 0000-0001-8900-0514 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
A fairness-centric approach to stealth assessment in collaborative game-based learning
Stealth assessment evaluates student competencies using rich interaction data and shows great potential in game-based learning for promoting collaborative problem solving (CPS). However, a key challenge is assessing the impact of algorithmic bias in these models on target populations. To address this challenge, we propose a fairness-centric stealth assessment framework for collaborative game-based learning environments that (1) develops robust mo…
Enhancing stealth assessment in game-based learning through goal recognition
Stealth assessment in game-based learning analyzes gameplay behaviors to measure student competencies unobtrusively. Grounded in the theoretical premise that in-game objectives shape learning outcomes, this article investigates how goal recognition can enhance stealth assessment by using predictions about immediate gameplay objectives as evidence for modeling learning. We evaluated this approach with 119 middle school students using an educationa…
Early prediction of student knowledge in game‐based learning with distributed representations of assessment questions
Game‐based learning environments hold significant promise for facilitating learning experiences that are both effective and engaging. To support individualised learning and support proactive scaffolding when students are struggling, game‐based learning environments should be able to accurately predict student knowledge at early points in students' gameplay. Student knowledge is traditionally assessed prior to and after each student interacts with…
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Early prediction of student knowledge in game‐based learning with distributed representations of assessment questions
Game‐based learning environments hold significant promise for facilitating learning experiences that are both effective and engaging. To support individualised learning and support proactive scaffolding when students are struggling, game‐based learning environments should be able to accurately predict student knowledge at early points in students' gameplay. Student knowledge is traditionally assessed prior to and after each student interacts with…
A fairness-centric approach to stealth assessment in collaborative game-based learning
Stealth assessment evaluates student competencies using rich interaction data and shows great potential in game-based learning for promoting collaborative problem solving (CPS). However, a key challenge is assessing the impact of algorithmic bias in these models on target populations. To address this challenge, we propose a fairness-centric stealth assessment framework for collaborative game-based learning environments that (1) develops robust mo…
Enhancing stealth assessment in game-based learning through goal recognition
Stealth assessment in game-based learning analyzes gameplay behaviors to measure student competencies unobtrusively. Grounded in the theoretical premise that in-game objectives shape learning outcomes, this article investigates how goal recognition can enhance stealth assessment by using predictions about immediate gameplay objectives as evidence for modeling learning. We evaluated this approach with 119 middle school students using an educationa…
Educational Games and Gamification (3 works) · Educational technology (2 works) · Electronic learning (2 works) · Experiential learning (2 works) · Intelligent Tutoring Systems and Adaptive Learning (2 works) · Teaching method (2 works) · Technology integration (2 works) · Artificial Intelligence (1 works) · Artificial Intelligence (1 works) · Collaborative learning (1 works)