Assessment HTN (A-HTN) for Automated Task Performance Assessment in 3D Serious Games
Kevin Desai, Omeed Ashtiani, Balakrishnan Prabhakaran

TL;DR
This paper introduces Assessment HTN (A-HTN), a novel framework that models tasks and automatically assesses user performance in 3D serious games, matching expert evaluations.
Contribution
A-HTN is the first to integrate task modeling with automatic performance assessment in 3D serious games, enabling efficient and accurate evaluation.
Findings
High correlation between system scores and instructor evaluations
Effective in both single-user and multi-user assessments
Demonstrated success in VR serious game scenarios
Abstract
In the recent years, various 3D mixed reality serious games have been developed for different applications such as physical training, rehabilitation, and education. Task performance in a serious game is a measurement of how efficiently and accurately users accomplish the game's objectives. Prior research includes a graph-based representation of tasks, e.g. Hierarchical Task Network (HTN), which only models a game's tasks but does not perform assessment. In this paper, we propose Assessment HTN (A-HTN), which both models the task efficiently and incorporates assessment logic for game objectives. Based on how the task performance is evaluated, A-HTN automatically performs: (a) Task-level Assessment by comparing object manipulations and (b) Action-level Assessment by comparing motion trajectories. The system can also categorize the task performance assessment into single user or multi-user…
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Taxonomy
TopicsHuman Pose and Action Recognition · Educational Games and Gamification · Virtual Reality Applications and Impacts
