Using NGOMSL for Formative Feedback Generation in a Virtual Learning Environment
Technology and Media
Science, Math, and Technology
This research paper presents a feedback generation system using the Natural GOMS Language (NGOMSL) to describe the learner’s tasks and performance expectation in a virtual reality-based learning environment. The acronym GOMS stands for Goals, Operators, Methods, and Selection Rules, and is derived from Card, Moran, and Newell’s Human Processor Model. It is a representation of a series of “how-to’s” required by a system in order to accomplish the intended tasks. NGOMSL is a variation of GOMS that provides an easy-to-read framework that highlights the underlying procedural rules of the task and the assessment of learner mastery of the task. A robust NGOMSL model will be able to provide the essential instructions regarding how the learner’s perceptual, cognitive, and motor subsystems should work, and also improve the learner’s ability to complete the task with proficiency.
In this paper we first use the domain of industrial robot programming as an example to discuss the difference between the Virtual Reality (VR) based learning environment and the traditional learning environment. Second, using a typical task in industrial robot programming as the point of discussion, our previous work in GOMS and its limitation are discussed. Next the rationale of using NGOMSL to model the procedural knowledge in a VR-based learning environment is presented, along with a comparison of using the NGOMSL model for the same task previously described using the GOMS model. The preliminary results of time duration measurement is presented, along with the discussion of how the proposed feedback system will work. This paper will conclude with a discussion of future work needed to implement the NGOMSL instructional models in a classroom setting.
Chang, Yi-hsiang Isaac and Devine, Kevin L., "Using NGOMSL for Formative Feedback Generation in a Virtual Learning Environment" (2019). Scholarship of Teaching and Learning Publications. 121.