Cognitive Knowledge-Based Algorithms for Dynamic Knowledge Representation of Adaptive Feedback - Andrew Thomas Bimba (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Historically, the creation of knowledge-based systems was perceived as a human transfer of expertise to the developed system. This perspective operated on the assumption that the necessary knowledge already existed and merely needed to be gathered and incorporated. Typically, this involved acquiring knowledge through expert interviews and translating it into production rules. However, this approach encountered challenges in adequately representing diverse knowledge types. The presence of various knowledge types and the lack of robust justifications for the rules rendered the system maintenance time-consuming and arduous. Consequently, this method was primarily viable for constructing prototypes, prompting a transition from the transfer method to the modeling approach. The modeling approach diverges from simulating the entire cognitive process of an expert and instead aims to create a model that produces similar outcomes in problem-solving. While several knowledge modeling techniques for delivering feedback in computer-based learning environments have been proposed, our research indicates that these techniques are often static, involve a manual knowledge elicitation process, and heavily rely on the volatile knowledge of experts. Consequently, there is a pressing need to streamline this process with a dynamic approach to knowledge representation in an adaptive feedback environment. This research seeks to introduce and assess the performance of knowledge elicitation, knowledge bonding, and adaptive feedback algorithms in representing knowledge for adaptive feedback. The proposed strategy utilizes the Cognitive Knowledge Base (CKB) to formalize knowledge based on an Object-Attribute-Relation (OAR) model. This technique empowers the CKB to autonomously decide on the type of feedback to provide. Conclusions drawn from the recommendations of the adaptive feedback algorithm align with prior research affirming the appropriateness of feedback in specific scenarios.