Design of a personalized cognitive layered framework for optimal extraction of mathematical teaching techniques.

Mathematical teaching techniques encompass a diverse range of approaches for students to grasp mathematical concepts. Currently, various mathematical teaching techniques exist, and it is crucial to extract personalized teaching techniques based on students' knowledge level. Cognitive Computing systems in the learning domain open a substantial range of possible alternatives, thereby helping both students and teachers. These systems enhance the human cognitive capabilities by developing cognitive assistant, which is a flexible and adaptive learning software and a continuous learning system. In this paper, a personalized cognitive layered framework is proposed to map the student's reasoning level with the mathematical teaching techniques. The analysis is done with real time data collected from the School for Grades 1 to 6 and mathematical teaching techniques extracted from mathematical research articles. The Hypothesis Evidence Extraction (HypoEE) algorithm is proposed to extract optimal teaching techniques. The proposed system follows the pipeline of cognitive computing processes such as extraction of factors of importance, knowledge assertion graph construction, node prioritization, correlation extraction, text summarization, hypothesis identification, evidence extraction and scoring, hypothesis refinement, inference and decision formulation. The system is evaluated using performance metrics and cognitive metrics. The validation is done by three mathematical subject experts. The results showcase a Confidence Weighted Score (CWS) of 0.96, an accuracy of 0.994, 0.92 precision, 0.88 recall, 0.89 F1 score, 0.83 specificity and cognitive metric scores indicating a taskability of 0.89, 0.86 knowledge capacity and 0.89 knowledge utilization. [Display omitted] • A personalized cognitive layered framework for deriving the most effective and personalized mathematical teaching strategy. • A cognitive system driven by evidence-based inference to personalize education. • A text summarization algorithm crafted to produce concise summaries from large volumes of text. • Application of the Hypothesis Evidence Extraction (HypoEE) algorithm to extract optimal mathematical teaching techniques based on individual reasoning levels. • Analyzing performance through expert assessments and cognitive evaluation metrics. [ABSTRACT FROM AUTHOR]