A component-based knowledge domain model for adaptive human learning systems

Abstract

Adaptive human learning systems (AHLSs) are important tools to personalise learning. However, the used domain representation formalisms lack the needed precision and flexibility those make the domains efficiently adaptable and intensively reusable. To address this issue, we propose a component based knowledge domain for an AHLS that aims to improve the adapting efficiency and provides intensive reuse of the pre-built (sub) knowledge domains. To show the feasibility and the benefits of the proposed AHLS, a prototype that experiments the explanation variants method is implemented. So, unlike the other, our AHLS achieves the adapted learning by (re)selecting and sequencing the appropriate linear combination of the component variants explaining the corresponding concepts. Also, as a more challenging task, to get a compromised solution of the conflicting learning goals and to reduce the substantial overhead, the adapting is formulated as a multi-objective component variants selection problem and it is implemented using Genetic Algorithms.Reference to this paper should be made as follows: Boulehouache, S., Maamri, R. and Sahnoun, Z. (2015) 'A component-based knowledge domain model for adaptive human learning systems', Int.

Estimating the minimum number of opportunities needed for all students to achieve predicted mastery

“…In addition to BKT, AFM, PFA, and artificial neural network models, other predictive models include linear, Markov, and rule induction models (Zukerman & Albrecht, 2001) as well as regression, instance-based, regularization, decision-tree, clustering, deep learning, dimensionality reduction, and ensemble models (Brownlee, 2013). There are also adaptive learning models (Boulehouache et al, 2015) such as Felder-Silverman (Kolekar, Pai, & Pai, 2016).…”