Fuzzy-Based Knowledge Design and Delivery Model for Personalised Learning

EasyChair Preprint 13126

Abstract

Adaptation to the level of knowledge of each student remains one of the key challenges of e-learning and education in general. E-learning systems provide opportunity for systematic data collection about learning activities offering valuable insights into the students’ knowledge. In order to achieve the personalised learning, this study introduces a Knowledge Design and Delivery Model (KDDM) for intelligent tutoring systems. This model uses a hybrid approach that combines traditional overlay student models with fuzzy logic and multi-criteria decision-making methods. Unlike popular machine learning approaches, these methods do not require existing datasets and they allow direct teacher involvement in knowledge delivery. The KDDM associates student stereotypes with Bloom’s revised taxonomy levels, providing a reference point for the cybernetic model. KDDM has been successfully implemented and examined in a two-year experiment which confirmed its effectiveness on 370 participants from two universities in two countries.

Keyphrases: Intelligent Tutoring System, Multi-critera decision, cybernetic model, personalised learning, student modeling

@booklet{EasyChair:13126,
  author    = {Tomislav Volaric and Hrvoje Ljubić and Robert Rozić},
  title     = {Fuzzy-Based Knowledge Design and Delivery Model for Personalised Learning},
  howpublished = {EasyChair Preprint 13126},
  year      = {EasyChair, 2024}}