Date of Award

Winter 3-2021

Document Type

Thesis

Degree Name

Bachelor Degree

Department

Computer Science

First Advisor

Krenare Pireva Nuçi

Language

English

Abstract

A substantial disadvantage of traditional learning is that all students follow the same curriculum sequence, but not all of them have the same background of knowledge, the same preferences, the same learning goals, and the same needs. Traditional teaching resources, such as textbooks, in most cases orient students to follow fixed sequences during the learning process, thus impairing their performance. Curriculum sequencing is an important research issue for learning process because no fixed learning paths will be appropriate for all learners. For this reason, many research papers are focused on the development of mechanisms to offer personalization on learning sequences, considering the learner needs, interests, behaviors, and abilities. In most cases, these researches are totally focused on the student's preferences, ignoring the level of difficulty and the relation degree that exists between various concepts in a course. This work presents a genetic algorithm-based model to offer personalization on learning paths, considering the level of difficulty and relation degree of the constituent concepts of a course. The experimental result shows that the genetic algorithm is suitable to generate optimal learning paths based on learning object difficulty level, duration, rating, and relation degree between each learning object. Furthermore, it indicates that using the proposed genetic-based approach for personalized learning path generation is superior to the traditional curriculum sequencing.

DOI

10.33107/ubt-etd.2021.2649

Download

Share

COinS