Modeling affective conceptual change using Bayesian networks

Sam, Yok Cheng (2014) Modeling affective conceptual change using Bayesian networks. Masters thesis, Multimedia University.

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Abstract

The acquisition of scientific inquiry skills through computer-based scientific inquiry learning environment is a challenge for computer-based learning researcher. The learning process in scientific inquiry learning environment is based on exploratory learning approach, where the student is free to explore their ideas and accumulate their scientific skills. When doing so, the student inherently experiences dissatisfaction with their prior knowledge, and by iteratively exploring into the learning environment, they confirm their new ideas. This process is the core of conceptual change learning process. In the process of conceptual change in learning, a student’s existing knowledge might be changed or fundamentally replaced, is influenced by a student’s affective states during process of learning. In its conception, conceptual change is an implicit and internalized process that takes place in a student’s mind, and therefore is affected by other factors such as affect and classroom contextual factors. Affective attributes played an important role in determining conceptual change learning outcomes in a classroom context. The role of affective characteristic such emotion and attitude, which included, but not limited to motivation, frustration, self-confidence, excitement, confusion, fatigue, and boredom should not be downplayed, as these emotion and attitude factors influences a student’s goals, intentions, self-efficacy, expectations, and learning purposes. Education research work has proven that insufficient focus for suitable student properties can resort to inaffective learning, and consequently the failure in achieving learning objectives. Similarly, in a computer-based learning environment, monitoring a student’s affect is crucial in inferring conceptual change occurrence in learning.

Item Type: Thesis (Masters)
Additional Information: Call No.: QA279.5 S26 2014
Uncontrolled Keywords: Bayesian statistics
Subjects: Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 12 Jan 2016 07:29
Last Modified: 12 Jan 2016 07:29
URI: http://shdl.mmu.edu.my/id/eprint/6265

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