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Influence of ICT-supported learning environment perceptions, academic ability, and prior educational experience on students’ approaches to learning principles of accounting in Malaysian secondary schools


Citation

Tan, Boon See (2017) Influence of ICT-supported learning environment perceptions, academic ability, and prior educational experience on students’ approaches to learning principles of accounting in Malaysian secondary schools. Doctoral thesis, Universiti Putra Malaysia.

Abstract

It was found that students of Principles of Accounting have yet to adopt a deep approach to learning. There is a lack of ICT usage in the accounting class; accounting students do not connect what they learn in class with the actual working place; neither is ICT used as a means to improve social interaction and other skills. The main purpose of this research was, thus, to investigate the factors that influence students’ deep and surface approaches to learning for the subject of Principles of Accounting in learning environments which are supported by ICT. The proposed predictors were ICT-supported learning environment perceptions, academic ability, and prior educational experience. This study adopted a correlational research design by using a set of questionnaire with scales measuring the predictors and approaches to learning. The sample consisted of 371 Form Four students who were studying Principles of Accounting in an ICT-supported learning environment. The confirmatory factor analysis found that the construct of deep approach to learning was formed according to Students’ Approaches to Learning (SAL) theory, which consists of sub constructs of deep motive and deep strategy. However, the surface approach was unidentifiable in the current learning context while a new learning approach named as future-oriented approach emerged in this study. This new approach could be the influence from the sociocultural and educational contextual factors in Malaysia. Descriptive statistics found that deep approach to learning was practised by students at a moderate extent (Mean = 3.22; Standard Deviation = .64); however, future oriented approach was adopted at high extent (Mean = 3.92; Standard Deviation = .74). Furthermore, based on the structural equation model generated in this study – the Malaysian Students’ Approaches to Learning Accounting in ICTsupported Environment (MySAL-AcICT), several significant paths were found. These significant paths are: 1) ICT-supported learning environment perceptions influenced deep approach to learning (β = .848, p < .001); 2) ICTsupported learning environment perceptions influenced future-oriented approach to learning (β = .734, p < .001); 3) academic ability influenced ICTsupported learning environment perceptions (β = .149, p < .01); and 4) prior educational experience influenced ICT-supported learning environment perceptions (β = .151, p < .01). In addition, it was found that the construct of ICT-supported learning environment perceptions was a total mediator between academic ability and approaches to learning, and between prior educational experience and approaches to learning. Consequently, the variance of deep approach was substantially (73.4%) explained by the model; while the model also explained more than half (54.5%) of the variance of future-oriented approach. This study proposes that students’ learning approaches are influenced by both their immediate and social environments. Educators and policy makers, thus, need to consider the total environments of students for the sake of cultivating the culture of deep learning.


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Additional Metadata

Item Type: Thesis (Doctoral)
Subject: Environmental education
Subject: Technological innovations
Subject: Information technology - Study and teaching (Secondary) - Malaysia
Call Number: FPP 2019 43
Chairman Supervisor: Professor Wong Su Luan, PhD
Divisions: Faculty of Educational Studies
Depositing User: Ms. Nur Faseha Mohd Kadim
Date Deposited: 06 Sep 2021 05:58
Last Modified: 06 Sep 2021 05:58
URI: http://psasir.upm.edu.my/id/eprint/90713
Statistic Details: View Download Statistic

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