Predicting Students’ Inclination to TVET Enrolment Using Various Classifiers

Hong, Chia Ming; Ch’ng, Chee Keong; Roslan, Teh Raihana Nazirah

doi:10.47836/pjst.31.1.28

Cited by 3 publications

(1 citation statement)

References 29 publications

(30 reference statements)

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“…A decision tree is a tree-structured classifier where core nodes represent the characteristics of a dataset, branches represent the decision rules, and leaf nodes represent the outcomes (Ch'ng & Mahat, 2014;Hong et al, 2023). Classification and regression tree algorithm (CART) is a type of classification algorithm that uses Gini's impurity index to build a decision tree model (Breiman et al, 1984).…”

Section: Classification and Regression Treementioning

confidence: 99%

Hybrid Machine Learning Approach for predicting E-wallet Adoption among Higher Education Students in Malaysia

Ch'ng

2024

JICT

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In today’s digitised world, e-wallets have been sprouting thick and fast in Malaysia as they contribute significantly to expediting onlinetransactions. The E-wallet system is not only a mechanism for businesses to acquire profit but is also one of the most secure paymentoptions for customers, particularly during the COVID-19 pandemic. However, the adoption of e-wallets among higher education students remains unfavourable, eliciting only minimal response. This study aims to analyse higher education students’ adoption of e-wallets using a hybrid machine learning method, combining clustering and decision trees. This approach provides deep insights into user behaviour, improving prediction accuracy and enabling personalised strategies for enhanced user experiences. It profiles and classifies students based on demographics and traits such as age, year of study, gender, frequency of use, future use intention, lifestyle compatibility, perceived trust, risk perception, convenience, and security factors. The analysis reveals the segmentation of the dataset into four distinct clusters, each characterised by shared attributes. These clusters are subsequently labelled descriptively and incorporated into the dataset. The dataset, now enriched with cluster information, serves as the foundation for constructing a decision tree model. The outcome of the decision tree indicates that Cluster 2 and Cluster 3 are hesitant towards e-payment. In contrast, Cluster 1 and Cluster 4 are more receptive despite security concerns, as e-wallets offer convenience despite lacking full trust, with security being a prominent concern amidst rising cyber threats. This study helps the Malaysian government and service providers promote cashless transactions and shape students’ financial independence based on their traits.

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Section: Classification and Regression Treementioning

confidence: 99%

Hybrid Machine Learning Approach for predicting E-wallet Adoption among Higher Education Students in Malaysia

Ch'ng

2024

JICT

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Malaysian youth’s propensity towards ambidextrous learning: an in-depth analysis of behavioural insights dimensions

Shaari,

Haron,

Abdul Wahab

et al. 2024

Humanit Soc Sci Commun

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The aim of this experimental study was to compare the results before and after an intervention that included ambidextrous training to accelerate the enrolment of adolescents in technical, vocational, and educational training (TVET). In particular, the effects of an ambidextrous learning-based training programme on increasing the intention to enter TVET were examined. The independent variables constructed in this study include self-efficacy and TVET knowledge, while the dependent variable is TVET intention. The sample consisted of 480 16-year-old students from government schools in four Malaysian regions selected by cluster sampling. The study used a randomised experimental design with a no-treatment control group. The intervention included training developed as part of Behavioural Insights. Methods of analysis included descriptive and bivariate analysis of mean differences and structural equation modelling to describe the predictive model. The results showed significant differences between the actual and the control groups in terms of post-test results across all factors, including TVET intention, TVET knowledge, and self-efficacy. The structural equation modelling test found that TVET knowledge emerged as the strongest predictor of TVET intention, surpassing self-efficacy. As knowledge about TVET is the most important factor influencing a person’s intention to choose TVET, these initiatives should provide clear and comprehensive explanations of the concepts of TVET, the different TVET clusters available, and the potential career opportunities within these sectors.

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Assessing the Impact of Student Employability Using Decision Tree Classifier in Education 4.0

Singh,

Srivastava

2023

Architecture and Technological Advancements of Education 4.0

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After graduation, student employability is a crucial issue that has an impact on people's lives, society, and the economy as a whole. Employers are looking for applicants in the present employment market who not only have the required technical abilities but also have the flexibility to adapt to shifting work conditions. In this study, investigate how a decision tree classifier machine learning approach affects graduates' employability in education 4.0. For this, a dataset is used for employability that contains a variety of variables, like academic achievement, real-world experience, and soft skills. Using the decision tree classifier approach to assess the dataset, predictions are then made regarding the factors that would affect employment after graduation. This study finds that the decision tree classifier, when compared to other machine learning algorithms, is more accurate and better suited for use in improving student employability by identifying the key competencies and characteristics needed for various job roles and matching them with qualified candidates in education 4.0.

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Predicting Students’ Inclination to TVET Enrolment Using Various Classifiers

Cited by 3 publications

References 29 publications

Hybrid Machine Learning Approach for predicting E-wallet Adoption among Higher Education Students in Malaysia

Hybrid Machine Learning Approach for predicting E-wallet Adoption among Higher Education Students in Malaysia

Malaysian youth’s propensity towards ambidextrous learning: an in-depth analysis of behavioural insights dimensions

Assessing the Impact of Student Employability Using Decision Tree Classifier in Education 4.0

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