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Khor, Ean Teng
Preferred name
Khor, Ean Teng
Email
eanteng.khor@nie.edu.sg
Department
Office of Education Research (OER)
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ORCID
14 results
Now showing 1 - 10 of 14
- PublicationMetadata onlySystematic review on the application of multimodal learning analytics to personalize students’ learning(Association of Southeast Asian Teacher Education Network (AsTEN), 2024)
; 45 - PublicationOpen AccessA learning analytics approach using social network analysis and binary classifiers on virtual resource interactions for learner performance predictionThe COVID-19 pandemic induced a digital transformation of education and inspired both instructors and learners to adopt and leverage technology for learning. This led to online learning becoming an important component of the new normal, with home-based virtual learning an essential aspect for learners on various levels. This, in turn, has caused learners of varying levels to interact more frequently with virtual resources to supplement their learning. Even though virtual learning environments provide basic resources to help monitor the learners’ online behaviour, there is room for more insights to be derived concerning individual learner performance. In this study, we propose a framework for visualising learners’ online behaviour and use the data obtained to predict whether the learners would clear a course. We explored a variety of binary classifiers from which we achieved an overall accuracy of 80%–85%, thereby indicating the effectiveness of our approach and that learners’ online behaviour had a significant effect on their academic performance. Further analysis showed that common patterns of behaviour among learners and/or anomalies in online behaviour could cause incorrect interpretations of a learner’s performance, which gave us a better understanding of how our approach could be modified in the future.
Scopus© Citations 3 109 151 - PublicationOpen AccessA learning analytics conceptual framework to understand networked learning in the workplace(2021)Workers use social infrastructure known as networks in their everyday jobs to solve work-related issues. Networks, in this case, can be defined as a platform of social partnerships among workers that reveals the transfer of knowledge in their workplace. Hence, it is vital to understand how workers develop knowledge through these networks. This paper attempts to propose a conceptual framework to study networked learning in the workplace by examining how workers build connections through their networks and their learning interests. The framework deploys a multi-method design to triangulate and contextualise the findings. The research design of this study is tailored to address the two main research questions. It deploys analytic methods such as social network analysis and text analysis to understand learning interactions and learning needs respectively. The paper discusses the methodology approach that could be used to implement to address the research questions. The paper also presents a few conceptual examples of expected outcomes to demonstrate how to gain insights into the use of social network analysis and text analysis.
164 222 - PublicationOpen AccessStudent perceptions of using generative AI chatbot in learning programming(Asia-Pacific Society for Computers in Education, 2024)
; ; Interest in Generative AI (GenAI) chatbots has exploded across the education industry, subsequently expanding in sophistication and usage. However, students' perceptions on utilizing this technology ultimately determines how well these chatbots promote learning. Hence, this study examines the factors influencing how Singapore secondary school-aged computing students perceive using MyBotBuddy (MBB), a GenAI chatbot, to assist them with programming tasks. A thematic analysis determined that students' perceptions were influenced by reliability, utility, cognitive effort needed, satisfaction, and enjoyment. The study contributes to the literature on GenAI chatbots supporting secondary school programming students and may guide the development of such tools in secondary school classrooms more effectively.30 245 - PublicationMetadata onlyA data mining approach using machine learning algorithms for early detection of low-performing studentsPurpose The purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance. Design/methodology/approach For the first step, the author performed exploratory data analysis to analyze the dataset. The process was then followed by data pre-processing and feature engineering (Step 2). Next, the author conducted data modelling and prediction (Step 3). Finally, the performance of the developed models was evaluated (Step 4). Findings The paper found that the decision trees algorithm outperformed other machine learning algorithms. The study also confirms the significant effect of the academic background and virtual learning environment (VLE) interactions feature categories to academic performance. The accuracy enhancement is 17.66% for decision trees classifier, 3.49% for logistic regression classifier and 4.89% for neural networks classifier. Based on the results of CorrelationAttributeEval technique with the use of a ranker search method, the author found that the assessment_score and sum_click features are more important among academic background and VLE interactions feature categories for the classification analysis in predicting students' academic performance.
WOS© Citations 2 164 - PublicationOpen AccessA learning analytics approach to model and predict learners’ success in digital learning(2019)
; Learning analytics methods are widely applied in the educational field to gain insights on hidden patterns from educational data. Methods like predictive learning analytics are used to identify and measure patterns in learning data and extrapolate future behaviours. It can be used to enable the learners to be more self-aware of their learning behaviours and to enable the instructor to take appropriate actions informed by the trace of data. Thus such methods can empower learners as they progress through online training, and allows them to be self-regulated in order to solidify their learning and develop positive habits that will enhance their learning experiences. This paper reports on the use of a popular decision tree classification algorithm using behavioural features from a public domain dataset to develop a predictive model for predicting learning performance. Among the five behavioural features, we find that the measure of visited resources provides the most discriminating rules in the classifier.509 451 - PublicationEmbargoEducational chatbots for personalised learning: A systematic reviewWith the rapid increase in technology in recent years, there has been a shift towards improving education using artificial intelligence, such as chatbots. Studies have shown chatbots' potential in tutoring, teaching and completing administrative tasks. However, when it comes to reviewing the application and effectiveness of chatbots under the personalised learning approach, studies have yet to conduct a review on personalised learning chatbots on students. A systematic review was hence conducted to evaluate their effects on students and their potential application in education. Fifteen articles are included in the final review after thorough filtering from three well-known databases. Results have shown that personalised learning chatbots are commonly applied as assistants or teachers to class content, as well as virtual alternatives to lessons. While they showed potential in enhancing students' learning via tailored content, feedback, and engagement, most studies still encountered functional and feasibility challenges during their implementation. With multiple studies showing how personalised teaching has significantly improved students' grades and understandings of class content, insights obtained from this study could encourage further studies on the possible improvement and enhancement that could be made or even recommend educators to implement such technology in their curriculum for the betterment of future education.
25 - PublicationOpen AccessWorking adults' intentions to participate in Microlearning: Assessing for measurement invariance and structural invariance(Frontiers, 2021)
; The current study set out to understand the factors that explain working adults’ microlearning usage intentions using the Decomposed Theory of Planned Behaviour (DTPB). Specifically, the authors were interested in differences, if any, in the factors that explained microlearning acceptance across gender, age and proficiency in technology. 628 working adults gave their responses to a 46-item, self-rated, 5-point Likert scale developed to measure 12 constructs of the DTPB model. Results of this study revealed that a 12-factor model was valid in explaining microlearning usage intentions of all working adults, regardless of demographic differences. Tests for measurement invariance showed support for invariance in model structure (configural invariance), factor loadings (metric invariance), item intercepts (scalar invariance), and item residuals (strict invariance) between males and females, between working adults below 40 years and above 40 years, and between working adults with lower technology proficiency and higher technology proficiency levels. While measurement invariance existed in the data, structural invariance was only found across gender, not age and technology proficiency. We then assessed latent mean differences and structural path differences across groups. Our findings suggest that a tailored approach to encourage the use of microlearning is needed to suit different demographics of working adults. The current study discusses the implications of the findings on the use and adoption of microlearning and proposes future research possibilities.WOS© Citations 1Scopus© Citations 2 97 156 - PublicationMetadata onlyA systematic review of the role of learning analytics in supporting personalized learning
Personalized learning is becoming more important in today’s diverse classrooms. It is a strategy that tailors instruction to each student’s abilities and interests. The benefits of personalized learning include students’ enhanced motivation and academic success. The average teacher-to-student ratio in classes is 1:15.3, making it challenging for teachers to identify each student’s areas of strength (or weakness). Learning analytics (LA), which has recently revolutionized education by making it possible to gather and analyze vast volumes of student data to enhance the learning process, has the potential to fill the need for personalized learning environments. The convergence of these two fields has, therefore, become an important area for research. The purpose of this study is to conduct a systematic review to understand the ways in which LA can support personalized learning as well as the challenges involved. A total of 40 articles were included in the final review of this study, and the findings demonstrated that LA could support personalized instruction at the individual, group, and structural levels with or without teacher intervention. It can do so by (1) gathering feedback on students’ development, skill level, learning preferences, and emotions; (2) classifying students; (3) building feedback loops with continuously personalized resources; (4) predicting performance; and (5) offering real-time insights and visualizations of classroom dynamics. As revealed in the findings, the prominent challenges of LA in supporting personalized learning were the accuracy of insights, opportunity costs, and concerns of fairness and privacy. The study could serve as the basis for future research on personalizing learning with LA.
Scopus© Citations 3 114 - PublicationOpen AccessMining educational data to predict learners' performance using decision tree algorithm(2018)Data mining is gaining increasing traction in the field of education as its applications in the education sector has increased over the past few years. Different data mining methods can be used to gain insights into educational data, including the uncovering of hidden patterns and prediction of output. The methods include classification analysis, association rule learning, anomaly or outlier detection, clustering analysis, and regression analysis. In this study, the classification analysis is used with decision tree algorithms to predict learners' performance. The findings reveal that the algorithm can be used to build a predictive model with good performance measure based on accuracy level, true positive (TP) rate, and false positive (FP) rate.
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