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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.

The 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.

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.

Predictive modelling in the education domain can be utilised to significantly improve teaching and learning experiences. Massive Open Online Courses (MOOCs) generate a large volume of data that can be exploited to predict and evaluate student performance based on various factors. This paper has two broad aims. Firstly, to develop and tune several Machine Learning (ML) models to perform classification tasks on the dataset to predict student performance, including Linear Regression, Logistic Regression, Random Forests, K-Nearest Neighbours, and more. Secondly, to evaluate the efficacy of these ML models and identify those which are best suited to this task. The categories of data utilised in achieving these aims include (i) demographic information, (ii) academic background, and (iii) interaction with MOOC course materials. The research procedure comprises five phases: data exploration to analyse the dataset, feature engineering which involves discerning the most important features and converting them into a format decipherable by the ML models, model building, model evaluation by measurement of accuracy, and subsequent comparative evaluation between the different models. The results achieved in this study are expected to have implications on how MOOC platforms utilise data to improve user experience. As indicated by the findings of this study, the data collected by these platforms may be used to predict performance with accuracy of over 77%; this extracted information can be exploited to enhance educational theory or practices in the context of MOOCs, for instance by implementing varying teaching methodologies or providing different types of resources based on predicted performance.

Virtual collaborative learning has been gaining in popularity in open and distance learning (ODL) over the last decade. In collaborative learning environments, the instructors facilitate and initiate discussion on particularissues of concern. Students are given the chance to share their knowledge in a way theyhave not been used to and the possibility of participating in a coordinated effort to solve problems together. A preliminary study showed that collaborative learning activities enhancedstudent satisfaction, achievedthe course learning outcomes and encouragedgroup participation. Peer-to-peer interaction has been shown to be successful and can be engaged in during collaborative learning activities. Wiki is widely promoted as a virtual collaborative tool and has been integrated into several learning management systems. However, there are only limited studies on the effectiveness of moodle-wiki for virtual collaborative learning. Therefore, the aim of this study is to evaluate the effectiveness of moodle-wiki in terms of students' perceptions and performance. The data collected were analysed and evaluated, and the statistical results demonstrate that the students hadpositive perceptions of moodle-wiki and the collaborative tool enhanced their learning performance. This study will be useful for instructors and course designers as a guide to investigating students' perceptions and evaluating the effectiveness of a collaborative tool. The findings of this research are also useful for stakeholders to maximize students' academic learning achievement.

Machine learning and data mining techniques have been widely used in educational settings to identify the important features that tend to influence students' learning performance and predict their future performance. However, there is little to no research done in the context of Singapore's education. Hence, this study aims to fill the gap by developing an early detection model for weaker students using various machine learning techniques and investigate potential factors that may affect students' learning performance. All the data analysis and model development are done using Python. Firstly, exploratory data analysis is performed to analyse the dataset. Secondly, data pre-processing and feature engineering were performed. Next, three models were developed using decision tree, random forest, and neural network. The performance of the developed models was then evaluated. From our analysis, we found that neural network achieved the highest accuracy of 74.46%. From the confusion matrix, most of the values are within or near the matrix diagonal, indicating that the model is a good fit. Further model improvements could also be done, such as pruning of the decision trees, or the use of ensemble models such as soft voting classifiers to prevent overfitting and improve overall accuracy.

Purpose: This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course. Design/methodology/approach: The exploration and visualisation of the data were first carried out to gain a better understanding of the students, the course(s) each student was enrolled in and each course's virtual learning resources. Following this, the construction of the social network graphs was performed to depict how each student behaved online before the degree centralities were computed for each of the nodes in a social network graph. Data pre-processing to assign labels based on the final result a student obtained in a course was then performed before we trained and tested models to predict which students did or did not graduate. Findings: The study's findings demonstrate that the constructed predictive model has good performance, as shown by the accuracy, precision, recall and f-measure metrics. The outcomes also showed that students' use of online resources is a crucial element that influences how well they perform in their academics. Originality/value: The similarity index is as low as 9%.