International students comprise an increasingly larger proportion of higher education students globally. Empirical evidence about the health and well-being of these students is, however, limited. We sought to examine the health and well-being of international students, primarily from Asian countries, attending the University of Tasmania, Australia, using domestic students as a comparison group. Ethics approval was given to invite (via email) all currently enrolled students to participate in the study by completing a pilot-tested, online survey. The survey was completed by 382 international students (response rate = 8.9%) and 1013 domestic students (9.2%). Independent samples t-tests, analysis of variance (ANOVA) and chi-square tests were used for bivariate comparisons between international and domestic students, and between subgroups of international students. Regression models were used to examine the associations between student status (international vs. domestic) and health outcomes, controlling for demographic and enrolment variables. International students, particularly male students, were found to be at increased risk of several adverse health outcomes while also being less likely to seek help for mental health and related problems. The findings indicate the need for accessible, targeted, culturally-sensitive health promotion and early intervention programs.

Student performance predictive analysis has played a vital role in education in recent years. It allows for the understanding students’ learning behaviours, the identification of at-risk students, and the development of insights into teaching and learning improvement. Recently, many researchers have used data collected from Learning Management Systems to predict student performance. This study investigates the potential of clickstream data for this purpose. A total of 5341 sample students and their click behaviour data from the OULAD (Open University Learning Analytics Dataset) are used. The raw clickstream data are transformed, integrating the time and activity dimensions of students’ click actions. Two feature sets are extracted, indicating the number of clicks on 12 learning sites based on weekly and monthly time intervals. For both feature sets, the experiments are performed to compare deep learning algorithms (including LSTM and 1D-CNN) with traditional machine learning approaches. It is found that the LSTM algorithm outperformed other approaches on a range of evaluation metrics, with up to 90.25% accuracy. Four out of twelve learning sites (content, subpage, homepage, quiz) are identified as critical in influencing student performance in the course. The insights from these critical learning sites can inform the design of future courses and teaching interventions to support at-risk students.

This study aimed to identify factors influencing student engagement in online and blended courses at one Australian regional university. It applied a data science approach to learning and teaching data gathered from the learning management system used at this university. Data were collected and analysed from 23 subjects, spanning over 5500 student enrolments and 406 lecturer and tutor roles, over a five-year period. Based on a theoretical framework adapted from Community of Inquiry (CoI) framework by Garrison et al. (2000), the data were segregated into three groups for analysis: Student Engagement, Course Content and Teacher Input. The data analysis revealed a positive correlation between Student Engagement and Teacher Input, and interestingly, a negative correlation between Student Engagement and Course Content when a certain threshold was exceeded. The findings of the study offer useful suggestions for future course design, and pedagogical approaches teachers can adopt to foster student engagement.

Much research has been carried out looking into the psychological effects of Facebook usage. Research reveals that Facebook use can influence subjective wellbeing, either positively or negatively. Few studies have investigated the use of Facebook for online social support groups, but none has yet looked into social support group for parents of children with Autism Spectrum Disorder (ASD) particularly in Asian context. The aim of this preliminary study was to examine the types of support being exchanged on Facebook and the frequency of the postings. Using the deductive thematic analysis, 320 Facebook postings made by members of an online social support group were analysed during a two weeks period. The results show that sharing of personal experience is the purpose of common postings, accompanied with other themes on social support. Facebook social support group can provide great avenue for these parents and can also be extended to cater for other groups