Numerous studies have been conducted on the influence of peers on students' learning processes and their participation in online forums. However, these studies are limited in terms of system functionality and lack realtime analysis. In this study, we present InPath, a real-time analytics forum system, to rank and provide feedback on students' online participation performance. We leveraged other students' online discussion forum performance as an effective reference point to inspire forum participation. A set of learning metrics was generated to analyze students' contributions to online forums. The K-means clustering method was used to classify students into three broad levels: Hall of Fame, All Star, and Rookies. The results showed that students with higher badge levels were more likely to spend more time on forums. In summary, this study highlights the implications of this state-of-the-art system for learning analytics on online forums, including supporting instructors and students in determining overall and individual performance on the forum.INDEX TERMS real-time learning analytics, online discussion forums, feedback system, ranking system

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Online gaming is now a well-known electronic entertainment for people all around the world, especially university students. However, the increasing popularity of online gaming may lead to addiction, a problem that has received significant attention. This study aims to identify the factors relating to online gaming addiction. Based on 118 responses collected from online questionnaires, the bivariate correlation test was employed to examine the relationship between depression, loneliness, motivation for escapism and motivation for achievement with online game addiction. Cohen’s effect size f2 for each path is calculated. The findings show that depression, loneliness, motivation for achievement and motivation for escapism have a high positive correlation with online game addiction with large effect size. Keywords: Online Music Streaming Service, Perceived Value, Tangibility Preference, Music Affinity, Online Music Piracy

Prior education research has focused on using learning analytics to predict the academic performance of Massive Online Learning Courses (MOOCs) and e- learning courses in universities. There is limited research on online learning that has been transitioned from physical classes and that has continued to use active learning approaches in an online environment. This study aims to determine the variables affecting students’ academic performance for a computing course in a research-intense university during the COVID-19 pandemic. Variables that are indicative of self-regulated learning such as time management, frequency of accessing learning materials and the Learning Management System (LMS), participation in assessment activities and discussions, and the results of formative assessments were extracted from the LMS reports and log files to predict the students’ total marks and final exam results. The findings revealed that good time management and active participation are important for academic success. The results also supported the model for the early prediction of summative assessment performance using formative assessment results. Additionally, this study concludes that the gap in predictive power between formative assessment results and online learning behaviors is small. This research is considered unique because it demonstrates predictive models for students’ academic success for an institution that was forced to transition from physical to online learning. It highlights the importance of self-regulated learning behavior and formative assessments in the contemporary era.