COVID-19 has drastically changed the teaching patterns of higher education from face-to-face to online learning, and it has also affected students’ engagement socially and academically. Understanding the nature of students’ engagement during online learning can help in identifying related issues so that various initiatives can be implemented in adapting to this situation. In this study, social network analysis is conducted to gain insights on students’ engagement during COVID-19. Directed and weighted networks were used to visualize and analyze friendship as well as peer tutor networks obtained from online questionnaires answered by all students in the class. Contrasting friends and peer tutors reveals some hidden interactions between students and shines some light on dynamics of the online learning community. The results indicate that, popular and important peer tutors may not be high achievers and thus possibly contributing to the spread of misinformation in the online learning community. By comparing weighted indegree and betweenness centrality values, we suggest approaches to cultivate a healthy online learning community. This study highlights the use of social network analysis to assist and monitor students’ engagement and further formulate strategies in order to make the class a conducive online learning community, particularly in the advent of online learning in higher education institutions.

This article utilizes social network analysis in addition to a measure of genre diversity to quantify the quality and capacity of actors in the Malay language film industry. We built a dataset by collecting data from various websites pertaining to Malay films. The data consists of 180 Malay films released from 2015 until 2020. The actor network is then built by connecting actors co-starring in a movie together and is compared to small world networks. We quantified the quality of actors in the network using five measures: number of films (TFA), degree centrality (DC), strength centrality (SC), betweenness centrality (BC), and normalized Herfindahl–Hirschman Index (NHHI). TFA, DC and SC indicate experience in the industry, since a high TFA shows that an actor has acted in more films. A high DC shows an actor has worked with many co-stars, and a high SC reflects an actor’s frequency of co-occurrence relationship. Actors with high TFA, DC, and SC are popular in this sense. Meanwhile, BC highlights the social importance of an actor in the network where they are the middlemen that connect actors from different genres of movies in the network, and we found that high BC actors are voice actors that may not have a high TFA, DC, or SC. NHHI highlights the actor’s capability to work with different types of film, and it serves as an important measure of an actor’s versatility. Moreover, we also calculated the average shortest path in the network to search for the “Kevin Bacon” of the Malay language film actor network. Using NHHI as an indicator of genre diversity, we also show that most of the actors diversify their work over the years and that genre diversity is an important benchmark for an actor.