December 10, 2021
We are delighted to inform that Mohammad Khalil’s two full research papers have been accepted at the highly prestigious LAK conference coming up March next year. Khalil is a Senior researcher at SLATE. His main research interest includes Learning Analytics, Massive Open Online Courses (MOOCs), Visualizations, Big Data, Educational Technology, as well as Security/ Ethics/ Privacy in all these fields. Learn about Mohammad from his website. SLATE technical staff Gleb Belokrys and Martin Heitmann are authors on one of the papers.
More information about the LAK conference can be found her.
Below you will find abstracts for Khalil’s papers to be presented at LAK:
Title: A Comparison of LearningAnalytics Frameworks: a Systematic Review
Authors: Mohammad Khalil, Paul Prinsloo, Sharon Slade
Abstract: While learning analytics frameworks precede the official launch of learning analytics in 2011, there has been a proliferation of learning analytics frameworks since. This systematic review of learning analytics frameworks between 2011 and 2021 in three databases resulted in an initial corpus of 268 articles and conference proceeding papers based on the occurrence of “learning analytics” and “framework” in titles, keywords and abstracts. The final corpus of 46 frameworks were analysed using a coding scheme derived from purposefully selected learning analytics frameworks. The results found that learning analytics frameworks share a number of elements and characteristics such as source, development and application focus, a form of representation, data sources and types, focus and context. Less than half of the frameworks consider student data privacy and ethics. Finally, while elements or design/process elements of these frameworks may be transferable and scalable to other contexts, users in different contexts will be best- placed to determine their transferability/scalability.
Title: Tweetology of Learning Analytics: What does Twitter tell us about the trends and development of the field?
Authors: Mohammad Khalil, Jacqueline Wong, Erkan Er, Martin Heitmann and Gleb Belokrys
Abstract: Twitter is a very popular microblogging platform that has been actively used by scientific communities to exchange scientific information and to promote scholarly discussions. The present study aimed to leverage the tweet data to provide valuable insights into the development of the learning analytics field since its initial days. Descriptive analysis, geocoding analysis, and topic modeling were performed on over 1.6 million tweets related to learning analytics posted between 2010-2021. The descriptive analysis reveals an increasing popularity of the field on the Twittersphere in terms of number of users, twitter posts, and hashtags emergence. The topic modeling analysis uncovers new insights of the major topics in the field of learning analytics. Emergent themes in the field were identified, and the increasing (e.g., Artificial Intelligence) and decreasing (e.g., Education) trends were shared. Finally, the geocoding analysis indicates an increasing participation in the field from more diverse countries all around the world. Further findings are discussed in the paper.