The rising number of students in computer science presents a challenge for educators to analyse student work and identify problem areas at scale. Learning analytics systems often fall short in offering detailed, actionable insights that educators can use to enhance their teaching. To address this, we propose a system leveraging Large Language Models to analyse programming submissions and generate actionable analytics. The study focused on two primary research questions regarding the accuracy of LLMs in classifying programming submissions and identifying common issues, and educators' perceptions of the usefulness of the system. Through a system evaluation and focus group, we found that LLMs can analyse SQL assignment submissions with reasonable accuracy and can identify common issues. Educators found the insights potentially useful but noted areas for improvement such as the need for concrete statistics, accurate lists of submissions with specific issues, and more readable reports. Future research should address these implications for the system, evaluate the system in other courses with different programming languages, and involve stakeholders directly related to the course.

Spam calls are a widespread problem as people receive about 14 spam calls per month on average. In response, tens of applications are available in Google’s Play Store that aim to block these calls. While these apps have hundreds of millions of installations, there’s a lack of research into these applications. Research has been done on the user experience of these apps, but the inner workings of the apps have not been explored. Analyzing the inner workings can provide useful insights to combat spam calls more effectively. In this paper, we analyze the elements of the AndroidManifest.xml file of a set of 10 spam call blocking applications to identify aspects that are related to the blocking of spam calls and to find commonalities among the apps. Occurrences of specific elements and their attributes are summarized in this paper. Permissions related to managing phone calls and accessing the internet were found to be common, and services were identified that screen and manage phone calls. 2 apps were found to allow other apps to use their phone number information database, but this analysis did not identify usages by other apps. A complete analysis of all available spam call blocking apps could show the general usage of these and other components.