The growing adoption of Large Language Models (LLMs) is transforming learning in higher education. While they provide flexible support, concerns remain about over-reliance, uncritical acceptance of outputs, and excessive cognitive offloading. To address these challenges, we foreground self-regulated learning for LLMs (SRL-for-LLM), defined as learners' ability to plan, monitor, and reflect on AI interactions to support learning. Guided by SRL theory, we introduce ChatWise, a proof-of-concept browser extension that combines conversational scaffolding with learning analytics to classify student prompts by SRL strategies and deliver adaptive metacognitive feedback in real time. A two-phase study included the design of ChatWise and a within-subject field study with STEM undergraduates. Log analyses using mixed-effects modeling showed that access to ChatWise more than doubled the likelihood of producing high-quality prompts. Findings further indicate improved prompt refinement, greater strategic awareness, and more reflective engagement with AI-supported learning. These results highlight the potential of SRL-aligned, analytics-driven scaffolding to support more effective student–LLM interactions.

Reading skills are crucial for students' success in education and beyond. However, reading proficiency among K-12 students has been declining globally, including in Sweden, leaving many underprepared for post-secondary education. Additionally, an increasing number of students have reading disorders, such as dyslexia, which require support. Generative artificial intelligence (genAI) technologies, like ChatGPT, may offer new opportunities to improve reading practices by enhancing the readability of educational texts. This study investigates whether ChatGPT-4 can simplify academic texts and which prompting strategies are most effective. We tasked ChatGPT to re-write 136 academic texts using four prompting approaches: Standard, Meta, Roleplay, and Chain-of-Thought. All four approaches improved text readability, with Meta performing the best overall and the Standard prompt sometimes creating texts that were less readable than the original. This study found variability in the simplified texts, suggesting that different strategies should be used based on the specific needs of individual learners. Overall, the findings highlight the potential of genAI tools, like ChatGPT, to improve the accessibility of academic texts, offering valuable support for students with reading difficulties and promoting more equitable learning opportunities.

Goal setting is the first and driving stage of the self-regulated learning cycle. Studies have shown that supporting goal setting is an effective means of improving academic performance among higher education students. However, doing so can be complex and resource intensive. In this study, a goal-setting conversational agent was designed and deployed to support higher education students in setting academic goals. Across 5-weeks, we tested the effects of goal-setting prompts (guided vs. unguided) and adaptive feedback (with vs. without) when delivered via a goal-setting conversational agent. We explored the effects of these supports (i.e., guidance and feedback) on students’ 1) goal quality and 2) goal attainment. Findings showed that guidance and feedback combined had the largest positive effect on goal quality. They also revealed that guidance alone produced initially high-quality goals which decreased in quality overtime, whereas feedback had a delayed but cumulative effect on quality across multiple goal setting iterations. However, neither guidance nor feedback had significant effects on goal attainment, and there was no significant relationship between goal quality and attainment. This study provides insights into how a goal-setting conversational agent and adaptive feedback can be used to support the academic goal setting process for higher education students.

AI-powered education technologies can support students and teachers in computer science education. However, with the recent developments in generative AI, and especially the increasingly emerging popularity of ChatGPT, the effectiveness of using large language models for solving programming tasks has been underexplored. The present study examines ChatGPT’s ability to generate code solutions at different difficulty levels for introductory programming courses. We conducted an experiment where ChatGPT was tested on 127 randomly selected programming problems provided by Kattis, an automatic software grading tool for computer science programs, often used in higher education. The results showed that ChatGPT independently could solve 19 out of 127 programming tasks generated and assessed by Kattis. Further, ChatGPT was found to be able to generate accurate code solutions for simple problems but encountered difficulties with more complex programming tasks. The results contribute to the ongoing debate on the utility of AI-powered tools in programming education.

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.

Learning analytics dashboards (LADs) are designed as feedback tools for learners, but until recently, learners rarely have had a say in how LADs are designed and what information they receive through LADs. To overcome this shortcoming, we have developed a customisable LAD for Coursera MOOCs on which learners can set goals and choose indicators to monitor. Following a mixed-methods approach, we analyse 401 learners’ indicator selection behaviour in order to understand the decisions they make on the LAD and whether learner goals and self-regulated learning skills influence these decisions. We found that learners overwhelmingly chose indicators about completed activities. Goals are not associated with indicator selection behaviour, while help-seeking skills predict learners’ choice of monitoring their engagement in discussions and time management skills predict learners’ interest in procrastination indicators. The findings have implications for our understanding of learners’ use of LADs and their design.

Massive Open Online Courses (MOOCs) allow learning to take place anytime and anywhere with little external monitoring by teachers. Characteristically, highly diverse groups of learners enrolled in MOOCs are required to make decisions related to their own learning activities to achieve academic success. Therefore, it is considered important to support self-regulated learning (SRL) strategies and adapt to relevant human factors (e.g., gender, cognitive abilities, prior knowledge). SRL supports have been widely investigated in traditional classroom settings, but little is known about how SRL can be supported in MOOCs. Very few experimental studies have been conducted in MOOCs at present. To fill this gap, this paper presents a systematic review of studies on approaches to support SRL in multiple types of online learning environments and how they address human factors. The 35 studies reviewed show that human factors play an important role in the efficacy of SRL supports. Future studies can use learning analytics to understand learners at a fine-grained level to provide support that best fits individual learners. The objective of the paper is twofold: (a) to inform researchers, designers and teachers about the state of the art of SRL support in online learning environments and MOOCs; (b) to provide suggestions for adaptive self-regulated learning support.

A Massive Open Online Course (MOOC) is a type of online learning environment that has the potential to increase students' access to education. However, the low completion rates in MOOCs suggest that student engagement and progression in the courses are problematic. Following the increasing adoption of gamification in education, it is possible that gamification can also be effectively adopted in MOOCs to enhance students' motivation and increase completion rates. Yet at present, the extent to which gamification has been examined in MOOCs is not known. Considering the myriad gamification elements that can be adopted in MOOCs (e.g., leaderboards and digital badges), this theoretical research study reviews scholarly publications examining gamification of MOOCs. The main purpose is to provide an overview of studies on gamification in MOOCs, types of research studies, theories applied, gamification elements implemented, methods of implementation, the overall impact of gamification in MOOCs, and the challenges faced by researchers and practitioners when implementing gamification in MOOCs. The results of the literature study indicate that research on gamification in MOOCs is in its early stages. While there are only a handful of empirical research studies, results of the experiments generally showed a positive relation between gamification and student motivation and engagement. It is concluded that there is a need for further studies using educational theories to account for the effects of employing gamification in MOOCs.

We present a mobile game app (EUR Game) that has been designed to complement teaching and learning in higher education. The mobile game app can be used by teachers to gauge how well students are meeting the learning objectives. Teachers can use the information to provide 'just-in-time' support and adapt their lessons accordingly. For the students, the game app is a study tool that can be used to test their own understanding and monitor their study progress. This, in turn, supports students' self-regulated learning. Gamification elements are also included in the game app to enhance the learning experience. During the demonstration, participants will experience the features of the game app and be engaged in an interactive session to explore the possible ways to use the mobile game app to support teaching and learning.

How can we design for more active participation and engagement in massive online virtual worlds? While many online communities, be they online games or virtual worlds, have been created or used ostensibly with learning goals in mind, relatively little has been studied about how to motivate members collectively (rather than individually or in small groups) in productive activities, especially with children and teens. To this end we describe the design and impact of a virtual epidemic in a massive online community called Whyville.net that engaged youth players in an infectious disease outbreak. Our analyses of click data captured in log files and observations reveal which factors impacted players' changes in participation, including searches for information, engagement in prevention, and donations to vaccine design. In the discussion, we address what we have learned about identity, agency and timing as levers in designing participation in massive community experiences.

In this paper, we report on the study of a new virtual epidemic called the Dragon Swooping Cough, a newly designed virus unleashed on the youth virtual world of Whyville.net in two stages during December 2015 and April 2016. Our overall goal in this study was to design experiential learning of infectious disease in a safe but epidemiologically and educationally sound way. The virtual virus targeted personal, social, and economic aspects of online life in Whyville in order to mirror real-world viruses and to trigger player emotions. Our analysis of pre/post surveys and online behavior log files for survey (N = 747) and non-survey (N = 3348) participants revealed that the virtual epidemic promoted participation, primarily through engagement in prevention against the virtual virus, that increased in the second outbreak. Furthermore, emotional engagement played an intriguing role in both behavioral and information-seeking behaviors. In the discussion we address what we learned about opportunities and challenges in designing a virtual epidemic for educational engagement.

Measuring the experience of audience of arts events is essential in the “experience economy” of this day and age, but it is a difficult task. The value of such information goes beyond evaluating the impact of the arts, as it can provide insights and feedback to enhance the work of artists and the experiences of other audience members. Through in-depth understanding of the needs of the providers and consumers of the arts, we progressively developed a biosensor infrastructure that was deployed in theaters. Over the years, we identified the challenges and issues related to developing and deploying a biosensor infrastructure in theaters. These collective experiences and identified issues were categorized into three main areas: processes, data, and system. A total of seven heuristics are developed across the three main areas. Processes place the stakeholders and audiences at the core of the research; data provides guidelines for data validity, collecting a variety of data, and supporting real-time data gathering; and systems covers the concurrency, scalability, deployment and feedback of the infrastructure. We believe that this set of heuristics forms the foundation for an adequate infrastructure to measure audience experience in the wild and it is a valuable source of guideline for future work.