L-AT-S

It is our great pleasure to present the Proceedings of the Eleventh Annual ACM Conference on Learning at Scale, L@S 2024, held July 18-20, 2024 at Georgia Tech in Atlanta, Georgia, USA.

The Learning at Scale conference was created by the Association for Computing Machinery (ACM), inspired by the emergence of Massive Open Online Courses (MOOCs) and the accompanying shift in thinking about education. During the last few years, new opportunities for scaling up learning have emerged, like hybrid learning environments combining online and face-to-face, and informal learning enabled by all sorts of platforms (e.g., gamified language learning, citizen science communities, and collaborative programming communities). In the recent two years, the unprecedented development of generative AI has brought profound opportunities to scale the teaching and learning experiences, with the goal of enhancing learning for the increasingly diverse group of learners in both formal and informal contexts. L@S has evolved along with these emergent massive learning scenarios and opportunities and is today one of the most prominent venues for discussion of the highest quality of research on how learning and teaching can be transformed at scale, in diverse learning environments.

The theme of L@S 2024 is Scaling Learning in the Age of AI. Rapid advances in AI have created new opportunities but also challenges for the Learning@Scale community. The advances in generative AI show potential to enhance pedagogical practices and the efficacy of learning at scale. This has led to an unprecedented level of interest in employing generative AI for scaling tutoring and feedback. The prevalence of such tools calls for new practices and understanding on how AI-based methods should be designed and developed to enhance the experiences and outcomes of teachers and learners.

Learning@Scale 2024 solicits empirical and theoretical papers on, but not limited to, the following topics (in no particular order): 1) Instruction at scale: studies that examine how teachers and educators scale their instructions, what aspects of instruction could be scaled effectively, and which of these instructional strategies are the most effective for learning. 2) Interventions at scale: studies that examine the effects of interventions on student learning and performance when implemented at scale. We welcome studies that use both qualitative and quantitative methods. 3) The use of generative AI to scale learning: studies that investigate stakeholders' experiences with generative AI, students' and teachers' interactions with generative AI, and the potentials and limitations of using generative AI in education. 4) Systems and tools to support learning at scale: research that designs and develops systems and tools to support learning at scale. For example, this involves scaling learning through web-based systems, MOOCs, visualization, intelligent tutoring systems, gamification, immersive techniques (AR/VR/MR), mobile technologies, tangible interfaces, and various other technologies. 5) The evaluation of existing learning at scale systems and online learning environments using but not limited to the above-mentioned technologies. 6) Methods and algorithms that model learner behavior: research that contributes methods, algorithms, and pipelines that process large student data to enhance learning at scale. 7) Scaling learning in informal contexts: studies that explore how people take advantage of online environments to pursue their interests informally. 8) Review and synthesis of existing literature related to learning at scale. 9) Empirical studies and interventions that address equity, trust, algorithmic transparency and explainability, fairness and bias when using AI in education. 10) Research that addresses accessibility in learning at scale contexts. 11) Design and deployment of learning at scale systems for learners from underrepresented groups.