CROSSMMLA

Description

As Multimodal Learning Analytics (MMLA) evolves, it’s crucial to critically assess its methodologies, especially with Generative AI (GenAI) transforming educational data collection and analysis. GenAI offers significant potential for processing diverse multimodal data, enabling richer insights and personalized learning experiences. However, this rapid integration also brings challenges, including ethical concerns, algorithmic bias, transparency, and privacy issues. Addressing these complexities is essential to ensure that GenAI enhances learning without perpetuating inequities.

The upcoming workshop explores how GenAI could reshape MMLA research, addressing both opportunities and challenges. Researchers and practitioners will discuss practical approaches for integrating GenAI into MMLA responsibly, focusing on ethical, scalable, and transparent AI-powered tools. This workshop will build on the CROSSMMLA series’ legacy, offering insights into how GenAI can enhance learning analytics while maintaining ethical standards and promoting equitable access in education.

The half-day workshop will accommodate up to 40 participants, catering to newcomers and experts in multimodal analyses. Participants do not need technical expertise but should be interested in methodology and research design in MMLA. The workshop includes reflective and hands-on activities to develop a deep understanding of GenAI research in MMLA.

Workshop Objectives

The workshop will equip participants with practical skills for integrating GenAI into MMLA research. Participants will develop hands-on expertise in automating data collection, analysis, and feedback generation across multiple modalities while addressing bias, privacy, and scalability challenges. Ethical considerations will be a significant focus, including fairness, transparency, and societal impact.

The workshop also aims to generate best practices and methodological insights for implementing GenAI in educational contexts. Discussions will foster multidisciplinary collaboration and contribute to the learning analytics community through a publication or report summarizing key insights, challenges, and solutions.

Details of Activities

Elevator Pitches and Framing Discussions (30 minutes): Participants introduce themselves and share a brief pitch about their research interests. Facilitators will provide key definitions and curated research examples to frame discussions and encourage active participation.

Diving into GenAI Research in MMLA (1.5 hours): Organizers will present examples of GenAI tools applied in MMLA research, followed by hands-on activities where participants can apply these tools to their own or provided datasets.

Future Considerations for GenAI Research (1 hour): Participants will break into small groups to discuss challenges and opportunities for applying GenAI to MMLA, focusing on themes like data privacy, algorithmic bias, environmental impact, and transparency.

Reflections and Next Steps (30 minutes): The workshop concludes with a reflection survey and final discussion to summarize the outputs and insights gained.