supervisor: hannie gijlers
Topic
Learning genetics is crucial for understanding biodiversity, disease mechanisms, and genetic engineering. It not only helps students develop a deep understanding of life sciences but also facilitates recognition of human genetic diseases and traits, significantly impacting modern medicine and biotechnological fields. Therefore, genetics education is a fundamental component of science education. However, learning genetics is often considered challenging due to its reliance on extensive professional terminology and abstract concepts such as gene mutations, dominant and recessive inheritance, and gene expression regulation. These concepts require students to possess strong abstract thinking abilities. Moreover, experimental genetics learning involves hands-on laboratory work, which demands high technical skills and equipment, further complicating the learning process. Traditional teaching methods may not effectively explain these complex concepts nor inspire deep interest and understanding in students. To address these challenges, we have designed a collaborative learning environment specifically tailored for high school students. This environment features two unique 30-minute tasks that encourage active discussion and teamwork among students, designed to deepen their comprehension of genetics:
- Collaborative Learning without Clair Intervention: Students will engage in traditional collaborative learning methods, relying on peer interactions without technological mediation
- Collaborative Learning with Clair: Students will interact with Clair, a conversational AI agent, specifically designed to facilitate and enhance their discussions by providing guided prompts.
Method
This experimental study will adapt and implement an collaborative learning environment in a high school setting. Data collection will involve logging interactions within the learning environment and gathering responses through surveys and questionnaires before and after the completion of the learning tasks. The effectiveness of these educational interventions will be evaluated by examining changes in students’ understanding of genetics concepts. This method will allow us to measure the efficacy of traditional versus AI-enhanced collaborative learning in improving students' understanding toward genetics.
references
de Jong, T. (2019). Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead. Journal of computer assisted learning, 35(2), 153-167.
Thomson, N., & Stewart, J. (2003). Genetics inquiry: Strategies and knowledge geneticists use in solving transmission genetics problems. Science Education, 87(2), 161–180. https://doi.org/10.1002/sce.10065
de Araujo, A., Papadopoulos, P. M., McKenney, S., & de Jong, T. (2023). Automated coding of student chats, a trans-topic and language approach. Computers and Education: Artificial Intelligence, 4, 100123.
de Araujo, A., Papadopoulos, P. M., McKenney, S., & de Jong, T. (2024). A learning analytics‐based collaborative conversational agent to foster productive dialogue in inquiry learning. Journal of Computer Assisted Learning, 1–15. https://doi.org/10.1111/jcal.13007