Scholarship of Teaching and Learning Faculty Learning Community

Daibao’s mixed-methods study examines how Magic School, a generative AI tool, affects preservice teachers’ lesson-planning skills in social studies and shapes their views on classroom AI integration. Using a quasi-experimental design, two groups will create lesson plans with and without AI assistance. The study will analyze lesson quality and explore teachers’ perceptions of AI benefits, limitations, and ethical considerations through reflections and interviews.

Jenée’s project develops an undergraduate laboratory module using transient absorption (TA) spectroscopy to teach ultrafast photophysics and excited-state dynamics. Recently accessible through compact laser systems, TA spectroscopy will give students hands-on experience with real-time molecular dynamics while reinforcing quantum mechanics, thermodynamics, and kinetics concepts.

This mixed-methods study examines whether solving real-world problems enhances pre-service teachers’ AI literacy, self-efficacy, and actual AI integration practices. Conducted across two education technology course sections, the research aims to inform how to better prepare teachers for AI integration in their future classrooms.

This SoTL project examines how explicit teamwork instruction through Collaboration Futures professional development helps students learn collaboration skills in project-based courses. The instruction focuses on communication, role clarity, feedback, and inclusive team processes that support diverse working styles and engagement needs.

Kirsten’s project examines whether early success in CMGT 310 predicts final course performance by analyzing pass rates on the first six test objectives against final grades. It will also compare student groups to assess how attendance and worksheet requirements affect this relationship. Findings will inform potential interventions to improve early student success and overall course outcomes.

Lorraine’s project evaluates whether a redesigned Introduction to R Programming (DATA-R155) improves student learning and progression in an AI-rich era. Although many students are digital natives, they are not automatically digital learners; readily available code generation can both help and hinder learning. The redesign bundles updated materials, a shorter 7-week format, a new instructor, and specifications grading (checklist-based pass/not-yet with revision tokens) that emphasizes transparent standards and process documentation.

Lorraine presented a Lightning Talk at the 2026 Innovative Idaho conference on her research. Watch her presentation!

Megan’s SoTL study investigates a chemistry intervention for BIOL191 and BIOL227 students to address knowledge gaps that contribute to high DFW rates in these COAS-SRI identified courses. It will evaluate both the intervention’s effectiveness and student engagement with the optional program.

This study examines misalignment between student intent in course evaluation feedback and instructor interpretation. Through student surveys about their evaluation approach and instructor focus groups analyzing the same comments, the research aims to improve the evaluation process and outcomes for both parties. Benefits include better guidance for student feedback and enhanced instructor understanding of evaluation comments.