- Year 2024
- NSF Noyce Award # 2151141
- First Name Elsa
- Last Name Villa
- Institution The University of Texas at El paso
- Role/Position Principal Investigator (PI)
- Proposal Type Workshop
- Workshop Category Track 2: Teaching Fellowships
- Workshop Disciplines Audience Computer Science
- Target Audience Noyce Master Teachers, Noyce Teaching Fellows, Undergraduate and/or Graduate Noyce Scholars
- Topics Culturally Relevant Pedagogy
- Additional Presenter(s)
Mariana Alvidrez/malvidrz@nmsu.edu; Christabel Wayllace/cwayllac@nmsu.edu; Kevin Sias/kesias@utep.edu; Seth Sias/sesias@utep.edu
Goals
Through hands-on activities and insightful discussions, participants will gain an insight into (1) the classification of reinforcement learning (RL) algorithms, (2) the fundamentals of a basic RL algorithm, (3) parallels and implications for both machine and human learning, and (4) the importance of embracing mistakes as valuable learning resources.
Evidence
Research on learning from mistakes in mathematics classrooms and initial research findings on developing a framework to learn from mistakes used in an AI course in computer science.
Proposal
This workshop provides preservice and practicing teachers with opportunities to explore Reinforcement Learning (RL), emphasizing the pedagogical value of mistakes in CS and mathematics classrooms. Through hands-on activities and insightful discussions, participants will gain an insight into (1) the classification of RL algorithms, (2) the fundamentals of a basic RL algorithm, (3) parallels and implications for both machine and human learning, and (4) the importance of embracing mistakes as valuable learning resources. In Computer Science (CS), the common practice of debugging a program might implicitly imply teachers’ productive framing of students’ mistakes. However, are CS and mathematics teachers capitalizing on their students’ mistakes to develop their knowledge and skills beyond the mere act of debugging? Participants in this workshop will explore the intriguing idea of embracing mistakes as valuable learning resources in the context of mathematics and computer science education by discussing a framework that illustrates teachers’ framings of errors and students who err. Additionally, we will discuss the preliminary findings of a pilot study conducted in an artificial intelligence course, where an innovative assessment strategy based on error analysis has been designed and implemented to promote students’ knowledge and skill development. This approach will provide teachers who are searching for new ways to assess and promote their metacognition skills with research-base
