More Like this

1. Toward a Theory of Youth Pedagogical Development: How Young People Learn to Teach.

   Yu, X; Gaston, A M; Freeman, C; Frankel, K K; Tucker-Raymond, E; Olivares, M C; Houston-King, A; Gutierrez, J M (April 2023, NA)

   NA (Ed.)

   This article introduces the concept of Youth Pedagogical Development (YPD), defined as an ongoing process of youth learning to teach other youth as they engage with academic knowledge and pedagogical strategies. To conceptualize and provide empirical support for YPD, we look at three case studies with community-based organizations that teach young people how to teach and mentor in STEM. We examine how youth’s teaching, mentoring, and learning from other near-peer youth transforms how youth identify as STEM teachers, learners, and doers. We propose that this development of youth teaching other youth creates more humanizing learning spaces where Black and Brown youth feel supported, cared for, and agentic. 

   more » « less
2. Towards a Framework for Smart Classrooms that Teach Instructors to Teach

   Gerritsen, David; Zimmerman, John; Ogan, Amy (July 2018, International Conference of the Learning Sciences)

   Teaching Assistants (TAs) play a major role in higher education; however, they receive little if any training on how to teach. Quality training requires access to grounded feedback and relevant suggestions for improvement. We developed a framework for using features of a smart classroom. This work reframes the instructor as the learner. It provides training on discursive practices with feedback based on the instructor’s in-class behaviors. We built and deployed a system based on this framework to five STEM TAs as part of a larger study. This paper: discusses the action-reflection-planning framework we used, provides evidence for how the framework addresses TA learning goals, and discusses how other researchers might make use of the framework. 

   more » « less
3. Teaching and Learning Through Pedagogical Environment Design

   Liquin, EG; Luzuriaga, N; Gureckis, TM (July 2023, Cognitive Science Society)

   People often rely on knowledgeable teachers to help them learn. Sometimes, this teaching is direct: teachers provide in- structions, examples, demonstrations, or feedback. But other times, teaching is more subtle: teachers construct the physi- cal environment in which a learner explores. In the present research, we investigate this more subtle form of teaching in an artificial grid-based learning environment. How do people construct the physical environment to teach, and how does the (pedagogical) design of the physical environment affect peo- ple’s learning? Study 1 shows that people pursue multiple ap- proaches to pedagogical environment design. Study 2 shows that learners make systematic, often accurate inferences from pedagogically designed environments, even in the absence of exploration. Together, these studies add to our understanding of the myriad ways in which experts communicate their knowl- edge to novices—a ca 

   more » « less
4. An Analysis of Prerequisites for Artificial Intelligence / Machine Learning-Assisted Malware Analysis Learning Modules

   https://doi.org/10.53735/cisse.v11i1.177  

   Pusey, Portia; Gupta, Maanak; Mittal, Sudip; Abdelsalam, Mahmoud (February 2024, Journal of The Colloquium for Information Systems Security Education)

   This paper presents the findings of action research conducted to evaluate new modules created to teach learners how to apply machine learning (ML) and artificial intelligence (AI) techniques to malware data sets. The trend in the data suggest that learners with cybersecurity competencies may be better prepared to complete the AI/ML modules’ exercises than learners with AI/ML competencies. We describe the challenge of identifying prerequisites that could be used to determine learner readiness, report our findings, and conclude with the implications for instructional design and teaching practice. 

   more » « less
5. Leveraging artificial intelligence to improve people’s planning strategies

   https://doi.org/10.1073/pnas.2117432119  

   Callaway, Frederick; Jain, Yash Raj; van Opheusden, Bas; Das, Priyam; Iwama, Gabriela; Gul, Sayan; Krueger, Paul M.; Becker, Frederic; Griffiths, Thomas L.; Lieder, Falk (March 2022, Proceedings of the National Academy of Sciences)

   Human decision making is plagued by systematic errors that can have devastating consequences. Previous research has found that such errors can be partly prevented by teaching people decision strategies that would allow them to make better choices in specific situations. Three bottlenecks of this approach are our limited knowledge of effective decision strategies, the limited transfer of learning beyond the trained task, and the challenge of efficiently teaching good decision strategies to a large number of people. We introduce a general approach to solving these problems that leverages artificial intelligence to discover and teach optimal decision strategies. As a proof of concept, we developed an intelligent tutor that teaches people the automatically discovered optimal heuristic for environments where immediate rewards do not predict long-term outcomes. We found that practice with our intelligent tutor was more effective than conventional approaches to improving human decision making. The benefits of training with our cognitive tutor transferred to a more challenging task and were retained over time. Our general approach to improving human decision making by developing intelligent tutors also proved successful for another environment with a very different reward structure. These findings suggest that leveraging artificial intelligence to discover and teach optimal cognitive strategies is a promising approach to improving human judgment and decision making. 

   more » « less