skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Towards a Framework for Smart Classrooms that Teach Instructors to Teach
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
Award ID(s):
1747997 1464204
PAR ID:
10073948
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
International Conference of the Learning Sciences
Volume:
3
Page Range / eLocation ID:
1779-1782
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; (, Springer Nature Switzerland)
    Cristea, Alexandra; Walker, Erin; Lu, Yu; Santos, Olga (Ed.)
    This project examines the prospect of using AI-generated feedback as suggestions to expedite and enhance human instructors’ feedback provision. In particular, we focus on understanding the teaching assistants’ perspectives on the quality of AI-generated feedback and how they may or may not utilize AI feedback in their own workflows. We situate our work in a foundational college Economics class, which has frequent short essay assignments. We developed an LLM-powered feedback engine that generates feedback on students’ essays based on grading rubrics used by the teaching assistants (TAs). To ensure that TAs can meaningfully critique and engage with the AI feedback, we had them complete their regular grading jobs. For a randomly selected set of essays that they had graded, we used our feedback engine to generate feedback and displayed the feedback as in-text comments in a Word document. We then performed think-aloud studies with 5 TAs over 20 1-hour sessions to have them evaluate the AI feedback, contrast the AI feedback with their handwritten feedback, and share how they envision using the AI feedback if they were offered as suggestions. The study highlights the importance of providing detailed rubrics for AI to generate high-quality feedback for knowledge-intensive essays. TAs considered that using AI feedback as suggestions during their grading could expedite grading, enhance consistency, and improve overall feedback quality. We discuss the importance of decomposing the feedback generation task into steps and presenting intermediate results, in order for TAs to use the AI feedback. 
    more » « less
  2. ; ; ; ; ; (, Springer Nature Switzerland)
    Cristea, Alexandra; Walker, Erin; Lu, Yu; Santos, Olga (Ed.)
    This project examines the prospect of using AI-generated feedback as suggestions to expedite and enhance human instructors’ feedback provision. In particular, we focus on understanding the teaching assistants’ perspectives on the quality of AI-generated feedback and how they may or may not utilize AI feedback in their own workflows. We situate our work in a foundational college Economics class, which has frequent short essay assignments. We developed an LLM-powered feedback engine that generates feedback on students’ essays based on grading rubrics used by the teaching assistants (TAs). To ensure that TAs can meaningfully critique and engage with the AI feedback, we had them complete their regular grading jobs. For a randomly selected set of essays that they had graded, we used our feedback engine to generate feedback and displayed the feedback as in-text comments in a Word document. We then performed think-aloud studies with 5 TAs over 20 1-hour sessions to have them evaluate the AI feedback, contrast the AI feedback with their handwritten feedback, and share how they envision using the AI feedback if they were offered as suggestions. The study highlights the importance of providing detailed rubrics for AI to generate high-quality feedback for knowledge-intensive essays. TAs considered that using AI feedback as suggestions during their grading could expedite grading, enhance consistency, and improve overall feedback quality. We discuss the importance of decomposing the feedback generation task into steps and presenting intermediate results, in order for TAs to use the AI feedback. 
    more » « less
  3. ; ; ; ; (, Computers and Education Open)
    To address the increasing demand for AI literacy, we introduced a novel active learning approach that leverages both teaching assistants (TAs) and generative AI to provide feedback during in-class exercises. This method was evaluated through two studies in separate Computer Science courses, focusing on the roles and impacts of TAs in this learning environment, as well as their collaboration with ChatGPT in enhancing student feedback. The studies revealed that TAs were effective in accurately determining students’ progress and struggles, particularly in areas such as “backtracking”, where students faced significant challenges. This intervention’s success was evident from high student engagement and satisfaction levels, as reported in an end-of-semester survey. Further findings highlighted that while TAs provided detailed technical assessments and identified conceptual gaps effectively, ChatGPT excelled in presenting clarifying examples and offering motivational support. Despite some TAs’ resistance to fully embracing the feedback guidelines-specifically their reluctance to provide encouragement-the collaborative feedback process between TAs and ChatGPT improved the quality of feedback in several aspects, including technical accuracy and clarity in explaining conceptual issues. These results suggest that integrating human and artificial intelligence in educational settings can significantly enhance traditional teaching methods, creating a more dynamic and responsive learning environment. Future research will aim to improve both the quality and efficiency of feedback, capitalizing on unique strengths of both human and AI to further advance educational practices in the field of computing. 
    more » « less
  4. ; (, ACM Technical Symposium on Computer Science Education)
    null (Ed.)
    As CS enrollments continue to grow, introductory courses are employing more undergraduate TAs. One of their main roles is performing one-on-one tutoring in the computer lab to help students understand and debug their programming assignments. What goes on in the mind of an undergraduate TA when they are helping students with programming? In this experience report, we present firsthand accounts from an undergraduate TA documenting her 36 hours of in-lab tutoring for a CS2 course, where she engaged in 69 one-on-one help sessions. This report provides a unique perspective from an undergraduate's point-of-view rather than a faculty member's. We summarize her experiences by constructing a four-part model of tutoring interactions: a) The tutor begins the session with an initial state of mind (e.g., their energy/focus level, perceived time pressure). b) They observe the student's outward state upon arrival (e.g., how much they seem to care about learning). c) Using that observation, the tutor infers what might be going on inside the student's mind. d) The combination of what goes on inside the tutor's and student's minds affects tutoring interactions, which progress from diagnosis to planning to an explain-code-react loop to post-resolution activities. We conclude by discussing ways that this model can be used to design scaffolding for training novice TAs and software tools to help TAs scale their efforts to larger classes. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; ; et al (, Nature Communications)
    Abstract High-density phase change memory (PCM) storage is proposed for materials with multiple intermediate resistance states, which have been observed in 1T-TaS2due to charge density wave (CDW) phase transitions. However, the metastability responsible for this behavior makes the presence of multistate switching unpredictable in TaS2devices. Here, we demonstrate the fabrication of nanothick verti-lateralH-TaS2/1T-TaS2heterostructures in which the number of endotaxial metallicH-TaS2monolayers dictates the number of resistance transitions in 1T-TaS2lamellae near room temperature. Further, we also observe optically active heterochirality in the CDW superlattice structure, which is modulated in concert with the resistivity steps, and we show how strain engineering can be used to nucleate these polytype conversions. This work positions the principle of endotaxial heterostructures as a promising conceptual framework for reliable, non-volatile, and multi-level switching of structure, chirality, and resistance. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email