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.
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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.
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- PAR ID:
- 10073948
- 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
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Abstract This descriptive study focuses on using voice activity detection (VAD) algorithms to extract student speech data in order to better understand the collaboration of small group work and the impact of teaching assistant (TA) interventions in undergraduate engineering discussion sections. Audio data were recorded from individual students wearing head‐mounted noise‐cancelling microphones. Video data of each student group were manually coded for collaborative behaviours (eg, group task relatedness, group verbal interaction and group talk content) of students and TA–student interactions. The analysis includes information about the turn taking, overall speech duration patterns and amounts of overlapping speech observed both when TAs were intervening with groups and when they were not. We found that TAs very rarely provided explicit support regarding collaboration. Key speech metrics, such as amount of turn overlap and maximum turn duration, revealed important information about the nature of student small group discussions and TA interventions. TA interactions during small group collaboration are complex and require nuanced treatments when considering the design of supportive tools.
Practitioner notes What is already known about this topic
Student turn taking can provide information about the nature of student discussions and collaboration.
Real classroom audio data of small groups typically have lots of background noise and present challenges for audio analysis.
TAs have little training in how to productively intervene with students about collaborative skills.
What this paper adds
TA interaction with groups primarily focused on task progress and understanding of concepts with negligible explicit support on building collaborative skills.
TAs intervened with the groups often which gave the students little time for uptake of their suggestions or deeper discussion.
Student turn overlap was higher without the presence of TAs.
Maximum turn duration can be an important real‐time turn metric to identify the least verbally active student participant in a group.
Implications for practice and/or policy
TA training should include information about how to monitor groups for collaborative behaviours and when and how they should intervene to provide feedback and support.
TA feedback systems should keep track of previous interventions by TAs (especially in contexts where there are multiple TAs facilitating) and the duration since previous intervention to ensure that TAs do not intervene with a group too frequently with little time for student uptake.
Maximum turn duration could be used as a real‐time metric to identify the least verbally active student in a group so that support could be provided to them by the TAs.
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Abstract Grading can shape students’ learning and encourage use of effective problem solving practices. Teaching assistants (TAs) are often responsible for grading student solutions and providing feedback, thus, their perceptions of grading may impact grading practices in the physics classroom. Understanding TAs’ perceptions of grading is instrumental for curriculum developers as well as professional development leaders interested in improving grading practices. In order to identify TAs’ perceptions of grading, we used a data collection tool designed to elicit TAs’ considerations when making grading decisions as well as elicit possible conflicts between their stated goals and actual grading practices. The tool was designed to explicate TAs’ attitudes towards research-based grading practices that promote effective problem solving approaches. TAs were first asked to state their goals for grading in general. Then, TAs graded student solutions in a simulated setting while explicating and discussing their underlying considerations. The data collection tool was administered at the beginning of TAs’ first postgraduate teaching appointment and again after one semester of teaching experience. We found that almost all of the TAs stated that the purpose of grading was formative, i.e. grading should encourage students to learn from their mistakes as well as inform the instructor of common student difficulties. However, when making grading decisions in a simulated setting, the majority of TAs’ grading considerations focused on correctness and they did not assign grades in a way that encourages use of effective problem solving approaches. TAs’ perceptions of grading did not change significantly during one semester of teaching experience.
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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
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As datacenter network speeds rise, an increasing fraction of server CPU cycles is consumed by TCP packet processing, in particular for remote procedure calls (RPCs). To free server CPUs from this burden, various existing approaches have attempted to mitigate these overheads, by bypassing the OS kernel, customizing the TCP stack for an application, or by offloading packet processing to dedicated hardware. In doing so, these approaches trade security, agility, or generality for efficiency. Neither trade-off is fully desirable in the fast-evolving commodity cloud. We present TAS, TCP acceleration as a service. TAS splits the common case of TCP processing for RPCs in the datacenter from the OS kernel and executes it as a fastpath OS service on dedicated CPUs. Doing so allows us to streamline the common case, while still supporting all of the features of a stock TCP stack, including security, agility, and generality. In particular, we remove code and data of less common cases from the fastpath, improving performance on the wide, deeply pipelined CPU architecture common in today's servers. To be workload proportional, TAS dynamically allocates the appropriate amount of CPUs to accommodate the fastpath, depending on the traffic load. TAS provides up to 90% higher throughput and 57% lower tail latency than the IX kernel bypass OS for common cloud applications, such as a key-value store and a realtime analytics framework. TAS also scales to more TCP connections, providing 2.2x higher throughput than IX with 64K connections.more » « less
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Accepted Manuscript1.0
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