Enhancing Programming Pair Workshops: The Case of Teacher Pre-Prompting

• URL: http://arxiv.org/abs/2506.20299v1
• Date: Wed, 25 Jun 2025 10:22:43 GMT
• Title: Enhancing Programming Pair Workshops: The Case of Teacher Pre-Prompting
• Abstract summary: We investigate how brief teacher-initiated questions can help shape problem interpretation and division of labor.<n>Our findings suggest that such prompts can foster structured discussions, clarify task requirements, and create opportunities for shared learning experiences.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores the pedagogical potential of "teacher pre-prompting" as a means of guiding student collaboration in programming education. In particular, we investigate how brief teacher-initiated questions posed before students engage in pair programming workshops can help shape problem interpretation and division of labor. Based on qualitative analysis of video data from a university course in systems development, we identify five distinct pre-prompting patterns. Our findings suggest that such prompts can foster structured discussions, clarify task requirements, and create opportunities for shared learning experiences.

Related papers

• Design and Deployment of a Course-Aware AI Tutor in an Introductory Programming Course [3.8144358095849946]
  Large Language Models (LLMs) have become part of how students solve programming tasks, offering immediate explanations and even full solutions.<n>Previous work has highlighted that novice programmers often heavily rely on LLMs, thereby neglecting their own problem-solving skills.<n>We designed a course-specific online Python tutor that provides retrieval-augmented, course-aligned guidance without generating complete solutions.
  arXiv  Detail & Related papers  (2026-04-12T08:17:25Z)
• From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understanding [5.459797813771498]
  This work introduces an annotated educational dialogue dataset of student conversations featuring knowledge construction and task production discourse.<n>We also establish baseline models for automatically predicting these discourse properties for each turn of talk within conversations, using pre-trained large language models GPT-3.5 and Llama-3.1.<n> Experimental results indicate that these state-of-the-art models perform suboptimally on this task, indicating the potential for future research.
  arXiv  Detail & Related papers  (2025-11-25T17:46:00Z)
• Towards AI Agents for Course Instruction in Higher Education: Early Experiences from the Field [3.1941554288428193]
  This article presents early findings from designing, deploying and evaluating an AI-based educational agent deployed as the primary instructor in a graduate-level Cloud Computing course at IISc.<n>We detail the design of a Large Language Model (LLM)-driven Instructor Agent, and introduce a pedagogical framework that integrates the Instructor Agent into the course workflow.<n>We report early experiences on how students interact with the Agent to explore concepts, clarify doubts and sustain inquiry-driven dialogue during live classroom sessions.
  arXiv  Detail & Related papers  (2025-10-23T06:23:35Z)
• LearnLens: An AI-Enhanced Dashboard to Support Teachers in Open-Ended Classrooms [3.7748177458129852]
  generative AI (GenAI)-enhanced teacher-facing dashboard designed to support problem-based instruction in middle school science.<n>LearnLens processes students' open-ended responses from digital assessments to provide various insights.
  arXiv  Detail & Related papers  (2025-09-11T23:06:54Z)
• INSIGHT: Bridging the Student-Teacher Gap in Times of Large Language Models [0.7499722271664147]
  INSIGHT is a proof of concept to combine various AI tools to assist teaching staff and students in the process of solving exercises.<n>We analyze students' questions to an LLM by extracting keywords, which we use to dynamically build an FAQ from students' questions.
  arXiv  Detail & Related papers  (2025-04-24T15:47:20Z)
• Multimodal Programming in Computer Science with Interactive Assistance Powered by Large Language Model [0.0]
  An interactive homework help system based on DeepSeek R1 is developed and first implemented for students enrolled in a large computer science beginning programming course.<n>It wraps student work in a personalized prompt that advances our educational objectives without offering answers straight away.
  arXiv  Detail & Related papers  (2025-03-09T10:48:47Z)
• MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors [76.1634959528817]
  We present MathTutorBench, an open-source benchmark for holistic tutoring model evaluation.<n>MathTutorBench contains datasets and metrics that broadly cover tutor abilities as defined by learning sciences research in dialog-based teaching.<n>We evaluate a wide set of closed- and open-weight models and find that subject expertise, indicated by solving ability, does not immediately translate to good teaching.
  arXiv  Detail & Related papers  (2025-02-26T08:43:47Z)
• Code Interviews: Design and Evaluation of a More Authentic Assessment for Introductory Programming Assignments [15.295438618760164]
  We describe code interviews: a more authentic assessment method for take-home programming assignments. Code interviews pushed students to discuss their work, motivating more nuanced but sometimes repetitive insights. We conclude by discussing the different decisions about the design of code interviews with implications for student experience, academic integrity, and teaching workload.
  arXiv  Detail & Related papers  (2024-10-01T19:01:41Z)
• Exploring Programming Task Creation of Primary School Teachers in Training [14.355436881937193]
  Inadequate resulting example code may negatively affect learning, and students might adopt bad programming habits or misconceptions. To avoid this problem, automated program analysis tools have the potential to help scaffolding task creation processes. For example, static program analysis tools can automatically detect both good and bad code patterns, and provide hints on improving the code.
  arXiv  Detail & Related papers  (2023-06-24T07:26:24Z)
• UKP-SQuARE: An Interactive Tool for Teaching Question Answering [61.93372227117229]
  The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course. We introduce UKP-SQuARE as a platform for QA education. Students can run, compare, and analyze various QA models from different perspectives.
  arXiv  Detail & Related papers  (2023-05-31T11:29:04Z)
• Opportunities and Challenges in Neural Dialog Tutoring [54.07241332881601]
  We rigorously analyze various generative language models on two dialog tutoring datasets for language learning. We find that although current approaches can model tutoring in constrained learning scenarios, they perform poorly in less constrained scenarios. Our human quality evaluation shows that both models and ground-truth annotations exhibit low performance in terms of equitable tutoring.
  arXiv  Detail & Related papers  (2023-01-24T11:00:17Z)
• UniDU: Towards A Unified Generative Dialogue Understanding Framework [62.8474841241855]
  We investigate a unified generative dialogue understanding framework, namely UniDU, to achieve information exchange among DU tasks. We conduct experiments on ten dialogue understanding datasets, which span five fundamental tasks. The proposed UniDU framework outperforms task-specific well-designed methods on all 5 tasks.
  arXiv  Detail & Related papers  (2022-04-10T09:32:34Z)
• Challenging but Full of Opportunities: Teachers' Perspectives on Programming in Primary Schools [13.453052164371734]
  computational thinking in school curricula requires teachers to introduce children to programming already at primary school level. As this is a recent development, primary school teachers may neither be adequately prepared for how to best teach programming, nor may they be fully aware why they have to do so. By surveying 200 teachers who have taught programming at primary schools and 97 teachers in training, we identify relevant challenges when teaching programming, opportunities that arise when children learn programming, and strategies how to address both of these in practice.
  arXiv  Detail & Related papers  (2021-11-01T10:02:00Z)
• Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms [50.19997675066203]
  We build an end-to-end neural framework that automatically detects questions from teachers' audio recordings. By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions.
  arXiv  Detail & Related papers  (2020-05-16T02:17:04Z)
• Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning [50.5572111079898]
  Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks.
  arXiv  Detail & Related papers  (2020-02-27T04:36:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.