Automated Feedback to Peer Tutors Increases Their Talk Moves and Learner Reasoning but Not the Balance of Talk

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1. Très proche Même question Cité par cet article Pas pertinent

   Enhancing Tutoring Effectiveness Through Automated Feedback: Preliminary Findings from a Pilot Randomized Controlled Trial on SAT Tutoring

   Joy Yun, Yann Hicke, Mariah Olson et al. · 2024 · ACM Conference on Learning @ Scale · cité 7 fois · Pas encore résumé

   L’essai pilote évalue le retour automatisé aux tuteurs de SAT et à leurs élèves, comme l’étude principale, mais ses résultats préliminaires sont annoncés.

   Résumé scientifique

   To address educational inequities, high-quality SAT tutoring is crucial for students who need support. Many tutors, however, are novices and require coaching to be effective. To examine this issue, we conducted a pilot randomized controlled trial (RCT) to evaluate the effectiveness of personalized, automated feedback for novice tutors. This feedback aimed to enhance their tutoring skills and, consequently, improve student outcomes. In our RCT, we not only assessed the effectiveness of the feedback provided to novice tutors but also examined the impact of extending this feedback to both tutors and their students, compared to just the tutors alone. Furthermore, we explored how the use of socia…

2. Très proche Autre approche du problème Cité par cet article Pas pertinent

   Can Automated Feedback Improve Teachers’ Uptake of Student Ideas? Evidence From a Randomized Controlled Trial in a Large-Scale Online Course

   Dorottya Demszky, Jing Liu, H. Hill et al. · 2023 · Educational Evaluation and Policy Analysis · cité 145 fois · Pas encore résumé

   M-Powering Teachers teste par essai randomisé un retour automatisé sur la reprise des idées d’élèves dans un cours en ligne, plutôt qu’en tutorat bénévole.

   Résumé scientifique

   Providing consistent, individualized feedback to teachers is essential for improving instruction but can be prohibitively resource-intensive in most educational contexts. We develop M-Powering Teachers, an automated tool based on natural language processing to give teachers feedback on their uptake of student contributions, a high-leverage dialogic teaching practice that makes students feel heard. We conduct a randomized controlled trial in an online computer science course (N = 1,136 instructors), to evaluate the effectiveness of our tool. We find that M-Powering Teachers improves instructors’ uptake of student contributions by 13% and present suggestive evidence that it also improves stude…

3. Très proche Autre approche du problème Cité par cet article Pas pertinent

   Does Feedback on Talk Time Increase Student Engagement? Evidence from a Randomized Controlled Trial on a Math Tutoring Platform

   Dorottya Demszky, Rose E. Wang, Sean Geraghty et al. · 2024 · International Conference on Learning Analytics and Knowledge · cité 9 fois · Pas encore résumé

   Le Talk Meter teste un retour automatisé sur le ratio de parole en tutorat de mathématiques et rapporte une hausse de la parole des élèves.

   Résumé scientifique

   Providing ample opportunities for students to express their thinking is pivotal to their learning of mathematical concepts. We introduce the Talk Meter, which provides in-the-moment automated feedback on student-teacher talk ratios. We conduct a randomized controlled trial on a virtual math tutoring platform (n=742 tutors) to evaluate the effectiveness of the Talk Meter at increasing student talk. In one treatment arm, we show the Talk Meter only to the tutor, while in the other arm we show it to both the student and the tutor. We find that the Talk Meter increases student talk ratios in both treatment conditions by 13-14%; this trend is driven by the tutor talking less in the tutor-facing c…

4. Très proche Même méthode, autre problème Pas pertinent

   Does Increased Choice over Learning Topic Improve the Effectiveness of Automated Feedback for Educators?

   Dorottya Demszky, Heather C. Hill, E. Taylor et al. · 2025 · Education sciences · Pas encore résumé

   Cette étude teste par essai randomisé le retour automatisé aux enseignants, mais examine le choix du sujet du retour et ses effets sur l’assiduité.

   Résumé scientifique

   Educator agency in the form of choice over learning experiences is widely thought to enhance educator engagement and instructional improvement, yet causal evidence is scarce. We conducted a preregistered randomized controlled trial in an online computer science course with volunteer instructors who teach students worldwide. All instructors (N = 583) received automated feedback on their instruction, with half randomly assigned to have choice over the feedback topic. Choice alone did not increase feedback engagement or yield observable changes in practice, but it raised student attendance—an effect that was strongest for instructors who voluntarily engaged with additional training resources, i…

5. Proche Même méthode, autre problème Pas pertinent

   High School English Teachers Reflect on Their Talk: A Study of Response to Automated Feedback with the Teacher Talk Tool

   Sean Kelly, Gizem Guner, Nicholas C. Hunkins et al. · 2024 · International Journal of Artificial Intelligence in Education · cité 9 fois · Pas encore résumé

   Le Teacher Talk Tool analyse automatiquement le discours et fournit un retour formatif, étudié ici par les réactions de cinq enseignants d’anglais.

   Résumé scientifique

   We present the Teacher Talk Tool, which automatically analyzes classroom audio and provides formative feedback on key aspects of teachers’ classroom discourse (e.g., use of open-ended questions). The tool was designed to promote teacher learning by focusing attention and sense-making on their discourse. We conducted a feedback-response study where five English & Language Art teachers used the Teacher Talk Tool in eight classroom sessions. Teachers completed repeated-measure surveys and semi-structured interviews providing quantitative and qualitative evidence of feedback response. Results indicated that the majority of automated feedback was perceived to be accurate and prompted a high degre…

6. Proche Contexte plus large Pas pertinent

   Chat-Based Support Alone May Not Be Enough: Comparing Conversational and Embedded LLM Feedback for Mathematical Proof Learning

   Eason Chen, Sophia Judicke, Kayla Beigh et al. · 2026 · International Conference on Artificial Intelligence in Education · cité 6 fois · Pas encore résumé

   GPTutor étudie le retour d’un système de tutorat par IA sur les preuves mathématiques, et non le discours de tuteurs en séance.

   Résumé scientifique

   We evaluate GPTutor, an LLM-powered tutoring system for an undergraduate discrete mathematics course. It integrates two LLM-supported tools: a structured proof-review tool that provides embedded feedback on students'written proof attempts, and a chatbot for math questions. In a staggered-access study with 148 students, earlier access was associated with higher homework performance during the interval when only the experimental group could use the system, while we did not observe this performance increase transfer to exam scores. Usage logs show that students with lower self-efficacy and prior exam performance used both components more frequently. Session-level behavioral labels, produced by …

7. Proche Même méthode, autre problème Pas pertinent

   Scaling Teacher Professional Learning Through Automated Feedback: An Evaluation in K-12 Classrooms

   Jessica Vitale, Alyssa Van Camp, Sarah Miller et al. · 2026 · International Conference on Learning Analytics and Knowledge · cité 1 fois · Pas encore résumé

   Cette étude évalue TeachFX, qui fournit un retour automatisé sur les pratiques enseignantes, dans des classes de la maternelle au secondaire.

   Résumé scientifique

   Automated feedback provides a scalable approach to supporting teacher reflection and teaching practice. However, there is limited evidence on how these technologies impact multiple dimensions of teacher and student outcomes in authentic school settings. This mixed-methods study evaluated the implementation and short-term impacts of a 4–-5 week pilot providing K–-12 teachers with professional learning and automated feedback on teaching practices via the TeachFX application. We examined TeachFX’s impact on teacher well-being, teaching practice, classroom talk, and student engagement. Findings indicate that automated feedback is associated with reduced teacher burnout, improved self-efficacy, a…

8. Proche Même méthode, autre problème Pas pertinent

   M-Powering Teachers: Natural Language Processing Powered Feedback Improves 1:1 Instruction and Student Outcomes

   Dorottya Demszky, Jing Liu · 2023 · ACM Conference on Learning @ Scale · cité 38 fois · Pas encore résumé

   M-Powering Teachers fournit un retour automatisé sur les pratiques dialogiques en mentorat individuel et mesure ses effets sur les élèves.

   Résumé scientifique

   Although learners are being connected 1:1 with instructors at an increasing scale, most of these instructors do not receive effective, consistent feedback to help them improve. We deployed M-Powering Teachers, an automated tool based on natural language processing to give instructors feedback on dialogic instructional practices ---including their uptake of student contributions, talk time and questioning practices --- in a 1:1 online learning context. We conducted a randomized controlled trial on Polygence, a research mentorship platform for high schoolers (n=414 mentors) to evaluate the effectiveness of the feedback tool. We find that the intervention improved mentors' uptake of student con…

Les travaux antérieurs sur lesquels il s’appuie

1. Proche* Même méthode, autre problème Pas pertinent

   Automated feedback improves teachers' questioning quality in brick-and-mortar classrooms: Opportunities for further enhancement

   Dorottya Demszky, Jing Liu, Heather C. Hill et al. · 2024 · Comput. Educ. · cité 49 fois · Pas encore résumé

   Cette étude porte sur un retour automatisé visant la qualité des questions des enseignants en classe, mais son résumé ne précise pas ses résultats.

2. Proche Contexte plus large Pas pertinent

   From Tutor Moves to Tutoring States: Modeling the Timing and Sequencing of Pedagogical Strategies for Student Engagement

   Kirk P. Vanacore, Jinsook Lee, Bakhtawar Ahtisham et al. · 2026 · ACM Conference on Learning @ Scale · cité 1 fois · Pas encore résumé

   Cette étude modélise les séquences de stratégies pédagogiques dans des séances de tutorat en mathématiques, sans tester de retour automatisé.

   Résumé scientifique

   Understanding how tutoring unfolds requires capturing not only what tutors and students say, but also the multi-turn pedagogical strategies that structure their conversation. In this study, we analyze 77 online math tutoring sessions (about 40 hours of tutoring) using a combination of LLM-assisted annotation and statistical modeling. Building on utterance-level annotations with a taxonomy of tutor moves, we identify three recurrent latent states using a Hidden Markov Model: Inquiry Elicitation (dominated by prompting and probing student reasoning), Direct Instruction (centered on explanation and scaffolding), and Affective Support (characterized by praise and socioemotional support). Sequent…

3. Proche Même question Pas pertinent

   Unpacking the Complexity: Why Current Feedback Systems Fail to Improve Learner Self-Regulation of Participation in Collaborative Activities

   Xavier Ochoa, Xiao-Meng Huang, Adam Charlton · 2024 · Journal of Learning Analytics · cité 4 fois · Pas encore résumé

   Cette étude examine si des systèmes de retour modifient la participation équitable aux activités collaboratives et ne trouve pas d’amélioration mesurable.

   Résumé scientifique

   Even before the inception of the term learning analytics, researchers globally had been investigating the use of various feedback systems to support the self-regulation of participation and promote equitable contributions during collaborative learning activities. While some studies indicate positive effects for distinct subgroups of learners, a common finding is that the majority of learners do not modify their behaviour, even after repeated interventions. In this paper, we assessed one such system and, predictably, did not find measurable improvements in equitable participation. Informed by self-regulated learning theory, we conducted a mixed-methods study to explore the diverse paths that …

4. Proche* Même méthode, autre problème Pas pertinent

   Promoting rich discussions in mathematics classrooms: Using personalized, automated feedback to support reflection and instructional change

   Jennifer Jacobs, Karla Scornavacco, Charis Harty et al. · 2022 · Teaching and Teacher Education : An International Journal of Research and Studies · cité 88 fois · Pas encore résumé

   Cette étude porte sur un retour personnalisé et automatisé destiné à soutenir la réflexion et le changement des pratiques en classe de mathématiques.

5. Proche Données, outil ou méthode en commun Pas pertinent

   A Deep Transfer Learning Approach to Modeling Teacher Discourse in the Classroom

   Emily Jensen, Samuel L. Pugh, Sidney K. D'Mello · 2021 · International Conference on Learning Analytics and Knowledge · cité 45 fois · Pas encore résumé

   Cette étude compare des méthodes d’apprentissage automatique pour modéliser des caractéristiques du discours enseignant, utiles à la création de retours automatisés.

   Résumé scientifique

   Teachers, like everyone else, need objective reliable feedback in order to improve their effectiveness. However, developing a system for automated teacher feedback entails many decisions regarding data collection procedures, automated analysis, and presentation of feedback for reflection. We address the latter two questions by comparing two different machine learning approaches to automatically model seven features of teacher discourse (e.g., use of questions, elaborated evaluations). We compared a traditional open-vocabulary approach using n-grams and Random Forest classifiers with a state-of-the-art deep transfer learning approach for natural language processing (BERT). We found a tradeoff…