Framing the 5% Problem: Teachers' Perspectives on Persistence in Educational Technology
- URL: http://arxiv.org/abs/2606.22294v1
- Date: Sun, 21 Jun 2026 01:35:56 GMT
- Title: Framing the 5% Problem: Teachers' Perspectives on Persistence in Educational Technology
- Abstract summary: This study reports on a 90-minute participatory design workshop with 12 U.S. middle school mathematics teachers who use i-Ready Math weekly.<n>Thematic analysis identified four recurring dimensions of low persistence spanning motivation and buy-in, cognitive roadblocks, resilience under challenge, and contextual barriers.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Adaptive K-12 mathematics platforms often show low sustained student use, a pattern termed the 5% problem. Although prior work has developed analytics and interventions to identify disengagement, less is known about how teachers make sense of persistence challenges in individualized learning classrooms. This study reports on a 90-minute participatory design workshop with 12 U.S. middle school mathematics teachers who use i-Ready Math weekly. Teachers generated needs through open-ended prompts, card sorting, and collective voting. Thematic analysis identified four recurring dimensions of low persistence spanning motivation and buy-in, cognitive roadblocks, resilience under challenge, and contextual barriers. Teachers emphasized the need to identify where students become stuck and recognize silent disengagement, prioritizing support for diagnosis and timely instructional response over aggregate usage metrics. These findings reframe the 5% problem as a situated instructional challenge and suggest a need for teacher-facing systems that support interpretation of student persistence through both cognitive and contextual evidence.
Related papers
- SimpleOPD: Simple Tokenizer-Agnostic On-Policy Distillation for Long-Context Reasoning [54.402550664505576]
On-policy distillation (OPD) offers a promising way to transfer reasoning capabilities from stronger teacher models to short-context students.<n>We study this setting by transferring proof-reasoning capabilities from the long-context reasoning model SU-01 to short-context student models.<n> Experiments on both same-family and different-family student models, including Qwen3, Qwen3.5, Intern-S2, GLM-4.7, Gemma-4, show consistent gains in mathematical reasoning.
arXiv Detail & Related papers (2026-08-14T12:57:02Z) - Sticky Help, Bounded Effects: Session-by-Session Analytics of Teacher Interventions in K-12 Classrooms [4.863262234062219]
This study investigates how students' prior help history and their engagement states shape teachers' decisions.<n>We analyzed 1.4 million student-system interactions from 339 students across 14 classes in the MATHia intelligent tutoring system.<n>Help coincided with immediate learning within sessions, but did not predict skill acquisition in later sessions.
arXiv Detail & Related papers (2026-01-20T02:15:01Z) - Teaching the social media generation: rethinking learning without sacrificing quality [0.0]
This generation prefers short, visual materials and fast feedback but struggles with focus, critical thinking, and deep learning.<n>This study presents a blended learning redesign of a first-year technical course at a Dutch university.<n>The results were promising: attendance increased by nearly 50%, and none of the regularly attending students failed the exam.
arXiv Detail & Related papers (2025-05-05T16:31:10Z) - Can Language Models Teach Weaker Agents? Teacher Explanations Improve
Students via Personalization [84.86241161706911]
We show that teacher LLMs can indeed intervene on student reasoning to improve their performance.
We also demonstrate that in multi-turn interactions, teacher explanations generalize and learn from explained data.
We verify that misaligned teachers can lower student performance to random chance by intentionally misleading them.
arXiv Detail & Related papers (2023-06-15T17:27:20Z) - MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties
Grounded in Math Reasoning Problems [74.73881579517055]
We propose a framework to generate such dialogues by pairing human teachers with a Large Language Model prompted to represent common student errors.
We describe how we use this framework to collect MathDial, a dataset of 3k one-to-one teacher-student tutoring dialogues.
arXiv Detail & Related papers (2023-05-23T21:44:56Z) - Computationally Identifying Funneling and Focusing Questions in
Classroom Discourse [24.279653100481863]
We propose the task of computationally detecting funneling and focusing questions in classroom discourse.
We release an annotated dataset of 2,348 teacher utterances labeled for funneling and focusing questions, or neither.
Our best model, a supervised RoBERTa model fine-tuned on our dataset, has a strong linear correlation of.76 with human expert labels and with positive educational outcomes.
arXiv Detail & Related papers (2022-07-08T01:28:29Z) - Faculty Distillation with Optimal Transport [53.69235109551099]
We propose to link teacher's task and student's task by optimal transport.
Based on the semantic relationship between their label spaces, we can bridge the support gap between output distributions.
Experiments under various settings demonstrate the succinctness and versatility of our method.
arXiv Detail & Related papers (2022-04-25T09:34:37Z) - The Wits Intelligent Teaching System: Detecting Student Engagement
During Lectures Using Convolutional Neural Networks [0.30458514384586394]
The Wits Intelligent Teaching System (WITS) aims to assist lecturers with real-time feedback regarding student affect.
A CNN based on AlexNet is successfully trained and which significantly outperforms a Support Vector Machine approach.
arXiv Detail & Related papers (2021-05-28T12:59:37Z) - Fixing the Teacher-Student Knowledge Discrepancy in Distillation [72.4354883997316]
We propose a novel student-dependent distillation method, knowledge consistent distillation, which makes teacher's knowledge more consistent with the student.
Our method is very flexible that can be easily combined with other state-of-the-art approaches.
arXiv Detail & Related papers (2021-03-31T06:52:20Z) - The Challenges of Assessing and Evaluating the Students at Distance [77.34726150561087]
The COVID-19 pandemic has caused a strong effect on higher education institutions with the closure of classroom teaching activities.
This short essay aims to explore the challenges posed to Portuguese higher education institutions and to analyze the challenges posed to evaluation models.
arXiv Detail & Related papers (2021-01-30T13:13:45Z) - 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)
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.