Use early-session timing signals, interpreted relative to proficiency, to identify students needing adaptive support¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that timing-based signals be used to identify early risk and guide context-sensitive interventions rather than as global indicators of effort quality. Because the RT–learning relationship is most structured in the earliest session slice, the authors suggest early-session response-time patterns could trigger adaptive supports, noting that "timing-based signals are best used to identify early risk and guide context-sensitive interventions". Heuristics equating fast responding with efficiency or slow responding with disengagement risk misclassifying higher-proficiency students whose slower pacing may reflect constructive processing.

Design Implications¶

Context¶

Requirements¶

• Response-time propensities estimated with difficulty adjustment (student and knowledge-component random effects)
• Interpretation conditional on learner proficiency and position within the session

Constraints¶

• The article states the analyses are correlational and causal interpretations are not warranted; experimental or quasi-experimental tests of timing-triggered supports are a next step
• Extended latencies among lower-proficiency students early in practice may signal emerging difficulties with task initiation or persistence

Target Learners¶

• middle- and high-school mathematics students in classroom tutoring deployments

Target Learning Goals¶

• detecting emerging disengagement and low persistence; targeting adaptive support when effort is most diagnostic

Affordances¶

• Response Time Propensities Construct

Related Strategies¶

• Use an early warning system based on linked school and human services data to target resources and lessen risks before they become more serious
• Use postsecondary readiness indicators in an early warning system and combine cultural responsiveness with tiered interventions to address disparities
• Use early cheating-risk predictions only for low-stakes support with human review, never for disciplinary decisions
• Use LCF risk clusters to target Supplemental Instruction recruitment and resource allocation

Examples¶

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Key Sources¶

• Conrad Borchers, Lijin Zhang, Kexin Yang, Tomohiro Nagashima, and Benjamin W. Domingue. 2026. Understanding Student Effort Using Response-Time Propensities During Problem Solving. Proceedings of the Thirteenth ACM Conference on Learning @ Scale (L@S '26). https://doi.org/10.1145/3774398.3811624