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Student-level recency-weighted proportional correctness feature (RKC,SPFA)

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

This feature extends Galyardt and Goldin's KC-level proportional decay measure by tracking the recency-weighted proportion of correct responses at the level of the student across KCs. The authors hypothesized this additional feature "could stand in for student learning differences as well as learning transfer across KCs". It requires two ghost attempts (one incorrect, one correct) to center the measure at .5 so it can adapt upward or downward, and the authors have implemented a version in a running system.

Accounts

  • Student-level recency-weighted proportional correctness feature (RKC,SPFA): This feature extends Galyardt and Goldin's KC-level proportional decay measure by tracking the recency-weighted proportion of correct responses at the level of the student across KCs. The authors hypothesized this additional feature "could stand in for student learning differences as well as learning transfer across KCs". It requires two ghost attempts (one incorrect, one correct) to center the measure at .5 so it can adapt upward or downward, and the authors have implemented a version in a running system. (Eglington et al. (2022))

Claims

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Key Sources

  • Eglington, L.G., Pavlik, P.I. (2022). How to Optimize Student Learning Using Student Models That Adapt Rapidly to Individual Differences. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-022-00296-0
Page metadata
Field Value
type research-method
id student-level-recency-weighted-proportional-correctness-feature-rkcspfa
title Student-level recency-weighted proportional correctness feature (RKC,SPFA)
description This feature extends Galyardt and Goldin's KC-level proportional decay measure by tracking the recency-weighted proportion of correct responses at the level of the student across KCs.
status review
generated by process:settle-candidates on 2026-10-27
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