Handling Missing Data in CALL - A Data Quality-Driven Imputation Framework for Learner Analytics: Uncertainty and Sensitivity Analysis

Abstract

Uncertainty and sensitivity analysis reveal whether a reported conclusion survives plausible changes in measurement, preprocessing, assumptions, and parameter choices. This structured evidence review evaluates "Handling Missing Data in CALL: A Data Quality-Driven Imputation Framework for Learner Analytics" alongside nine author-disjoint, topically matched publications in data quality in learner analytics. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through measurement uncertainty and sensitivity analysis, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda varies measurement choices and assumptions systematically, reports uncertainty, and identifies conclusions that are not robust.