Handling Missing Data in CALL - A Data Quality-Driven Imputation Framework for Learner Analytics: Evaluation Design and Construct Validity

Abstract

Evaluation is persuasive only when the measured outcome corresponds to the construct claimed by the study and the comparison answers the stated research question. 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 evaluation design and construct validity, 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 aligns claims, outcomes, comparators, sampling, and uncertainty before any performance estimate is interpreted.