Handling Missing Data in CALL - A Data Quality-Driven Imputation Framework for Learner Analytics: Deployment and Systems Integration

Abstract

Operational value depends on latency, resource use, interface dependencies, and reliability within the systems that must host the method. 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 deployment constraints and systems integration, 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 tests end-to-end latency, failure recovery, resource demand, and compatibility with existing operational interfaces.