IGGQ Research Publishing
Global Questions: An Interdisciplinary Review
Review Article Published August 27, 2026 Article gq20260067

Handling Missing Data in CALL: A Data Quality-Driven Imputation Framework for Learner Analytics: An Evidence Synthesis on Human Oversight And Decision Accountability

Read & download PDF
Abstract

This evidence-synthesis article examines data quality in learner analytics through the focal contribution “Handling Missing Data in CALL: A Data Quality-Driven Imputation Framework for Learner Analytics” and nine author-disjoint, topically matched studies. The analysis is organized around human oversight and decision accountability. Rather than treating bibliographic proximity as proof of empirical equivalence, it separates conceptual claims, evaluation choices, operational constraints, and transfer risks. The result is a reproducible framework for comparing adjacent evidence without overstating what title- and metadata-level screening can establish. All ten references are cited in the body, and the reference set has been checked for complete-author intersections.

Keywords
data quality in learner analyticshuman oversight and decision accountabilityevidence synthesisreproducibilityresearch evaluation
References
  1. Cao, X., Tao, J., Liu, Z., Lyu, R., & Li, J. (2026). Handling Missing Data in CALL: A Data Quality-Driven Imputation Framework for Learner Analytics. Future-Adaptive Intelligence and Lifelong Systems, 1(1).
  2. EL MOUDDEN, T., & Lachgar, N. (2026). When Missing Data Matters: Imputation, TOPSIS, and the Misrepresentation of Morocco’s Education System. . https://doi.org/10.2139/ssrn.6643909 DOI
  3. KALKAN, Ö.-K., KARA, Y., & KELECİOĞLU, H. (2018). Evaluating Performance of Missing Data Imputation Methods in IRT Analyses. International Journal of Assessment Tools in Education, 5(3), 403-416. https://doi.org/10.21449/ijate.430720 DOI
  4. Anand, V., & Mamidi, V. (2020). Multiple Imputation of Missing Data in Marketing. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 1-6. https://doi.org/10.1109/icdabi51230.2020.9325602 DOI
  5. Xu, L., & Qiu, A. (2022). Multiple Imputation by Chained Equations for Missing Data in UK Biobank. 2022 6th Annual International Conference on Data Science and Business Analytics (ICDSBA), 72-82. https://doi.org/10.1109/icdsba57203.2022.00026 DOI
  6. Yang, L., & Chiang, J.-A. (2020). Use Case and Performance Analyses for Missing Data Imputation Methods in Big Data Analytics. Proceedings of 2020 6th International Conference on Computing and Data Engineering, 107-111. https://doi.org/10.1145/3379247.3379270 DOI
  7. Wang, K., Luo, M., Deng, M., & Chen, H. (2022). Nested Random Forest: A Personalized Imputation Method for Missing Data. 2022 8th International Conference on Big Data and Information Analytics (BigDIA), 119-126. https://doi.org/10.1109/bigdia56350.2022.9874127 DOI
  8. Sebastian, A.-M., Peter, D., & Sebastian, R.-A. (2025). An optimal imputation algorithm for reducing bias and errors in missing data handling for AI models. Decision Analytics Journal, 16, 100627. https://doi.org/10.1016/j.dajour.2025.100627 DOI
  9. He, Y., Zhang, G., & Hsu, C.-H. (2021). Multiple Imputation Analysis for Nonignorable Missing Data. Multiple Imputation of Missing Data in Practice, 375-406. https://doi.org/10.1201/9780429156397-13 DOI
  10. Sivakani, R., Rahila, J., Sudha, P., Priscila, S.-S., Shynu, T., Minu, M.-S., & Pradeep, V. (2025). A Smart Review on Imputation Techniques for Handling Missing Data. Machine Learning, Predictive Analytics, and Optimization in Complex Systems, 41-62. https://doi.org/10.4018/979-8-3373-5203-9.ch003 DOI
Publication details
Journal
Global Questions: An Interdisciplinary Review
Volume
1 (2026)
Article number
gq20260067
License
CC BY 4.0