A harmonized longitudinal dataset of engineering students for learning analytics and predictive modeling.
- 1Hanoi University of Industry, Hanoi, Vietnam.
Abstract
This article presents a high-dimensional longitudinal dataset (1,656 student records x 56 variables) synthesized from institutional academic administrative records of undergraduate students in an automotive engineering program at an application-oriented university in Vietnam. Unlike static datasets derived from fixed schemas, this resource was extracted from disparate, spreadsheet-based management files across multiple academic years, requiring a systematic process of cleaning, standardization, and harmonization to resolve inconsistencies in nomenclature and coding. The dataset retains real-world curriculum variation across academic years, including differences in course naming, course sequencing, semester placement, and elective pathways. The released data are provided in two parallel tabular formats: a labeled version for immediate interpretation and a harmonized coded version designed for reproducible analytical pipelines. The 56 variables encompass student-level contextual information, harmonized course grades, cumulative performance indicators, scholarship records, and academic status. Beyond descriptive analysis and learning analytics, this repository provides a standardized reusable resource for educational data mining, feature engineering, interpretable machine learning, and predictive modeling studies, including analyses of academic performance and student progression. It also provides a reference dataset for methodological research on data harmonization, the reuse of heterogeneous administrative records, and curriculum analytics.
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BibTeX
@article{nguyen2026harmonized,
title = {{A harmonized longitudinal dataset of engineering students for learning analytics and predictive modeling.}},
author = {Thi-Hoa-Hue Nguyen and Ngo Thanh Doan and Hoang Duy Kien and Dang Trong Hop and Nguyen Thi Kim Son},
journal = {Data in brief},
year = {2026},
month = aug,
doi = {10.1016/j.dib.2026.113320},
url = {https://europepmc.org/article/MED/42927250},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - A harmonized longitudinal dataset of engineering students for learning analytics and predictive modeling.
AU - Thi-Hoa-Hue Nguyen
AU - Ngo Thanh Doan
AU - Hoang Duy Kien
AU - Dang Trong Hop
AU - Nguyen Thi Kim Son
PY - 2026
DA - 2026/08/01
JO - Data in brief
DO - 10.1016/j.dib.2026.113320
UR - https://europepmc.org/article/MED/42927250
AB - This article presents a high-dimensional longitudinal dataset (1,656 student records x 56 variables) synthesized from institutional academic administrative records of undergraduate students in an automotive engineering program at an application-oriented university in Vietnam. Unlike static datasets derived from fixed schemas, this resource was extracted from disparate, spreadsheet-based management files across multiple academic years, requiring a systematic process of cleaning, standardization, and harmonization to resolve inconsistencies in nomenclature and coding. The dataset retains real-world curriculum variation across academic years, including differences in course naming, course sequencing, semester placement, and elective pathways. The released data are provided in two parallel tabular formats: a labeled version for immediate interpretation and a harmonized coded version designed for reproducible analytical pipelines. The 56 variables encompass student-level contextual information, harmonized course grades, cumulative performance indicators, scholarship records, and academic status. Beyond descriptive analysis and learning analytics, this repository provides a standardized reusable resource for educational data mining, feature engineering, interpretable machine learning, and predictive modeling studies, including analyses of academic performance and student progression. It also provides a reference dataset for methodological research on data harmonization, the reuse of heterogeneous administrative records, and curriculum analytics.
SN - 2352-3409
ER -