{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Hanoi University of Industry, Hanoi, Vietnam."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Thi-Hoa-Hue Nguyen"
    },
    {
      "affs": [
        0
      ],
      "name": "Ngo Thanh Doan"
    },
    {
      "affs": [
        0
      ],
      "name": "Hoang Duy Kien"
    },
    {
      "affs": [
        0
      ],
      "name": "Dang Trong Hop"
    },
    {
      "affs": [
        0
      ],
      "name": "Nguyen Thi Kim Son"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bmp27jh04c3kj.bib",
  "cited_by": 0,
  "doi": "10.1016/j.dib.2026.113320",
  "fulltext_url": "https://europepmc.org/articles/PMC13695103",
  "id": "bmp27jh04c3kj",
  "issns": [
    "2352-3409"
  ],
  "landing_url": "https://europepmc.org/article/MED/42927250",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1706|Online Learning and Analytics"
    ]
  ],
  "openalex": "W7221180642",
  "pdf_url": "https://europepmc.org/articles/PMC13695103?pdf=render",
  "pmcid": "PMC13695103",
  "pmid": "42927250",
  "published": "2026-08-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bmp27jh04c3kj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:18Z",
      "native_id": "MED:42927250",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42927250"
    }
  ],
  "subjects": [],
  "title": "A harmonized longitudinal dataset of engineering students for learning analytics and predictive modeling.",
  "title_text": "A harmonized longitudinal dataset of engineering students for learning analytics and predictive modeling.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:21:18Z",
  "url": "https://research.draconitic.com/paper/bmp27jh04c3kj",
  "venue": "Data in brief",
  "versions": []
}