{
  "abstract": "Traditional teaching feedback and student assessment methods often struggle to provide timely, personalized, and comprehensive evaluations in large-scale digital learning environments. To address this core challenge, this study proposes an intelligent teaching feedback and student performance assessment framework based on natural language processing (NLP) and multimodal learning analytics. The framework integrates textual assignments, online discussions, behavioral records, and emotional information to construct a comprehensive learner model. By combining semantic analysis, sentiment analysis, and multimodal data fusion, the proposed approach enables automated performance evaluation and personalized feedback generation. Experimental results from a 16-week controlled trial with 346 students demonstrate that the proposed framework improves student engagement by 88.65%, increases knowledge retention by 65.9%, and achieves a personalized feedback satisfaction rate of 91.3%. In addition, multimodal data fusion lifts comprehensive assessment accuracy from 72% (single-modal text analysis) to 89%, and students’ final exam scores improve by 15% compared with the preintervention baseline. These findings demonstrate the effectiveness of the proposed framework in supporting intelligent educational assessment, personalized learning, and data-driven instructional decision-making.",
  "abstract_shown": true,
  "affiliations": [
    "Zhengzhou University of Science and Technology, Zhengzhou 450000, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jiasheng Ma"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4q6e5se8tpspm.bib",
  "cited_by": 0,
  "doi": "10.6180/jase.202701_36.037",
  "fulltext_url": "https://jase.tku.edu.tw/jase/?tkuisotope=jase-202701-36-037",
  "id": "4q6e5se8tpspm",
  "issns": [
    "2708-9967",
    "2708-9975"
  ],
  "landing_url": "https://doi.org/10.6180/jase.202701_36.037",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QC1-999"
    ],
    [
      "lcc",
      "TA1-2040"
    ],
    [
      "lcc",
      "TP155-156"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-31",
  "publisher": "Tamkang University Press",
  "ris": "https://research.draconitic.com/paper/4q6e5se8tpspm.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T14:22:55Z",
      "native_id": "b00331621e9a42eaac2ae2b74207fb7d",
      "source": "doaj",
      "url": "https://doi.org/10.6180/jase.202701_36.037"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "lcc:QC1-999",
      "subfield": "general"
    }
  ],
  "title": "Towards Intelligent Educational Assessment: A Multimodal NLP Framework for Automated Feedback Generation and Personalized Learning Analytics",
  "title_text": "Towards Intelligent Educational Assessment: A Multimodal NLP Framework for Automated Feedback Generation and Personalized Learning Analytics",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T14:20:20Z",
  "url": "https://research.draconitic.com/paper/4q6e5se8tpspm",
  "venue": "Journal of Applied Science and Engineering",
  "versions": []
}