Students' performance evaluation in higher education using data visualization techniques.

Assessment is a process that produces information for educators that they may use to make decisions regarding students' learning. Nowadays one of the great difficulties that educational institutions face, is the increasing growth of educational data and the use of it to improve the quality of making...

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Publicado en:Annals of Philosophy, Social & Human Disciplines Vol. 2; pp. 43 - 56
Autores principales: LLAHA, Olta, ALIU, Azir
Formato: Artículo
Publicado: Suceava: "Stefan cel Mare" University 2022
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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          LLAHA, Olta
          ALIU, Azir
        affil: SEE University, Tetovo, North Macedonia
      su:
        Data modeling
        Data visualization
        Assessment of education
        Higher education
        Visualization
        School enrollment
        Decision making
      sug:
        subj:
          Data modeling
          Data visualization
          Assessment of education
          Higher education
          Visualization
          School enrollment
          Decision making
      keyword:
        Data Visualization
        Higher Education
        Knowledge Discovery
      ab: Assessment is a process that produces information for educators that they may use to make decisions regarding students' learning. Nowadays one of the great difficulties that educational institutions face, is the increasing growth of educational data and the use of it to improve the quality of making decisions in regard to education. Data visualization methods help to create useful and valid analyses from higher education databases. They also ensure students maximize their academic output. Here we present the evaluation of students for different subjects, graduation levels, student enrollment and by the use of data visualization techniques we make an analysis of the study results. We show the importance of data visualization in data analysis which has a significant impact on the accuracy of the predicted results.
      pubtype: Academic Journal
      doctype: Article
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    language: English
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