Innovative Model for Student Project Evaluation Based on Text Mining

Yousef Abuzir

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Abstract


At present, there is a huge amount of unstructured documentation that is generated by students who study the course project management and evaluation. These unstructured documents cannot be used in direct processing to extract useful information or knowledge. At the same time, make use of them to assess student achievement. In this work, we propose text-mining techniques for the evaluation of the progress of student in a course project management and evaluation. The most straightforward approach is looking for the enrichment of the vocabulary, the casual and condition relations, and the structure of the terms in the different phases of the project. In our paper, we used text mining and the structure of the vocabularies as a tool to measure the success and the progress of the students. It is important to evaluate the effectiveness and efficiency of our approach. The experimental results illustrate an overall performance of project evaluation. By looking into the number of indexing terms, the causal relations and the positive conditional relations, and levels of the hierarchical structure of the terms there is an evidence from this research that the vocabulary enrichment had an impact on the students evaluation and learning ability.

Keywords


Text Mining, Project Management, Statistical Evaluation, Student Evaluation

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References


Abuzir, Y. (2018). Innovative model for student project evaluation based on text mining. International Journal of Research in Education and Science (IJRES), 4(2), 409-419. DOI:10.21890/ijres.409481


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Copyright (c) 2018 International Journal of Research in Education and Science



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International Journal of Research in Education and Science (IJRES)
 
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Editor: Wilfried Admiraal, The Netherlands

Place of Publication: Turkey & Name of Publisher: Ismail Sahin

ISSN: 2148-9955 (Online)