Effective Active Course Learning Method for Education Data Using Feature Base Classification Model

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2019 by IJETT Journal
Volume-67 Issue-10
Year of Publication : 2019
Authors : Mrs.R.Nirmala Devi, Ms.Anupriya Sharma
DOI :  10.14445/22315381/IJETT-V67I10P229


MLA Style: Mrs.R.Nirmala Devi, Ms.Anupriya Sharma   "Effective Active Course Learning Method for Education Data Using Feature Base Classification Model" International Journal of Engineering Trends and Technology 67.10 (2019):182-186.

APA Style:Mrs.R.Nirmala Devi, Ms.Anupriya Sharma, Effective Active Course Learning Method for Education Data Using Feature Base Classification Model  International Journal of Engineering Trends and Technology, 67(10),182-186

Educational researchers commonly use a variety of methods such as classroom observation, content analysis, surveys and interviews to collect data and analyze teachers’ reflective thinking. This paper describes the different methods and reveals in depth meanings within the unstructured data, but is time-consuming and cannot be implemented in a large scale. The objectives from these paper studies may be subject to the subjective impression of educational researchers. Moreover, the results obtained from these methods are lagging behind and cannot help teacher trainers make timely intervention policies. On the other hand, the data-driven approaches such as educational data mining and learning analytics are able to analyze mass of relative data and visualize results. The proposed approaches, however, are mainly used for the analysis of structured data, including learning behavior data, performance data and administrative data recorded in effective course review management systems (ECMS) or online learning environments (OLE) such as Moodle. In this proposed system analysis the inductive content analysis and a common classification method for analyzing review content.


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Active Education Data Mining, Teacher Reflection Data, TF-IDF Classification, Machine Learning.