2015IJITRRequires access

"Educational Data Mining" Case Study - Bangalore

Manjula, A. N. Nandakumar

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Abstract

Educational Data Mining (EDM) describes a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings (e.g., universities and intelligent tutoring systems). At a high level, the field seeks to develop and improve methods for exploring this data, which often has multiple levels of meaningful hierarchy, in order to discover new insights about how people learn in the context of such settings. In doing so, EDM has contributed to theories of learning investigated by researchers in educational psychology and the learning sciences. The field is closely tied to that of learning analytics, and the two have been compared and contrasted. In This Paper we have conducted a Questionnaire Survey on 500 Software Engineers to understand the present scenario of EDM.

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What this paper is about

Educational Data Mining (EDM) describes a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings (e.g., universities and intelligent tutoring systems). At a high level, the field seeks to develop and improve methods for exploring this data, which often has multiple levels of meaningful hierarchy, in order to discover new insights about how people learn in the context of such settings. In doing so, EDM has contributed to theories of learning investigated by researchers in educational psychology and the learning sciences. The field is closely tied to that of learning analytics, and the two have been compared and contrasted. In This Paper we have conducted a Questionnaire Survey on 500 Software Engineers to understand the present scenario of EDM.

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Available abstract

Educational Data Mining (EDM) describes a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings (e.g., universities and intelligent tutoring systems). At a high level, the field seeks to develop and improve methods for exploring this data, which often has multiple levels of meaningful hierarchy, in order to discover new insights about how people learn in the context of such settings. In doing so, EDM has contributed to theories of learning investigated by researchers in educational psychology and the learning sciences. The field is closely tied to that of learning analytics, and the two have been compared and contrasted. In This Paper we have conducted a Questionnaire Survey on 500 Software Engineers to understand the present scenario of EDM.

Key concepts: Educational data mining, Field (mathematics), Data science, Learning analytics, Context (archaeology), Computer science, Software, Knowledge management

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