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A Review On Research Areas In Educational Data Mining And Learning Analytics
[Full Text]
AUTHOR(S)
AbhilashaSankari, Dr. ShraddhaMasih, Dr. Maya Ingle
KEYWORDS
Educational Data Mining, Learning Analytics
ABSTRACT
Over the last two-decade or so, educational data mining has evolved as an emerging discipline to analyze the type of data that comes from academics. Several research studies has carried outin Intelligent Tutoring System (ITS), Difficulty Factor Assessments, Latent Knowledge Estimation, Knowledge Inferences, Recommender System and Social Network Analysis.Gathering evidence of learning from educational setup has laid the foundation of learning analytics and educational data mining. Bayesian Knowledge Tracing (BKT), Q-Metrics, Performance Factor Analysis and Latent Knowledge Estimation methods are useful for the study of student’s success. Other methods like matrix factorization and knowledge components are suited for analyzing the student’s knowledge and performance. On the other hand, knowledge engineering and clustering is useful to develop student models for educational software.The current scope of research areas and methods utilized in educational data mining and learning analytics has discussed in this paper
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