Data Mining: A prediction for performance improvement using classification
By Brijesh Kumar Bhardwaj, S. Pal. arXiv.org, 2012
395 citations recorded by the source corpus.
Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. The performance in higher education in India is a turning point in the academics for all students. This academic performance is influenced by many factors, therefore it is essential to develop predictive data mining model for students' performance so as to identify the difference between high learners and slow learners student. In the present investigation, an experimental methodology was adopted to generate a database. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 300 student records, which were used for by Byes classification prediction model construction. Keywords- Data Mining, Educational Data Mining, Predictive Model, Classification.
Subfields: Computer Science Applications
View Paper: https://www.semanticscholar.org/paper/2fa8397f26abee21547a93bf4dc043a8c6b1300f
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In Computer Science Applications
Citations in the corpus
- Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. (2016, background): “…Analytics Benefits Althubaiti and Alkhazim (2014) AlShammari, Aldhafiri, and Al-Shammari (2013) Arnold and Pistilli (2012 Armayor and Leonard (2010) Bhardwaj and Pal (2011) DiCerbo (2014) Dietz-Uhler and Hurn (2013) Grummon (2009) Hsinchun, Chiang, and Storey (2012) Hung and Zhang (2012)…”
- Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients (2015, methodology): “The principle of Naïve Bayes is based on Bayes rules of simple conditional probability used to estimate the likelihood of a property given small amount of training data to estimate parameters such as mean and variance necessary for classification [39].”