ANALYSIS AND PREDICTIONS ON BLENDED LEARNING READINESS AMONG INDIAN STUDENTS AT UNIVERSITIE SUSING DECISION TREE CLASSIFIER IN SCIKIT-LEARN ENVIRONMENT

Abstract

Decision trees classifiers are modest and hasty data classifiers, usually used in data mining to study the data and generate the tree and its rules that will be used to formulate predictions. One of the key challenges for knowledge discovery and data mining systems stands in developing their data analysis capability to discover out of the conventional models in data. Since the Union budget 2017, the debate around the quality of Higher Education in India has been acquiring momentum, which laid emphasis on skills development, employability and digitisation of the education process. The key lies in blended learning, a model that is fast gaining pace in the Indian context,where online tools are combined with classroom and instruction to provide an overall improvement in educational outcomes activities.The explanatory variables are students’ attitude towards learning flexibility, online learning, classroom learning, degree and stream. This paper represents an implementation of the decision tree classifier algorithm using scikit-learn library for python on data collected from the survey with the purpose of predicting whether a particular student is ready for blended learning throughdecision trees.