An approach to generate students' response on learning environment using Association Rule Mining
Rajni Jindal, Dutta Borah Malaya · 2014
To enhance the quality and cost effectiveness of the education, students participation and a better understanding of the learning is important. The proliferation of learning innovations such as distributed and heterogeneous applications and resources requires the adoption of appropriate technique to deal with the various information flows to support learning. Association Rule Mining is one the prominent Data Mining technique that helps to find out the items frequently appears in the dataset, relationships among different attributes in a large search space. The focal point of this work is to measure learner's beliefs, interests in particular subjects and ultimately generate students' response in on learning environment using Association Rule Mining. For training and testing purpose, we used the 'Motivation and Metacognition in Chinese Vocabulary Learning, Experiment 3 (27,421 transactions) & 5 (72,249 transactions) dataset accessed via DataShop.