Student Learning Behavior: an Artificial Neural Network approach - Oza, KS; Kamat, RK; Naik, PG (International Journal of Computer Sciences and Engineering, 2019)
E-Learning has made learning easy with most of the courses floated online for convenient 24X7 learning at learners ease. With virtual learning environment, learning behavior of the online learner has become one of the significant factor. To facilitate fast learning on virtual platform, there is need to analyze online learning pattern of learner. Once the pattern are mined personalized learning environment can be created for the learner as per his/her learning behavior, which will make online learning interesting faster. For finding learning pattern artificial intelligence can be a good tool. Proposed work classifies the learning behavior of the learners with application of artificial neural networks. Proposed work used two types of students test data, one where test was conducted on Moodle server with objective questions and negative marking and second was descriptive test in pen paper mode. First test was conducted to analyze fundamental concepts and their applications in problem solving and second test was to check the innovative thinking ability of students. Three artificial neural networks were trained to classify students in to each three categories based on their number of attempts in the test. All the three models classified the students accurately with negligible mean square error.