Assessing Learning Behaviors Using Gaussian Hybrid Fuzzy Clustering (GHFC) in Special Education Classrooms
Dr. Udayakumar RDean, CS & IT, Kalinga University rsukumar2007@gmail.com0000-0002-1395-583X
Muhammad Abul KalamResearch Scholar, Bharath Institute of Higher Education and Research. Assistant Professor, CMR Institute of Technology. makalam.cse@gmail.com0000-0002-9130-1444
Dr. Sugumar RProfessor, Institute of CSE, Saveetha School of Engineering, Saveetha Institute of Medical & Technical Sciences sugu16@gmail.com0000-0002-0801-6600
Dr. Elankavi RAssociate Professor, Department of Computer Science and Engineering, Siddharth Institute of Engineering & Technology kavirajcse@gmail.com0000-0001-5661-7278
Keywords: Affective State Transition, Unsupervised Learning, Cognitive State Transition, Likelihood Metric, Fuzzy Clustering
Abstract
The article suggests an unsupervised model for featuring student’s learning patterns in an open-ended learning scenario. The article proceeds by generating powerful metrics to characterize the learner’s behavior and efficacy through Coherence investigation. Then, the selected features are combined through a Gaussian Hybrid Fuzzy Clustering (GHFC) that categorizes students based on their learning patterns. The proposed system features the essential behaviors of every group and associate the behaviors with ability to develop right models to gauge the learning gains between pre- and post-test scores. Also, this article explains the deployment of behavior characterization to be developed as a adaptive framework of learning behaviors.