LEARNING ANALYTICS TO QUANTIZE AND IMPROVE THE SKILLS DEVELOPMENT AND ATTAINMENT IN LARGE CLASSES

Ishwari Aghav, Jagannth Aghav

Abstract: The intervention of technology in the teaching and learning processes is bringing change. There is requirement of methods and techniques to analyze the generated data in these processes. Traditional way of delivery and assessment is becoming more focused by the application of technology. In this paper, we propose and illustrate an algorithmic methodology that shall allow the stakeholders in education to focus more on skills attainment effectively. In large size class, there is huge data generated by registration system, administration system, and the learning management system together. We propose the methodology that mainly maps and quantizes the learning outcomes, and that relates to the successful and weaker range of attainments of stakeholders. This algorithmic methodology reads the data viz. course details, outcomes, and assessment scores of a large class and analyzes the skills attainment with respect to the planned each course outcome. The detailed report is generated with the average of attainment of the class strength, highest attainment, and lowest attainment in each of the course outcomes for each student

Keywords: Learning Analytics, Software tool, System Architecture, Course Outcomes, and Skills Attainment

DOI: https://doi.org/10.15623/ijret.2014.0310057