Affiliation:
1. Amity University, Gwalior, India
Abstract
Organizations of every size and industry are embracing data analytics to extract insightful information from the data with the goal of enhancing decision-making, boosting productivity, streamlining workflows, and reducing costs. The adoption of data, more especially learning analytics, which is the act of obtaining, measuring, and analyzing data on learners in order to better understand their requirements and learning preferences, is an excellent possibility for the education industry. Predictive learner analytics can be used to gain a variety of insights that can help institutions improve learner retention, engagement, and performance measurement, particularly in asynchronous online learning where students feel helpless and distracted. In light of this, the chapter suggests a methodology and model for integrating asynchronous learning and predictive analytics.
Reference71 articles.
1. Intelligent Tutoring Systems
2. Using decision tree algorithm to predict student performance.;M.Apolinar-Gotardo;Indian Journal of Science and Technology,2019
3. Learning analytics methods, benefits, and challenges in higher education: A systematic literature review.;J. T.Avella;Online Learning : the Official Journal of the Online Learning Consortium,2016
4. Predictive modelling and analytics of students’ grades using machine learning algorithms
5. Bailie, J. L. (2020). Online learner analytics of asynchronous discussions as a predictor of final grades. Journal of Instructional Pedagogies, 24.