Personalized Formative Feedbacks and Recommendations Based on Learning Analytics to Enhance the Learning of Java Programming

Abstract

AbstractLearning analytics is becoming increasingly popular in higher education. During this COVID-19 pandemic, in particular, e-learning has grown increasingly popular. Students will have additional options for learning based on their interests and requirements as a result of this. To be effective in online and hybrid learning environments, students must develop self-regulation skills and self-directed learning. In online learning, learning analytics appear to have the ability to deliver tailored feedback, personalized recommendations, and support. It is possible to assist students in managing their learning process and self-evaluating their performance through external support and supervision in online learning environments, as well as the usage of learning analytics tools. Sixty-nine undergraduate students participated in the study. Students were challenged on a variety of programming topics. The goal of this research is to use the KNN algorithm to provide automated personalized feedback to novices by conducting continuous assessments and generating recommendations for further improvement using the decision tree algorithm in order to improve their overall skill development in the Java programming language. As a finding of the research, the benefits of individualized recommendations and guided feedback related to learning analytics in increasing students’ overall performance were determined.KeywordsPersonalized feedbackPersonalized recommendationsLearning analyticsQuality educationPerformanceKNNNEP 2020Lifelong learningCOVID-19 pandemic