The Use of Learning Analytics to Track and Improve Linguistic Competency

Abstract

The purpose of this study is to investigate how children from low-income backgrounds might benefit from playing serious games in order to improve their language skills. With the use of learning analytics, we were able to covertly measure students' progress and provide them with individualized feedback after they played serious games in class. A total of 85 kids, including 41 males and 44 girls, ranging in age from 9 to 12 (m=10.6; SD=0.7), were surveyed for the study. All of the participants were deemed at risk owing to their socioeconomic status. To determine how well two games aimed at enhancing language abilities worked, researchers used a pre-experimental design that included both pre- and post-tests. Learning analytics can effectively evaluate the competencies that students have acquired and identify their specific needs when they are facing academic challenges. Additionally, this analytics approach can predict academic performance using scores collected during game-based learning. The results show that (a) incorporating serious games into the curriculum can greatly improve linguistic competence in disadvantaged students. (b) With targeted systematic interventions, these students can achieve performance levels comparable to their peers. These findings help fill in some of the blanks in our understanding of how to best use educational technology and instructional practices to support at-risk students.