LASI is an acronym that stands for "Learning Analytics and Educational Data Mining." It refers to the growing field of study within education that utilizes various analytical techniques and data mining methods to gain insights into learning processes and educational systems. LASI involves the analysis of vast amounts of data generated during online learning activities, such as student interactions with learning content, online discussions, assessments, and other forms of engagement.
By leveraging computational methods, LASI aims to extract meaningful patterns, trends, and correlations from educational data to inform and improve teaching and learning practices. These data-driven insights can help educators and educational institutions better understand student behavior, identify areas of improvement, and develop personalized learning experiences.
LASI draws from disciplines such as machine learning, data mining, statistics, and human-computer interaction to support evidence-based decision-making in education. It encompasses the collection, storage, cleaning, integration, analysis, and visualization of educational data sets from multiple sources. The ultimate goal of LASI is to enhance educational outcomes, including student achievement, engagement, and retention rates, by using data-driven methodologies.
Researchers and practitioners in LASI explore different areas of interest, such as predictive modeling, recommending learning resources, detecting learning patterns, and evaluating the effectiveness of educational interventions. This interdisciplinary field aims to bridge the gap between research and practice by providing actionable insights and fostering data-informed decision-making in educational contexts.
