Learning Analytics for Monitoring Progress in Inclusive Classrooms

Abstract

The present day classroom arrangement where diversity is gradually turning into the new reality of the day requires the implementation of some innovative solutions that can be utilized so that the process of students learning in a non-discriminating way were to be followed. The suggested research provides a detailed learning analytics concept, which will help to track the progress of learning and personalized learning in general classrooms. The model is associated with the usage of various types of data sources such as Learning Management Systems (LMS), IoT-based classroom environment, and supportive technology to visualize and analyze real-time data about learners. Premier analytics methods (including descriptive, predictive and prescriptive analytics) are used to identify the patterns of learning, define at-risk students and offer them specific interventions. One of the offered models can be considered an adaptive learning, feedback, and data-driven decision making, which can be helpful to help educators meet the needs of diverse learners. Using visualization dashboards, students can view actionable data about student engagement and student performance and trends in student behaviour that can inform and intervene in a timely manner. Examples of performance indicators that can be utilized in measuring the model are engagement, academic performance, retention and effectiveness of interventions. The findings show that the model has achieved a lot of advancement in all the measures; specially at-risk and special needs students, which demonstrates that it is a decent model in enhancing inclusive education. In addition to the same, the paper records the relevance of ethical data management, the integration of the system and the stakeholder participation in the achievement of learning analytics systems. The results indicate that the suggested model does not only lead to academic success but equity, access, and individualized learning opportunities. This study will help in coming up with scalable and adaptive learning systems that will fill the gap existing between technological progress and all-inclusive pedagogies.