Learning Analytics (LA) data streams obtained from 'genuine' student learning activities may open up new paths for acquiring important information on students' learning patterns and could be used to adjust the learning design in real time. We believe that Learning Analytics applied within Learning Design (LD) and shown on a learning dashboard has the potential to lead to more personalized learning experiences if correctly implemented.

LA analyzes data using a range of methods, including visual data analysis tools, social network analysis, semantic, and educational data mining techniques such as prediction, clustering, relationship mining, model development, and data separation for human judgment. Focused course offerings, curriculum creation, student learning outcomes, behavior and process, tailored learning, increased teacher performance, post-educational employment choices, and improved education research are among the benefits. Among the problems are issues with data monitoring, gathering, evaluation, and analysis; a lack of connection to learning sciences; optimizing learning settings; and ethical and privacy considerations. This comprehensive analysis provides professors, course developers, and administrators with an integrative report on the methodology, benefits, and limitations of LA, allowing them to more effectively use LA to improve teaching and learning in higher education.

LA increases learners' and instructors' awareness of their current situations, helping them to make better decisions and perform their tasks more effectively.

To speed up the flow of information and provide an orderly approach for acquiring and evaluating data in learning analytics, researchers presented a macro-level technique for performing learning analytics in educational contexts.

LA combines and applies approaches such as data mining, data visualization, machine learning, learning sciences, psychology, social network analysis, semantics, artificial intelligence, e-learning, and social aspects analysis.

An initial benefit of using big data analysis in education is the capacity of educational institutions to select targeted courses that more closely align with student needs and preferences for their program of study. Monitoring patterns in student enrollment and interests in various disciplines allows institutions to focus educational and instructional resources in programs that optimize student enrolment in the most needed fields of study.

Using big data allows instructors to make changes and alterations to the educational system's curriculum development, such as through the use of data-driven curricular mapping.

This data can also be used to evaluate instructor performance.

Using big data, educational institutions may be able to identify post-secondary career opportunities for graduates and help target training that is more closely matched with labor market needs.

The use of big data in education serves both the academic and research communities. Researchers will be able to exchange and collaborate more easily. They are able to identify gaps between industry and academia in order to do study on how to fix problems. Furthermore, relevant data analysis is a critical component of researchers' ability to develop information and advance in their fields of study.

LA Implementation Obstacles

Data collecting in Los Angeles might be tricky. Nonetheless, it is a vital component in future educational program expansion planning.