Educational data mining and learning analytics are two aspects of big data in the field of education. I would like to discuss about learning analytics.

Learning analytics is about gathering data about learners in their environment or in a particular context and using that data to analyze and understand how learners learn. This powerful data analysis can have significant impact on optimizing learning tasks and assessing learners as well as improving their learning experience.

Educators have always had traditional methods of measuring learner data and utilizing it for their students' benefit. However, with the advent of technology surrounding Big Data, analytics has improved in leaps and bounds.

Big data is named as such because the amount of data collected today is enormous due to digitization of traditional methods, in this case, learning. Moreover, all kinds of data is collected, for example, data in various media forms such as audio, video, and text. Data can also be structured, semi-structured, and unstructured. All this increases data variability. Thus, analyzing this data in order to extract meaning from it is essential because of the myriad ways it can help improve learning and assessment. Starting from automated scoring of hand-written essays to delivering automated feedback to students, the scope and possibilities of learning analytics are far-reaching.

Furthermore, learning analytics must be adopted in institutes attended by student belonging to various demographics, from high-end to low income neighborhoods. This is clearly delineated in this paper regarding widespread adoption of learning analytics.

Acknowledgements:

https://www.researchgate.net/publication/327032518_Big_Data_and_Learning_Analytics_A_Big_Potential_to_Improve_e-Learning

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