Learning Analytics to expose the learner for pedagogical advancements

Teachers have a significant role to play in the educational process to provide high-quality teaching by identifying and coping the learner differences, desires, requirements and their learning patterns (Andriotis, 2016). Correctly examined data in a teaching and learning environment reflect the students’ uniqueness, achievements, motivations and other stakeholders’ interactions with the learning process (DQC, 2019). The data in an educational environment can be collected using explicit methods like surveying and interviewing or/and implicit methods like access logs, behavioural observations, results and marks (Vagale & Niedrite, 2012). Each data contains some hidden information. This phenomenon was caused to emerge a new concept in learning named Learning Analytics (LA). There is no concrete definition for LA. But in the 1st International Conference on Learning Analytics and Knowledge organised by the Society for Learning Analytics Research LA concept was defined as follows (Siemens & Long, 2011).

 “LA is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”.

The above definition describes the LA in different perspectives but not sufficient to designate the whole LA process and its significance in the educational field. Therefore, the following definition is proposed considering the personal experiences of LA in the local context.

 “Learning Analytics is a technique of revealing patterns based on the data obtained from the access logs, surveys, feedbacks, interviews, observations, historical facts, students’ marks and results in educational environments which can be used to formulate descriptive, predictive, diagnostic or prescriptive information models for proposing innovative resolutions to address the teaching and pedagogical concerns. LA is more targeted of changing teaching and learning techniques at the individual level.”

Although Learning Analytics concept resonates advanced, quality and motivated online learning, inaccurate and inappropriate analytic reports can be harmful to the learners increasing the dropout rate in online courses. Therefore, data and pieces of evidence should be correctly analysed to deter the wrongful use of big data in LA research studies (Dringus, 2012).   

References:

Andriotis, N., 2016. Know your Audience! A smart guide for analyzing your learners’ needs. [Online] Available at: https://www.efrontlearning.com/blog/2016/10/guide-learners-needs-analysis.html

DQC, 2019. Why Education Data?. [Online] Available at: https://dataqualitycampaign.org/why-education-data/

Dringus, L. P., 2012. Learning Analytics Considered Harmful. Journal of Asynchronous Learning Networks, 16(3), pp. 87-100.

Siemens, G. & Long, P., 2011. Penetrating the Fog: Analytics. [Online]
Available at: https://er.educause.edu/-/media/files/article-downloads/erm1151.pdf

Vagale, V. & Niedrite, L., 2012. Learner Models Utilization in the e-learning environments. Vilnius, Lithuania, s.n.



Leave a Reply

Your email address will not be published. Required fields are marked *