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What is learning analytics?

Learning analytics is all about optimizing the learning environments to lay emphasis on how students learn and how teachers or advisors teach. Interestingly, it can expose student behaviorsthat can lead to assumptions which impacts student success. At the same time, learning analytics can recognize the students at risk, based on student activity, demographics, and outcomes.  Based on the information provided, instructors can lay razor sharp focus on students who need a lot of attention. It is the process of using data to augment learning and teaching.  With widespread availability of big datasets around learner activity and digital footprints left by the students in their surroundings, analytics helps to augment learning. Moreover, learning analytics provide information in real-time on the basis of educational information from digital environments and social platforms. Infact, a dynamic education environment is used for modeling, prediction,and educational decision making. Today, educational institutions are distressed with low completion rates of students – a Learning Analytics platform generates insights in real-time that help the decision makers to take immediate actions for course completion and ensure higher success rates for all the students.

Benefits of Learning Analytics

Benefits of Learning Analytics

Quality assurance:Learning analytics helps teachers see how the students are attempting their course, and the hurdles they are facing. Accordingly, they can develop their curriculum design by tweaking the components of the course. Also, when the teaching module is taking place, analytics can monitor student performance.

Identify risky sub-groups:Among the student population, learning analytics can be a powerful tool to identify differential outcomes and gauge theprogress of socio-economical sub-groups. That said, with this tool, educational institutions can offer supplementary support to the students who hail from a particular sub-group in order to augment their learning experience and ensure their retainment.

Learning Analytics challenges

Student motivation

Learning analytics empowers learning for studentsas they know the frequency at which they are imbibing information. In short, it motivates them to work harder. For instance, at Purdue university several students said that their ability to notice their personal analytics has increased their inclination to learn more, leading to a positive experience.

Challenges in adopting Learning Analytics at universities

Integration with systems:

Learning analytics may act like a crucial tool for the universities, but there is a challenge in its disposition. At a university, data is mostly in silos and there is a requirement to integrate different systems in order to get the unified flow of data needed for analytics.

Ethical concerns with learning analytics

Ethical issues are a cause of concern for the educational use of data in learning analytics. That means how personal data is collected, stored, analyzed,and presented to various stakeholders. Thus, the various procedures to regulate access and the usage of educational data need to be highlighted before learning analytics frameworks are implemented. For predictive modeling, it will include transparency of applied algorithms and weightage of educational data.

Validity of data

All the educational data provided is not relevant. The validity of data and its analysis is important for generating real-time and predictive insights. The insights generated pertain to educational technology, learning design, data management,and artificial intelligence. The roadblocks are to examine the complex processes with learning analytics and comprehend their long-term effects on learning process.

educational data

Linking unstructured data to educational data

Within an institution’s environment, information from unstructured data cannot be linked to educational data. Accumulation of such data and uncontrolled relations to existing educational data may lead to invalid analysis, decisions, and predictions. The difficulty is to develop mechanisms to filter biased information.

Leadership and training opportunities

There is a dearth of leadership competencies to ensure that execution of learning capabilities is tactfully planned and delivered. At the same time, there are inadequate training opportunities to prepare end users to implement learning analytics.

Future of learning analytics

At present, the implementation of learning analytics cannot be narroweddown to only universities.Since employees in the businesses are anticipatedto learn and re-learn to meet the dynamic demands of a modern workplace, the significance of corporate learning programs cannot be over-emphasized. Learning analytics is supremely important because it predicts the learning patterns of employees and creates tailor-made learning plans. It is a golden opportunity for corporate employees to progress and reap rich dividends.


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