Learning Analytics (LA) is a growing area of the higher educational field. With the shift from classroom learning to online learning, there are now more opportunities to capture what is being learnt, what is being learnt and understood.

LA itself is defined as a measurement, collection, analysis and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which it occurs. LA is very much related to educational data mining.

Usually, LA is used to track learners’ activity so as to improve retention and attainment, with a view to maximise learners’ learning potential and experience.

Following on Dr Cope’s explanation on the video ‘Recursive Feedback, Part 4C: Crowdsourcing Prospective or Constitutive Assessment’, it will not be too far before we stop assessing students at the end. This is because as we are able to capture learners’ progress and understanding along the way, we can correlate this to data captured on learner’s understanding before the course and automatically calculate improvement in learner’s understanding (e.g. as a percentage increase).

Below is a personal reflection on the issues around LA.

Personal Reflection on Issues:

1. One thing that I am concerned with, is how far are we willing to go with LA. Big data* is currently an issue everywhere. For instance, in some MOOCs, there are 1000’s of participants and a MOOC can run several times per year. In this case, if we run it 5 times per year, we end up with 5000 students roughly.

We spend a humongous amount of resources (time, effort, money) to collect a significantly large amount of data from the 1000 learners, but can we fully tap the meta data around these data sets?

Unfortunately, we are lagging behind in terms of technologies. We do not have enough computational resources or Artificial Intelligence software to make effective use of the data that we have.

[*Big data are extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.]

2. Besides, while learning analytics are on the radar of many institutions, the logic behind has to be constantly revisited. E-Learning technologies and methods are constantly changing. The first graduate program focused specifically on learning analytics was created and launched only in 2015. Hopefully, LA will continue to attract researchers/educators to keep rethinking the design and approach behind the LA algorithm they are using.

3. Let’s face it, most MOOCs are free and there is always a high percentage of registrants who do not complete their courses. How much effort are institutions/educators involved in MOOCs willing to put to generate analytics, and to modify materials / approaches taken, bearing in mind that learners’ retention and sometimes achievements are poor. Should we focus LA on how it benefits learners, rather than the MOOCs institutions/educators? Mostly LA is more effective when used in an institution where learners pay fees, where there is tangible benefits to both learners and the institution to analyse the way the students learn. So does that mean that we to tap the benefits of LA, it should best be applied only paid courses?

4. Whilst LA is still a fairly new area, there are growing concerns about the legal and ethical aspects of LA. In most MOOCs, learners are asked to complete their profile and load up a photo as a mandatory step. That’s useful data for LA, but can these data be misused by potentially hundreds of thousands of participants geographically dispersed around the globe?  Are data held in US for an EU learner legal? Who owns the data, is it the company doing the LA on behalf of an institution, or is it the institution? It is only in January of this year, that the EU launched their Bricks to Clicks report and issues related to LA will keep cropping.

Conclusion

Whilst this personal reflection has highlighted only the issues around LA, it is to be noted that LA is highly beneficial, such as, increasing retention and achievement, increasing learner's experience and understanding.

REFERENCES:

• Effective learning analytics – JISC https://www.jisc.ac.uk/rd/projects/effective-learning-analytics
• MOOC on Wikipedia https://en.wikipedia.org/wiki/Massive_open_online_course
• Big data on Wikipedia https://en.wikipedia.org/wiki/Big_data
• Report: From Bricks to Clicks - The Potential of Data and Analytics in Higher Education. Available at: http://www.policyconnect.org.uk/hec/research/report-bricks-clicks-potential-data-and-analytics-higher-education
• A taxonomy of ethical, legal and logistical issues of learning analytics v1.0. Available at: https://analytics.jiscinvolve.org/wp/2015/03/03/a-taxonomy-of-ethical-legal-and-logistical-issues-of-learning-analytics-v1-0/