The recent growth in data has been phenomenal.  It is estimated that there are 2.7 zettabytes¹ of data in existence today² and the amount is expected to grow exponentially to 180 zettabytes by 2025³.  As examples of this trend, ASDA/Walmart now handles more than 1 million customer transactions per hour and every minute that passes see 48 hours of new video uploaded to YouTube⁴.

Education is witnessing this growth too. According to the March 2017 report from the Department for Digital, Culture, Media and Sport⁵, Education Technology accounts for 4% of all digital companies and is one of the fastest growing sectors in the UK.  This growing investment, coupled with the rise of social media, the adoption of new technologies, decreasing costs for data collection and storage and improved ways to process data are now allowing us to unlock the power stored in data.  This unlocking creates opportunities to make better use of our data and to develop and improve the ways in which we support students on their learning journey at BU.

The beginning of the implementation of Brightspace is a significant step in tapping into and unlocking BU’s digital education data.  Now more than ever in the past, we are collecting large amounts of data about users’ interaction with the VLE.  For example, since its launch in August 2017, content items within Brightspace have been accessed more than 3 million times and 191,000 times in the last week (10 – 17 January 2018).

The analysis of the data from the learning environment (including the VLE but potentially other sources such as library usage) is collectively known as learning analytics.  Learning analytics is defined as the process of examining learning data with the objective of transforming the data into information and then using that information to generate conclusions that can form the basis of informed decisions.  Within the HE sector, many HEIs are starting to investigate learning analytics because of the potential for improving student attainment, retention and satisfaction⁶.

Learning analytics can help attainment by identifying students who are not performing to their potential in order that the teaching team can decide how best to support a student.  It is a common misconception that the system intervenes with students but this is almost never the case.  Instead, the system alerts staff and it is the responsibility of staff to choose how to respond to that information.  Another area in which learning analytics can be very beneficial is in the identification of students close to grade boundaries so that they can be helped to improve their performance.  From my own experience, I can remember conversations with students after their final unit marks is released and who achieved a 58 or 69 say, and them saying that they would have worked harder if they knew they were that close to the next grade boundary.  In the past, I did not have the tools available to provide information at this level of granularity but learning analytics does offer the potential to provide better information and thus help students at all levels of performance.

Student retention is another area that HEIs are looking to address as exemplified by the recent What Works? programme of the HEA⁶.  The final report from the HEA, published in April 2017, commented how “every student that drops out of their higher education course is a loss: a loss to their university or college, a loss to the future economy and, above all, a loss to that individual.”  Research indicates that students who withdraw from programmes often follow a similar path, for example, marked by lower than average attendance and poorer than average assessments.  Learning analytics can potentially help here by looking for changes in interactions with the VLE that may indicate something has changed, allowing staff to decide on the best course of action before a decision to withdraw is made by the student.

Overall, student satisfaction can be boosted by learning analytics because it empowers and informs staff and thereby enables them to better support students on their learning journey.  HEIs using learning analytics have reported increased levels of engagement, belonging, confidence and attainment by students.

BU is currently in the first phase of its own learning analytics journey.  A working group has been looking at the BU policy, staff guidelines and, crucially, a guide for students on how and why learning analytics can be used to aid student success.  We want to be open and transparent about the intended use of learning analytics before its first use.   The working group is also now assessing the implications of learning analytics for the pending General Data Protection Regulations (GDPR).  The working group is very excited about the potential benefits of learning analytics and is seeking to unlock the power of our learning data to benefit our students.

For more information on learning analytics, please contact David Biggins in the first instance.

Sources:

1.  Zetta indicates multiplication by the seventh power of 1,000 or 10²¹.  A thousand Gigabytes equal a Terrabyte, 1,000 Terrabytes equals a Petabyte, 1,000 Petabytes equals an Exabtye and, finally, 1,000 Exabytes equals a Zettabyte.

2.  https://martech.zone/ibm-big-data-marketing/

3.  https://www.simplilearn.com/data-science-vs-big-data-vs-data-analytics-article

4.  https://www.waterfordtechnologies.com/big-data-interesting-facts/

5.  UK Digital Strategy 2017.

6.  https://www.jisc.ac.uk/rd/projects/effective-learning-analytics