Traditionally, the success or failure of a course has been determined by evaluating students, assessing grades and test scores, and gathering observations from course instructors – usually once the course has finished.
One of the best ways to ensure that participants will benefit from the education you’re giving them is by studying their reactions to a course, and their behaviour as they’re taking it. That’s where learning analytics comes in.
The process involves the collection, analysis, and reporting of information gathered from the participants as they undertake an eLearning course. Learning analytics is an ongoing, real-time process, rather than a retrospective one.
Analytics take in observations like how often your learners log into the eLearning platform, how they are making their way through its various modules, their activities in discussion forums, and how they perform in tests, examples, and quizzes.
Information gathered from day to day observations in real time may be used by course developers and facilitators to predict their learners’ level of success, and to make amendments and improvements to course materials.
How eLearning Benefits
Learning analytics can reveal how a course participant is performing today, and use this information to help predict how they will perform as the eLearning course progresses. Study of their performance and behaviour may indicate whether supplementary materials, help from instructors or other learners, or even a complete change of tack will be most appropriate to ensure their success.
Learners benefit from a personally tailored eLearning experience. If an individual is observed to be struggling over a particular course module, he or she can be offered access to custom tools and resources like links to specific and related websites, or audio-visual supplements that throw a greater light on the subject.
Learners may come from a wide range of educational and cultural backgrounds. Using learning analytics, the same eLearning course may take very different paths, for each of the learners involved. But at the end of the day, all participants may achieve the required levels of success.
Success breeds confidence, and assisting a learner’s progress with learning analytics encourages him/her to complete the course, and to actively participate in its modules.
Collating and analysing all the course observations can help improve the eLearning experience for future participants. Problem areas, unpopular course modules, and suitable levels of difficulty in the course material can be red-flagged by studying the results from all the learners – and used to design a better course, for next time.
For course designers and those hosting an eLearning course, learning analytics can reveal which aspects of a training scheme are working best, and which are failing to meet your expectations. This can be a guide to where resources (and money) may best be used to achieve your learning goals.
Analytics in Practice
Commerce, media, government bodies, intelligence agencies and the military have long known the benefits of gathering and analysing data from various sources to help in formulating policies and making decisions.
With the increasing overlap between technology and education, software manufacturers, research centres and training establishments have begun to recognise the value of data analysis in their operations.
Learning analytics is fuelling the development of machine learning tools, social and content recommendation systems, network and discourse analysis, predictive analysis, content development, support and intervention strategies. Leading the way are organisations like the Society for Learning Analytics Research (SoLAR) and the International Educational Data Mining Society (IEDMS).
Learning analytics are being used to enhance the role of facilitators and instructors, to develop tools for automation within eLearning course structures, and to promote research.
Some Questions to Ask:
- Is the course effective, and is it meeting the needs of your learners?
- Which exercises, forums, and methods of interaction are most effective?
- How could these aspects be improved?
- How does student performance compare with your anticipated goals, and the learning targets you’ve set?
What to Expect of Your Learning Analytics Tools
Learning analytics seeks to assist learners and instructors by developing eLearning environments that can adjust their content, resources and support systems to suit individual needs by acting on data gathered and analysed on a continuous basis.
A well-rounded set of learning analytics tools should draw on aspects from academic analytics, business intelligence, educational data mining, action and web analytics.
Web analytics involves the gathering, analysis and reporting of information gleaned from online activity. This may include not only your course participants, but the potentially millions of users who visit the websites and resources concerned.
Retention of knowledge gained by learners during a course and their rates of success are central to academic analytics, which combines this data with predictive models and statistical tools to help in making decisions based on the results.
As an adjunct to this, educational data mining looks for underlying patterns in the data collected.
By analysing information from learners, instructors, and facilitators, learning analytics makes it possible to interpret the mountains of data generated during an eLearning course, and to apply it in improving both the learner experience and your levels of success.