We live in a world where people and objects produce ever-increasing quantities of data. Data that, thanks to big data technology, can be collected, processed and analysed. And it is through learning analytics that these practices have reached education.
At the 1st International Conference on “Learning Analytics and Knowledge” (2011), held in Alberta, Canada, a first definition of learning analytics was drafted:
“The measurement, collection, analysis and reporting of data about students and their contexts in order to understand and optimise learning and the environments in which it occurs.”
Learning analytics is therefore a set of techniques to help us get to know students through the traces, in other words the “digital trail”, left by the student within digital learning environments. And from this, to be able to adapt and personalise teaching to their specific needs.
The field of learning analytics has now made its way into the different educational levels and effective models have been established that contribute to increasing the quality and results of the teaching-learning process in the everyday classroom.
In addition, a number of initiatives have emerged within EdTech companies to develop tools that collect the maximum amount of data in relation to learning environments and the way we learn.
Behavioural patterns: the next level for the EdTech industry
The emergence of learning analytics is closely related to the gradual process of digitalisation that has been taking place in education for some years now. First with the incorporation of ICT and the use of devices in the classroom, and then with learning platforms.
It is through those digital learning platforms that students leave digital traces of their activity, such as the number of accesses, connection times, homework completed, etc.
Therefore, in order to bring the personalisation of learning a little closer, the educational technology company BlinkLearning has launched a data analysis project within its platform. This project has now been translated into BlinkAnalytics, a very user-friendly statistics dashboard within the teachers’ profile.
Through this new dashboard, teachers can visualise their students’ academic activity: how often they access the platform, the time spent on homework, or average grades, among other metrics.
For Gonzalo Baranda, CEO of BlinkLearning, “data analysis will increasingly allow us to move towards a true personalisation of learning. By analysis of the data obtained, a teacher could monitor whether their student is making good progress or not. And, most importantly, provide a solution in real time. But for this to happen, a data-driven culture needs to be embedded in educational institutions to make decisions based on evidence, not on assumptions.”
What are the benefits of applying learning analytics in education?
The biggest benefit brought by big data technology is the ability to improve the teaching-learning process on four levels:
- Descriptive, or ‘what happens’.
- Diagnostic, or ‘why it happens’.
- Predictive, or ‘what might happen’.
- Prescriptive, or ‘how it can be improved’.
In addition, several specialists in the field agree on the following benefits of using learning analytics in education. It enables you to:
- Implement personalised education derived from a thorough knowledge of each student.
- Analyse the performance of each student: academic results, absences and any other information that may have an influence on their school performance.
- Collect information on each student in real time and therefore get instant feedback and act accordingly.
- Find out how students use different educational resources, and which methods or techniques work best.
- Provide teachers with up-to-date training tools to enable them to adapt their teaching methods and techniques to the new educational models.
- Measure interest or motivation in the subject through the frequency of connection. Predict which students are at risk of failing and/or dropping out.
However, we should not lose sight of the fact that talking about the use of big data technology in education implies approaching debates such as: the effectiveness of the data collected, ethics in the use of this data and adequate teacher training.
The value of data analysis in education is growing along with the development of the technology itself. However, we should not lose sight of the fact that tangible results depend to a large extent on the adoption of an integrated digital project. As with the use of digital platforms or devices in the classroom, when properly implemented, cutting-edge technology can boost the quality of the teaching-learning process.
