Educational institutions are increasingly using big data and analytics to process the data they have collected and derive patterns from it, highlight certain trends, and inform their decision-making accordingly.
- What is big data?
- What can we do with big data?
- Big data in use in education
- Potential problems
Few fields contain within them the potential for so much simultaneous improvement and controversy as big data and analytics. In our age of big tech and social media, big data has become something of a buzzword, eliciting feelings of excitement and apprehension depending on the context. The gathering and use of big data are driven by a universal fact: the more you know, the more you can act accordingly and effectively. However, this is a sensitive topic in more ways than one, and it is not a certainty that attitudes are going to change to allow for what is often seen as more intrusive data harvesting. In the context of education, this can be particularly tricky.
What is big data?
The concept of big data is one of those things whose name really says it all. It means data, and lots of it. An industry analyst by the name of Doug Laney, in the early years of the new millennium, laid out what is often considered to be the mainstream or standard definition of ‘big data’, which revolves around the concept of three V’s: volume, velocity, and variety. Volume refers to the sheer amount of data that is being collected and stored. Not too long ago, the ability to store vast quantities of usable data was prohibitively expensive or difficult. Now, storing methodologies like data lakes have made it possible. Velocity is a reference to the speed with which businesses and other organisations are constantly receiving new data. With more objects becoming connected to the wider internet, there is an increasingly greater volume of data just tumbling in from every direction. Variety, as the name implies, refers to the fact that data is being gathered in various forms. It can be structured or unstructured, numerical or non-numerical; it can relate to individual transactions or preferences and browsing habits.
What can we do with big data?
Big data can be utilised to reduce operating costs, identify new products or services for development, inform current decision-making, and reduce time waste and inefficiency. Big data analytics can therefore identify why some things fail and others succeed. It can also help organisations detect and act upon potentially fraudulent behaviours and calculate and manage risks. Another use of big data, whose benefits are more indirect, relates to its implications for artificial intelligence (AI). As AI is fed with more data, it becomes cleverer and more sophisticated, which reduces the need for human intervention or oversight.
Generally speaking, we can conceive of the benefits for education in two broad areas: the learning process and the management of educational institutions. For example, a school or university may be experiencing variations in enrolment numbers. Moreover, the reasons behind these variations might not be immediately apparent. The benefit of big data and analytics is that it can be used to process all of the relevant data that the institution has collected and derive patterns from it. This can be used to highlight certain trends. Are enrolment numbers dropping in some departments more than others? Are all student enrolments dropping or only a certain type of student? Does this correlate to certain professors or teachers? Insights like these can be invaluable guides when it comes to decision-making.
Admissions can also make use of big data and analytics by utilising the data about prospective students gathered from their many other interactions with the IoT and similar. This data and the corresponding insights can be used to identify students who are most likely to complete the course, those most likely to drop out, and even target certain groups for advertising or marketing. Regarding the academic side of things, big data and analytics can be quite useful here too. Data gathered on courses, student performance, attendance, feedback, and more can all be gathered using a variety of digital means. These can be used to assess the performance of both the students themselves and the academic staff.
With more teaching and learning being done digitally, both remotely and in the classroom, there is a corresponding influx of new data that can be utilised. Student activity, participation, time management, performance, and more can also be leveraged. This can be used to identify which teachers are best at maintaining student engagement, how this varies by course, and even if the teacher resonates more with a certain type of student. If a certain student (or a group of students) is struggling with a certain aspect of a course, real-time data analytics can detect that they are starting to fall behind, allowing teaching staff to address the issue as soon as possible. This means that education can be better tailored to the individual student, thereby improving average performance.
Big data in use in education
Digital learning platform provider Apex Learning develops solutions for various educational levels, both for physical and virtual environments. The idea is to replace periodical assessments like mid-year reports with a continually evolving progress tracker. This real-time solution is accessible to both students and teachers, and it can help the latter when it comes to adapting or devising lesson plans that are more effective. This can point teachers in the right direction if there is trouble, such as students struggling or falling behind. In Texas, Santa Anna High School has already adopted this innovative solution. The school was considering changes to its curriculum and the integration of existing materials with supplementary digital ones. Thanks to the insights provided by the software, the school was able to determine its priorities and make the necessary changes to its curriculum such that they fit with the needs of students.
Grading systems can also benefit from data analytics. Ashford School in the United Kingdom has adopted big data tools from providers like Classroom Monitor, Nearpod, and Socrative. These solutions allow the school to observe its students’ behaviour and performance in real time, thereby granting the school the ability to make timely changes and alterations to its educational approach for the best results. In terms of grading, the patterns that can be derived from analysis of individual student grades can give an indication of a student’s interests and where they are likely to excel in future. Teachers can make use of these insights when it comes to advising students on their elective classes or programmes and even help them advise students on further education and career paths.
Anybody who has not been living under a rock knows the controversies that surround big data gathering and analytics. Most often, we associate these sorts of problems with big tech companies like Facebook and Twitter. This may be simply due to the fact that these companies gather more data than anyone else, whereas schools and universities are relative latecomers to big data and analytics. The relationship between a student and a school or university is also different to that between a company and a customer or user and implies a greater degree of trust. While this can mean that schools and universities may not be in for as much criticism, it does raise the stakes if something does go wrong. This is especially true for schools, where the majority of students will usually be underage and therefore not able to consent to their data being harvested or used. At university, this problem does not arise, as students will generally be consenting adults. However, this does not mean they are happy about their data being gathered and analysed. Indeed, we have already seen students organise petitions in many universities against certain digital technologies being introduced for fear that any data being gathered will not be secure.
Additionally, there is a problem related to a distinct lack of talent when it comes to data scientists and data analysts. At present, demand for these skills far exceeds supply, and many educational institutions are lagging behind the needs of the labour market and not offering effective data science and analysis courses. On the other hand, the supply of data is much greater than our present ability to store and analyse it. There just isn’t enough physical hardware and storage space to take it all in, process it, and derive anything meaningful therefrom. Finally, we have the problem of security. The security protocols that already exist were never designed for the sort of big data we are currently witnessing, and there will be a need for these to be reworked if these unprecedented volumes are going to be used effectively and securely.
When all is said and done, many of the troubles that plague big data are natural when we consider how relatively young this technology is. Not only do we have to come to terms with some of the ethical considerations that are involved, but we must also acknowledge that the hardware requirements (in terms of raw computing power) are like nothing we have ever seen before. Moreover, schools and universities need to start producing individuals who are capable of managing, analysing, and otherwise handling large volumes of data. These are just a few of the hurdles yet to be overcome. But once they are, as it is certainly only a matter of time, then the real potential of big data is going to be tapped.