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It is natural to imagine the future, but there is no assurance that the future would be what we imagined. Did anyone of us think that the development of digital classrooms could turn to be virtual and remote so soon? It is the reality of today's education system.
However, the stable sight of education relies on emphasising new technologies like ubiquitous computing devices, flexible classroom designs and innovative visual displays. When the classrooms soothe the students and the staffers, the big data and analytics shape the future of the education system. Data is the asset of the tech world. It is changing the way that the education system works by predicting accurate decisions that improve organizational output and productivity. Still, many believe in their gut feeling and experience when it comes to predictions in higher education levels.
Data has an influence on all industries. It has taken a remarkable stand on establishing its state in the medical field. The old school way of doctors going through all the patients' files and reports to get to a guess on their current report is long gone. The data collections and build-ups on the patients' former illness, medical reports and prescriptions will lead the data analytics to come to a predicted accurate answer on the patients' health condition. It also reminds the risk factors in the patients' current state. Insurance companies are also switching to the analytics model to predict high-risk customers.
Education may have turned its face to modern technologies now, but for ages, it has been inefficient in data use. Higher education delayed in adapting to the analyzed evident data and feedback. There was a delay in action and opportunities for intervention due to lack of evaluation on student dropouts every year.
Things need to be changed and improved in order to get to the next level. Guidelines were given on the education development for decades now to reform the efficiency and quality of the subject. Now, it is time to stick to the internet, mobile technologies and open education to upgrade the level. Apparently, the decision making is still at a hanging state without proper analysis. Thus, by including analytics into education will help in a better framework on decision making.
Explicit data is the byproduct of the internet, computer, mobile devices and enterprise learning management systems (LMS). General classroom teachings and hallway conversations tend to wither from memory at a certain point of time. But online learning, digital students' records, student cards, sensors and mobile devices now attract data trails and activity streams. Educators could improve the learning process by having an insight into the techniques.
A deeper analysis of data of students will also provide an overview on their performance and indicates if a person is at high-risk to dropout. Some higher education has begun considering using data analysis to increase the success rate and confidence among students, better their learning techniques and understanding. For example, EDUCAUSE and the Next Generation Learning Challenge (NGLC) is focusing on possibilities that can be achieved by modelling learning interactions based on large-scale data collection.
More data is also trouble as it suffocates the running system. Using analytics on big data requires careful thinking about what we need to know and what data is most likely to tell us what we need to.
Marissa Mayer, the current spokesperson of Google has defined data by three elements:
Speed- It is possible to process and act on data instantaneously as the availability has ten-folded recently.
Scale- The increase in computing power continues to be doubling.
Sensors- New types of data on Internet of Things (IoT) like data economy, data published by physics, real-world objects including sensors, smart grids and connected devices are at use.
Datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyse data is called big data. To strengthen and develop the technologies used to manage data, the new breeds like Hadoop, data-mining or knowledge discovery in the database are brought to existence.
The fundamental of data has changed with the massive data inputs across the globe. Big data is a point of or a path to value generation in organisations. Data is not something that could be contained at a single organisation, it is spread across various government, corporations and higher education institutions.
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their context, for the purpose of understanding and optimising learning and the environments in which it occurs. It revolves around the learner and the faculty in predictive modelling and patterns of success or failure.
Academic analytics is contrary to learning analytics. It is the application of business intelligence in education. It emphasises analytics at institutional, regional and international levels. Academic analytics involves the whole education industry including administrators, funders, marketing, national government and educational authorities.
The education sector should become a more intentional, intelligent organisation with the scope and range of activities in higher education. The changes involve affecting the administration, research, teaching and learning, support resources which should be adopted with open arms. The central role is played by data, evidence and analytics.
Some ways in which big data and analytics generate value for higher education
• Data analytics improve administration decision-making and organisational resource allocation.
• They can predict the future and identify high-risk students. It will provide intervention to assist learnings in achieving success.
• Through the transparent data and analysis, data can create a shared understanding of the institution's success and challenges.
• They can bring in innovation and transformation through academic models and pedagogical approaches.
• They can provide recommendations to the learners with an insight into their own learning habits.
LMS captures the data that is structured and reflects the learners' interaction within a system according to its adaptability. But the drawback about LMS is that it cannot capture activity by online learners outside of the system.
Most other analytic systems also failed to capture out of box data like the library usage, access to learning support, or academic advising. Clickers in the classroom can be integrated with the data from learners' activity in online environments, providing additional insight into factors that contribute to the learners' success.
In education, the value of analytics and big data can be found in their role in guiding reform activities in higher education and how they assist educators to improve teaching and learning. By adding analytics to the learning system, it brings a new opening and learning style to the education sector. It is good to be aware of the bad implication too. Learning could turn cosy and futuristic soon with the help of analytics.
