Pressure from state and private stakeholders to increase effectiveness, efficiency and output has compelled higher education institutions to rethink their strategic plans and to gain perspectives that can accommodate the rapid changes of technology and societal needs.

The education sector no longer suffers from an information deficit. Facilitated by highly advanced technologies, institutions of higher learning in particular are facing an unprecedented influx of data, making the traditional face-to-face classroom instruction no longer germane. Education is entering the era of big data, which has the potential to revolutionize and even to disrupt the learning industry in the coming years.

Thus, learning is no longer a matter of acquiring knowledge, but rather an acquisition of strategy of sorting and analyzing knowledge relevant and useful to one’s life purposes. Technology can mediate this acquisition of strategy in no insignificant ways, making the attainment of quality education possible.

After all, as not all knowledge or information presented to students holds relevance to them and comes from credible sources, learning strategies that emphasize such skills as classification, dissection, synthesis, critical evaluation and creation become ineluctably important. These strategies are needed to handle and use the flood of data presented to them in an effective and efficient manner.

Yet, it is important to note that technology is only a means to support our educational goals. Its utility needs to suit the very needs of students and learning goals they are aiming to attain. To make the best use of big data as a technological instrument to enhance learning and teaching, for instance, it is necessary for educators and learners to understand how data analytics can be manipulated to increase and support learning.

The focus in making use of big data in education is to instill in students independent learning as well as passion about innovating and creating. Their teacher’s role will be reduced to a collegial one rather than as a knowledge possessor.

The perceived benefit of big data use has been that it helps personalize students’ learning experience. With the varying degrees of knowledge they possess, students can experiment with the data or information they obtain, make use of it, and gauge it based on their interests and learning objectives. They can even use their individual experiences as a basis for modifying and creating it anew.

For teachers, using data analytics helps reduce their burden in supervising their students. For example, they can monitor where each individual student is still grappling or has accomplished with the assigned tasks. In so doing, they can offer different starting material for each student within the same course. In the end, they can assist their students in channeling their interest in the subject, and in indicating to whom and when specific learning content should be delivered.

Another benefit is that big data analytics provide new opportunities to accelerate the education process by helping teachers and learners make appropriate decisions earlier in the process of setting educational objectives. Educational strategic planning, measurement, evaluation and monitoring can be made more efficient through the use of big data. Any idiosyncrasies in planning can be detected in advance, so that corrective steps can be immediately taken.

Likewise, students’ learning hindrances can be noticed at the outset and teachers’ feedback be given as a response to these problems. Learning progress can also be monitored and further action be taken to maintain and even accelerate this progress. As a process of planning, measurement and monitoring cannot escape individual teachers’ subjectivity and biases. Big data analytics help strengthen the ability to address any unconscious idiosyncrasies teachers may have in making any important educational decisions.

Big data analytics have also been useful for understanding what industry sectors need from university graduates, and what kinds of curricular designs can meet their expectations. Trends in job markets and emerging ideas in labor forces surely need to be accommodated in the curriculum, in order for university graduates to be ready to compete in job markets.

Finally, big data can also be employed to monitor how well university graduates have progressed in performing in the job market. Such monitoring will serve as useful input for universities to evaluate and revise their curriculum accordingly.

Now with the multitude of data available, learning institutions can speed up the information gathering process, easily adapt their educational approach and respond to the individual needs of students and society at large.

In fact, technological revolution has offered huge benefits that help pave the way to the achievement of quality education, if managed properly and handled wisely. The challenges lie in the preparedness of our higher education institutions. Several things need to be anticipated.

First, big data requires changes for IT staff, administrative staff, teachers and students alike that are still grappling with rapid change management in their institutions, mass customization concepts and the adoption of radically new learning and working strategies that will have implications for the learning and working process. Technology agility, prescriptive systems, adaptive and analytic skills, creative and innovative minds are what higher education institutions now need to inculcate in their academic and administrative staff.

Second, the cost of technology associated with collecting, storing, developing and managing algorithms to mine data is still relatively high. A big data system means a big investment in equipment procurement, operational budgets concerning staffing, training and software licensing. Therefore, investment in big data technology should be included in the universities’ strategic plans.

Last, ethical issues in dealing with big data can become significant challenges to its implementation in higher education. Developing awareness among university staff about the ethics of data collection and interpretation with regard to the quality of data, privacy, security and ownership must become an important consideration. This implies adequate provision of funds for training the staff should be ensured. Access to big data needs to be supplemented with training of academic and administrative staff, so that inaccuracy and overgeneralization in data interpretation can be avoided at all costs.

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The writer is a professor of applied English linguistics at Graduate School of Applied English Linguistics, Atma Jaya Catholic University, Jakarta. He can be reached at setiono.sugiharto@gmail.com.

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