In some ways, academics’ methods of teaching and research have hardly changed since Plato and Aristotle were discussing philosophy and mathematics in the Academy in Ancient Greece. Students still learn from a tutor in small discussion groups and lectures, and many scholars still study physical manuscripts in libraries. But in this blog, we will explore a recent change that has revolutionized academia: big data analytics.
The application of big data analytics has generated new and innovative partnerships between universities and companies on research projects. Companies have access to a lot of data on their customers, and universities have data science experts who can interpret and analyze that data. The result is mutually beneficial—companies get better insights about their customers and products, and universities get new research ideas and much-needed funding from partner firms. For example, earlier this year the pharmaceutical firm Novartis announced a partnership with the University of Oxford’s Big Data Institute. The Oxford experts are using machine learning to analyze data from Novartis’ clinical trials to identify patterns in how patients respond to different medicines.
Academics in virtually every academic discipline are exploring how AI and big data analytics could change the way they do their research. A study in the journal Nature found that ‘big data’ and AI were the third and fourth most-searched terms on a scholarly database in 2018. LexisNexis attended Dubai’s Global Education and Skills Forum earlier this year and found many examples of academics who were using AI to do research that would otherwise be have been impossible. Milo Comerford, a senior analyst at the Tony Blair Institute for Global Change, said that AI and machine learning have “changed the game” for academic research. He gave the example of his own research which uses AI to trawl through thousands of extremist propaganda documents and come up with new insights. “I wouldn’t have been able to do [this] manually and through my own study,” he said.
Universities’ core purposes to research and to teach, and big data analytics are changing the latter as well as the former. Teachers and lecturers in universities are using big data to gather data on their students and then customize their lesson plans. Lecturers no longer have to wonder whether their lectures are going over students’ heads—now they can use data from e-learning software to work out which parts of the course students struggle with, and give extra support to those who find a particular area difficult. Back in 2015, in a paper in the journal Procedia Economics and Finance, Logica and Magdalena argued that big data offers teachers “a chance to refine the educational process”. “If [teachers] want relevant insights on their real efficiency and the progress of their students, they need to integrate this kind of solutions,” they wrote. In the four years since this paper was published, more and more educators have followed this advice.
The next step could be for some of these lecturers to be replaced by AI—indeed, some universities are already employing AI in their teaching process. In 2016, students on Georgia Tech University’s online master’s in computer science who asked questions on the course’s online forum were often answered by Jill Watson, who was apparently a teaching assistant at the university. When the course finished, it was revealed to them that Jill was in fact an AI bot based on IBM’s Watson platform. Most students had had no idea. AI and big data offers universities a way to save money on teaching, and reach a greater number of students.
Universities are even using big data to help them decide which students to admit every autumn. Traditionally, universities have employed a large team of admissions officers who weigh up thousands of applications before deciding who to admit, but big data analytics allows them to automatically sort candidates by test scores and other admissions criteria. Universities rely on attracting large numbers of eligible students to enroll, and data analytics expert Eric Spear says big data analytics “holds the key to solving the problem of declining enrolment numbers”. Analytics allow an institution to track which courses are more popular with students from different demographics, and which courses have a problem with drop-outs. Removing the element of human judgement in the admissions process could also benefit universities’ reputations—earlier this year, top U.S. colleges were alleged to have accepted donations from wealthy Americans in exchange for university places. An AI-driven process would likely have prevented that from happening.
What should you do?
Big data analytics can make your life easier, whether you’re an academic, a university president or a corporation considering a research partnership with data scientists. But the insights available from this analysis are only as good as the data being fed into them. LexisNexis’ Data as a Service provider helps universities by integrating data with flexible APIs that deliver normalized big data in a semi-structured XML format. This enables universities to add bespoke tags and other enrichments to meet the unique requirements of the research being undertaken. Additionally, when retrieving data, researchers can take advantage of our own robust topical classifications to identify and retrieve highly-relevant result datasets, ensuring a faster time to insight.