The information revolution has opened up an impressive scenario of management solutions, but also an important amount of raw data that is collected and put on a digital shelf, failing to analyze and process it.
Nevertheless, much of that information can help us improve the quality of higher education. Institutions must make very complex decisions, many times in uncertain scenarios, as they project, distribute, plan, expand or concentrate different elements of their educational offer. Moreover, they are required by law to provide more detailed information and indicators about their performance by governments, accreditation agencies and the very students they teach.
Sometimes, indicators were already there to be calculated and studied, but nobody had found the proper way to collect them.
The rise of Big Data in Higher Education
“Big data” simply refers a massive amount of information available. By itself, this bulk of information has no value, but its extraction can give us interesting insights. Most information hasn’t been processed, its density can overwhelm conventional data processing software and teams. The purpose of Big Data analysis through different software and procedures, is to transform an otherwise unreadable and inconclusive set of information into trends and statistics that can help decision-making both in strategic definitions and real-time problems.
Today, higher education is increasingly analyzing and cross-referencing student, academic, administrative and industry information in order to account for their achievements, reviewing and improving their performance. According to the University of Berkeley’s School of Information, Big Data produces “vast amounts of data on student learning outcomes, data of the sort that was previously unavailable to students and educators. It can allow institutions to better deliver and market their degrees to the right type of students, and it can let students personalize their educational experience to best suit their needs increasing the chance they graduate and succeed.”
Experts from Berkeley regard that data from online education can help monitor a student’s behavior and level of engagement, and analyze patterns within an institution to:
Nowadays, the input of information from higher education teams has increased with the use of e-learning platforms, internet sites, intranet interfaces and social media both for academic, accreditation, management and informational uses. Higher education has been able to get input such as
Analytics tools can help increase student retention, improve services and increase student grades, and educational data mining can help discover new information.
Internally, it can review the order certain classes become more effective for specific students within a 4 year major, how effective was a teacher in specific issues or what topics or subjects have higher demand and require more literature in the student library.
At the same time, academics have been given access to large amounts of open access data about education from governments, researchers and software corporations. Higher education institutions can then benchmark their students, faculty and curriculum and assess similar universities, research and development institutions and the labor market, to identify trends and make informed decisions.
As part of their accountability agendas, countries such as the United States, the United Kingdom, Chile, Mexico, Peru or Colombia, government institutions have put forward statistics and important information in order to address both students, parents, academics and educational institutions.
www.data.gov is the home of the U.S. Government’s open data, where people can find information, tools, and resources to conduct research, develop web and mobile applications or design data visualizations.
https://data.gov.uk/ compiles bulks of public information available in different formats.
The Chilean Ministry for Education’s Center for Studies provides statistics in several levels of the Chilean education system such as student enrollment, priority students, teacher surveys and statistical analysis.
The Mexican Ministry of Public Education has opened up statistical information such as figures, projections, simulations and geo-referenced mapping for planning and development.
The Peruvian Ministry of Education has a unit for Statistics of Educational Quality, with databases, indicators and trends.
The Colombian Ministry for National Education provides a follow-up and comparisons from public reports with institutions such as the OECD.
Privacy concerns
The use of these tools have raised issues regarding privacy, legitimacy and the subsequent use of information. In the United States, a Student Privacy pledge has been signed, requesting authorities to legally address the commercialization of student information, the educational use of data, privacy policies, parental consent and accountability about the collection and use of data.
Another important concern is the internal use of data involving teacher’s assessments, grading, attendance or dropouts, as well as the use of evidence and class records with written consent from students, the increase of online data input from management offices, all of which require a new set of skills by software engineers and data managers. The most sensitive issue is the input of information the very students put into the system, as they need to be properly informed how is their personal information collected and used.
Online legal privacy issues are diffused and depend on a specific country’s legislation. Therefore, it comes for institutions to specifically differentiate which data is private domain – and shouldn’t be part of public repositories – and which data can be made public when it is uploaded and sent to the government or a regulator.
In higher education, internal data should be private. Nevertheless, the bulk of information of each separate institution does not amount to become “big data”. This can be different when it comes to using online education platforms such as EdX, Udacity or Coursera. They can bulk a lot of information, such as time spent online, number of pages visited, as well as learning and dropout ratios.
In that regard, Big Data analysis can help re-engineer courses and make them more effective, but the right to analyze or share certain information is also under question. For instance, how are MOOCs going to estimate their demand, calculate dropout rates and assess the number of teachers needed for a specific course?
In your institution, where do you see improvements with the use of big data? What experiences do you have with it? Do you have concerns regarding its proper use?