You’ve probably heard it said (or seen it written) a thousand times already, that learning is a lifelong process. It doesn’t apply only to students – leaders, engineers, doctors, scientists, businessmen, and even teachers and educators themselves must view learning as a continuous process (if they want to achieve sustained success, that is). Learning is a process that involves gaining information that can be used to make sense of situations, which helps to take the most appropriate action in every situation.
Put simply, learning means gaining knowledge about how things happen, why they happen, and how to respond to them. For students, gaining knowledge is generally a structured process overseen by governments, educational leaders, and teachers. For students, the source of learning is their teaching faculty and books. For the educators, that source of learning is educational data mining, which they use to gain feedback on institutional management and student outcomes. Effectively using data analysis in education to gain insights into the efficacy of programs, people, and processes has become the hallmark of leading universities across the US.
What top universities do better than the others
Top universities, the ones that always receive applications beyond their enrolment capacities, the ones where a majority of the students report being engaged in learning, and where the graduation rates always exceed others by miles, don’t get there by accident. Their position at the top of the educational system is the result of a deliberate process of goal-setting, institutional planning, timely execution of strategies, and most importantly, gathering and acting on feedback. The final step, which turns the series of repetitive improvement initiatives that get suboptimal results into a continuous cycle of improvement that achieves better outcomes at every iteration, makes the difference between the most efficient, successful institutions and ones that bleed money through ineffective projects.
Nearly all universities and community colleges implement initiatives aimed at improving student engagement, designing better educational programs, and getting increased enrolments. With the emergence and propagation of digital technology, institutions have gone digital in their approach towards teaching. However, very few institutions have used digital technology for the purpose of learning.
The universities that achieve success are the ones that learn from their existing processes – what they are doing right, what they are doing wrong, and what can be done better – using different feedback channels. The most effective of these channels has been educational data mining, which is increasingly being used by top colleges to evaluate and improve their institutional planning.
How educational data mining helps achieve student success
Educational institutions, just like every other organization in every industry, are going digital. Students and teachers use digital channels to access resources and manage information, respectively. Students are increasingly using the digital systems to take courses online, clock in their attendance, submit assignments, and check their grades, among other activities. Teachers use these systems to track student progress, both academic and behavioral. Every interaction between these digital systems and the participants (students and faculty) generates multiple data points, which can be analyzed to give educational leaders, such as deans and provosts, valuable insights into campus life, student affairs, and overall institutional effectiveness.
Administrators can use educational data mining to monitor student affairs on a granular, individual level, as well as on a broad, institutional level. Academic performances of individual students can be monitored across different timelines to identify students for rewards and scholarships. It can also help the faculty to identify students whose performances are following a downward trend so that they can plan steps to help those students keep up with their peers. Doing this for the entire student body will not only motivate the high performers but will also ensure that the overall graduation rate stays at a satisfactory level, due to fewer students dropping out on account of academic failure. The institution can also identify patterns in student behavior that may indicate low engagement. This can help teachers to perform timely interventions and minimize the drop out rates by bringing students back on track for graduation.
Using data analysis in education can help institutions to spot general trends in academics, such as identifying courses with high dropout rates. They can further investigate the causes for the same and rectify any issues associated with it.
In addition to learning about the state of the institution, educators should also employ tools that can help in analyzing the conditions outside the institution, such as the job market. To complement educational data mining, the use of data analysis tools in education that can help plan better curriculums based on labor market data can be used to complete an institution’s digital strategy. Using labor market analytics tools like Talismatic can enable educators to identify high-performing courses in terms of market demand. Such tools can also help universities and community colleges identify low-performing courses so they can stop investing courses that are approaching redundancy, and the funds and resources saved can be invested in other areas or the high-demand courses.