USING DATA SCIENCE TO SOLVE EDUCATION ISSUES
With the adoption of technology in more schools and with a push for more open government data, there are clearly a lot of opportunities for better data gathering and analysis in education. However, education is today at least, a black box. Society invests significantly in primary, secondary, and higher education. Unfortunately, we don’t really know how our inputs influence or produce outputs. We don’t know, precisely, which academic practices need to be curbed and which need to be encouraged. We are essentially swatting flies with a sledgehammer and doing a fair amount of peripheral damage.
Learning analytics are a foundational tool for informed change in education. Over the past decade, calls for educational reform have increased, but very little is understood about how the system of education will be impacted by the proposed reforms. We need a means, a foundation, on which to base reform activities. In the corporate sector, business intelligence serves this “decision foundation” role. In education, data science and learning analytics will serve this role. Once we better understand the learning process — the inputs, the outputs, the factors that contribute to learner success — then we can start to make informed decisions that are supported by evidence.
Learning analytics can provide dramatic, structural change in education. For example, today, our learning content is created in advance of the learners taking a course in the form of curriculum like textbooks. This process is terribly inefficient. Each learner has differing levels of knowledge when they start a course. An intelligent curriculum should adjust and adapt to the needs of each learner. We don’t need one course for 30 learners; each learner should have her own course based on her life experiences, learning pace, and familiarity with the topic. The content in the courses that we take should be as adaptive, flexible, and continually updated. The black box of education needs to be opened and adapted to the requirements of each individual learner.
Programs of study should also include non-school-related learning (prior learning assessment). A student that volunteers with a local charity or a student that plays sports outside of school is acquiring skills and knowledge that is currently ignored by the school system. “Whole-person analytics” is required where we move beyond the micro-focus of exams. For students that return to university mid-career to gain additional qualifications, recognition for non-academic learning is particularly important.
Much of the current focus on analytics relates to reducing attrition or student dropouts. This is the low-hanging fruit of analytics. An analysis of the signals learners generate (or fail to — such as when they don’t login to a course) can provide early indications of which students are at risk for dropping out. By recognizing these students and offering early interventions, schools can reduce dropouts dramatically.
So to summarize, learning analytics serve as a foundation for informed change in education, altering how schools and universities create curriculum, deliver it, assess student learning, provide learning support, and even allocate resources.