Understanding Educational Data Mining

Technology is revolutionizing many sectors, and education is one of them. Just like big data is allowing businesses to delve deep into data to recover insights and improve decision making, educational data mining does the same for the process of learning. One of the biggest evergreen challenges for learning is to get learners willfully involved in the process, rather than compelling them to. Naturally so, because learning that is self-motivated achieves far better results when compared to learning which is compulsory.

Educational data mining (EDM) digs deep into the data which is generated as a result of student interaction with online learning systems to understand students’ preferences vis-a-vis the settings in which they learn. EDM explores data patterns to answer questions like:

•  Which student prefers what sequence of topics so that she may learn most effectively?
•  Which learner actions indicate satisfaction and engagement with the elearning course?
• What are the best features of the online learning program in terms of making effective learning possible?
• What student actions are associated with better learning and better test performance?

These questions are not random; rather they work towards achieving some key goals of EDM. By using techniques from statistics and machine learning, EDM tools and algorithms work to predict learners’ future learning behavior as well as to study the pedagogical models that will work best to align with that behavior. It also tries to find out optimal instructional sequences for each learner. All these goals are pursued with the background of detailed information about every learner’s knowledge, motivation, cognition, and attitude.

All this process happens in a cycle. Here’s how: Imagine a learner who logs on to the LMS and browses through the learning material, during which he also interacts with the online systems in the form of clicks and text inputs. At the back-end, the LMS stores detailed information about the learner behavior in a database which is then used to predict the learner’s future performance. These predictions and feedback are displayed on a visual dashboard which makes the information easy to comprehend for instructors and trainers. The next time the learner logs on to his LMS, the LMS already knows his learning-related attributes and thus learning material is presented based on his performance level and interest. The role of the trainer is to intervene and help as and when necessary.

Such a system has two-way advantage. On one hand, it gives better control to learners, which is especially important in the workplace where learners are adult, mature employees. On the other hand, it enables the trainer to be a facilitator of learning, so that he can contribute more towards intelligent content design.

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