In the education world, the analysis of big data is called learning analytics. Big data in simple words is enormous unstructured information. In learning analytics, the unstructured data can be the number of questions, marks, identification numbers, etc. Furthermore, the unstructured data is senseless if not used as a part of the solution. Hence to derive the solution, learning analytics is playing a huge role in strategic decisions and insights.
In today’s ever-changing world, big data is acting as a bridge for C suite executives to reinforce powerful decisions. After all, the leaders are flooded with salient questions. Hence, they are reiterating the big data strategies for maximizing profits and productivity. eLearning company leaders are especially surrounded by a lot of data after the pandemic hit and the demand for eLearning platforms increased several folds. As you read further you may discover how data analytics is providing insights for leaders to personalize content.
Stage 1: Data Classification
Learning is distinct for different people. So personalization is the next best solution to address the issues of learners. Moreover, Edtech leaders often ask a question on what are the best ways to classify the unstructured data so as to develop unique course content? These three data classification models do justice to answering this question. Broadly, there are three types of classification models used by eLearning platforms.
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1. VAK Model:
The Visual, Audio and Kinesthetic (VAK) learning model as the same suggests focusing on classification on the basis of three learning styles.
2. Kolb’s Model:
David Kolb, an American educational theorist, developed another model based on a four-stage learning cycle. It starts with Concrete Experience (CE) & Reflective Observation (RO) which provide a basis for observations and reflections. Secondly, the reflections are assimilated and distilled into abstract concepts producing new implications for action. This process is also called Abstract Conceptualization (AC). Lastly, Active Experimentation (AE) takes place. To put it in another way, the resultant actions are actively tested in turn creating new experiences.
3. Felder-Soloman Model:
The model proposes classification based on how we process, perceive, receive, and understand information. There are a total of 16 possible classifications. That is to say, the student’s learning style can be active, sensing, verbal and global. This model classifies the learning styles in a more granular way as opposed to Kolb’s model.
Hence, knowing to which category a learner belongs can help the content developer personalize the course text. Moreover, the classification helps the content developers to know what information to add and how to add it.
Stage 2: Recommendations
This step in data analytics for eLearning personalization helps make informed decisions about the action plan after the classification model works on the unstructured data. The purpose of this step is to come up with insights that will maximize the user experience of the learner.
In a similar context, platforms like Udemy and Coursera examine the user data and recommend similar courses for the future. The recommendation algorithms can vary based on the platforms yet aim to forecast students’ learning interests.
Moreover, one of the case studies uses a data analytics tool so that the analysis helps the instructors adapt their content based on the student feedback. This tool helps store student interactions data in the platform. In turn, performing text analysis on the stored data. The results are then sent directly to the instructor.
Future Opportunities
As can be seen, data analytics algorithms tackle the challenges of eLearning personalization. However, there are more opportunities yet to be explored in this area. One opportunity is to design a universal test and predict students’ final grades. In turn, classify them into different levels of performance groups. The classification can help instructors identify weak students that may need help in the course. This analysis can help students to pinpoint hard concepts or learning outcomes.
Furthermore, the analysis can come in handy for instructors to research more on the harder concepts to deliver better clarity as per the student. Instructors can even facilitate student collaborations if this type of system is implemented in our educational institutions. The instructor might recommend a weak student collaborate with a bright student to bridge his/her learning gaps. Similarly, there are more challenges like evaluation and assessment, enabling technologies in primitive education systems, and transmission & delivery of concepts to students.
