Machine Learning in Education Sector

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 Machine Learning is the next trending concept in various fields related to education. It involves the study of learning processes and computer modelling in different contexts. The authors in (Carbonell, 1983) rightly stated that ML can be used in task-oriented studies, theoretical analysis and cognitive simulation. Machine Learning in Education field falls in the category of task-oriented studies since it involves developing and analyzing learning systems to improve performance of education institutions through predetermined set of actions.

Classification Techniques in Education

The main classification techniques used in education include Artificial Neural Networks, Decision Trees, Logical Regression and Support Vector Machine. As stated by Nieto et. al. in the paper, the decision making model offers support in decision making on various aspects in educational industry. ML can be useful in increasing student retention and mitigating dropout rate, strategic planning using the knowledge of future, managing interventions and enhancing education institutions quality indicators.

Benefit of ML to Education Institutions in Decision Making

The issue faced by various institutions is that they have to rely on decision making arbitrarily, lacking any logical reasoning. The factors responsible for such random decision making are lack of past experiences, delayed decisions, incomprehensive observation and constrained academic impact of the decision. ML can be beneficial in making strategic decision related to student retention, institution resource planning, curriculum planning and managing dropout. Approaches and models based on ML can help the institutions to perform reliable predictive analytics through in-depth analysis of hidden patterns.

AI predicting Slow Learners

The most interesting service of AI in education is how it can predict slow learners. ML combined with data mining can be used in such prediction. The authors of (Kaur, 2015) state that slow learners can be determined using classification and regression technique of data mining prediction model and ML techniques of SVM and Decision Tree can be used to improve the prediction outcomes. Another area where combination of ML and DM is effective includes enhancing online education. The global crisis of 2020 has led to an urgent need of online education. It requires Information and Communication Technologies in order to support student to access education while remaining confined geographically. The authors of (Villegas, 2020) state that ML can learn from past experiences with a specific objective. ML as a tool can predict, model and control systems while performing in-depth analysis of data. The authors have proposed two main strategies for implementing ML model in education. Stakeholders are given tasks in order to generate data for the tool so that it can learn about the task which it has never performed. Supervised learning technique shall require the tool to be introduced to previous datasets so that it can learn from recognition patterns and identify accurately or learn through regression for predicting accurate outcome.

Other Benefits

Machine Learning algorithms can be deployed to predict graduation rate, student retention and drop-out rates from real data in higher educational institutions. The techniques of logistic regression, decision tree and random forest may be used to analyze information and achieve better understanding of data contained in a particular context. The accuracy and analysis of these techniques shall help in evaluating the best outcome. These techniques can identify students who may not graduate. They can further predict academic performance of students as well as their drop-out rate. The results shall offer insightful decision making in various other aspects of education like curriculum designing, resource planning, etc. Such predictions early in the strategic planning of the institutions will support governance of the institutions.

References

Carbonell, J., G., Michalski, R., S., Mitchell, T., M., “1 - An Overview of Machine Learning”, Pages 3-23, ISBN 9780080510545, 1983, https://doi.org/10.1016/B978-0-08-051054-5.50005-4.

Villegas-Ch, W.; Román-Cañizares, M.; Palacios-Pacheco, X. Improvement of an Online Education Model with the Integration of Machine Learning and Data Analysis in an LMS. Appl. Sci. 2020, 10, 5371.

Y. Nieto, V. Gacía-Díaz, C. Montenegro, C. C. González and R. González Crespo, "Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions," in IEEE Access, vol. 7, pp. 75007-75017, 2019, doi: 10.1109/ACCESS.2019.2919343.

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