Use machine learning to identify a given educational content
Machine Learning and Artificial intelligence are increasingly used in various fields, including higher education's shift to digital learning. Online education systems store vast student-related data, presenting an opportunity to leverage these technologies for enhancing digital education [1].
Machine Learning and Artificial intelligence roles in higher education are becoming increasingly significant, enabling a personalized approach to learning tailored to students' unique experiences and preferences. Digital learning solutions powered by these techniques can adjust to individual students' knowledge levels, learning paces, and goals, maximizing the effectiveness of their education. Moreover, these tools have the potential to analyze students' past learning histories, pointout weaknesses, and recommend courses for an enhanced personalized learning experience [1].
Machine Learning and Artificial intelligence roles in higher education are becoming increasingly significant, enabling a personalized approach to learning tailored to students' unique experiences and preferences. Digital learning solutions powered by these techniques can adjust to individual students' knowledge levels, learning paces, and goals, maximizing the effectiveness of their education. Moreover, these tools have the potential to analyze students' past learning histories, pointout weaknesses, and recommend courses for an enhanced personalized learning experience [1].
Example:
An extensive literature review about artificial intelligence and machine learning approaches in digital education is presented in [1]. The research outcomes reveal six key themes associated with the integration of machines in digital education. These themes encompass the utilization of intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning, learning styles, analytics and group-based learning, and automation. Among the identified themes, artificial neural network and support vector machine algorithms emerge as the most commonly employed, with additional use of algorithms such as random forest, decision tree, naive Bayes, and logistic regression.
Reference:
[1] Munir, H.; Vogel, B.; Jacobsson, A. Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision. Information 2022, 13, 203. https://doi.org/10.3390/info13040203
Author of the tip:
Florbela P. Fernandes
Instituto Politécnico de Bragança