Prospects and Challenges of Using Machine Learning for Academic Forecasting

Table 1

Summary of related works.

Authors	Outcome
Musso et al. [10]	The study successfully forecasted students’ academic success one year ahead using the ANN based on cognitive and demographic traits
Hudson and Cristiano [7]	The results suggest that ML can generate dependable results in prediction
Elhaj et al. [13]	The study was empirical, and it showed the ability of KNN in prediction of learning patterns of students
Ahajjam et al. [24]	The paper provided AI-based solutions to track students’ performance and was able to recommend diagnosis for the Moroccan students
Pranav et al. [25]	The paper provided evidence on the significance of AI in management of education data and decision-making
Lidia et al. [26]	The paper concluded that ML will be required more in the future because of the need to assist  students to overcome learning difficulties and also enhance their productivity in learning
Phauk and Takeo [27]	The study recommended the use of the hybrid machine learning algorithm approach to solve misclassification issues and improve academic prediction accuracy
Onan and Korukoğlu [28]	The research proposed an ensemble method to feature selection that combines the results of numerous independent feature lists generated by various features that may be used in education
Onan [29]	The study provided a better approach for managing students’ information system via ML
Hassen et al. [30]	The study showed that the student’s success with the aid of machine learning can be monitored using their previous performance data before they engaged in the current program
Ibtehal [31]	The study affirmed the applicability of ML in education technology development and deployment
Feders and Anders [32]	They developed a smart algorithm that assessed the teaching methods of teachers and how it affects the understanding of their lessons by students in the class taking into consideration the former knowledge of students
Popenici and Kerr [33]	They examined the various implications of ML and other relevant AI-driven systems in higher education