Higher education confers numerous benefits both to the individual and to society, including higher earnings, lower rates of unemployment and government dependency, an increased tax base, and greater civic engagement. Access to higher education remains a challenge for many families. The emergence of Industry 4.0 will not only affect technological changes but also people in the labour market. As such, higher education institutions with academic responsibilities of formally training students to better adapt to such changes are not left out. Since the introduction of the Master of Business Administration (MBA) in the United States of America in 1908 and over one hundred years of history, this prestigious programme has suffered a rapid decline in enrolments worldwide. Thus, it has gradually lost its value under the current industrial revolution. This paper evaluates and prioritizes barriers affecting the decline in international MBA enrolments. Understanding the key barriers to MBA enrolments regarding the dynamics will contribute to the successful implementation of those barriers. With scarce information, this study innovatively applies the grey incidence analysis (GIA) method to prioritizing the identified barriers. When applied, this method provided a robust prediction of results. For international MBA enrolments decline, the barrier "employment difficulties" (H3) should receive much attention from policymakers because it scored the highest among the barriers ranked. Further, other important barriers that should be considered are "lack of entrepreneurship skills" (H11), "high cost" (H10), "longer payback duration" (H1), "10 yr. ROI" (H9), and "lack of data analytic skills" (H7). Therefore, during the formulation and implementation of policy to address these prioritized barriers, scarce resources must be committed to them. This paper also contributes to the literature a first-hand, formal study on the prioritization of barriers to international MBA enrolments.

Education is the cultivation of people to promote and guarantee the development of society. Education reforms can play a vital role in the development of a country. However, it is crucial to continually monitor the educational model’s performance by forecasting the outcome’s progress. Machine learning-based models are currently a hot topic in improving the forecasting research area. Forecasting models can help to analyse the impact of future outcomes by showing yearly trends. For this study, we developed a hybrid, forecasting time-series model by long short-term memory (LSTM) network and self-attention mechanism (SAM) to monitor Morocco’s educational reform. We analysed six universities’ performance and provided a prediction model to evaluate the best-performing university’s performance after implementing the latest reform, i.e., from 2015–2030. We forecasted the six universities’ research outcomes and tested our proposed methodology’s accuracy against other time-series models. Results show that our model performs better for predicting research outcomes. The percentage increase in university performance after nine years is discussed to help predict the best-performing university. Our proposed algorithm accuracy and performance are better than other algorithms like LSTM and RNN.

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