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Abstract
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.
Highlights
Education is the cultivation of people to promote and guarantee the development of society
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
The Institute for Development Research (IRD) in France has been paying attention to and researching Africa for 65 years. e institute advocates combining the needs of African countries to support the development of local scientific research
Summary
Asmaa Fahim ,1 Qingmei Tan, Mouna Mazzi, Md Sahabuddin, Bushra Naz, and Sibghat Ullah Bazai. 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. The Organization for Economic Cooperation and Development (OECD) supports and encourages countries to aid scientific research in Africa. E development of a forecasting model for performance prediction of educational reform is necessary. Many forecasting models are being used in higher education that is based on time-series data. Erefore, this article propose a hybrid method which is combination of LSTM with attention mechanism and found that this method effectively predicts time-series data and helps solve the above problems. We propose our new time-series forecasting model It is followed by a description of our experimental analysis of research performance in Moroccan universities with forecasts. We present our conclusions and recommendations for future directions
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