PerformanceX is a proposed system that combines Educational Data Mining (EDM) techniques to enhance student performance and reduce dropout rates. It employs a hybrid feature selection approach to identify the most significant attributes from student academic datasets, eliminating unnecessary features that are not crucial for predicting performance. The selectX algorithm, a critical component of PerformanceX, selects a limited number of high-performing features to optimize student learning effectiveness and prediction accuracy. The system applies various machine learning classifiers, including a fusion Voting Classifier, to different subsets of features, ultimately determining the best combination. The study achieved an impressive accuracy rate of 99.41%, with the selectX approach utilizing 10 features in conjunction with a random forest (RF) classifier offering the highest accuracy. These findings underscore the importance of categorizing student performance based on a concise yet meaningful set of features, leading to improved student quality and career progression. The research value of PerformanceX lies in the development of a performance forecasting system that eliminates irrelevant information and provides precise predictions for student performance. Its efficacy and efficiency make it an invaluable tool for educators and educational institutions. By assisting students in selecting appropriate courses to enhance their performance and advance their careers, PerformanceX contributes to diminishing dropout rates while fostering positive student outcomes.

The attempt to integrate ICTs into the education system in Ghana has existed close to two decades following the adoption of the ICT for Accelerated Development (ICTAD) policy in 2003. However, empirical quantitative studies to analyse the access of basic schools to electricity, which is the major power of technology devices, over the period appear non-existent. This article is a descriptive quantitative analysis of basic school access to electricity for the academic years 2010 / 2011 to 2017/2018 using secondary data obtained from the Educational Management and Information System (EMIS) through the lens of Access Theory. The study was undertaken using secondary educational statistical data and document reviews as data sources. The results of the data analysis indicate a low electricity access rate in basic schools in the Northern Region of the country. The study concludes that the gap between the ICT in education policy rhetoric and the reality in ICT for education (ICT4E) at the Ghanaian basic school level appears unhealthy and requires stakeholders’ immediate attention to realize the desired impact of ICT in education policy if the goal of the country to achieve the Sustainable Development Goals 4 and 10 need to be success. The findings of these studies provide valuable insights for policymakers and education stakeholders in designing effective policies and interventions to improve access to electricity and promote the integration of ICT in basic education.

Machine Reading Comprehension (MRC), known as the ability of computers to read and understand unstructured text and then answer questions, is still an open research field. MRC is considered one of the most research-demanding sub-tasks in Natural Language Processing (NLP) and Natural Language Understanding (NLU). MRC introduces multiple research challenges. One of these challenges is that the models should be trained to answer all questions and abstain from answering when the answer is not covered in the given context. Another challenge lies in dataset availability. These challenges are amplified for non-Latin-based languages; Arabic as an example. Currently, available Arabic MCR datasets are either small-sized high-quality collections or large-sized low-quality datasets. Additionally, they do not include unanswerable questions. This lack of resources depicts the model as incapable of real-world deployments. To tackle these challenges, this paper proposes a novel large-size high-quality Arabic MRC dataset that includes unanswerable questions, named “Arabic-SQuAD v2.0'”. The dataset consists of 96051 triplets {question, context, answer} in an attempt to help enrich the field of Arabic-MRC. Furthermore, a Machine Learning (ML)-based model is introduced that is capable of effectively solving Arabic MRC-with-unanswerable questions. The results of the proposed model are satisfactory and comparable with Latin-based language models. Furthermore, the results show a significant improvement of the current state-of-the-art Arabic MRC. To be exact, the model scores 71.49 F1-score and 65.12 Exact Match (EM). This proposed dataset and implementation pave the way to further Arabic MRC; aiming to reach a state when MRC models could mimic human text reasoning.

Throughout the past twenty years, we've seen a huge increase in the number of school universities. Given the intense competition among major universities and schools, this attracts students to apply for admission to these institutions. Early school dropout prediction is a critical problem for learners, and it is hard to tackle. And a wide number of factors can impact student retention. In order to attain the best accuracy, the conclusion of the program, the standard classification approach that was used to solve this problem frequently needs to be applied the majority of organizations and courses launched by universities operate on either an auto model, therefore they always prefer course enrollment over student caliber. As a result, many students stop taking the course after the first year. In order to manage student dropout rates, this research provides a data mining application. The predictive model may provide an effective predictive list of students who typically require the greatest help from the student dropout program given updated data on new students. The results indicate that the object classification algorithm Random Forest data mining technique can create a reliable prediction model using existing student academic data. Future research on student dropout rates will continue to be vital for informing policy decisions, identifying at-risk populations, evaluating interventions, enhancing support services, predicting trends, understanding long-term consequences, and promoting global learning and collaboration in education.

This research was conducted to explain that long before the Covid 19 outbreak hit. There has been a deadly plague that has long affected human life at large. Anticipatory actions of the Dutch Colonial government were carried out intensively to inhibit the rate of spread of plagues at that time such as cholera, malaria, and smallpox. The influenza and bubonic plague epidemics of 1918 and 1911 threatened successively and smallpox emerged at the same time, hampering the pace of life in all fields. Moving on from this time, the Covid 19 outbreak has affected all fields including education. So far, when face-to-face began to be done again after a long time online, here a polemic emerged. Vaccination for children aged 6-11 years at primary school age. In field research, there are several obstacles experienced. Based on the results of interviews conducted in this study, most respondents were afraid of vaccines when administered to their elementary school-age children. Fear of risks such as hoaxes circulating on social media. Based on the results of the research conducted, in general, the people of Padang City are not entirely aware of the Covid-19 vaccination policy for elementary school-age children (6-11 years) because for them this is very risky because not all children in their bodies can receive vaccines. The research implementation procedure. This research uses the historical method (heuristics, criticism, interpretation, historiography). The purpose of the historical method is used starting with the collection of sources: first, literature and document studies, and field studies through in-depth interviews with several parents of students, teachers, such as elementary school residents in Padang City. Second, criticizing the sources obtained, Third, analyzing the relationship between the facts found, and finally the fourth is writing the findings. The purpose of this research to be achieved is to be able to explain the implications that the covid 19 outbreak has a lot of impact when it is required to vaccinate as a condition for face-to-face learning to be carried out again.