مجلة منار الشرق للتربية و تكنولوجيا التعليم

الثورة الرقمية وأثرها على المنظومة التربوية

د. منير العكعاك — 2026-04-21

تتناول هذه الدراسة موضوع "الثورة الرقمية وأثرها على المنظومة التربوية" من خلال مقاربة تحليلية نقدية تستحضر التحولات العميقة التي أحدثتها الرقمنة في بنية التعليم ووظائفه وأدواره. وقد انطلقت الدراسة من إشكالية مركزية تتمثل في كيفية توظيف الثورة الرقمية لبناء منظومة تربوية منصفة وإنسانية، دون أن تتحول العملية التعليمية إلى منتج تقني خاضع لمنطق السوق.

اعتمدت الدراسة إطارا نظريا يستند إلى إسهامات كل من Klaus Schwab حول الثورة الصناعية الرابعة، وNeil Selwyn في قراءته النقدية لعلاقة التعليم بالتكنولوجيا، كما استحضرت نماذج بيداغوجية مفسرة لدمج التكنولوجيا في التعليم مثل نموذج SAMR وإطار TPACK ونظريات التعلم البنائي والتعلم بالمواقف.

وتوصلت الدراسة إلى أن الرقمنة أحدثت تحولا في أدوار الأستاذ، وانتقالا من نموذج الناقل إلى نموذج الميسر والفاعل الرقمي، كما أعادت تشكيل طرائق التدريس وأساليب التقويم في اتجاه مزيد من التفاعلية والتخصيص. غير أن هذه التحولات تصاحبها تحديات كبرى، أبرزها الفجوة الرقمية بين الأفراد والدول، وتهميش البعد الإنساني في العلاقة التربوية، وهيمنة المنصات الخاصة، ومخاطر انتهاك الخصوصية وحماية المعطيات.

وتخلص الدراسة إلى أن رقمنة التعليم يمكن أن تشكل فرصة حقيقية لإصلاح المنظومة التربوية وتعزيز الإنصاف وجودة التعلم، شريطة تبني سياسات عمومية واضحة تضمن العدالة الرقمية، والاستثمار في التكوين المستمر للأساتذة، وترسيخ بعد قيمي وإنساني يضع المتعلم في قلب العملية التعليمية.

Digital Accessibility Assessment of Sudanese Websites: A Multi-Tool Evaluation Across Governmental, Academic, and Service Sectors

Mohammed Tagelsir M.Elamin — 2026-04-21

Despite global progress in digital accessibility, the extent to which websites in developing countries comply with international accessibility standards remains largely underexplored. This study presents an exploratory baseline assessment of digital accessibility across seven high-impact Sudanese websites representing academic, governmental, and service sectors. The evaluation employs a multi-tool triangulation approach using WAVE, axe DevTools, and Lighthouse to assess compliance with WCAG 2.1 AA guidelines.The results reveal substantial variability in accessibility performance, with Lighthouse scores ranging from 76 to 88. However, deeper inspection uncovers critical accessibility barriers across all sites, including insufficient color contrast, missing alternative text, ambiguous link labels, and non-semantic HTML structures. Notably, discrepancies between tool outputs highlight the limitations of relying on a single evaluation method, as some sites achieved relatively high scores despite significant structural deficiencies. The findings establish the first empirical baseline for digital accessibility in Sudan and demonstrate that the primary barriers are not technical limitations but rather institutional awareness and implementation gaps. The study concludes with targeted recommendations for policy, institutional governance, and developer practices aligned with WCAG 2.1 AA.

Designing a Requirements Engineering Model to Achieve Software Quality

Mohammed Ali Badawy Alawad Kenen — 2026-04-21

One of the key issues addressed in this study is the ambiguity surrounding the understanding of software project requirements. Many users and clients often lack a clear vision of what they expect from the system or the purpose behind its development, which inevitably hinders the success of software projects. A primary objective of the researcher in this study was to establish a practical approach for writing requirements based on specific standards and a proposed model. This model consists of a structured and organized sequence of steps designed to facilitate accurate and effective requirement specification. Among the most significant findings of the study—based on a series of questionnaires distributed to various software companies and individuals—was the contribution of the survey in analyzing the current state of requirements engineering within software institutions and assessing the extent to which practical standards are applied. The survey revealed numerous insights, most notably the widespread absence of a standardized model for requirements gathering among both organizations and individual practitioners.

Customer Classification in E-Commerce Using Random Forest Algorithm

Noor Shobar Ali — 2026-04-28

This study investigates how well Random Forest Classification machine learning algorithms predict customer satisfaction levels when shopping online. A synthetic dataset of 350 customer records containing demographics, behavior and transaction data was assembled with the purpose of segmenting customers into three categories of satisfaction (Neutral/Satisfied/Unsatisfied). All data was divided into training and test datasets using an 80/20 stratified split, and all data was preprocessed and transformed into numerical codes. Different evaluation metrics were used for evaluating performance; these include Accuracy, Precision, Recall, F1 Score, Confusion Matrix, Cross Validation, and Multiclass ROC-AUC. The Random Forest algorithm outperformed the other algorithms with a total accuracy rate of 98.57%, a mean Cross Validation accuracy rate of 98.86%, and a mean ROC-AUC score of 0.9963. The feature importance analysis indicated that Days Since Last Purchase and Total Spend are the most important factors influencing customer satisfaction. The Random Forest algorithm has shown superior performance with large and complex e-commerce datasets, which can help retailers improve how they retain customers and create customized marketing programs. This study is part of an increasing body of knowledge aimed at using data to improve the quality of customer satisfaction forecasting for online retailers.