Название: Emerging Trends in Computer Science and Computer Engineering Education
Автор: Kevin Daimi, Abeer Alsadoon
Издательство: Springer
Год: 2026
Страниц: 540
Язык: английский
Формат: True PDF, True EPUB
Размер: 56.8 MB

This book focuses on a wide range of advances related to Computer Science and Computer Engineering Education which include, among others, Educational System Performance Understanding, Active Learning in Computer Science, Computer Engineering and Software Engineering, Case-Based Learning, Collaborative Learning, Problem-Based Learning, Web-based Learning, Curriculum Development, Virtual Learning, Evaluation of Computing Programs, Computer-Based Students Assessment, Pedagogical Approaches in Computer Science/Engineering Education, E-learning, Intelligent Educational Systems, Online Courseware Design, Pattern-Based Instruction, and Outcome-Based Learning. The Computer Science and Computer Engineering education topics highlights the concepts, techniques, methods, approaches and emerging trends desirable by computer science and computer engineering educators for enhancing their current knowledge and improving their teaching. The book is a valuable assortment of prudently selected and reviewed chapters written by diverse Computer Science and Computer Engineering faculty and experts in the above field and reviewed and edited by prominent Computer Science and Computer Engineering faculty, researchers, and specialists.

The first part presents a semantic framework for constructing personal learning trajectories (PLTs) for adult learners through integrating models, methods, and technologies tailored to the principles of andragogy. With the context of lifelong learning and the increasing demand for individualized education, PLTs offer a flexible mechanism for aligning the competencies of learners and their preferences and goals with the dynamic requirements of contemporary learning domains. A platform designed to be used by instructors and students and specifically allowing multi-format submissions, AI-driven feedback, progress tracking, and module/content management is then proposed. Instructors create modules, upload multimodal materials (text, code, diagrams), and generate quizzes using the GenAI component, and students attempt quizzes and submit free-form responses.

The second part concentrates on cybersecurity and privacy education. It first deals with cybersecurity education in the age of AI. Instead of viewing AI as merely an influencing factor, it introduces a framework that positions AI as an integral part of cybersecurity. The framework is grounded in the understanding that both AI and cybersecurity are inherently interdisciplinary fields, encompassing technical, business, regulatory, and ethical domains. This part then demonstrates a cohesive and extensible framework for developing intelligent learning ecosystems using the MVC Blazor components, the embedded computing paradigm, and the GraphQL data communication architecture with integrating three primary components, a biometric-enhanced SmartQuiz component that customizes tests, authenticates student identities, and maintains emotional state monitoring under asynchronous online testing situations, a dynamic dashboard framework that sends live analytics and performance visualization to both students and faculty, and a GraphQL-driven embedded computing integration.

The third part spans four topics in computing education management. It describes a technique for the automatic generation of tailored feedback during the grading of weekly SQL quizzes and examines a collaborative approach in which both the course instructor and the teaching assistants contribute sample feedback for comparison and evaluation purposes. Afterwards, a machine learning application in the field of educational computational psychology is introduced in this part.

Finally, the fourth part contributes three topics to this book. It first investigates the skill gaps between Higher Education Institutes (HEI) and industry in the United Kingdom by placing technical skills present in job adverts and university curricula into skill categories and then mapping them. The input data is collected from a range of sources using web scraping and text analysis tools that automatically extract and categorize skills from job descriptions. The primary method of analysis involves comparing the frequency of different skill categories across job adverts and curricula. Following that, an introduction of a practical approach aimed at enhancing students’ object-oriented programming skills with a focus on producing clean, maintainable, and reusable code is demonstrated by the fourth part. While most programming courses emphasize functional correctness, students often overlook non-functional qualities such as readability, modularity, and long-term maintainability.

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