Computational Intelligence In Digital Pedagogy

This book is a useful guide for the teaching fraternity, administrators and education technology professionals to make good use of AI across outcome-based technical education (OBTE) ecosystem and infuse innovations and affordable digital technologies to traditional pedagogic processes to make teaching-learning more independent of human factor (teacher/student quality), time and place and at the same time more impactful and enjoyable for the learners. Providing access to the digital media and learning tools (even to the extent of mobile apps) to the students would allow them to keep pace with innovations in learning technologies, learn according to their own pace and improve their understanding level and have instantaneous feedback and evaluation. The book explores these new challenges and scope of using computational intelligence in educational technology. The book also addresses how based on the data collected from the outcome of conventional educational system, intelligent diagnostic and feedback system is developed which will change the teaching strategies and corresponding teaching-learning process. The book covers a wider framework of digital pedagogy and its intelligent applications on various sectors of education system.

Intelligent Support for Computer Science Education presents the authors’ research journey into the effectiveness of human tutoring, with the goal of developing educational technology that can be used to improve introductory Computer Science education at the undergraduate level. Nowadays, Computer Science education is central to the concerns of society, as attested by the penetration of information technology in all aspects of our lives; consequently, in the last few years interest in Computer Science at all levels of schooling, especially at the college level, has been flourishing. However, introductory concepts in Computer Science such as data structures and recursion are difficult for novices to grasp. Key Features: Includes a comprehensive and succinct overview of the Computer Science education landscape at all levels of education. Provides in-depth analysis of one-on-one human tutoring dialogues in introductory Computer Science at college level. Describes a scalable, plug-in based Intelligent Tutoring System architecture, portable to different topics and pedagogical strategies. Presents systematic, controlled evaluation of different versions of the system in ecologically valid settings (18 actual classes and their laboratory sessions). Provides a time-series analysis of student behavior when interacting with the system. This book will be of special interest to the Computer Science education community, specifically instructors of introductory courses at the college level, and Advanced Placement (AP) courses at the high school level. Additionally, all the authors’ work is relevant to the Educational Technology community, especially to those working in Intelligent Tutoring Systems, their interfaces, and Educational Data Mining, in particular as applied to human-human pedagogical interactions and to user interaction with educational software.

This volume brings together advanced concepts from leading academic scientists, educationalists, administrative policymakers, and researchers on their experiences and research results on many aspects of digital educational methods and teaching practices. It provides an interdisciplinary compilation of recent innovations, trends, and concerns as well as the challenges encountered and solutions adopted in the fields of digital pedagogies and educational design. It is becoming increasingly important to develop adaptive, robust, scalable, and digital teaching-learning mechanisms in academics. This volume addresses this need by discussing the advancements in flipped and blended learning, student- and teacher-centric learning in technical institutes, critical digital pedagogies, and the complex analyses and collaborations with organizations outside the academy. This book also deals with protocols for educational and administrative policies, IoT-based teaching-learning methodology, teaching education and the process of assessment, testing and evaluation, integration of technology with digital education, and different case study-based approaches in digital teaching-learning methodology.

Big data has the power to transform education and educational research. Governments, researchers and commercial companies are only beginning to understand the potential that big data offers in informing policy ideas, contributing to the development of new educational tools and innovative ways of conducting research. This cutting-edge overview explores the current state-of-play, looking at big data and the related topic of computer code to examine the implications for education and schooling for today and the near future. Key topics include: · The role of learning analytics and educational data science in schools · A critical appreciation of code, algorithms and infrastructures · The rise of ‘cognitive classrooms’, and the practical application of computational algorithms to learning environments · Important digital research methods issues for researchers This is essential reading for anyone studying or working in today′s education environment!

This book systematically reviews a broad range of cases in education that utilize cutting-edge AI technologies. Furthermore, it introduces readers to the latest findings on the scope of AI in education, so as to inspire researchers from non-technological fields (e.g. education, psychology and neuroscience) to solve education problems using the latest AI techniques. It also showcases a number of established AI systems and products that have been employed for education. Lastly, the book discusses how AI can offer an enabling technology for critical aspects of education, typically including the learner, content, strategy, tools and environment, and what breakthroughs and advances the future holds. The book provides an essential resource for researchers, students and industrial practitioners interested and engaged in the fields of AI and education. It also offers a convenient handbook for non-professional readers who need a primer on AI in education, and who want to gain a deeper understanding of emerging trends in this domain.

This book brings together the fields of artificial intelligence (often known as A.I.) and inclusive education in order to speculate on the future of teaching and learning in increasingly diverse social, cultural, emotional, and linguistic educational contexts. This book addresses a pressing need to understand how future educational practices can promote equity and equality, while at the same time adopting A.I. systems that are oriented towards automation, standardisation and efficiency. The contributions in this edited volume appeal to scholars and students with an interest in forming a critical understanding of the development of A.I. for education, as well as an interest in how the processes of inclusive education might be shaped by future technologies. Grounded in theoretical engagement, establishing key challenges for future practice, and outlining the latest research, this book offers a comprehensive overview of the complex issues arising from the convergence of A.I. technologies and the necessity of developing inclusive teaching and learning. To date, there has been little in the way of direct association between research and practice in these domains: A.I. has been a predominantly technical field of research and development, and while intelligent computer systems and ‘smart’ software are being increasingly applied in many areas of industry, economics, social life, and education itself, a specific engagement with the agenda of inclusion appears lacking. Although such technology offers exciting possibilities for education, including software that is designed to ‘personalise’ learning or adapt to learner behaviours, these developments are accompanied by growing concerns about the in-built biases involved in machine learning techniques driven by ‘big data’.

This book presents recent advances in intelligent educational machines. It will be of particular interest to engineers, researchers, and graduate students in Computational Intelligence.