The delivery of quality education to students relies heavily on the actions of an institution’s administrative staff. Effective teaching strategies allow for the continued progress of modern educational initiatives. Student Engagement and Participation: Concepts, Methodologies, Tools, and Applications provides comprehensive research perspectives on the multi-faceted issues of student engagement and involvement within the education sector. Including innovative studies on learning environments, self-regulation, and classroom management, this multi-volume book is an ideal source for educators, professionals, school administrators, researchers, and practitioners in the field of education.

"The goals of higher education have evolved through time based on the impact that technology development and industry have on productivity. Nowadays, jobs demand increased technical skills, and the supply of prepared personnel to assume those jobs is insufficient. The system of higher education needs to evaluate their practices to realize the potential of cultivating an educated and technically skilled workforce. Currently, completion rates at universities are too low to accomplish the aim of closing the workforce gap. Recent reports indicate that 40 percent of freshman at four-year public colleges will not graduate, and rates of completion are even lower for community colleges. Some efforts have been made to adjust admission requirements and develop systems of support for different segments of students; however, completion rates are still considered low. Therefore, new strategies need to consider student success as part of the institutional culture based on the information technology support. Also, it is key that the models that evaluate student success can be scalable to other higher education institutions. In recent years machine learning techniques have proven to be effective for such purpose. Then, the primary objective of this research is to develop an integrated system that allows for the application of machine learning for student success prediction. The proposed system was evaluated to determine the accuracy of student success predictions using several machine learning techniques such as decision trees, neural networks, support vector machines, and random forest. The research outcomes offer an important understanding about how to develop a more efficient and responsive system to support students to complete their educational goals"--Abstract, page iv

The International Conference on ICT for Digital, Smart, and Sustainable Development (ICIDSSD’20) aims to provide an annual platform for the researchers, academicians, and professionals from across the world. ICIDSSD’20, held at Jamia Hamdard, New Delhi, India, is the second international conference of this series of conferences to be held annually. The conference majorly focuses on the recent developments in the areas relating to Information and Communication Technologies and contributing to Sustainable Development. ICIDSSD’20 has attracted research papers pertaining to an array of exciting research areas. The selected papers cover a wide range of topics including but not limited to Sustainable Development, Green Computing, Smart City, Artificial Intelligence, Big Data, Machine Learning, Cloud Computing, IoT, ANN, Cyber Security, and Data Science. Papers have primarily been judged on originality, presentation, relevance, and quality of work. Papers that clearly demonstrate results have been preferred. We thank our esteemed authors for having shown confidence in us and entrusting us with the publication of their research papers. The success of the conference would not have been possible without the submission of their quality research works. We thank the members of the International Scientific Advisory Committee, Technical Program Committee and members of all the other committees for their advice, guidance, and efforts. Also, we are grateful to our technical partners and sponsors, viz. HNF, EAI, ISTE, AICTE, IIC, CSI, IETE, Department of Higher Education, MHRD and DST for sponsorship and assistance.

The digital transformation of higher education institutions has accelerated in the last decade due to the confluent development of digital technologies. Understanding how artificial intelligence-enabled changes and improvements in universities in relation to teaching, management, sustainability, and research allows researchers to understand the advances and identify the challenges that may arise. This knowledge provides technological instruments as well as cognitive, philosophical, and epistemological tools to address different current issues. Strategy, Policy, Practice, and Governance for AI in Higher Education Institutions offers both empirical and theoretical information focused on artificial intelligence and its various applications in higher education institutions. It includes research results, authoritative overview articles, high quality analysis on trends, comparative studies, and analysis of cases that focus on issues including ethical issues and risks for applying AI in higher education, policies to introduce AI in curricula, and applications in teaching and learning. Covering topics such as artificial intelligence ethics, energy efficiency, and postsecondary administrative leadership, this premier reference source is an essential resource for computer scientists, AI scientists, administration of higher education institutions, educators and faculty of higher education, pre-service teachers, researchers, IT professionals, and academicians.