The concept of algorithm, as used in teaching and learning, is defined. Characteristics of algorithms are identified and described. The elements (operator, discriminator, syntactic structure) are described and illustrated. Methods of representing algorithms are portrayed. Differences between identification algorithms, transformation algorithms, and search algorithms are discussed. Use of algorithms in instruction and training are suggested. Several research and development tasks are proposed. (Author).
A critique of what lies behind the use of data in contemporary education policy While the science fiction tales of artificial intelligence eclipsing humanity are still very much fantasies, in Algorithms of Education the authors tell real stories of how algorithms and machines are transforming education governance, providing a fascinating discussion and critique of data and its role in education policy. Algorithms of Education explores how, for policy makers, today’s ever-growing amount of data creates the illusion of greater control over the educational futures of students and the work of school leaders and teachers. In fact, the increased datafication of education, the authors argue, offers less and less control, as algorithms and artificial intelligence further abstract the educational experience and distance policy makers from teaching and learning. Focusing on the changing conditions for education policy and governance, Algorithms of Education proposes that schools and governments are increasingly turning to “synthetic governance”—a governance where what is human and machine becomes less clear—as a strategy for optimizing education. Exploring case studies of data infrastructures, facial recognition, and the growing use of data science in education, Algorithms of Education draws on a wide variety of fields—from critical theory and media studies to science and technology studies and education policy studies—mapping the political and methodological directions for engaging with datafication and artificial intelligence in education governance. According to the authors, we must go beyond the debates that separate humans and machines in order to develop new strategies for, and a new politics of, education.
This book deals with large-scale or macro-level instructional design, which is referred to by other authors variously as curriculum development, course design, training system design or instructional systems design. The emphasis throughout the book is on the application of a systems approach, which implies both a way of thinking about the problem and a methodology for seeking and developing solutions. Thus the approach of the book is problem-oriented. The successful problem-solver requires more than a technique or procedure. He requires experience of similar problems, some general principles that he can apply to the class of problems and a great deal of creativity to develop an optimal method of solving each problem. This book brings together the theories and practical experience that have been built up by instructional technologists over the last two decades, the techniques that are currently most used for the analysis of problems in education and for their solution, and a range of new ideas specially developed by the author to encourage the creative element (so often missing from educational materials). This book is intended for anyone involved in instructional design. It is designed on a ‘grid’ structure to facilitate the reader’s choice of chapters. Those who wish to gain a general overview may concentrate on the chapters at the theory base and analysis levels. Those more practically concerned with course design will find much of use in the synthesis and evaluation levels. Those who wish simply to discover ‘what’s new’ in this book and its treatment of instructional design will find what they are seeking principally in the analysis and evaluation levels.
The opening section of this paper discusses these issues and attempts to define what algorithmic prescriptions have to offer for the instructor. Next, the question of effectiveness and efficiency is examined in two experiments. The first study provides preliminary data regarding the form of algorithm and the presentation modes to be used. The second study asks 77 undergraduate volunteers to master and utilize one of three forms of an algorithm: a prose discourse, a flowchart procedure, or a mastery-inducing flowchart technique. Also examined in a factorial design was the effect of the presence or absence of the algorithm in subsequent problem solving and the learners' ability to retain the algorithmic content over a period of time. The results provided very strong data for the overall effectiveness of algorithms, showing from 52% to over 80% accuracy following an average of only 15 minutes of instruction and practice. Surprisingly, almost no decrement (only 1%) in performance was observed across groups after a one-week delay. Although scores were high after the algorithm representation was withheld, subjects did better when allowed to continue using it. In general, the prose and flowchart representations were equally effective, with the mastery-flowchart group performing significantly worse. The flowchart group alone was much more efficient than the other groups.
In "Unleashing AI: Harness the Power of Artificial Intelligence in Instructional Design," seasoned instructional systems designer Ruchir Bakshi explores the transformative potential of Artificial Intelligence (AI) in Instructional Design (ID). This groundbreaking book serves as a comprehensive guide for instructional designers, educators, and anyone interested in harnessing the power of AI to create engaging, personalized, and effective learning experiences. Bakshi expertly navigates the complex landscape of AI, providing readers with a deep understanding of its applications in ID. From leveraging machine learning algorithms for data-driven insights to utilizing natural language processing for personalized content creation, this book covers a wide range of AI technologies and their practical applications in the field. Through the eyes of Sofia, a fictional instructional designer, readers embark on a captivating journey that brings the concepts to life. Sofia's experiences showcase real-world examples of how AI can seamlessly integrate into the ID process, making the content relatable and inspiring. Bakshi also addresses the challenges and ethical considerations surrounding AI in education, offering guidance on navigating these complexities while prioritizing learner needs and well-being. The book emphasizes the importance of human judgment and oversight in using AI, ensuring that the technology enhances, rather than replaces, the creativity and expertise of instructional designers. "Unleashing the Power of AI in Instructional Design" is not just a book; it's a roadmap for the future of learning. Bakshi's passion for innovation and social responsibility shines through on every page, making this an essential resource for anyone seeking to revolutionize education and training. As a combat veteran and mental health advocate, Bakshi brings a unique perspective, infusing the book with insights from his diverse experiences and tireless pursuit of knowledge. His engaging and authoritative tone guides readers through AI-powered instructional design's exciting world, inspiring them to create transformative learning experiences. Whether you're a seasoned instructional designer looking to stay ahead of the curve, an educator eager to explore new frontiers in learning, or simply someone curious about the future of education, "Unleashing AI: Harness the Power of Artificial Intelligence in Instructional Design" is an indispensable guide. Embrace the power of AI and join Ruchir Bakshi on this transformative journey to revolutionize learning for generations to come.
Instructional design theory and practice has evolved over the past 30 years from an initial narrow focus on programmed instruction to a multidimensional field of study integrating psychology, technology, evaluation, measurement, and management. The growth of instructional design (ID) has occurred because of direct needs, problems, and goals from society. Its application in planning instruction first developed in the United States with the Department of Defense during World War II with the purpose of meeting immediate concerns for effective training of larger numbers of military personnel. From the beginning, ID has rapidly expanded into applications in industrial and executive training, vocational training, classroom learning, and professional education. Although ID has its roots in the U.S., applications and theoretical growth is an international activity. However, literature at the international level is still limited to either individual author contributions or collections primarily represented by single countries. As a result, there is no standard reference source that contains the rich variety of theories and applications to form the international foundation for the field. The goal of this two-volume set is to establish international foundations for ID theory, research, and practice within the framework of the two following objectives: * to identify and define the theoretical, research, and model foundations for ID, and * to bridge the gap between ID foundations and application. Volume I includes chapters on philosophical and theoretical issues on learning theory and ID models. Volume II provides an overview of the state of the art of solving ID problems. The contributors offer contrasting points of view which provide a rare opportunity to see the diversity and complexity in the field. The editorial committee has selected a wide range of internationally known authors to make presentations in the topic areas of the field.
This textbook on Instructional Design for Learning is a must for all education and teaching students and specialists. It provides a comprehensive overview about the theoretical foundations of the various models of Instructional Design and Technology from its very beginning to the most recent approaches. It elaborates Instructional Design (ID) as a science of educational planning. The book expands on this general understanding of ID and presents an up-to-date perspective on the theories and models for the creation of detailed and precise blueprints for effective instruction. It integrates different theoretical aspects and practical approaches, such as conceptual ID models, technology-based ID, and research-based ID. In doing so, this book takes a multi-perspective view on the questions that are central for professional ID: How to analyze the relevant characteristics of the learner and the environment? How to create precise goals and adequate instruments of assessment? How to design classroom and technology-supported learning environments? How to ensure effective teaching and learning by employing formative and summative evaluation? Furthermore, this book presents empirical findings on the processes that enable effective instructional designing. Finally, this book demonstrates two different fields of application by addressing ID for teaching and learning at secondary schools and colleges, as well as for higher education.
Modes and models of learning and instruction have shown a significant shift from yesterday's conventional learning and teaching given this era’s current educational and social contexts. Learners are no longer learning and communicating with human-generated, computed, and mediated—or traditional—learning and instructional practices, paving the way for machine-facilitated communication, learning, and teaching tools. Learning and instruction, communication and information exchange, as well as gathering, coding, analyzing, and synthesizing data have proven to be in need of even more innovative technology-moderated tools. Applications of Machine Learning and Artificial Intelligence in Education focuses on the parameters of remote learning, machine learning, deep learning, and artificial intelligence under 21st-century learning and instructional contexts. Covering topics such as data coding and social networking technology, it is ideal for learners with an interest in the deep learning discipline, educators, educational technologists, instructional designers, and data evaluators, as well as special interest groups (SGIs) in the discipline.
Order affects the results you get: Different orders of presenting material can lead to qualitatively and quantitatively different learning outcomes. These differences occur in both natural and artificial learning systems. In Order to Learn shows how order effects are crucial in human learning, instructional design, machine learning, and both symbolic and connectionist cognitive models. Each chapter explains a different aspect of how the order in which material is presented can strongly influence what is learned by humans and theoretical models of learning in a variety of domains. In addition to data, models are provided that predict and describe order effects and analyze how and when they will occur. The introductory and concluding chapters compile suggestions for improving learning through better sequences of learning materials, including how to take advantage of order effects that encourage learning and how to avoid order effects that discourage learning. Each chapter also highlights questions that may inspire further research. Taken together, these chapters show how order effects in different areas can and do inform each other. In Order to Learn will be of interest to researchers and students in cognitive science, education, machine learning.
