The Application of Algorithms to Instructional Design
The Application of Algorithms to Instructional Design

Author: Richard F. Schmid

Publisher:

Published: 1977

Total Pages: 46

ISBN-13:

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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.

Algorithms in Learning, Teaching, and Instructional Design
Algorithms in Learning, Teaching, and Instructional Design

Author: Vernon S. Gerlach

Publisher:

Published: 1975

Total Pages: 67

ISBN-13:

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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).

Unleashing AI
Unleashing AI

Author: Ruchir Bakshi

Publisher: Independently Published

Published: 2024-03-26

Total Pages: 0

ISBN-13:

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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.

The Algorithm Design Manual
The Algorithm Design Manual

Author: Steven S Skiena

Publisher: Springer Science & Business Media

Published: 2009-04-05

Total Pages: 742

ISBN-13: 1848000707

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This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: • Doubles the tutorial material and exercises over the first edition • Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video • Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them • Includes several NEW "war stories" relating experiences from real-world applications • Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java

Mastering Machine Learning Algorithms
Mastering Machine Learning Algorithms

Author: Giuseppe Bonaccorso

Publisher: Packt Publishing Ltd

Published: 2018-05-25

Total Pages: 567

ISBN-13: 1788625900

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Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.

Deep Learning: Algorithms and Applications
Deep Learning: Algorithms and Applications

Author: Witold Pedrycz

Publisher: Springer

Published: 2019-11-04

Total Pages: 360

ISBN-13: 9783030317591

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This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.

Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques
Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques

Author: Kulkarni, Siddhivinayak

Publisher: IGI Global

Published: 2012-06-30

Total Pages: 464

ISBN-13: 1466618345

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Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.

The Impact and Importance of Instructional Design in the Educational Landscape
The Impact and Importance of Instructional Design in the Educational Landscape

Author: Calhoun, Christie F.

Publisher: IGI Global

Published: 2023-08-25

Total Pages: 310

ISBN-13: 1668482096

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Instructional design is pivotal to the landscape of education. Shifts in the educational landscape require different approaches to meet different needs. While it is important to realize that education in modern society looks much different than decades ago, it is essential to understand that the basic components of instructional design have not changed. No matter the classroom, all learning must begin with clear goals and objectives, learning activities, and assessments. From there, instruction is designed using a number of models or instructional designs as a foundation to develop learning. The Impact and Importance of Instructional Design in the Educational Landscape provides relevant theoretical instructional design models and the latest research findings related to these models. Covering topics such as co-teaching, lesson planning and delivery, and universal design for learning (UDL), this premier reference source is an excellent resource for pre-service and in-service teachers, teacher educators, instructional technology professionals, library media specialists, educational administrators, instructional leaders, researchers, and academicians.