Review: Designing Adaptive and Personalized Learning Environments


Developing Adaptive and Personalized Learning Environments

Authored by Kinshuk

2016, Routledge; New York, NY

ISBN: 978-1-138-01306-3

Four sections, 12 Chapters, 182 pages, Test for Understanding Questions, Learning Activities, Resource lists by Chapter.


Technology continues to offer expanding opportunities for learners to gain knowledge in any environment, with all manner of devices. With the growth of open learning environments, online learning, ubiquitous computing, and learning analytics, adaptive and personalized learning environments have the potential to optimize learning and collaboration, while minimizing frustration and misconceptions. All of this promise comes with a complex set of challenges, and Kinshuk has written a thorough and well-structured book to walk instructional designers and instructors through many of the key factors to be considered. This review will discuss the overall structure of the book, along with a summary of each of the book’s four main sections.

The book is divided into four sections. The first section provides an overview of adaptation and personalization, with definitions, an explanation of how adaptivity and personalization work in different learning contexts, and the benefits of adaptivity for both instructors and learners throughout the lifespan. The second section of the book outlines the various theoretical underpinnings of adaptive and personalized environments. The third sections moves into considerations for implementation, with example scenarios for each application. Finally, the fourth section discusses methods for evaluating and optimizing learning environments, as well as a look at directions for future research and technologies.

Key Features

The book includes a number of key features that make it useful both as a possible textbook, and as a reference manual for practitioners. Each chapter includes one or more reflection activities, tests for understanding, and learning activities, which ask the reader to consider the content that has been covered, or to create a scenario based on the principles of the current chapter. References for further reading are provided at the end of each chapter, and links to useful resources are provided through the book at the end of appropriate chapters. The overall structure of the book provides a logical progression from foundations to theory to implementation and evaluation. Many of the scenarios and examples are referenced throughout the book, providing a consistent thread of concepts from start to finish.

Part One

Part 1 of the book includes three chapters and provides the foundation for the discussions in the rest of the work. The first chapter focuses on defining adaptation and personalization, and the different levels of each that can be applied in a learning environment. Kinshuk also discusses both the benefits and limitations of adaptation and personalization. He closes the chapter with a scenario that summarizes the many functions that an adaptive and personalized learning environment would typically perform in a real-world situation. Chapter 2 outlines the concepts of adaptivity and personalization in the context of life-long learning. Given the availability of information in nearly any context at any time, and the demands placed on students and professionals alike, learning needs to take place according to the learner’s location, learning objectives, and the device upon which they are going to access the learning environment. In Chapter 3, Kinshuk defines the many aspects of the context in which learning can take place. Context can refer to the physical environment, the mode of communication, the discipline of the content being learned, and the interaction between the learner and their device.

Part 2

In Part 2, four chapters are devoted to covering the theory that forms the basis for adaptive and personalized learning environments. This is the largest section of the book, and goes into considerable detail concerning cognitive theory, different learning styles, and the ways in which a learning environment can be structured to respond to the needs of the learner, based on a wide range of feedback.

The first chapter in Part 2 describes cognitive theory, and some of the key characteristics that affect a student’s capabilities and learning style. Kinshuk describes how a learning environment can gather data to create a cognitive trait model for each student, and use that information to make decisions about how and when to present a student with certain content. As the chapter notes, a cognitive trait model for a particular student tends to be stable and useful independent of the content being studied. This makes it a valuable component of an adaptive learning environment.

Chapter 5 deals with the ways in which a learning environment can present the same content in different ways, to best suit the student’s profile, and the demands of the type of content being learned. Examples include the decision to present text, audio, images, or video, based on the needs of any given scenario. This presentation-based adaptation also takes into account the location and tools available to the student, such as minimizing the use of video if the student is using a mobile device or has an unstable Internet connection.

The next two chapters deal with different the different types of adaptation that can be applied to exploratory learning and mobile or ubiquitous learning. For exploratory learning, with its roots in constructivist theory, students must be given the freedom to choose their own learning path and the format of the content that they wish to receive. However, in a completely open virtual environment, the sheer number of choices may be overwhelming, especially for novice learners. This is where Kinshuk describes the benefits of effective adaptation and personalization. An effective learning environment recognizes both the ability and the learning preferences of the student, and will constrain the number of choices available. This leaves the student freedom to explore, while minimizing the cognitive load that can arise from being overwhelmed by options.

In mobile and ubiquitous environments, there are a number of ways in which a system can personalize the learning process. First, given the constraints of mobile devices, the system must recognize that certain types of media may not be optimal (i.e. large images or video files). Second mobile environments can vary in their ambient conditions, such as lighting and background noise. The learning environment can utilize various sensors to analyze the environment around the student, and adjust its delivery of content accordingly. Ubiquitous computing offer the opportunity for students to have authentic learning experiences based on their location, and adaptive learning environments can sense objects in the area around the student that can become part of the lesson. In addition, specific location-based lessons can be created and validated by instructors anywhere in the world, so that students can be alerted to new learning opportunities when they are nearby.

Part 3

The third section of the book focuses on implementation and practical considerations. Chapter 8 describes a model for implementing adaptive and personalized learning environments. In this section, Kinshuk outlines several principles that should be considered when implementing a system. One of the key concepts in this chapter is that the system should empower the student to learn in the absence of an instructor, and that the learning must be available at different levels, depending on the needs of the student at that time. The chapter offers a practical scenario in which a system implementation is described. This chapter also discusses factors such as cultural and cognitive differences, which could lead to misunderstandings in synchronous communications, and how and adaptive learning environment can intervene to minimize these potential issues.

Chapter 9 deals with cognitive skills acquisition, using a method called cognitive apprenticeship, as well as the potential effectiveness of simulated environments. The cognitive apprenticeship model is a form of scaffolding, and Kinshuk describes a real-world scenario involving carpentry, and how this can be related to a learning environment that follows a similar model. A case study is discussed involving a simulation for medical students to assess and diagnose problems using a virtual model of the ear. Various aspects of the model can be manipulated by either the instructor or the student, and the adaptation engine can select certain variables to present, based on the needs of the student.

Chapter 10 deals with the concept of reusability in adaptive learning environments. Given the need for each lesson to have access to a range of different resources, in multiple formats, the sheer volume of content required for an effective learning environment is daunting. Kinshuk describes the need for repositories for content that can be used by instructors, and presents examples of existing standards and repositories that may be useful. There is a clear need for standardization, and at present, there are multiple formats and styles for packaging content.

Part 4

In Part 4, the book discusses various methods of validating learning environments, and looks to the future of adaptive and personalized learning, based on trends in technology and theory. Chapter 11 outlines the evaluation principles that can be applied to both internal and external evaluation of adaptive and personalized learning environments. He distills the evaluation process down to two essential questions: how does the environment impact student learning, and is the desired effect achieved by the personalization and adaptation of the system? A number of different methods are described, and the benefits and limitations of each are outlined. A practical example is discussed.

Chapter 12 describes the potential for adaptive and personalized learning environments in the future, and suggests avenues for future research in the field. Key among the potential future developments in the growth of mobile and ubiquitous learning. As the power of mobile devices and the connectivity of wireless networks continues to expand, the opportunities for learning literally follow the student everywhere they go. Opportunities for authentic, context-based learning can be created by students and instructors alike, and accessed by anyone who has the technology to interact with the environment. Advances in sensor technology will lead to further adaptation and personalization, as biophysical cues can trigger the system to increase or decrease the pace or complexity of the content if the student’s cognitive load becomes too high or too low. Finally, the growing field of learning analytics and data mining will provide more and more accurate guidance for adaptive systems to predict and respond to the needs of different types of learners. Kinshuk concludes with a description of a smart learning environment, an ecosystem of data, content, devices, interaction among students and instructors, and the system that allows each student to achieve their maximum potential at their ideal pace.


As technology and access continue to improve, and if adaptive and personalized learning systems gain wider adoption in a variety of learning contexts, there will be a need for guidelines to build effective environments. Kinshuk has provided a concise explanation of the key theories behind adaptive and personalized learning systems. He has also given many practical examples and recommendations for evaluating learning environments. Finally, he looks to the future, and considers the potential of mobile and ubiquitous computing, combined with the personalization and optimization that will be made possible by data mining and learning analytics.

This is not a step-by-step guide to building a personalized learning environment. While there are practical examples provided, this is neither a programming guide, nor a manual that will enable readers to add personalization to their own learning environments. Kinshuk does not give detailed descriptions of specific personalization modules. Rather, the book provides a theoretical and practical framework for how an adaptive and personalized learning environment could be conceptualized, designed, and evaluated.

The many learning activities and reflection questions would make this book suitable as a textbook for a course on adaptive and personalized learning environments. The well-designed structure and practical examples also make this a useful manual for the practitioner. For anyone interested in adaptive and personalized learning environments, this book will serve as a valuable foundation and reference.

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