Designing instruction for complex learning: 4C/ID in higher education

Objectives‐based instructional design approaches break down tasks into specific learning objectives and prescribe that instructors should choose the optimal instructional method for teaching each respective objective until all objectives have been taught. This approach is appropriate for many tasks where there is little relation between the objectives, but less effective for teaching complex professional tasks that require the integration of knowledge, skills, and attitudes and the coordination of different skills. For the latter, a task‐centred approach that starts designing instruction from whole, real‐life tasks, is more appropriate. This article describes one task‐centred instructional design model, namely the Four‐Component Instructional Design (4C/ID) model and illustrates its application by reflecting on three educational programs in higher education designed with 4C/ID. The first case presents a design for a course that focuses on the development of mobile apps at the Amsterdam University of Applied Sciences in the Netherlands. The second case illustrates the integration of information problem‐solving skills at Iselinge University of Professional Teacher Education, a teacher training institute in the Netherlands. The third case is an example from general practice education at the KU Leuven, Belgium. Future developments and issues concerning the implementation of task‐centred educational programmes are discussed.     

Preparing Instructional Designers for Game-Based Learning: Part 2

As noted in part I of this article (published in TechTrends 54(3)), advances in technology continue to outpace research on the design and effectiveness of instructional (digital video) games. In general, instructional designers know little about game development, commercial video game developers know little about training, education and instructional design, and relatively little is understood about how to apply what we know about teaching and learning to optimize game-based learning. In Part I, a panel of recognized and emerging experts in the design of instructional (digital video) games set the context for this three part series and one of four panelists discussed what he believes instructional designers should know about instructional game design (Hirumi, Appleman, Rieber, Van Eck, 2010). In Part II, two faculty members who teach courses on instructional game design presents their perspectives on preparing instructional designers for game-based learning. Part III will present a fourth perspective along with conclusion that compares the four views.     

Behaviorism,Cognitivism, Constructivism: Comparing Critical Features from an Instructional Design Perspective

The way we define learning and what we believe about the way learning occurs has important implications for situations in which we want to facilitate changes in what people know and/ or do. Learning theories provide instructional designers with verified instructional strategies and techniques for facilitating learning as well as a foundation for intelligent strategy selection. Yet many designers are operating under the constraints of a limited theoretical background. This paper is an attempt to familiarize designers with three relevant positions on learning (behavioral, cognitive, and constructivist) which provide structured foundations for planning and conducting instructional design activities. Each learning perspective is discussed in terms of its specific interpretation of the learning process and the resulting implications for instructional designers and educational practitioners. The information presented here provides the reader with a comparison of these three different viewpoints and illustrates how these differences might be translated into practical applications in instructional situations.     

How Do Instructional‐Design Practitioners Make Instructional‐Strategy Decisions?

Many theories have been proposed to help instructional designers make instructional‐strategy decisions, yet it is not clear if these theories are actually used by ID practitioners. This study used a web‐survey to examine the design strategies of 113 ID practitioners. The survey asked respondents to rate how frequently they used learning or ID theories as well as 10 other design strategies, to help make instructional‐strategy decisions. Respondents were also asked how often they used different information sources to learn about new theories, trends, and strategies, and to respond to a set of contrasting statements depicting objectivist vs. construc‐tivist assumptions. The results indicate that ID practitioners most often rely on interaction with others both as a means of making instructional‐strategy decisions and of learning about new theories, trends, and strategies. Only fifty percent of the respondents said they regularly use theories when making instructional‐strategy decisions, using other design strategies more frequently instead; and most practitioners are eclectic in their underlying philosophical assumptions. Based on these results, we discuss implications for training and ongoing support of instructional designers.     

Preparing Instructional Designers for Game-Based Learning: Part 1

Like many rapidly growing industries, advances in video game technology are far outpacing research on its design and effectiveness. Relatively little is understood about how to apply what we know about teaching and learning to optimize game-based learning. For the most part, instructional designers know little about game development and video game developers may know little about training, education and instructional design. In this three part series of articles, four recognized and emerging experts in instructional game design discuss their perspectives on preparing instructional designers to optimize game-based learning. In Part I, we set the context for the series of articles and one of four faculty members who teach a graduate level course on game design discusses what he believes instructional designers should know about instructional game design based on his experiences. Part II will present alternative perspectives from two additional faculty members who teach courses in instructional game design, and Part III will present a fourth perspective along with conclusion that compares the four views.     

Learning styles fray: Brilliant or batty?

Many school educators, instructional designers, and corporate training specialists believe that learning styles are important and that much of training success relies on the craft of incorporating learning styles into learning materials. But how do common views and popular wisdom on learning styles align with what the psychological sciences tell us today? Does matching instructional design to learning styles really result in learning enhancement? This article seeks to examine such questions based on current research in psychological sciences.     

“There’s got to be a better way to do this”: a qualitative investigation of informal learning among instructional designers

This study employed a qualitative research design to investigate informal learning among practicing instructional designers. Prior research has examined how instructional designers spend their time, make decisions, use theory, solve problems, and so on, but no published research has explored the nature and role of informal learning in instructional design work. Based on intensive interviews of practitioners in the field, this study produced eight themes organized according to two metathemes: (a) the nature of informal learning in instructional design practice and (b) instructional design as informal learning. Specific themes concerned what instructional designers learn through informal practical experience, how they learn it, and the meaning of this kind of learning for various aspects of their work. Overall, these results suggest that informal learning is a vital part of instructional design practice and that design itself can be thought of as a specialized type of informal learning. Other conclusions regarding informal learning in design are discussed and future directions for research are offered.     

The Effects of Instructional Training on University Teaching Assistants

This study addressed the need for empirical tests of the global instructional design (ID) model as a toolkit for classroom teachers in authentic settings; and the performance improvement challenge of finding effective, efficient methods of professional development for preparing graduate assistants to teach. Participants were eighteen teaching assistants (TAs) with primary instructional responsibilities at a large Midwestern university. Twelve were given a training intervention in instructional design, while the other six served as a control group. The intervention was based on the iterative, five‐phase ADDIE model, and principles from educational psychology. Dependent measures were TAs' ID knowledge, teaching self‐efficacy, satisfaction with knowledge and strategies, perceived teaching competence, teaching performance and teaching effectiveness, and their students' engagement and perceived learning. All of the study's seven hypothesized relationships were found statistically significant. The intervention, though brief, measurably increased the ID knowledge of participating TAs, along with their teaching‐related self‐perceptions, and student outcomes. Instructional design emerges as a potentially powerful training tool for organizing teachers' and trainers' knowledge related to the complex practice of classroom instruction.     

Learning theories for artificial intelligence promoting learning processes

This paper discusses a three-level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities.

Practitioner notes

What is already known about this topic

• Numerous learning theories exist with significant cross-over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning.
• Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified.
• Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices.

What this paper adds

• A three-level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education.
• A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels.
• The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels.
• Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity.

Implications for practice and policy

• Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems.
• Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.

     

A reflexive model for teaching instructional design

Although there are numerous models to practive instructional design (ID), few instructional models to teach instructional design have been documented. This article documents a five-year study of two instructors who collaborated on formally studying their teaching of a master's level instructional design course. A reflexive instructional approach was used, in which the teachers examined their teaching while students were being prompted to reflect on their learning of instructional design through a course-long ID project. In this article we summarize our views on learning, teaching, and instructional design. A design and development framework from developmental research (Richey & Nelson, 1996) was used to describe our teaching in terms of the design decisions, model implementation, and model evaluation across six deliveries of the ID course from 1994–1998.     

Cognitive design for instructional design

There is strong interest in the field of instructional design in building expert systems that can provide advice to inexperienced instructional designers. This paper questions whether the expert systems model to advice-giving is in fact appropriate for a design process such as instructional design. An alternate approach based on case-based reasoning and the critic approach to advice-giving is considered to be better adapted to the cognitive needs of this task. The cognitive constraints of the task are used to orient the design of an instructional design workbench. The concept of an ID workbench illustrates the direct application of cognitive science to a complex practical task in the area of design.     

Organising industrial knowledge dissemination on frontier technology

This paper describes the challenges faced by frontier technology education, typical among large integrated EU projects. These include an evolving nature, the scarcity of experts and established material, and the need for relevant material. Classical approaches to learning seem to not adequately address the needs of frontier technology alone. Following this observation we develop a training model, where the instructional model, architectural design, and delivery mechanisms are developed according to the needs, goals, homogeneity, and distance among learners. We then use principles of constructivist methodology to address the needs of frontier technology. Throughout the paper radio frequency identification (RFID) training at the Cambridge Auto ID labs is taken as an example. The methodology leads to a successful training delivery where students have achieved the targeted success criteria.     

From concepts of motivation to its application in instructional design: Reconsidering motivation from an instructional design perspective

This paper explores the concepts of motivation, including extrinsic motivation and intrinsic motivation. It describes how motivation becomes a major concern in the field of instructional design (ID). Furthermore, a motivation model—the ARCS model—is identified and discussed. Finally, it provides an example of how to apply the motivational design process in ID. The aim of this paper is to facilitate a deeper understanding of motivation and to inform professionals about its importance in learning     

Rapid Prototyping in the Instructional Design Process

Instructional models guide designer activity as they attempt to solve instructional problems. Models provide structure to the project, problem solving strategies, evaluation, and feedback. This paper is designed to examine Rapid Prototyping, a model born in the computer age. Rapid Prototyping embraces computer design strategies, constructivist learning theory, and cognitive psychology. This paper will first look at the “classic” forms of instructional design models, which form the foundation of instructional design. A review of the critical elements of these models will provide the framework to understand the concepts behind the Rapid Prototyping model. Next, this paper will examine how researchers define Rapid Prototyping, how the model is used, whether it is successful, and why some consider it a major shift in the way instruction is designed. By clarifying how RP is structured to solve instructional problems and the processes it uses to produce instructional materials, this paper will strive to determine whether it is a viable alternative to traditional design models.     

A Job Aid for Effective Learning: The ID Ready Reckoner

A blueprint for effective learning can be created quickly using the ID Ready Reckoner. This is a job aid that assists learning professionals in selecting appropriate instructional techniques for different levels of learning objectives based on their content types. The job aid also recommends the most suitable assessment formats for a given learning objective level. Instructional techniques and assessment formats together provide an effective instructional strategy for classroom training design. The use of this job aid requires little or no specialization in instructional design.     

Cognitive task analysis for instructional design: Applications in distance education

The increasing complexity of the subjectmatters taught through distance education calls for a cognitive approach to instructional design. Cognitive task analysis‐based instructional design (CTA‐BID) is especially appropriate for distance education, which requires high‐fidelity instructional materials that teach cognitive content with little or no classroom instruction. CTA‐BID organises instructional materials around the results of a cognitive task analysis that identifies the optimal knowledge structures, mental models, strategies, and skills underlying expertise in the subject matter. CTA‐BID has produced innovative instructional programmes aimed at teaching cognitive skills, accelerating the development of expertise, and improving student performance and training efficiency. This article provides an overview of CTA‐BID and its applications in the design of instructional and testing materials for distance education. I also review recent developments in education, psychology, and instructional design that complement CTA‐BID.