Various theories and models have been published that guide the design and development of digital learning technologies. While these approaches can be useful for promoting cognitive or affective learning outcomes, user-centered design methods and processes from the field of human-computer interaction can also be of value to those in the learning/instructional design and technology community. In this chapter, we present user-centered design techniques and processes derived from human-computer interaction, human-centered computing, user experience design, and design thinking. These techniques can lead to highly usable and satisfying digital learning experiences. We begin with foundational theories particularly relevant to the field of learning experience design. We then outline specific, human-centered design techniques that can be applied during the design and development of digital learning experiences. The descriptions of these techniques include both the goals of the techniques, as well as the ideal stage in which to apply them.

Introduction

Educators and learners are increasingly reliant on digital tools to facilitate learning. However, educators and learners often use technology in ways that are different than developers originally intended (Straub, 2017). For instance, educators may be challenged with trying to determine how to assess student learning within their learning management system (LMS), so they use a different tool than the one provided in the LMS and then copy/paste the results. Or they might spend time determining workarounds to administer lesson plans because the LMS does not directly support a particular pedagogical approach or learning strategy. From the perspective of learners, experiencing the challenges of navigating an interface or finding homework details might result in frustration or even missed assignments. When an interface is not easy to use, users tend to develop alternative paths to complete a task to accomplish a learning goal. Long recognized in the field of human-computer interaction (HCI), such adjustments, accommodations, and improvisations are the result of design flaws (cf. Orlikowski, 1996; Grudin, 1988). These design flaws are often the result of the software development team failing to consider the user (or in this case, the learner) sufficiently in the design process. This extends to the field of learning/instructional design and technology (collectively LIDT) and can create barriers to effective instruction (Jou et al., 2016; Rodríguez et al., 2017).

The principles of human-computer interaction (HCI) and user-centered design (UCD) have implications for the design of learning experiences in digital environments. While the field of LIDT has focused historically on theories that guide learning design (e.g., scaffolding, sociocultural theory), less emphasis has been placed on learning technology design from the view of HCI and UCD (Okumuş et al., 2016). Increasingly, user experience design (UXD) and usability research are being accepted as particularly useful in supporting positive, enjoyable, or memorable learning experiences. This has emerged as a focus area in the field of LIDT and is referred to as learning experience design. Adoption of such techniques occurred alongside the field differentiating instructional design (Mor & Craft, 2012) from learning design (Saçak et al., 2022). At the same time, usability and user experience methods emerged from the field of software engineering (Hassenzahl, 2013), and practitioners of learning design began adopting these methods in their own design practice (Kilgore, 2016). Hence, the term learning experience designer was born (Georgiou & Ioannou, 2021; Harrati et al., 2016; Korkmaz, 2018; Minichiello et al., 2018) to describe designers engaging in the practice of learning experience design (LXD; Schmidt & Huang, 2022).

LXD is a relatively novel phenomenon in the field of LIDT. We recently published an edited volume titled Learner and User Experience Research: An Introduction for the Field of Learning Design & Technology (Schmidt et al., 2020). In our introduction to this book, we identified three areas in need of further articulation. Firstly, there is little agreement in terminology within our discipline. Secondly, LXD as an emerging area of research and practice has made neither substantial nor sufficient connections to the theoretical foundations of LIDT. Thirdly, although learning designers are applying methods and processes of UCD in their design contexts, there are as of yet no guidelines for this in LIDT. Since publishing this edited volume, some progress has been made in terms of defining LXD (as discussed in the following paragraph). However, progress elaborating theoretical foundations of LXD remains limited. In this chapter, we approach this issue by situating LXD within theories of cognitive load, distributed cognition, and activity theory. Finally, guidance regarding design techniques and evaluation methods for aspiring learning experience designers has yet to emerge. The current chapter (and its companion chapter in this volume, titled Evaluation Methods for Learning Experience Design [PROVIDE LINK]) speaks to this need, focusing primarily on design techniques for learning experience designers.

Learning experience design (LXD) is defined as “a human-centric, theoretically-grounded, and socioculturally sensitive approach to learning design, intended to propel learners towards identified learning goals, and informed by UXD methods” (Schmidt & Huang, 2022, p. 151). LXD is concerned with learners’ interactions with the learning environment, as well as with their interactions with the learning space (Tawfik et al., 2022). Importantly, the term LXD does not suggest that learning or experiences themselves can be designed or engineered, but instead that opportunities for learning can be designed so that positive and enjoyable learning experiences can happen. The practice and theories of LXD suggest it is a multidimensional construct that considers aspects of the individual learner’s experience across three separate dimensions: 

(a) the technological dimension, which includes learner-computer interaction with a given learning technology;

(b) the pedagogical dimension, which includes learner interaction with designed materials, instructions, activities, assessments, etc.; and

(c) the sociocultural dimension, which includes digitally-mediated social relationships, digital communication, and online social presence (Jahnke et al., 2020; Marell-Olsson & Jahnke, 2019), as detailed in Figure 1.

Theoretical Foundations

Usability and HCI principles are often situated in established theories such as cognitive load theory, distributed cognition, and activity theory. LIDT is a sibling of these disciplines; hence, these theories also have ramifications for the design and development of learning technologies. In the following sections, we discuss each theory and the importance for conceptualizing UCD, usability, and UX from the LIDT perspective. 

Human-Computer Interaction

Understanding how educators and learners interact with learning technologies is key to avoiding and remediating design flaws. HCI seeks to understand the interaction between technology and the people who use it from multiple perspectives (Rogers, 2012)—two of which are user experience (UX) and usability. UX describes the broader context of technology usage in terms of “a person’s perceptions and responses that result from the use or anticipated use of a product, system, or service” (International Organization for Standardization [ISO], 2010, Terms and Definitions section, para 2.15). UX considers all aspects of a user's interaction with technology, including how pleasing and usable the technology is. More specifically, usability describes how easy or difficult it is for users to interact with a user interface in the manner intended by the software developer (Nielsen, 2012). Highly usable user interfaces are easy for users to become familiar with, efficiently support users achieving their goals, and are easy to remember. From the perspective of learning design, these design factors are used strategically to focus cognitive resources primarily on the task of learning. 

Cognitive Load Theory

Cognitive load theory (CLT) contends that learning is predicated on effective cognitive processing; however, an individual only has a limited number of resources needed to process the information (Mayer & Moreno, 2003; Paas & Ayres, 2014). The three categories of CLT include: (a) intrinsic load, (b) extraneous load, and (c) germane load (Sweller et al., 1998) (see Figure 2). 

Firstly, intrinsic load describes the active processing or holding of verbal and visual representations within working memory, while also considering their complexity and relationships, referred to as element interactivity. Secondly, extraneous load includes the elements that are not essential for learning but are still present for learners to process (Korbach et al., 2017). Thirdly, germane load describes the relevant load imposed by the effective instructional/learning design of learning materials (hereafter referred to simply as learning design). Germane cognitive load is therefore relevant to schema construction as information is incorporated into long-term memory (Paas et al., 2003; Sweller et al., 1998; van Merriënboer & Ayres, 2005). It is important to note that the elements of CLT are additive, meaning that if learning is to occur, the total load cannot exceed available working memory resources (Paas et al., 2003). 

Extraneous load is of particular importance for UCD. Extraneous cognitive load can be directly manipulated by a designer (van Merriënboer & Ayres, 2005) through improved usability. When an interface is not designed with usability in mind, the extraneous cognitive load is increased, which impedes meaningful learning. From a learning design perspective, poor usability might result in extraneous cognitive load in many forms. For instance, a poor navigation structure in an online course might require the learner to extend extra effort to click through the learning modules to find relevant information. Further, when an instructor uses unfamiliar terms in digital learning materials that do not align with a learner’s mental model or the different web pages in a learning module are not consistently designed, the learner must exert additional effort toward understanding the materials. Another example of extraneous cognitive load is when a learner does not know how to progress in a digital learning environment, resulting in an interruption of learning flow. Although there are many other examples, each depicts how poor usability taxes finite cognitive resources. Creating highly usable digital environments for learning can help reduce extraneous cognitive load and allow mental resources to remain focused on germane cognitive load for building schemas (Sweller et al., 1998).

Distributed Cognition and Activity Theory

While CLT helps describe the individual experience of user actions and interactions, other theories and models focus on broader conceptualizations of HCI. Among the most prominent are distributed cognition and activity theory, which take into account the social context of learning and introduce the role of collaboration between various individuals. Distributed cognition suggests that knowledge is present both within the mind of an individual and across artifacts (Hollan et al., 2000). The theory focuses on the understanding of the coordination “among individuals and artifacts, that is, to understand how individual agents align and share within a distributed process” (Nardi, 1996, p. 39). From the perspective of LIDT, individual agents (e.g., learners, instructors) operate within a distributed process of learning, as facilitated by various artifacts (such as content, messages, and media). The distributed process of learning is mediated by intentional interaction and communication with learning technologies (e.g., learning management systems, web conferencing platforms) in pursuit of learning objectives (Boland et al., 1994; Vasiliou et al., 2014). For example, two learners collaborating on a pair of programming problems might write pseudo-code and input comments into a text editor. In this case, distributed cognition is evident in collaborating on the programming problem and by conceptualizing various solutions mentally but also by using a tool (the text editor) to extend their memory. Cognition in this case is distributed between people and tools; distributed cognition, therefore, would focus on the function of the tool within the broader learning context (Michaelian & Sutton, 2013). In contrast with the more narrow perspective of CLT that considers the degree to which a specific learner’s limited cognitive resources are affected when interacting with a technology system, distributed cognition adopts a broader cognitive, social, and organizational perspective (Rogers & Ellis, 1994).

Activity theory, on the other hand, is a systems-based, ecological framework that that shares some similarities with distributed cognition but distinguishes itself in its specific focus on activity and the dynamic interaction of actors, artifacts, and sociocultural factors within an interconnected system. Given its ecological lens, activity theory can be a useful framework for describing and understanding how a variety of factors can influence human activity. Central to activity theory is the concept of mediation. In activity theory, activity is mediated by tools, also called artifacts (Kaptelinin, 1996). From a technological perspective, the concept of tools is often in reference to digital tools or software. These technological tools mediate human activity within a goal-directed hierarchy of (a) activities, (b) actions, and (c) operations (Jonassen & Rohrer-Murphy, 1999). Firstly, activities describe the top-level objectives and fulfillment of motives (Kaptelinin et al., 1999). Secondly, actions are the more specific goal-directed processes and smaller tasks that must be completed in order to complete overarching activities. Thirdly, operations describe the automatic cognitive processes that group members complete (Engeström, 2000). However, operations do not maintain their own goals but are rather the unconscious adjustment of actions to the situation at hand (Kaptelinin et al., 1999). Engström’s (2000) sociocultural activity theory framework is commonly depicted as an interconnected system in the shape of a triangle, as depicted in Figure 3.

Activity theory is especially helpful for LXD because it provides a framework to understand how objectives are completed within a learning context. Nardi (1996) highlights the centrality to activity theory of mediation via tools/artifacts. These artifacts are created by individuals to control their own behavior and can manifest in the form of instruments, languages, or technology. Each carries a particular culture and history that stretches across time and space (Kaptelinin et al., 1999) and serves to represent ways in which others have solved similar problems. As applied to learning contexts, activity theory suggests that tools not only mediate the learning experience but that learning processes are often altered to accommodate the new tools (Jonassen & Rohrer-Murphy, 1999).

This belief in the role tools play in learning processes and experiences underscores the importance of considering the influence of novel learning technologies (e.g., LMSs, educational video games) from within a broader context of social activity when implemented by schools and/or organizations (Ackerman, 2000). The technological tools instituted in a particular workgroup should not radically change work processes but should present solutions on the basis of needs, constraints, history, etc. of that workgroup (Barab et al., 2002; Yamagata-Lynch et al., 2015). As learning is increasingly collaborative through technology (particularly online learning), activity theory and distributed cognition can provide important insights for learning designers into the broader sociocultural aspects of HCI.

Learning Experience Design Techniques

The brief overview of theoretical foundations provided in the previous sections highlights how theories of cognition and human activity in sociocultural contexts can be useful in the design of digital learning experiences. However, the question remains as to how one designs highly usable, engaging, and effective digital learning experiences on the basis of these theories. Answering this question is difficult because these theories are not prescriptive. Specific guidance for how they can be applied is lacking, meaning that how best to design theoretically inspired, highly usable, and pleasing learning environments is ultimately the prerogative of the designer. Iterative design approaches can be useful for confronting this challenge.

While the field of LIDT has recently begun to shift its focus to more iterative design and user-driven development models, there is a need to more intentionally bridge learning design and user experience design approaches to support effective, efficient, and satisfying learning experiences in digital environments. To this end, a number of existing learning design methods can be used or adapted to fit iterative approaches. For example, identifying learning needs has long been the focus of a front-end analysis. Ideation and prototyping are frequently used methods from UX design, and rapid prototyping (see Tripp & Bichelmeyer, 1990) is a typical design process. In addition, evaluation in learning design has a rich history of formative and summative methods. By applying these specific design methods within iterative design processes, learning experience designers can advance their designs in such a way that they can focus not only on intended learning outcomes but also on the learning experience and usability of their designs. 

In the following sections, LXD techniques are considered for incorporation into one’s learning design processes through (1) identifying user needs, (2) project requirements gathering, and (3) prototyping.

Developing Project Requirements Based on Learners’ Needs

One potential pitfall of any design process is when designers create systems based on assumptions of what users want. Only after designers have begun to understand the user should they begin to identify what capabilities or conditions a system must be able to support to meet the identified needs. These capabilities or conditions are known as “requirements.” The process a designer undertakes to identify these requirements is known as "requirements gathering." Generally, requirements gathering involves gathering and analyzing user data (e.g., surveys, focus groups, interviews, observations) and assessing user needs (Sleezer et al., 2014).

In the field of LIDT, assessing learner needs often begins with identification of a gap (the need) between actual performance and optimal performance (Rossett, 1987; Rossett & Sheldon, 2001). Needs and performance can then be further analyzed and learning interventions can be designed to address those needs. Assessing user (and learner) needs can yield important information about performance gaps and other problems. However, knowledge of needs alone is insufficient to design highly usable and satisfying learning environments. Further detail is needed regarding the specific context of use for a given tool or system. Context is defined by the learners (and others who will use the tool or system such as administrators or instructors), the tasks (what will learners do with the tool or system), and the environment (the local context in which learners use the tool or system).

Based on identified learner needs, a set of requirements is generated to define what system capabilities must be developed to meet those needs. Requirements are not just obtained for one set of learners, but for all learner types and personas (including instructors and administrators) that might utilize the system. Data-based requirements help learning designers avoid the pitfall of applying ready-made solutions to assumed learner needs, but instead position the learner and learner needs centrally in the design process, allowing for creation of design guidelines targeting an array of various learner needs.

Requirements based on learner data are therefore more promising in supporting a positive learning experience. However, given the iterative nature of UXD, requirements might change as a design evolves. Shifts in requirements vary depending on design, associated evaluation outcomes, and contextual considerations. Two methods commonly used in UXD for establishing requirements are persona and scenario development. Personas provide a detailed description of a fictional user whose characteristics represent a specific user group—thus helping designers approach design based on the perspective of the user. Meanwhile scenarios situate the learner in an authentic context by presenting narratives that describe user activity in an informal story format (Carroll, 2000).

Prototyping Digital Learning Experiences

Gathering data and designing and developing digital learning experiences is an iterative process. Based on personas and identified requirements, an initial prototype of the user interface or the online learning environment will be created. Prototyping is central to learning experience design practice and tends to follow a trajectory of development over time from low fidelity to high fidelity (Walker et al., 2002). Fidelity refers to the degree of precision, attention to detail, and functionality of a prototype. A LXD designer progresses in prototype levels towards greater and higher fidelity, testing each prototype with learners. Examples of the range of prototyping encouraged include: 

• Low-fidelity prototypes, which include the proverbial “sketch on a napkin” and paper prototypes, which can then be annotated/enhanced with digital tools These are typically evaluated through peer and expert review of the prototype.
• Medium-fidelity prototypes, such as wireframes, that visually convey structure but lack the functionality and visual elements of high-fidelity prototypes. Wireframing commonly occurs early in the design process after paper prototyping and allows designers to focus on things that paper prototyping does not, such as layout of content. These prototypes are typically evaluated through testing/feedback with small groups of target learners.
• High-fidelity prototypes, which can include non-functional “dummy” graphical mockups of interfaces and interfaces with limited functionality that allow for more refined user evaluation with target learners or can represent a full manifestation of a design. These can be evaluated through field testing, heuristic evaluation using established heuristic guidelines, and learner feedback from usage tests.

Typically, lower fidelity prototypes do not take much time to develop, and higher fidelity prototypes take longer because prototypes become more difficult to change as more details and features are added. Prototyping is a crucial skill for all learning experience designers, including those who create online courses by arranging various content, media, and interactive experiences to those who develop educational software such as educational video games or mobile apps.

To reiterate, the goal of UCD is to approach systems development from the perspective of the end-user. Using tools such as personas and prototypes, the learning design process becomes iterative, dynamic, and more responsive to learner needs. Learning designers often use these tools in conjunction with a variety of evaluation methods [LINK HERE] to better align prototype interface designs with learners’ mental models, thereby reducing cognitive load and improving usability.

Conclusion

As digital tools for learning have gained in popularity, there is a rich body of literature that has focused on designing learning experiences with and through these tools. Indeed, a variety of principles and theories (e.g., cognitive load theory, distributed cognition, activity theory) provide valuable insight to situate the learning design process. In this chapter, we have illustrated how the fields of HCI and UX intersect with the field of LIDT and have provided specific examples of how theories from within and outside the field of LIDT influence learning experience design. Moreover, we have provided a brief description of iterative design processes that can be employed to advance usable and pleasing learning designs. A design approach that connects the principles of UXD and HCI with theories and processes of LXD can help ensure that digital environments for learning are constructed to support learners’ achievement of their learning goals in ways that are effective, efficient, and satisfying.

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