Jan L. Plass

Our goal in this chapter is to take a conceptual look at personalization and ask, (1) Why should we pursue the design of personalized learning systems? Here, we will describe the benefits of personalization and will argue that the alternative to personalization, i.e., a one-size-fits-all approach, is no longer tenable in our current society. We will further ask, (2) Which sources and aims for personalization should be considered? Here, we will argue that the current focus on a narrow range of cognitive variables as sources for personalization, and cognitive aims of its impact, means we are missing out on other important opportunities to individually support learners by also considering affective, motivational, and socio-cultural factors as a basis for adaptivity. We then ask, (3) Which methods are used for personalization? Here, we discuss different approaches to personalization, their advantages, and disadvantages. We next turn to the question, (4) What trends exist in personalization? Here, we will discuss how generative AI, extended reality, and playfulness may result in broader opportunities for personalization. Finally, we will ask, (5) What can go wrong with personalization? Here, we will explore concerns related to equity and agency in personalization and discuss how to address them.

This chapter presents an integrated, goal-driven approach to cognitive load theory (CLT) as a first proposed revision to the theory. This approach recognizes that the classification of cognitive load as intrinsic or extraneous depends on instructional goals. It introduces the idea that activities aimed at motivation, engagement, or prior knowledge activation may generate cognitive load that is intrinsic to these objectives rather than extraneous. A three-level taxonomy of instructional goals highlights the need to integrate cognitive, motivational, and affective perspectives. Using simulations and game-based learning as examples, the chapter explores how different goals influence cognitive load, demonstrating the necessity of incorporating emotional and motivational elements into CLT. This expanded perspective ensures that CLT will remain relevant for modern instructional environments, supporting diverse learning objectives beyond traditional schema acquisition.

This chapter summarizes the modifications that have been made to cognitive load theory (CLT) to date. One such modification was the introduction of germane cognitive load as a third source of load to account for cognitive effort that enhances learning. However, differentiating germane from intrinsic load proved conceptually challenging, leading to the return to a dual-model framework distinguishing only intrinsic and extraneous load. This shift redefined intrinsic load to encompass all essential cognitive processes required for learning, rendering the concept of germane load redundant. A refined understanding incorporates motivation and engagement, recognizing that cognitive load is influenced by learner emotions and instructional goals. Integrating these factors ensures a more comprehensive framework that better addresses the design of complex learning tasks, aligning theory with contemporary educational research and practice. Another reviewed modification of CLT was the use of an evolutionary approach to ground human cognition in natural information processing systems.

This final chapter draws conclusions from the proposed revisions to cognitive load theory (CLT). It summarizes the evolution of CLT and the proposed revisions to better accommodate complex learning environments, integrating motivation, affect, and goal-driven strategies. It highlights three major shifts: adopting an interval view of cognitive load focused on specific goals and related activities, incorporating motivational factors to engage learners, and integrating affective elements recognizing the interplay between emotion and cognition. It describes how the reconceptualized CLT emphasizes microlevel cognitive processes, providing an approach to enhancing the precision of cognitive load measurement. It advocates for learner-centered, adaptive instruction tailored to various expertise levels, aligning learning tasks with specific cognitive, emotional, and motivational goals. The chapter calls for future research on optimal instructional sequences, real-time cognitive load assessment, and adaptive learning environments. This holistic framework transcends traditional cognitive efficiency models, offering a more nuanced approach to designing effective and engaging educational experiences that address diverse learner needs.

This chapter describes two additional proposed revisions to cognitive load theory (CLT) that expand on the goal-driven approach—the introduction of affective goals and motivational goals. After defining each of these constructs, the chapter discusses empirical evidence supporting the need to consider these goals in the context of CLT. It introduces a view of CLT that, starting with the learning goal, identifies specific subactivities of learning which, based on learner characteristics such as prior knowledge, level of motivation, and experienced emotion, determine whether specific instructional materials are considered intrinsic or whether they increase or decrease a learner’s extraneous cognitive load. The chapter redefines intrinsic and extraneous cognitive load to reflect this revised view of CLT.

This chapter reviews criticisms that cognitive load theory (CLT) has faced regarding its definitions, measurement methods, and limited consideration of motivation and emotion. Scholars have argued that intrinsic, extraneous, and germane load distinctions are conceptually unclear and often applied post hoc. Measurement challenges persist, as different types of cognitive load are difficult to assess separately in real time. Additionally, CLT’s traditional exclusive focus on schema acquisition neglects other instructional goals, such as motivation, engagement, and self-regulation. Critics suggest integrating cognitive, motivational, and affective processes, as seen in the cognitive-affective theory of learning with media and the integrated cognitive-affective learning model. The chapter suggests that to be better aligned with contemporary educational research and practice and be more broadly applicable across diverse modern learning environments, a revised CLT framework should incorporate goal-specific learner activities, an interval-based cognitive load perspective, and expanded definitions that consider time-dependent variations in cognitive processing.