Cognitive Load Theory (CLT) builds upon established models of human memory that include the subsystems of sensory, working and long-term memory. Working memory (WM) can only process a limited number of information elements at any given time. This constraint creates a "bottleneck" for learning. CLT identifies three types of cognitive load that impact WM: intrinsic load (associated with performing essential aspects of the task), extraneous load (associated with non-essential aspects of the task) and germane load (associated with the deliberate use of cognitive strategies that facilitate learning). When the cognitive load associated with a task exceeds the learner's WM capacity, performance and learning is impaired. To facilitate learning, CLT researchers have developed instructional techniques that decrease extraneous load (e.g. worked examples), titrate intrinsic load to the developmental stage of the learner (e.g. simplify task without decontextualizing) and ensure that unused WM capacity is dedicated to germane load, i.e. cognitive learning strategies. A number of instructional techniques have been empirically tested. As learners' progress, curricula must also attend to the expertise-reversal effect. Instructional techniques that facilitate learning among early learners may not help and may even interfere with learning among more advanced learners. CLT has particular relevance to medical education because many of the professional activities to be learned require the simultaneous integration of multiple and varied sets of knowledge, skills and behaviors at a specific time and place. These activities possess high "element interactivity" and therefore impose a cognitive load that may surpass the WM capacity of the learner. Applications to various medical education settings (classroom, workplace and self-directed learning) are explored.

Complex learning aims at the integration of knowledge, skills, and attitudes; the coordination of qualitatively different constituent skills; and the transfer of what is learned to daily life or work settings. Recent instructional theories stress authentic learning tasks as the driving force for learning; but due to the complexity of those tasks, learning may be hampered by the limited processing capacity of the human mind. In this article we present a framework for scaffolding practice and just-in-time information presentation, aiming to control cognitive load effectively. We briefly describe a design model for complex learning consistent with cognitive load theory. Theoretical and practical implications of the presented framework are discussed.Recent instructional theories tend to focus on authentic learning tasks that are based on real-life tasks as the driving force for learning (Merrill, 2002; Reigeluth, 1999a;. The general assumption is that such tasks help learners to integrate the knowledge, skills, and attitudes necessary for effective task performance; give them the opportunity to learn to coordinate constituent skills that make up complex task performance; and eventually enable them to transfer what is learned to their daily life or work settings. This focus on authentic, whole tasks can be found in practical educational approaches, such as project-based education, the case method, problem-based learning, and competency-based learning; and in theoretical models, such as Collins, Brown, and Newman's (1989) theory of cognitive apprenticeship learning, Jonassen's (1999) theory of constructive learning environments, Nelson's (1999) theory of collaborative problem solving, and Schank, Berman, and MacPerson's (1999) theory of goal-based scenario.A severe risk of all of these approaches is that learners have difficulties learning because they are overwhelmed by the task complexity. The aim of this article is to discuss managing cognitive load when rich learning tasks are used in education. First, methods for scaffolding whole-task practice are discussed, including simple-to-complex sequencing of learning tasks and the use of alternative tasks, such as worked-out examples and completion tasks. Second, methods for just-in-time information presentation are discussed, including timely presentation of information to support practice on learning tasks and the direct, step-by-step presentation of procedural information. Third, we briefly sketch an instructional design model for complex learning fully consistent with cognitive load theory (CLT). We conclude that CLT offers useful guidelines for decreasing intrinsic and extraneous cognitive load, so that sufficient processing capacity is left for genuine learning.

Despite the popularity of peer assessment (PA), gaps in the literature make it difficult to describe exactly what constitutes effective PA. In a literature review, we divided PA into variables and then investigated their interrelatedness. We found that (a) PA's psychometric qualities are improved by the training and experience of peer assessors; (b) the development of domain-specific skills benefits from PA-based revision; (c) the development of PA skills benefits from training and is related to students' thinking style and academic achievement, and (d) student attitudes towards PA are positively influenced by training and experience. We conclude with recommendations for future research.

Motivation can be identified as a dimension that determines learning success and causes the high dropout rate among online learners, especially in complex e-learning environments. It is argued that these learning environments represent a new challenge to cognitive load researchers to investigate the motivational effects of instructional conditions and help instructional designers to predict which instructional configurations will maximize learning and transfer. Consistent with the efficiency perspective introduced by Paas and Van Merriënboer (1993), an alternative motivational perspective of the relation between mental effort and performance is presented. We propose a procedure to compute and visualize the differential effects of instructional conditions on learner motivation and illustrate this procedure on the basis of an existing data set. Theoretical and practical implications of the motivational perspective are discussed.

In this paper we advocate for a joining of forces. By combining elements of PBL and TBL, we could create varied instructional approaches that are in keeping with current instructional design principles, thereby combining the best of both worlds to optimize student learning.