Cognitive load theory was introduced in the 1980s as an instructional design theory based on several uncontroversial aspects of human cognitive architecture. Our knowledge of many of the ...characteristics of working memory, long-term memory and the relations between them had been well-established for many decades prior to the introduction of the theory. Curiously, this knowledge had had a limited impact on the field of instructional design with most instructional design recommendations proceeding as though working memory and long-term memory did not exist. In contrast, cognitive load theory emphasised that all novel information first is processed by a capacity and duration limited working memory and then stored in an unlimited long-term memory for later use. Once information is stored in long-term memory, the capacity and duration limits of working memory disappear transforming our ability to function. By the late 1990s, sufficient data had been collected using the theory to warrant an extended analysis resulting in the publication of Sweller et al. {Educational Psychology Review, 10, 251-296, 1998). Extensive further theoretical and empirical work have been carried out since that time and this paper is an attempt to summarise the last 20 years of cognitive load theory and to sketch directions for future research.
According to cognitive load theory, instructions can impose three types of cognitive load on the learner: intrinsic load, extraneous load, and germane load. Proper measurement of the different types ...of cognitive load can help us understand why the effectiveness and efficiency of learning environments may differ as a function of instructional formats and learner characteristics. In this article, we present a ten-item instrument for the measurement of the three types of cognitive load. Principal component analysis on data from a lecture in statistics for PhD students (
n
= 56) in psychology and health sciences revealed a three-component solution, consistent with the types of load that the different items were intended to measure. This solution was confirmed by a confirmatory factor analysis of data from three lectures in statistics for different cohorts of bachelor students in the social and health sciences (
n
s = 171, 136, and 148), and received further support from a randomized experiment with university freshmen in the health sciences (
n
= 58).
Cognitive-load researchers attempt to engineer the instructional control of cognitive load by designing methods that substitute productive for unproductive cognitive load. This article highlights ...proven and new methods to achieve this instructional control by focusing on the cognitive architecture used by cognitive-load theory and aspects of the learning task, the learner, and the learning environment.
Although the theoretical framework of cognitive load theory has acknowledged a role for the learning environment, the specific characteristics of the physical learning environment that could affect ...cognitive load have never been considered, neither theoretically nor empirically. In this article, we argue that the physical learning environment, and more specifically its effects on cognitive load, can be regarded as a determinant of the effectiveness of instruction. We present an updated version of the cognitive load model of Paas and Van Merriënboer (Educational Psychology Review, 6:351-371, 1994a), in which the physical learning environment is considered a distinct causal factor that can interact with learner characteristics, learning-task characteristics, or a combination of both. Previous research into effects of the physical learning environment on cognitive performance that could inspire new cognitive load research is discussed, and a future research agenda is sketched.
A sample of 33 experiments was extracted from the Web-of-Science database over a 5-year period (2016–2020) that used physiological measures to measure intrinsic cognitive load. Only studies that ...required participants to solve tasks of varying complexities using a within-subjects design were included. The sample identified a number of different physiological measures obtained by recording signals from four main body categories (heart and lungs, eyes, skin, and brain), as well as subjective measures. The overall validity of the measures was assessed by examining construct validity and sensitivity. It was found that the vast majority of physiological measures had some level of validity, but varied considerably in sensitivity to detect subtle changes in intrinsic cognitive load. Validity was also influenced by the type of task. Eye-measures were found to be the most sensitive followed by the heart and lungs, skin, and brain. However, subjective measures had the highest levels of validity. It is concluded that a combination of physiological and subjective measures is most effective in detecting changes in intrinsic cognitive load.
Research has shown that taking ‘timeouts’ in medical practice improves performance and patient safety. However, the benefits of taking timeouts, or pausing, are not sufficiently acknowledged in ...workplaces and training programmes. To promote this acknowledgement, we suggest a systematic conceptualisation of the medical pause, focusing on its importance, processes and implementation in training programmes. By employing insights from educational and cognitive psychology, we first identified pausing as an important skill to interrupt negative momentum and bolster learning. Subsequently, we categorised constituent cognitive processes for pausing skills into two phases: the decision‐making phase (determining when and how to take pauses) and the executive phase (applying relaxation or reflection during pauses). We present a model that describes how relaxation and reflection during pauses can optimise cognitive load in performance. Several strategies to implement pause training in medical curricula are proposed: intertwining pause training with training of primary skills, providing second‐order scaffolding through shared control and employing auxiliary tools such as computer‐based simulations with a pause function.
Lee et al. suggest a systematic conceptualization of the medical pause, focusing on its importance, processes, and implementation in training programs.
In the past 50 years, the original McMaster PBL model has been implemented, experimented, revised, and modified, and is still evolving. Yet, the development of PBL is not a series of success stories, ...but rather a journey of experiments, failures and lessons learned. In this paper, we analyzed the meta-analyses and systematic reviews on PBL from 1992 to present as they provide a focused lens on the PBL research in the past 5 decades. We identified three major waves in the PBL research development, analyzed their impact on PBL research and practice, and offered suggestions of research gaps and future directions for the field. The first wave of PBL research (polarization: 1990–mid 2000) focused on answering the question “Does PBL work?” and the outcomes. The results were conflicting. The researchers took polarizing positions and debated over the merits of PBL throughout this wave. However, the contradictory results and the debates in fact pushed the researchers to look harder for new directions to solve the puzzle. These efforts resulted in the second wave (from outcomes to process: mid 2000–mid 2010) that focused on the question “How does PBL work?” The second wave of PBL research targeted at investigating the effects of implementation constituents, such as assessment formats or single versus curriculum wide implementations. The third wave (specialization: mid 2010 and onward) of PBL research focused on “How does PBL work in different specific contexts?” These research widened our perspectives by expanding our understanding of how PBL manifests itself in different contexts. Given the diversification of PBL and more hybrid PBL models, we suggest “Why does PBL with particular implementation characteristics for specific outcomes work or not work in the condition where it is implemented?” to be the question to answer in the next wave of PBL research.
Although self-regulated learning (SRL) is becoming increasingly important in modern educational contexts, disagreements exist regarding its measurement. One particularly important issue is whether ...self-reports represent valid ways to measure this process. Several researchers have advocated the use of behavioral indicators of SRL instead. An outstanding research debate concerns the extent to which it is possible to compare behavioral measures of SRL to traditional ways of measuring SRL using self-report questionnaire data, and which of these methods provides the most valid and reliable indicator of SRL. The current review investigates this question. It was found that granularity is an important concept in the comparison of SRL measurements, influencing the degree to which students can accurately report on their use of SRL strategies. The results show that self-report questionnaires may give a relatively accurate insight into students’ global level of self-regulation, giving them their own value in educational research and remediation. In contrast, when students are asked to report on specific SRL strategies, behavioral measures give a more accurate account. First and foremost, researchers and practitioners must have a clear idea about their research question or problem statement, before choosing or combining either form of measurement.
Summary
Students' ability to accurately self‐assess their performance and select a suitable subsequent learning task in response is imperative for effective self‐regulated learning. Video modeling ...examples have proven effective for training self‐assessment and task‐selection skills, and—importantly—such training fostered self‐regulated learning outcomes. It is unclear, however, whether trained skills would transfer across domains. We investigated whether skills acquired from training with either a specific, algorithmic task‐selection rule or a more general heuristic task‐selection rule in biology would transfer to self‐regulated learning in math. A manipulation check performed after the training confirmed that both algorithmic and heuristic training improved task‐selection skills on the biology problems compared with the control condition. However, we found no evidence that students subsequently applied the acquired skills during self‐regulated learning in math. Future research should investigate how to support transfer of task‐selection skills across domains.
This article takes a critical look at three pervasive urban legends in education about the nature of learners, learning, and teaching and looks at what educational and psychological research has to ...say about them. The three legends can be seen as variations on one central theme, namely, that it is the learner who knows best and that she or he should be the controlling force in her or his learning. The first legend is one of learners as digital natives who form a generation of students knowing by nature how to learn from new media, and for whom "old" media and methods used in teaching/learning no longer work. The second legend is the widespread belief that learners have specific learning styles and that education should be individualized to the extent that the pedagogy of teaching/learning is matched to the preferred style of the learner. The final legend is that learners ought to be seen as self-educators who should be given maximum control over what they are learning and their learning trajectory. It concludes with a possible reason why these legends have taken hold, are so pervasive, and are so difficult to eradicate.
PDF
