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    Klik hier om een link te hebben waarmee u dit artikel later terug kunt lezen.Beperken werkgeheugenbelasting als belangrijk instructieprincipe: tips & onderzoek
    Beperken van belasting van het werkgeheugen als belangrijk instructie-principe (=ook centraal principe in onze vakdidactische publicaties over leren lezen -DSM, rekenen ...)

    Educators would do well to recognise the motivating and engaging properties of clear, structured and well guided instruction, and the role this plays in students’ learning and achievemement

    LRI involves the following at some point in the learning and
    achievement process:
    (1) Reducing the difficulty of a task during initial learning
    (2) Instructional support and scaffolding through the task
    (3) Ample structured practice
    (4) Appropriate provision of instructional feedback, and
    (5) Independent practice and guided autonomy.

    These represent a useful organising frame- work for considering key elements of LRI.Here, these elements are briefly introduced.

    (1) Reducing the difficulty of a task during initial learning


    ■Teacher provides early instruction on the core elements of a task (e.g.identifying name, definition, location,function of topics or components) to assist subsequent learning

    Modelling important processes
    ■Teacher demonstrates how to complete a task; can also involve‘think-aloud’ strategies as the teacher conducts a task
    ■Teacher shares examples of good practices and good work to provide clarity on what constitutes good workand how to do it

    ■Teacher breaks a task into ‘bite-size’ components (or ‘chunks’) and encourages students to see the completion of each component as a success

    Preliminary (and spaced) reviews
    ■Teacher and students review prior learning at the outset of a new task or lesson; teacher reviews at regular (spaced) intervals (e.g. review prior week’s learning at the start of each week)

    (2) Instructional support and scaffolding through the task

    Reducing split-attention

    ■Two or more stimuli are integrated where feasible to reduce splitting students’ attention across disparate stimuli (e.g. integrate the equation for finding an angle into the angle itself on a given diagram)

    ■Teacher integrates the focus of a learning task with a meaningful problem (e.g. integrate instruction on punctuation into a student’s own essay)

    Information integration sequencing
    ■Teacher integrates two successive pieces of instructional material into the one instructional element (e.g. integrate the narration of how lightening is formed with an animation of that process)

    Harnessing different modalities
    ■Teacher presents different pieces of information (or stimuli) in a different modality (e.g. present an image with a narrative in order to reduce the burden on visual and auditory processers)

    Avoiding redundancy and increasing coherence
    ■Where possible, teacher presents information once (avoiding redundancy) and organises material so that extraneous or overly elaborate material that may be tangential to essential learning is reduced or removed (increasing coherence)

    ■Teacher provides cues to help the learner locate and focus on the essential material in a lesson or activity (e.g. teacher asks students to watch out for a particular event or character in a plot)

    Organising information thematically
    ■Teacher identifies a major/main theme in a task or learning activity and explicitly connects instruction to this theme

    Allowing appropriate instructional time
    ■Teacher schedules tasks and lessons to ensure sufficient instructional time occurs in a task, in a lesson, and across the day

    Checking for understanding
    ■Teacher employs checking strategies such as frequently posing questions and asking students to summarise major points or repeat explanations

    Worked examples (uitgewerkte voorbeelden)
    ■New material is presented to learners with completed samples of work that show how a particular problem can be solved or task is to be completed

    Providing Templates
    ■Materials are provided to learners that are formatted or structured to help the learner stay on track or that list the important features to include or address in a task

    ■Learners are strategically prompted to persist with and complete less structured tasks such as those found in comprehension and writing tasks (e.g. students are asked to identify the ‘what’, ‘who’, ‘why’, and ‘when’ in a

    Using Load Reduction Instruction (LRI) to boost motivation and engagementstimulus passage; this helps them extract specific information or articulate an answer or response)


    ■Teacher adjusts wording and/or administration of a task to involve the learner in a more personalised and individually-relevant way (e.g. Use instructions such as ‘Your goal in thistask is to …’ rather than ‘The goal for this task is to …’)

    (3) Ample structured practice

    Deliberate practice

    ■Teacher ensures rehearsal that is relevant to a specific skill, usually also involving feedback, and conducted by the student on his/her own

    Mental practice
    ■Learners imagine or mentally rehearse a concept or procedure (e.g. the student studies an example, then turns away and rehearses the example in his/her mind)

    Guided practice
    ■Learners are systematically guided through the steps of learning or problem solving (e.g. prompting responses through a task or providing part of a solution for a student to complete)

    (4) Appropriate provision of instructional feedback

    ■Concrete and specific information is provided on the correctness of an answer or the quality of application

    ■Concrete and specific information is provided on how the answer or quality of the application can be improved in future schoolwork

    (5) Independent practice and guided autonomy

    Independent practice
    ■When skills and knowledge become automated and fluent, the learner is encouraged to attempt similar problem tasks independently

    Guided discovery learning
    ■When the learner has engaged in successful independent practice, he/she is encouraged to undertake new tasks, move in new directions, or apply learning to ‘real-world’ problems that further enrich learning.



    Can educators reduce students’ cognitive load and boost motivation and engagement? Integrating explicit instruction and discovery learning through Load Reduction Instruction (LRI) Andrew J. Martin School of Education University of New South Wales, Australia

    The Psychology of Education Section of The British Psychological
    2 35th Vernon-Wall Lecture

    Vooraf: CONCLUSION The bulk of research into instructional techniques that directly or indirectly reduce cognitive load (i.e. Load Reduction Instruction; LRI) has focused on academic learning and achievement. Findings support the role of LRI in students’ learning and achievement gains. Less attention has been given to the role of LRI in promoting students’ motivation and engagement.

    The present review has harnessed motivation and engagement as a lens through which to consider LRI. It has examined key dimensions of motivation and engagement and explored the extent to which specific approaches and strategies under LRI address them.
    The review has also considered the learning process more broadly and highlighted the role of guided discovery approaches in the learning sequence to appropriately manage cognitive load and generate greater autonomy and independent learning. Thus, it is emphasised that LRI encompasses both explicit instructional approaches and guided discovery-oriented learning – and that this has significant implications for students’ academic motivation and engagement.

    Taken together, educators would do well to recognise the motivating and engaging properties of clear, structured and well guided instruction, and the role this plays in students’ learning and achievement.


    School is academically demanding and becomes more so as students move from elementary school to middle school to high school. Across these stages of schooling (and year levels within them), there is an escalation in homework, frequency and difficulty of assessment, content to be covered, subject difficulty, and competing deadlines

    This progressive escalation in challenge places increased cognitive demands on students. At the same time, there are well-documented declines in motivation and engagement as students move from elementary to and through high school. For example, Eccles and colleagues have identified significant

    Can educators reduce students’ cognitive load and boost motivation and engagement? Integrating explicit instruction and discovery learning through Load Reduction Instruction (LRI) Andrew J. MartinLoad Reduction Instruction(LRI) is an umbrella term referring to instructional approaches that seek to reduce cognitive load in order to optimise students’ learning and achievement.

    LRI typically encompasses explicit and direct instruction, and under particular conditions can also encompass less structured approaches such as guided discovery-, problem-, and inquiry-based learning. Theory and research support the role of LRI in students’ academic learning and achievement. Relatively less attention has been given to the role of LRI in students’ academic motivation and engagement. This review examines key dimensions of motivation and engagement and explores the extent to which specific approaches and strategies under LRI may promote them.

    A major tenet of the review is that students are at first novices with respect to academic skill and subject matter and that a structured and somewhat directional approach to instruction that reduces cognitive load is important for achievement, motivation, and engagement in the early stages of learning. LRI helps build the content of long-term memory and develops a level of fluency and automaticity that frees up working memory to apply to a given task or problem. As discussed, this fluency and automaticity has implications for students’ motivation and engagement. Importantly, as core skill, knowledge and automaticity further develop, LRI emphasises the centrality of guided discovery-, problem-, and inquiry-based learning. Introduced at the appropriate point in the learning process, these scaffolded exploratory approaches can also be a means to manage cognitive load, generate autonomous learning, and provide a further basis for students’ motivation and engagement. The review concludes by showing how these instructional practices that unambiguously emphasise the role of the teacher are in fact predominantly student-centered and student-salient. Taken together, it is considered important to recognise the motivating and engaging properties of clear, structured and well guided instruction, and the implications this has for students’ learning and achievement outcomes declines in academic expectancy and valuing between elementary and high school. Once in high school, Martin (2007, 2009) has shown that both motivation and engagement decline as students move from early high school to middle high school and that this follows from higher levels of motivation and engagement in elementary school. Eccles and Midgley (1989) proposed that motivation and engagement decline across the transition from elementary to middle/high school because the developmental needs of adolescents do not fit with the change of context and demands in high school – and nor do instructional approaches adequately meet the needs of the developing learner.

    The escalation in demands through school brings into consideration the need to approach instruction in ways that appropriately manage the burden on learners where possible and feasible. Cognitive psychology has been informative in identifying instructional approaches that are directly geared to managing the cognitive load on students to better help them learn and achieve. This article considers numerous instructional approaches that explicitly or implicitly appropriately manage the cognitive burden on students as they learn. ‘Load Reduction Instruction’ (LRI) is introduced here as an umbrella term that encompasses instructional models such as direct instruction and explicit instruction – as well as some less structured approaches to instruction (e.g. guided discovery learning) –that seek to optimally manage the cognitive burden on students in order to enhance their learning and achievement. To date, the bulk of research into LRI approaches has focused on their effects for learning and achievement.

    As discussed in this review, findings support the role of LRI in generating learning and achievement gains. Although learning and achievement are desirable ends in themselves, there are other factors that are considered desirable academic ends. Motivation and engagement are two such factors salient on the psychoeducational landscape. Indeed, from a cognitive psychological perspective, motivation and engagement are recognised as important factors in more complex learning (e.g. Van Merrienboer & Sweller, 2005) and factors that can increase the cognitive resources devoted to a task (e.g. Paas, Renkl & Sweller, 2003).

    The present review considers the relationship between motivation, engagement, and LRI. It examines key dimensions of motivation and engagement and explores the extent to which specific approaches and strategies under LRI can address them. In so doing, it seeks to complement the large body of work into LRI and its achievement effects with closer consideration of its potential yields for students’ motivation and engagement.

    Part 1. Load Reduction Instruction:

    (i) definition and description of LRI, (ii) a review of human cognitive architecture as relevant to LRI, (iii) consideration of fluency and automaticity, (iv) a summary of LRI effects on achievement, (v) consideration of LRI for diverse learners and subject areas, and (vi) identification of specific Load Reduction Instructional elements.

    Part 2. Motivation and Engagement: (vii) definition and description of motivation and engagement and (viii) a motivation and engagement framework for considering LRI.

    Part 3. Load Reduction Instruction, Motivation, and Engagement: (ix) LRI approaches for specific motivation and engagement dimensions. Part 4. Load Reduction Instruction and the Broader Process of Learning: (x) the role of guided discovery learning and (xi) understanding the optimal learning sequence. Part 5. Looking Forward: (xii) Opportunities for future research in LRI, explicit instruction, motivation, and engagement.
    Load Reduction Instruction (LRI) Instruction that appropriately reduces or manages the cognitive load on the student in the learning process.

    Key elements (1) Reducing the difficulty of a task during initial learning (2) Instructional support and scaffolding through the task (3) Ample structured practice (4) Appropriate provision of instructional feedback (5) Independent practice and guided autonomy.
    Major instructional approaches Explicit instruction and guided discovery learning.

    Specific instructional strategies Pre-training; Modelling important processes; Showcasing; Segmenting; Preliminary (and spaced) reviews; Reducing split-attention; Integrating; Information integration sequencing; Harnessing different modalities; Avoiding redundancy; Increasing coherence; Signalling; Organising information thematically; Allowing appropriate instructional time; Checking for understanding; Worked examples; Providing templates; Prompting; Personalising; Deliberate practice; Mental practice; Guided practice; Feedback; Feedforward; Independent practice; Guided discovery learning.

    Academic outcomes Learning, achievement, motivation, engagement.
    Figure 1: Organising themes and processes for this review.
    PART 1: LOAD REDUCTION INSTRUCTION Load Reduction Instruction (and Guided Discovery Learning) Load Reduction Instruction (LRI) is defined here as a mode of teacher-led instruction that involves the following at key points in the learning process: (1) Reducing the difficulty of a task during initial learning (2) Instructional support and scaffolding through the task (3) Ample structured practice (4) Appropriate provision of instructional feedback, and (5) Independent practice and guided autonomy A major tenet of LRI is that learners are at first novices with respect to academic skill and subject matter, that a structured and systemic approach to instruction is important in the early stages of learning, and that there is an appropriate time for guided discovery and exploratory approaches as novices become more developed in their learning ). Indeed, guided discovery learning can be another means by which to manage cognitive load for the student in the learning process. Accordingly, attention will also be given to the role of guided discovery learning as a part of LRI. As discussed below, following sufficient explicit input, guided practice and demonstration of independent learning, there is an important place for guided discovery learning, including with regards to motivation and engagement (Liem & Martin, 2013; Martin, 2013). Once learners progress beyond novice status and have sufficiently automated core skills and knowledge, they are ready to engage in meaningful discovery and exploratory learning that have motivational properties beyond the motivational yields experienced through LRI. LRI thus recognises that explicit and constructivist learning and teaching are inextricably intertwined such that the effectiveness of one is reliant on the effectiveness of the other. Although other frameworks have recognised the roles of both explicit and discovery approaches LRI is distinct in that its emphasis is on reducing or managing the cognitive burden on students as they learn. LRI is thus termed, framed, and developed deliberately to indicate why we engage its various instructional elements – namely, to deliver instruction and instructional support so as to appropriately reduce and manage the cognitive burden on the learner.

    The cognitive architecture of the human mind: Working and long-term memory When developing instructional approaches for students, it is important to understand the cognitive parameters relevant to learning. The architecture of the human mind –and its memory systems –is one of the core foundations underpinning the rationale for LRI approaches. This has implications for the development and delivery of LRI –as well as the ordering and balancing of explicit instruction and guided discovery learning. Working and long-term memory are primary mechanisms for learning (Kirschner, Sweller & Clark, 2006; Sweller, 2012; Winne & Nesbit, 2010). Working memory refers to the conscious component of cognition responsible for receiving and processing information, performing tasks, solving problems, etc. – particularly new information, new tasks, and novel problems. Learning is believed to occur when information is successfully moved from working memory and stored in longterm memory

    . Figure 2 shows the process, with stimuli received by the sensory register (e.g. sound, sight, touch etc.) sent to working memory, information in working memory is encoded and sent to longterm memory, and information in long-term memory is retrieved to working memory to be applied as necessary. If working memory is overly burdened or overloaded then there is a heightened risk that instructional content is not understood, information is misinterpreted or confused, information is not effectively encoded in longterm memory, and learning is markedly slowed down (Rosenshine, 1986, 2009; Tobias, 1982). Given this, there is a need to deliver instruction, present instructional material, and organise learning tasks that do not overly or unnecessarily burden students’ working memory (Kirschner et al., 2006). It is also the case that working memory is limited. Indeed, because a major function of working memory in the classroom is to process novel, unfamiliar information that comes from others (via listening, observing, or reading), working memory limits are highly relevant at many points of the learning process. This presents a substantial challenge to teachers as effective instruction relies on them navigating this limited conscious aspect of the cognitive structure (working memory) when teaching new material and presenting novel subject matter (Sweller, Ayres & Kalyuga, 2011; Winne & Nesbit, 2010). It has been speculated that information stored in working memory has a capacity of about seven elements (or even as low as four elements plus or minus one element). Further, this can be lost within about 30 seconds unless rehearsed (Baddeley, 1994). Clearly, a vast body of instructional material comprises information that exceeds seven (or so) elements or requires the student to be able to retain extended or complex concepts in conscious working memory for more than 30 seconds.
    This reality has led to research and theory into instructional approaches that aim to accommodate the boundary conditions inherent in learners’ working memory systems. Fortunately, long-term memory does not have the same limitations as working memory. Long-term memory has vast capacity. Thus, if information can be effectively and accurately stored in long-term memory and if working memory can efficiently access this long-term memory, successful learning can take place. Given this, there is a clear necessity to deliver instruction and develop instructional material that optimally assists the processing of information to long-term memory from working memory, the processing of information from long-term memory to working memory, and a working memory that is freed from unnecessary burden or load (Martin, 2015; Paas et al., 2003; Sweller, 2003, 2004; Winne & Nesbit, 2010). From a cognitive load perspective, learning thus very much relies on building long-term memory and effectively managing working memory to facilitate this (Kirschner et al., 2006; Sweller, 2012; Winne & Nesbit, 2010). According to Kirschner and colleagues: ‘Any instructional theory that ignores the limits of

    Figure 2: Process of sensory, working, and long-term memory.
    working memory when dealing with novel information or ignores the disappearance of those limits when dealing with familiar information is unlikely to be effective’ (2006, p.77). Indeed, cognitive load theorists suggest three goals for designing learning: reduce extraneous cognitive load, manage essential cognitive processing, and foster generative processing (Mayer, 2004; Mayer & Moreno, 2010; Moreno & Mayer, 2010). In all cases, it is recognised that cognitive capacity is limited and so it is important to reduce load on learners in order to facilitate the learning process. Notably, when dealing with familiar, organised information held in long-term memory, there are no known capacity or duration limits on working memory. Thus, students are transformed when information is transferred to long-term memory and this explains why education is transformative (Sweller, 2012).

    Fluency and automaticity According to Rosenshine (1986, 2009), fluency and automaticity are vital means of reducing the burden on working memory. This occurs when information is effectively stored in long-term memory and is accessed by working memory fluently and seemingly automatically. This frees up working memory that can then be used to process new information to long-term memory, to apply one’s learning, or for higher order thinking and guided discovery learning (Rosenshine, 1986, 2009). That is, as longterm memory builds and automaticity develops, the learner is ready for greater discovery, exploration, and inquiry approaches to instruction. The pedagogical approach traversing this process is herein referred to as LRI. Indeed, it is claimed that it is this automaticity that demarcates novice learners from expert learners. Expert learners derive and build their skill by drawing on the extensive information stored in long-term memory and quickly selecting and applying it to solve new problems (Kirschner et al., 2006). Accordingly, the aim of education is to increase the information held in long-term memory and this is achieved through instruction that optimises the capacity of working memory and long-term memory to process new information efficiently.

    Automaticity also demarcates the student who struggles academically from the student who does not (Martin, 2015). There are some students for whom working memory (or related executive functions) is impaired. These students are more likely to be cognitively overloaded than students without such impairments. Especially for these academically at-risk students, it is important that teachers implement instructional approaches that reduce the burden on working memory.

    Accommodating the boundary conditions of human cognitive architecture as relevant to learning thus relies on the teacher to structure learning material and learning activities in a way that reduces ambiguity, enhances clarity, builds in sequencing, and harnesses scaffolds. In so doing, the teacher manages the learning and instruction process in a way that optimises learner and learning efficiency. Notably, recent developments in cognitive psychology that have been applied to educational processes provide guidance on how material can be organised and presented to learners to free up working memory, optimise long-term memory, and enhance the processing of information from long-term to working memory – and in so doing, realise the aims of instruction intended to reduce the cognitive burden on students (Winne & Nesbit, 2010). However, as discussed below, as fluency and automaticity develop, the cognitive load inherent in instruction may be upwardly adjusted (e.g. via independent and guided discovery learning) to match the developing expertise of the learner.

    Load Reduction Instruction and evidence:

    Learning and achievement In numerous empirical studies, meta-analyses and reviews, the achievement-related merits of LRI approaches are evident (Cromley
    Byrnes, 2009; Lee & Anderson, 2013; Liem & Martin, 2013; Mayer, 2004). Across numerous subject domains and skill sets to be learned, LRI is positively associated with learning and/or achievement (e.g. see Cooper & Sweller, 1987; Klahr & Nigam, 2004; Matlen & Klahr, 2010; Strand-Cary & Klahr, 2008; Sweller & Cooper, 1985). In early work, Adams and Engelmann (1996) examined the effectiveness of major educational approaches (including those aligned with LRI) on numerous educational outcomes.

    Findings showed that explicit instruction, for example, yielded consistently positive effects on basic skills (e.g. word recognition, spelling, math computation) and cognitive skills (e.g. reading comprehension, math problem solving). Positive effects were also observed for motivational factors and affective outcomes (e.g. self-concept, attributions to success). In a meta-analysis by Haas (2005), the most effective method of teaching algebra was deemed to be explicit instruction. Its effectiveness was attributed to the focus on appropriate pacing and both guided and independent practice.

    Borman and colleagues (2003) conducted a meta-analysis of numerous school reform programs. They found that explicit instruction evinced the strongest systematic evidence of effectiveness. In a meta-analysis across 304 explicit (direct) instruction studies, Hattie (2009) ranked explicit instruction 26th out of 138 effects on achievement, placing it ‘among the most successful outcomes’ (p.205). Meta-analysis by Alfieri and colleagues (2011) showed that the specific techniques emphasised under LRI-oriented frameworks moderated the effects on achievement. For example, worked examples yielded the strongest results, followed by feedback, direct teaching, and explanations. When reviewing the range of meta-analyses conducted over the past two decades, Liem and Martin (2013) concluded that LRI approaches that allow teachers to be ‘activators’ of student learning (Hattie, 2009) are well placed to alleviate cognitive demands and assistworking memory and long-

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