2
How People Learn

Jeroen van Merriënboer

2.1 Introduction

Learning refers to the act, process, or experience of gaining knowledge, skills, and attitudes and as such, learning is inherent to all human life. People learn by doing, by working together, by exploring, by listening, by reading books, by studying examples, by being rewarded, by discovering, by making and testing predictions, by trial and error, by teaching, by abstracting away from concrete experiences, by observing others, by solving problems, by analyzing information, by repetition, by questioning, by paraphrasing information, by discussing, by seeing analogies, by making notes, and so forth. Learning is a broad container concept and this makes it very hard to answer the question “How do people learn?”

In order to make research on learning and instruction manageable, theories are typically developed within particular “paradigms of learning” (Van Merriënboer and De Bruin 2014) and/or they focus on particular domains of learning, such as models for declarative learning, emphasizing instructional methods for the construction of conceptual knowledge, models for procedural learning, emphasizing methods for acquiring skills, and models for affective learning, emphasizing methods for the formation of attitudes (Bloom 1956). This chapter takes a different stance. It starts from the basic assumption that all types of learning eventually lead to cognitive schemas in long-term memory, that is, patterns of thought or behavior that organize categories of information or actions and the relationships among them (Piaget1953). This assumption is based on a cognitive architecture for which ample support is provided in the literature (Sweller, Van Merriënboer, and Paas 1998; Van Merriënboer and Sweller 2005). It makes a distinction between learning processes that re/construct schemas (schema construction) and learning processes that automate these schemas (schema automation). Furthermore, it is assumed that people can often monitor and control their learning processes (i.e., self-regulated learning, or SRL).

Thus, the aim of this chapter is to discuss how people learn by constructing and automating cognitive schemas and how they regulate these processes; less attention is paid to particular views on how these learning processes are best supported. The structure of the remainder of this chapter is as follows. The second section describes the human cognitive architecture, including a description of induction and elaboration as basic learning processes that re/construct cognitive schemas, and knowledge compilation and strengthening as basic learning processes that automate these cognitive schemas. It also describes complex learning as a process where the four basic learning processes occur simultaneously, and it briefly describes four-component instructional design (4C/ID; Van Merriënboer 1997; Van Merriënboer, Clark, and De Croock 2002; Van Merriënboer, Jelsma, and Paas 1992; Van Merriënboer and Kirschner 2013) as an approach to support complex learning and to organize the use of media and technologies. The third section discusses SRL. The basic assumption is that people use cognitive cues to monitor their learning and to make control decisions (e.g., to restudy materials, to continue or stop practicing). These cues are different for the four basic learning processes and, unfortunately, learners often tend to use invalid cues. The concept of SRL is then further extended to include self-directed learning (SDL), where control decisions also concern the selection of new learning tasks and learning resources. The fourth and final section presents the main conclusions and raises issues for future research.

2.2 Human Cognitive Architecture and Learning Processes

A human cognitive architecture that is broadly accepted in the psychological literature and for which ample empirical support is available distinguishes a working memory with a very limited capacity when dealing with novel information from an effectively unlimited long-term memory. For learning to occur, novel information must be actively processed in working memory to construct new knowledge in long-term memory. This processing is heavily limited by the fact that only a few elements can be simultaneously active in working memory: about seven distinct elements that need to be stored (Miller 1956) or about two to four elements and their interactions if the elements need to be interrelated to each other (Cowan 2001). Furthermore, it is generally assumed that working memory can be subdivided into partially independent channels or processes. One channel consists of a phonological loop to deal with verbal material based on an auditory working memory; another channel consists of a visuospatial sketch pad to deal with diagrammatic or pictorial information based on a visual working memory. Using both the visual and auditory channels rather than either one channel alone increases the effective working memory capacity and thus facilitates learning (Mousavi, Low, and Sweller 1995).

Long-term memory alters the characteristics of working memory by reducing or even eliminating its limitations. Human expertise is thus the result of the availability of rich knowledge in long-term memory, not from an ability to engage in reasoning with many elements that yet need to be organized in long-term memory (the human mind simply does not allow for such many-elements processing). As indicated above, knowledge in long-term memory that reduces working memory limitations takes the form of cognitive schemas. Learning processes are either related to the construction of such schemas, including the formation of new schemas and the embellishment of existing schemas, or to the automation of these schemas. The next subsections briefly describe learning through schema construction and learning through schema automation.

2.2.1 The construction of cognitive schemas

Schema construction refers to the—often conscious and mindful—formation of an increasing number of ever more complex schemas by combining elements consisting of lower-level schemas into higher-level schemas. These schemas organize and store knowledge, but also heavily reduce working memory load because even highly complex schemas can be dealt with as one element in working memory (Sweller, Van Merriënboer, and Paas 1998). Thus, a large number of elements for one person may be a single element for another more experienced person who already has a cognitive schema available that incorporates the elements. For example, when you are asked to remember the phone number 30031959 this may be a cumbersome task because it contains eight elements. But for someone who recognizes this number as being his or her birthday (30 March 1959) it is easy to remember because it is organized as only one element in long-term memory and may thus be activated as one element in working memory. Similarly, novel information may be easy to understand by someone with relevant experience and very hard to understand by someone without this experience.

With regard to schema construction, a further distinction can be made between inductive learning, which refers to the construction of cognitive schemas by abstracting away from concrete experiences, and elaboration, which refers to the construction of schemas by relating already existing knowledge in long-term memory to novel information.

Inductive learning

People often “learn by doing,” that is, they learn from concrete experiences. Such inductive learning from concrete experiences may lead to both the generalization and discrimination of cognitive schemas (Holland, Holyoak, Nisbett, and Thagard 1989); it can be contrasted with deductive learning, where people are given general and/or abstract information which they must then apply to concrete cases (Van Merriënboer 1997). When learners generalize or abstract away from concrete experiences, they construct schemas that leave out the details so that they apply to a wider range of events or to events that are less tangible. For example, a child practicing addition may find out that 2 + 3 and 3 + 2 both add up to 5. One simple schema that might be induced here is “if you add two digits, the sequence in which you add them is not important for the outcome” (this is the law of commutativity). Discrimination is just the opposite of generalization. A more specific schema may be constructed if a set of failed solutions is available for a class of related tasks. Then, particular conditions may be added to the schema and restrict its range of use. For example, if the child finds out that 9 – 4 = 5 but 4 – 9 = –5 (minus 5), the more specific schema induced is: “if you perform a computational operation on two digits, and this operation is not subtraction (added condition), the sequence in which you perform it is not important for the outcome.” While this schema is still an overgeneralization, discrimination has made it more effective than the original schema. Induction through generalization and discrimination is typically a strategic and controlled cognitive process that requires conscious processing from the learner (see also section 2.3).

Elaboration

The elaboration of novel information refers to those cognitive activities that integrate new information with cognitive schemas already available in memory (Willoughby et al. 1997). When learners elaborate novel information, they first search their memory for general cognitive schemas that may provide a cognitive structure for understanding the information in general terms, and/or for concrete schemas or cases that may provide a useful analogy. These schemas are connected to the new information, and elements from the retrieved schemas that are not part of the new information are now related to it. For example, a learner who is learning about collapsing stars may understand their behavior better when it is linked to prior knowledge about ice skaters (they spin faster as they pull in their arms/their size shrinks). Thus, learners use what they already know about a topic to help them structure and understand the new information. Like induction, elaboration is a strategic and controlled cognitive process requiring conscious processing from the learners. Collaboration between learners and group discussion might stimulate elaboration. In collaborative settings, learners often must articulate or clarify their ideas to the other members of their group, helping them to deepen their own understanding of the domain (Van Boxtel, Van der Linden, and Kanselaar 2000). Group discussion in problem-based learning groups may also benefit the activation of relevant prior knowledge and so facilitate elaboration (Dochy et al. 2003).

2.2.2 The automation of cognitive schemas

Schema automation occurs if a task performer repeatedly and successfully applies a particular cognitive schema (Van Merriënboer and Sweller 2005). As is the case for schema construction, automation can free working memory capacity for other activities because an automated schema directly steers the routine aspects of behavior without the need to be processed in working memory. For example, young children may have a schema for doing multiplication, allowing them to compute the answer for 3 times 12 through the steps 3 times 2 is 6, 3 times 10 is 30, so the answer is 30 + 6 or 36. After repetitive practice, this schema may become automated, meaning that the children immediately give the answer 36 when prompted with 3 times 12, without the need to consciously do any computation. With regard to schema automation, a further distinction can be made between knowledge compilation, which refers to the preliminary automation of schemas by the construction of schemas that take the form of “cognitive rules” (IF condition, THEN action), and strengthening, which refers to the development of very high levels of automaticity through lengthy repetitive practice.

Knowledge compilation

Knowledge compilation refers to the process by which information is embedded in schemas that directly steer behavior, that is, evoke particular actions under particular conditions (Anderson 1993). Newly acquired schemas or worked examples may be used to yield an initial solution, and compilation is the process that creates highly specific schemas from this solution. For example, suppose that the following schema is used to make phone calls:

If you regularly make phone calls to your mother, whose phone number is 39475932, knowledge compilation may directly embed this information in the schema, yielding the following rule:

The process of embedding new information in schemas is called proceduralization (Anderson 1987). Another subprocess of knowledge compilation is composition, meaning that rules that consistently follow each other are combined into one new rule. For example, picking up the phone and dialing the number may eventually be combined in one rule, rather than in two different rules because these rules consistently follow each other. After the knowledge is compiled, the solution is generated by directly coupling the actions to the conditions in the specific schema. This places little load on working memory and greatly improves performance.

Strengthening

While knowledge compilation leads to highly specific schemas or cognitive rules, which are assumed to underlie accurate performance of a skill, it is usually assumed that an automated schema has a strength associated with it, determining the chance that it applies under the specified conditions as well as how rapidly it then applies (Palmeri 1999). Newly compiled rules, however, still have a weak strength. Repetitive practice, that is, long periods of overtraining eventually make it possible for learners to perform skills at a very high level of automaticity, such as touch-typists whose finger movements are directly driven by their thoughts regardless of the contents of the text they are typing, or trumpet players whose embouchure is directly driven by their interpretation of the music regardless of the musical piece they are playing. Strengthening is a straightforward learning mechanism. It is simply assumed that automated schemas accumulate strength each time they are successfully applied in a process of repetitive practice.

2.2.3 Complex Learning and Transfer

Many learning theories focus on only one particular type of learning, such as inductive learning by discovery, elaborative learning by group discussion, knowledge compilation by contingent tutoring, or strengthening by repetitive practice. Although these are powerful and highly relevant theories, a drawback is that as a result of the application of these theories skills, knowledge, and attitudes are often taught separately. For example, in many curricula knowledge is taught in lectures, skills are taught in a skills lab or practical, and attitudes are taught in role plays. This approach leads to compartmentalization and makes it difficult if not impossible for learners to integrate objectives from different domains of learning (Van Merriënboer and Kirschner 2013). A common complaint of students is that they experience their curriculum as a disconnected set of topics and courses, with implicit relationships between them and unclear relevance to their future profession.

This complaint prompted the initial interest in complex learning. The term was introduced in the 1990s to refer to forms of learning aimed at “integrative goals” (Gagné and Merrill 1990). Learning goals that require such integration are frequently encountered when instruction must reach beyond a single lesson or course, for example when professional competencies or complex skills are taught that should enable learners to work on real-life or professional tasks. Characteristic of complex learning is that integrative objectives are assumed to be rooted in different domains of learning, including the declarative or conceptual domain, the procedural or skills domain (including perceptual and psychomotor skills), and the affective or attitudes domain. It thus refers to the simultaneous occurrence of schema construction (i.e., induction and elaboration), schema automation (i.e., knowledge compilation and strengthening), and attitude formation.

With regard to outcomes, complex learning explicitly aims at transfer, that is, the ability to apply what has been learned to unfamiliar problems and/or in new situations. The main assumption is that complex learning yields a highly integrated knowledge base, organized in interrelated networks of cognitive schemas, which facilitates transfer (Van Merriënboer 1997). Automated schemas in this integrated knowledge base make it possible to perform familiar aspects of transfer tasks; it explains transfer by saying that acquired automated schemas are also applicable in performing the routine aspects of transfer tasks. This concerns the same use of the same—automated—knowledge, meaning that there are automated schemas or “identical elements” (Thorndike and Woodworth 1901) involved in performing both the learning tasks and transfer tasks. General or abstract schemas in the integrated knowledge base make it possible to understand a new situation in general terms and to act according to this general understanding. This concerns the different use of the same—general—knowledge, meaning that there are general and abstract schemas available enabling a task performer to interpret unfamiliar aspects of a transfer task in order to find a solution (e.g., through finding analogies; Gick and Holyoak 1983). Needless to say, transfer task performance becomes much more effective when automated schemas steer the routine aspects of the transfer task, so that more resources become available for interpreting schemas that can help to perform the unfamiliar problem-solving aspects of this task (Van Merriënboer 2013).

Most educational theories assume that complex learning occurs in situations where learning is driven by rich, meaningful tasks, which are typically based on real-life or professional tasks (Merrill 2013). Such tasks are called learning tasks, problems, enterprises, scenarios, or projects. Well-designed learning tasks explicitly aim at integrative objectives by forcing learners both to coordinate different aspects of task performance and to integrate knowledge, skills, and attitudes. The next sub-sections discuss an instructional design model for complex learning and the use of media and educational technologies according to this model.

Four-component instructional design

Four-component instructional design (4C/ID; Van Merriënboer 1997; Van Merriënboer, Clark, and De Croock 2002; van Merriënboer, Jelsma, and Paas 1992; Van Merriënboer and Kirschner 2013) is an instructional design approach for complex learning aimed at the training of complex skills and professional competencies. Its basic assumption is that educational programs for complex learning can always be described by four components that are based on the four basic types of learning, namely (a) learning tasks, which aim at inductive learning, (b) supportive information, which aims at elaboration, (c) procedural information, which aims at knowledge compilation, and (d) part-task practice, which aims at strengthening of selected routine aspects of tasks. Learning tasks provide the backbone of the training program; the three other components are connected to this backbone. Table 2.1 connects the four components to the basic types of learning and provides key instructional principles that are relevant for each component.

Table 2.1 Types of learning, related instructional components, and key instructional principles

Type of learningInstructional componentKey instructional principles
Schema constructionInductive learningLearning tasksVariability of practice
Simple-to-complex sequencing
Scaffolding (decreasing support and guidance)
ElaborationSupportive informationProvision of domain models and SAPs
Self-explanation prompts
Cognitive feedback
Schema automationKnowledge compilationProcedural informationHow-to instructions
Just-in-time information provision
Corrective feedback
StrengtheningPart-task practiceRepetitive practice
Distributed and spaced practice
Accuracy–speed–time-sharing

Learning tasks include case studies, projects, assignments, problems and so forth that can be performed in a real or simulated task environment. They are preferably authentic whole-task experiences based on real-life tasks and aim at the integration of skills, knowledge, and sometimes attitudes (Van Merriënboer and Kester 2008). In the field of medicine, for example, learning tasks could confront learners with patient descriptions or electronic virtual patients for which a diagnosis must be made. In engineering, learning tasks could confront a team of learners with a project in which they have to design and build some artifact. The whole set of learning tasks exhibits a high variability of practice because learning from varied experiences facilitates inductive learning, that is, the construction of rich schemas through generalization and discrimination. The learning tasks are organized in simple-to-complex classes of tasks, and have diminishing learner support and guidance for the equally complex tasks within the same class (this is also called “scaffolding”). Each learning task should be in the learner’s “zone of proximal development” (Vygotsky 1978), that is, the task must be challenging for the learner but thanks to the available support and guidance it can be successfully completed.

Supportive information helps students learn to perform non-routine aspects of learning tasks, which often involve problem solving, reasoning, and decision making. It explains how a domain is organized (i.e., provision of domain models) and how problems in that domain are approached, or should be approached according to experts (i.e., systematic approaches to problem solving or SAPs). In medicine, for example, supportive information pertains to knowledge of the human body as well as to a systematic approach for making a diagnosis (e.g., patient interview, physical examination, laboratory tests etc.). In engineering it may pertain to knowledge of materials, laws of mechanics and electricity etc. as well as systematic approaches for designing and developing artifacts. Self-explanation prompts can help learners to achieve a deep understanding of the information and cognitive feedback helps learners to critically compare and contrast their own domain models and cognitive strategies to those of experts and/or peer learners. Supportive information provides a bridge between what learners already know and what they need to know to work on the learning tasks. The basic underlying process for learning from supportive information is thus elaboration, that is, learning by connecting the new information to what is already known.

Procedural information allows students to learn to perform routine aspects of learning tasks that are always performed in the same way. It specifies exactly how to perform the routine aspects of the task (how-to instructions) and is best presented just in time, precisely when learners need it; corrective feedback immediately indicates errors and provides hints for how to continue. In medicine, for example, procedural information might be provided by an instructor who is giving directions to a student on how to conduct a physical examination (“You should now position the stethoscope right there,” “No, you should hold that instrument between your thumb and index finger”). In engineering, it might pertain to a quick reference guide explaining how to operate a particular tool or machine. Procedural information is quickly faded as learners gain more expertise and do not need it anymore. The basic underlying process for learning from procedural information is knowledge compilation, that is, learning by transforming new information into cognitive rules.

Finally, part-task practice pertains to additional practice of routine aspects so that learners can develop a very high level of automaticity for selected aspects for which this is necessary. In medicine, for example, part-task practice might be provided for critical routines such as giving intravenous or subcutaneous injections, auscultation, or resuscitation. In engineering, it may offer practice in making capillary joints or in using particular tools that need dexterity to be handled correctly. Part-task practice typically provides huge amounts of repetition and only starts after the routine aspect has been introduced in the context of a whole, meaningful learning task. It is best spaced or distributed in time (i.e., four practice sessions of one hour are more effective than one session of four hours) and first aims at accurate, errorless performance of the skill, then at high speed, and finally at time-sharing with other skills. The basic underlying process for learning from part-task practice is strengthening, that is, automating routine skills through repetitive practice.

The framework offered by 4C/ID offers good opportunities for collaborative and cooperative approaches to learning, especially when it concerns schema construction processes. First, learning tasks are based on real-life or professional tasks, meaning that collaborative learning will occur when this is indicated by real life. For example, when emergency skills are trained in the medical domain this will typically involve team training, where doctors, nurses and other professionals are trained as a team to deal with emergency situations. In engineering, typical design projects will also involve multiple students with different roles and responsibilities. Second, the elaboration of supportive information is promoted by cooperative learning such as computer-supported collaborative learning (CSCL). Brainstorming, discussion, and argumentation in a group are processes that help elaborate on new information. For example, in the medical domain learners in a problem-based learning group may discuss a patient case to share their prior knowledge and so increase understanding. In engineering, pairs of students may run experiments in a microworld to learn principles or laws because explaining their argumentation to a peer might help them to elaborate on the new information and increase their comprehension. Needless to say, the social context in which these types of collaborative and cooperative learning occur is particularly important.

Media and educational technologies

Some media are better to support particular learning processes than others (Van Merriënboer and Kester 2005). Because each of the four components aims at a different learning process, each of the components is associated with the use of particular media (see Table 2.2). Learning tasks help learners construct cognitive schemas in a process of inductive learning from concrete experiences. Suitable media must, thus, allow learners to work on learning tasks and will usually take the form of a real or simulated task environment, including serious games, virtual reality, augmented reality, computerized high-fidelity simulators, and so forth. Supportive information helps learners construct cognitive schemas in a process of elaboration; they must connect new information to prior knowledge already available in memory. Suitable media include hypermedia, multimedia, and microworlds (i.e., simulations of conceptual domains), but also social media and CSCL environments offering learners opportunities for cooperative knowledge construction. Procedural information helps learners automate their cognitive schemas via knowledge compilation. Suitable media include mobile technologies (smartphones, tablets) that can easily provide how-to instructions just in time while learners are working on a learning task, but also online job aids and help systems, wizards, and pedagogical agents. Finally, part-task practice helps learners automate the cognitive schemas that drive routine aspects of behavior through a process of strengthening. Suitable media include traditional drill-and-practice computer-based training (CBT), part-task trainers and also games directed at the acquisition of basic skills (e.g., spelling, grammar).

Table 2.2 Instructional components and media

Instructional componentLearning technologies
Traditional mediaNew media
Learning tasksReal task environment, role play, project groups, problem-based learning groupsComputer-simulated task environments, serious games, virtual reality, augmented reality, computerized high-fidelity simulators
Supportive informationTextbooks, dictionaries, lectures, realiaHypermedia (Internet), multimedia, microworlds, social media, CSCL
Procedural informationInstructor, job aids, learning aids, quick reference guides, manualsMobile technologies (smartphones, tablets), online job aids and help systems, wizards, pedagogical agents
Part-task practicePracticals, paper and pencil, skills laboratory, real task environmentDrill-and-practice CBT, part-task trainers, games for basic skills training

4C/ID shows how an educational program can be designed in such a way that all four basic learning processes occur simultaneously in a process of complex learning and how, eventually, transfer of learning can be realized. In such an educational program or environment we will typically find a rich mix of media and technologies, often including both traditional media and “new” technologies. This is not to say that each separate component cannot be important in its own right. For example, when a touch-typing course is designed only the instructional principles for part-task practice need to be applied, and when an animation of the heart-lung system is designed only the instructional principles for supportive information need to be applied. Regulation of learning processes will then also be different: In the touch-typing course learners may monitor whether they are fast enough and continue practicing when they are not; in the heart-lung animation learners may monitor whether they understand the working of the system and restudy the animation when they do not. The next section discusses such SRL processes in more detail.

2.3 Self-Regulated and Self-Directed Learning

SRL is an active, constructive, metacognitive process (metacognition is cognition about cognition, in this case, learning; Flavell 1979). Not only the acquisition of domain-specific complex skills as described in the previous section, but also the acquisition of SRL skills is of utmost importance in contemporary education, both because they are critical to effective learning in schools and because they are required in a fast-changing society where learners must be prepared to develop new knowledge and skills autonomously and continuously. However, research has shown that learners often have faulty ideas on how they learn and remember, which leads to ineffective forms of SRL (Bjork, Dunlosky, and Kornell 2013). It is thus critically important to explicitly teach learners not only domain-specific skills but also SRL skills.

Two important and complementary subprocesses in SRL are monitoring and control (Nelson and Narens 1990; Zimmerman and Schunk 2001). Monitoring is the term used to refer to the metacognitive thoughts learners have about their own learning. For example, learners who are reading a study text must monitor their level of comprehension of the text. Control refers to how learners respond to the environment or adapt their behavior based on their metacognitive thoughts. For example, if comprehension monitoring leads to the thought that an expository text is not yet well understood, the learner might decide to restudy one or more parts of this text. Monitoring and control are closely linked to each other in one and the same learning cycle: one is of little use without the other. To illustrate this, suppose you are in the passenger seat of a car and required to monitor rear-moving traffic in the rearview mirror. This would feel like a pointless exercise because it does not help you drive more safely. It only makes sense when you are in the driver seat, that is, when you are in control and can use the information on rear-moving traffic to drive more safely. The same is true in education: it only makes sense to ask learners to monitor or reflect on their performance when they are in a position to use their metacognitive thoughts to control or plan future actions.

SRL can take place at different levels. First, at the level of tasks or topics learners monitor how well they master a particular task, which affects how and how long they continue practicing it, or they monitor how well they comprehend, for example, a piece of text, animation, or video which affects how and how long they engage in studying or restudying it. Second, at the instructional-sequence level learners monitor how well they performed on one or more learning tasks after completing them, which then affects their selection of next suitable tasks and other learning resources. SRL at the task-sequence level is closely related to SDL. The next sub-sections briefly discuss, in order, learning and instruction of SRL skills and SDL skills.

2.3.1 Learning SRL skills

Metacognitive monitoring and control play an important role during the phases of acquisition of new knowledge and skills as well as their retention and retrieval (Nelson and Narens 1990). In this chapter, we focus on the acquisition phase. When students monitor their learning during this phase, their monitoring judgments are typically based on cognitive cues that are more or less predictive of future test performance (Koriat 1997). One example of an invalid cue that is not predictive of future performance is that information is easily recallable immediately after study; it is then easily recallable because it is still active in working memory not because it can be readily retrieved from long-term memory as will be required in a test. Thus, a much better cue is whether the information is easily recallable a few hours after study (the “delayed judgment-of-learning effect”; Dunlosky and Nelson 1992). Unfortunately, there is a tendency for people to use invalid and/or superficial cues, which may also explain why learners are typically overconfident when predicting their future performance. When learners use less valid cues and are overconfident this has negative consequences for their control decisions, for example they use surface rather than deep study strategies, they terminate practice or study too early, or they skip particular elements during practice or study. In turn, this will also have negative effects on their learning outcomes (Dunlosky and Rawson 2012).

Accurate monitoring must be based on valid cues, but what those valid cues are depends on the type of learning. When learners work on learning tasks and are involved in a process of inductive learning, they should monitor whether their activities help to construct more general/abstract cognitive schemas in long-term memory. If this is not the case, control may entail attempting alternative approaches to the task, consulting worked-example solutions, or comparing and contrasting the current approach to the task with approaches to previous tasks because these are activities that facilitate schema construction. As indicated above, a common problem is that learners often use invalid cues for monitoring, for example they solely monitor the quality of their current performance (fluency, accuracy, speed) and not the quality of constructed schemas as an indicator of successful learning (Bjork, Dunlosky, and Kornell 2013). Yet, the fact that a task is smoothly performed does not predict future performance on transfer tasks; trying out alternative approaches to a task, in contrast, may have negative effects on immediate performance (e.g., errors are made and it may take more time to complete the task) but positive effects on learning and transfer, an effect that is known as the “transfer paradox” (Van Merriënboer, De Croock, and Jelsma 1997). Instruction may take the form of metacognitive prompts that explicitly help learners focus on valid cues (i.e., improve monitoring) and undertake cognitive activities that promote schema construction (i.e., improve control; see Table 2.3 for some examples).

Table 2.3 Metacognitive prompts for monitoring and control in self-regulated learning

Instructional componentMetacognitive prompts
MonitorControl
Learning tasksWould you be able to perform this task in an alternative fashion?
How well do you expect to perform on future tasks that are a bit different from the current one?		Can you try out alternative approaches to this task?
Can analogies, worked-out examples or previous task solutions help you perform this task?
Supportive informationCan you self-explain the information you just studied?
Will you be able to answer test questions on the gist of the studied information?		Can you paraphrase, summarize, or build a diagram for the information you just studied?
Which parts do you want to restudy in order to increase your understanding?
Procedural informationWould you be able to perform this part-task without the just-in-time instructions?
Is your performance still dependent on corrective feedback?		Can you perform the task another time without consulting the procedural information?
If you make an error, are you able to recover from this error without asking for help?
Part-task practiceDoes it cost you any mental effort to perform this task?
Would you be able to perform it simultaneously with other tasks?		Should you continue with massed practice or plan another practice session (i.e., spacing practice)?
Can you perform the task under higher speed stress or under time-sharing conditions?

When learners study supportive information and are involved in a process of learning by elaboration, they need to monitor their level of comprehension or understanding, that is, how well they are able to interpret the new information in terms of what they already know. If such elaboration is not successful, control may entail restudying parts of the presented information, paraphrasing the information in their words, taking tests, or generating keywords, summaries, and diagrams that represent the studied information because such activities facilitate schema construction. In the context of studying supportive information invalid cues for comprehension or understanding that learners often use are, for example, ease of immediate recall or ease of studying the information because it is about a familiar topic, written in simple language, or depicted in an attractive animation or video. Such invalid cues might easily lead to an “illusion of understanding” (e.g., Paik and Schraw 2013); the fact that a text is familiar and easy to read or that an animation is easy to follow does not necessarily mean that it leads to the construction of rich cognitive schemas. Metacognitive prompts should thus help learners to base their monitoring on more valid cues, for example by making them aware whether they are able to self-explain the studied information or to answer future test questions. Similarly, prompts could also help learners undertake control activities that contribute to schema construction.

Monitoring and control are also important self-regulative processes for the consultation of procedural information as well as part-task practice, that is, for learning processes aimed at the automation of cognitive schemas. Again, instruction should first help learners to use valid cues for monitoring. When procedural information is consulted, the ability to perform the current task with the procedural information at hand is not a valid cue; instead, learners should ask themselves whether they will be able to perform the same task a next time without consulting the procedural information. Similarly, when learners are involved in part-task practice, the ability to perform the task accurately and without errors is not a valid cue for monitoring because this does not properly inform the learner about the achieved level of automaticity; instead, learners should use speed, invested mental effort, and time-sharing abilities as more valid cues for the achieved level of automaticity because an automated task can be performed very quickly, effortlessly, without conscious control, and thus together with other tasks (Van Merriënboer, Kirschner, and Kester 2003; Van Merriënboer and Sweller 2010). With regard to control, metacognitive prompts should make learners aware that it is not repetitive practice per se but also, for example, spaced practice, speed stress, and time sharing that might help to automate schemas.

2.3.2 Learning SDL skills

Learning is always self-regulated, even when learners have no control over the sequencing of instruction. It is simply impossible for a learner to work on learning tasks without monitoring his or her approach and adapting it accordingly, or to study supportive information without monitoring comprehension and adapting reading or viewing strategies accordingly (e.g., restudy or skip parts of a text, focus on other parts of an animation or video). However, when the learner is given control over the instructional sequence, with or without the advice of others, self-regulation also pertains to the selection of instructional activities and resources. This is often called SDL:

“…a process in which individuals take the initiative, with or without the help of others, in diagnosing their learning needs, formulating learning goals, identifying human and material resources for learning, choosing and implementing appropriate learning strategies, and evaluating learning outcomes”

(Knowles 1975, 15).

In the situation where learners lack SDL skills, a teacher or designer might decide to teach not only the domain-specific skills or professional competencies that the training program is aiming at, but also the SLD skills that will help learners become professionals who are able to continue learning in their future professions (Van Merriënboer et al. 2009). Yet it is important to teach SDL skills with great care because giving learners more control over instruction than they can handle negatively affects domain-specific learning outcomes (Kostons, Van Gog, and Paas 2012).

For the acquisition of SDL skills, the same principles as for developing domain-specific skills apply, namely variability, increasing complexity, and, above all, decreasing support and/or guidance in a process of second-order scaffolding (Van Merriënboer and Sluijsmans 2009). It is called second-order scaffolding because it does not pertain to domain-specific complex learning but to the SLD skills superimposed on it. In the context of educational programs developed on the basis of 4C/ID, SDL skills pertain to the selection of learning tasks, and to the identification and consultation of relevant supportive information, procedural information, and part-task practice. Table 2.4 provides examples of concrete resources for each component as well as examples of metacognitive prompts that might help learners with accurate monitoring and control.

Table 2.4 Learning resources and metacognitive prompts for monitoring and control in self'directed learning

Instructional componentExamples of resourcesMetacognitive prompts for monitoring and control
Learning tasksCollection or database of learning tasks (problems, projects, scenarios etc.)Did you make progress over learning tasks?
What are points for improvement?
Which future learning tasks will help you work on points of improvement and will improve overall performance?
Supportive informationStudy books, experts, Internet, multimedia, videos, animations, microworldsDid your understanding of this topic increase?
What other information or resources might help you increase your understanding?
What should you re/study in order to be able to perform future tasks?
Procedural informationManuals, quick-reference guides, online help, mobile technologiesDid your accuracy for routine aspects increase?
Did you make any errors?
Which how-to instructions can help you become more accurate and make less errors?
Part-task practiceDrill and practice exercises, part-task trainersDid your speed increase?
Did your investment of effort decrease?
Did you become better able to perform this part-task in combination with other tasks?
Should you continue practicing under more challenging speed stress and time sharing conditions?

For learning tasks, a so-called development portfolio may help learners monitor their progress over learning tasks. Such a portfolio keeps track of the tasks that have been performed, gathers assessment results for those tasks, and often provides overviews that indicate points of improvement or learning needs (Kicken et al. 2009). Furthermore, a collection or database with learning tasks should allow the learners to have control over the tasks they work on. Metadata for each learning task should be available to help the learner make an appropriate selection, such as associated standards that make it possible to work on identified points of improvement, its level of complexity, and available support and guidance.

Typically, learning SDL skills for learning-task selection will involve a gradual transition from a situation where the teacher or designer decides on the learning tasks to work on to a form of on-demand education, where it is the learner who decides on the next task or tasks to work on. With such second-order scaffolding the learner is given increasingly more control over the selection of tasks as his or her SDL skills further develop. This requires a form of “shared” control, where the teacher or other intelligent agent provides—first much but increasingly less—support and/or guidance to the learner for assessment of progress, identification of learning needs, and the selection of learning tasks that can fulfill these needs (Corbalan, Kester, and Van Merriënboer 2008). As an example of such second-order scaffolding, an e-Learning application may first present the learner with suitable learning tasks to work on, then present the learner with increasingly larger sets of suitable, pre-selected learning tasks from which the learners makes a final selection, and finally leave it up to the learner to select his or her own tasks. As another example, a teacher may first have frequent coaching meetings with the learner to discuss progress that has been made and provide advice on the selection of future learning tasks, then gradually decrease the frequency of those meetings, and finally leave it up to the learner to schedule such meetings only if deemed necessary. An electronic development portfolio is a very useful tool to use in such coaching meetings because learners and coaches can use the information from the portfolio to reflect on progress and points of improvement and to plan future learning.

For supportive information, the learning of SDL skills will typically involve a gradual transition from a situation where study books, multimedia, hypermedia, lectures, and other resources are prescribed for the learners so that it is guaranteed that necessary supportive information is available when needed, to a situation where learners must independently search and select their learning resources. These SDL skills are also called information literacy skills or information problem-solving skills (e.g., Brand-Gruwel, Wopereis, and Vermetten 2005). Second-order scaffolding of these skills may proceed through three phases. In the first phase, learners are given a limited list with relevant resources they should consult to be able to perform a learning task. In the second phase, learners are given a long list with relevant resources, for example all resources relevant for the learning tasks presented in one particular course, so that they must actually choose the resources relevant for the task at hand. In the final phase, learners are given no list of resources at all but must independently search for them in a library and/or on the Internet. Another example of second-order scaffolding relates to the teacher or tutor. In the early phases of the learning process, the tutor might give learners explicit advice on how and where to look for relevant resources. Later in the learning process, the tutor might only ask the learners how they plan to search for relevant resources and provide them with cognitive feedback on their intended search strategies. Finally, the tutor may provide no guidance at all.

Comparable approaches may be followed for the teaching of SDL skills relevant to procedural information and part-task practice. For procedural information, in a first phase how-to instructions can be explicitly presented to the learner together with the first learning task for which they are relevant (e.g., by a teacher who is acting as an assistant looking over your shoulder, or ALOYS). In a second phase, how-to instructions can be consulted by the learner on his smartphone or in a quick reference guide but the teacher closely observes the learner, refers to the procedural information when needed, and helps learners find the relevant how-to instructions. In a third phase, the learner can still consult the manual but receives no further guidance from the teacher. Similarly, for part-task practice, in a first phase the teacher may provide part-task practice to the learners and explain the most important principles underlying the provision of part-task practice. In a second phase, the teacher may provide an overview of all available workshops, skills-lab exercises, and drill-and-practice computer programs that learners might use for part-task practice; it is up to the learners to decide on doing part-task practice or not, although the teacher gives them advice and feedback on their choices. In the third phase, the teacher may make part-task practice available but fully leave it up to the learner if and when to use it.

2.4 Discussion

Learning is inherent to all human life and people learn in countless different ways. Yet, all these different types of learning eventually help learners construct cognitive schemas in long-term memory. Such cognitive schemas enable people to solve problems, to reason, and to make decisions in new situations. On the one hand, highly complex schemas can be activated as one element in working memory and so help to interpret unfamiliar aspects of the task in general or abstract terms; on the other hand, schemas might have been automated through repetitive practice and so help to perform familiar aspects of the task without error, very quickly and without effort, thereby freeing up additional working-memory resources for performing the unfamiliar aspects of the task.

Many learning theories focus on only one type of learning, such as declarative learning in the conceptual domain or procedural learning in the skills domain. Complex learning, in contrast, describes how learning processes interact with each other in order to enable people to perform real-life or professional tasks. Four fundamental learning processes were distinguished. With regard to the construction of schemas, people learn from concrete experiences in a process of inductive learning where they build general or abstract schemas, and they learn from newly presented information in a process of elaboration where they connect the new information to already existing knowledge in memory. With regard to the automation of schemas, people learn from “how-to” instructions in a process of knowledge compilation where associations are built between particular conditions and particular actions, and they learn from repetitive practice in a process of strengthening which eventually leads to full automaticity for performing familiar aspects of tasks. 4C/ID suggests instructional methods for learning environments that sustain a process of complex learning. Its four components directly relate to the four learning processes: learning tasks to inductive learning, supportive information to elaboration, procedural information to knowledge compilation, and part-task practice to strengthening.

Regulative processes govern learning. Monitoring refers to the thoughts learners have about their own cognition, and based on these metacognitive thoughts learners respond to the environment or adapt their behavior, which is termed control. Accurate monitoring must be based on valid cognitive cues, but what valid cues are depends on the type of learning. At the level of tasks or topics, predicted ability to perform transfer tasks is a more valid cue for inductive learning than the fluency of performing the current task, the ability to self-explain studied information is a more valid cue for elaborative learning than immediate recall of this information, the predicted ability to perform a future task without consulting procedural information is a more valid cue for knowledge compilation than being able to perform with the procedural information available, and speed, invested mental effort, and time-sharing abilities are more valid cues for strengthening than the ability to perform the task accurately and without errors. At the instructional-sequence level, which is directly related to SDL, learners also need to learn recognize valid cues that inform them which new learning tasks and other resources (supportive information, procedural information, part-task practice) will best help them achieve their learning goals.

The cognitive orientation of this chapter might be seen as a limitation. Yet this should certainly not be interpreted as an undervaluation of research paradigms that stress the importance of social learning, such as socio-cultural theory and social constructivism (Van Merriënboer and De Bruin 2014). As indicated before, learning tasks that are based on real-life or professional tasks will often involve team work and project work, and in modern society the importance of multidisciplinary team work is increasing and should thus be reflected in educational programs. Second, cooperative methods are particularly important for the provision of supportive information because group discussion and argumentation help learners activate prior knowledge and thus promote the linking of new information to available knowledge in long-term memory. Third, learning by observing others might be seen as the linking pin between supportive information/elaboration on the one hand and learning tasks/inductive learning on the other. “Modeling examples” (Van Gog and Rummel 2010) can be seen as a type of supportive information illustrating how experts or other task performers systematically approach tasks, but they can also be seen as learning tasks with a maximum level of support because they present not only the problem but also the problem-solving process an expert goes through in order to reach an acceptable solution. In the human motor domain, observing an expert who is performing a task actually activates the same neurons in the brain as performing the task yourself (i.e., the mirror neuron system; De Jong et al. 2009; Van Gog et al. 2009).

Instructional methods and approaches that are popular in socio-cultural theory and social constructivism are mainly related to schema construction processes, while methods that are sometimes labeled as instructivist or objectivist approaches (Jonassen 1991) are more related to schema automation processes. Furthermore, self-regulation processes play a central role in a variety of approaches such as resource-based learning, adult learning, and competency-based learning. In addition, SRL in cooperative and collaborative learning settings will not only refer to monitoring and controlling own learning processes, but also group learning processes. This chapter made no attempts to discuss the many different perspectives on how learning can best be promoted—this would fall beyond the scope of this book and also take too much space. Instead, a focus on learning processes per se was chosen while acknowledging that there are countless ways to help people learn and regulate their learning.

To conclude, people can learn from virtually everything they do. Yet, efforts to help people learn will be more successful when they focus on complex learning, enabling people to perform meaningful real-life or professional tasks, and when they emphasize SRL and SDL, enabling people to control their own learning based on valid cues. A clear implication for the field of learning technology is that it should not primarily study the effects of isolated technologies on particular learning processes, but foremost study how learning technologies can be fully integrated in educational programs and learning environments in such a way that they best promote a process of complex and Self Regulated Learning.

References

  1. Anderson, John R. 1987. “Skill acquisition: Compilation of weak-method problem solutions.” Psychological Review 94 2: 192–210. doi:10.1037/0033-295X.94.2.192.
  2. Anderson, John R. 1993. Rules of the mind. Hillsdale, NJ: Lawrence Erlbaum.
  3. Bjork, Robert A., John Dunlosky, and Nate Kornell. 2013. “Self-regulated learning: Beliefs, techniques, and illusions.” Annual Review of Psychology 64 1: 417–44. 10.1146/annurev-psych-113011-143823.
  4. Bloom, Benjamin 1956. Taxonomy of educational objectives; Handbook I: The cognitive domain. New York: David McKay.
  5. Brand-Gruwel, Saskia, Iwan G. J. H. Wopereis, and Yvonne Vermetten. 2005. “Information problem solving by experts and novices: Analysis of a complex cognitive skill.” Computers in Human Behavior 21 3: 487–508. doi:10.1016/j.chb.2004.10.005.
  6. Corbalan, Gemma, Liesbeth Kester, and Jeroen J. G. Van Merriënboer. 2008. “Selecting learning tasks: Effects of adaptation and shared control on efficiency and task involvement.” Contemporary Educational Psychology 33 4: 733–56. doi: 10.1016/j.cedpsych.2008.02.003.
  7. Cowan, Nelson. 2001. “The magical number 4 in short-term memory: A reconsideration of mental storage capacity.” Behavioral and Brain Sciences 24 1: 87–185. doi: 10.1017/S0140525X01003922.
  8. De Jong Ton, Tamara Van Gog, Kathleen Jenks, Sarah Manlove, Janet Van Hell, Jelle Jolles, Jeroen J. G. Van Merriënboer, Theo Van Leeuwen, and Annemarie Boschloo. 2009. Explorations in learning and the brain: On the potential of cognitive neuroscience for educational science. New York: Springer.
  9. Dochy, Filip, Mien Segers, Piet Van den Bossche, and David Gijbels. 2003. “Effects of problem-based learning: a meta-analysis.” Learning and Instruction 13 (5): 533–68. doi:10.1016/S0959-4752(02)00025-7.
  10. Dunlosky, John and Thomas O. Nelson. 1992. “Importance of the kind of cue for judgments of learning (JOL) and the delayed JOL effect.” Memory and Cognition 20 4: 374–80. doi:10.3758/BF03210921.
  11. Dunlosky, John and Katherine A. Rawson. 2012. “Overconfidence produces underachievement: Inaccurate self-evaluations undermine students' learning and retention.” Learning and Instruction 22 4: 271–80. doi:10.1016/j.learninstruc.2011.08.003.
  12. Flavell, John H. 1979. “Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry.” American Psychologist 34 10: 906–11. doi:10.1037/0003-066X.34.10.906.
  13. Gagné, Robert M. and M. David Merrill. 1990. “Integrative goals for instructional design.” Educational Technology Research and Development 38 1: 23–30. doi:10.1007/BF02298245.
  14. Gick, Mary L. and Keith J. Holyoak. 1983. “Schema induction and analogical transfer.” Cognitive Psychology 15 1: 1–38. doi:10.1016/0010-0285(83)90002-6.
  15. Holland, John H., Keith J. Holyoak, Richard E. Nisbett, and Paul R. Thagard. 1989. Induction: Processes of inference, learning, and discovery. Cambridge, MA: MIT Press.
  16. Jonassen, David. H. 1991. “Objectivism versus constructivism: Do we need a new philosophical paradigm?” Educational Technology Research and Development 39 3: 5–14. doi:10.1007/BF02296434.
  17. Kicken, Wendy, Saskia Brand-Gruwel, Jeroen J. G. Van Merriënboer, and Wim Slot. 2009. “Design and evaluation of a development portfolio: How to improve students’ self-directed learning skills.” Instructional Science 37 5: 453–73. doi:10.1007/s11251-008-9058-5.
  18. Knowles, Malcolm S. 1975. Self-directed learning: A guide for learners and teachers. Chicago: Association Press.
  19. Koriat, Asher 1997. “Monitoring one's own knowledge during study: A cue-utilization approach to judgments of learning.” Journal of Experimental Psychology. General 126 4: 349–70. doi:10.1037/0096-3445.126.4.349.
  20. Kostons, Danny, Tamara Van Gog, and Fred Paas. 2012. “Training self-assessment and task-selection skills: A cognitive approach to improving self-regulated learning.” Learning and Instruction 22 2: 121–32. doi:10.1016/j.learninstruc.2011.08.004.
  21. Merrill, M. David 2013. First principles of instruction. San Francisco, CA: Pfeiffer.
  22. Miller, George A. 1956. “The magical number seven, plus or minus two: some limits on our capacity for processing information.” Psychological Review 63 2: 81–97. doi:10.1037/h0043158.
  23. Mousavi, Seyed Y., Renae Low, and John Sweller. 1995. “Reducing cognitive load by mixing auditory and visual presentation modes.” Journal of Educational Psychology 87 2: 319–34. doi:10.1037/0022-0663.87.2.319.
  24. Nelson, Thomas O. and Louis Narens. 1990. “Metamemory: A theoretical framework and new findings.” In The psychology of learning and motivation: Advances in research and theory.26, edited by G. Bower: pp. 125–173. New York: Academic Press. doi:10.1016/S0079-7421(08)60053-5.
  25. Paik, Eugene S. and Gregory Schraw. 2013. “Learning with animation and illusions of understanding.” Journal of Educational Psychology 105 2: 278–89. doi:10.1037/a0030281.
  26. Palmeri, Thomas J. 1999. “Theories of automaticity and the power law of practice.” Journal of Experimental Psychology. Learning, Memory, and Cognition 25 2: 543–51. doi:10.1037/0278-7393.25.2.543.
  27. Piaget, Jean 1953. The origin of intelligence in the child. New York: Routledge and Kegan Paul.
  28. Sweller, John, Jeroen J. G. Van Merriënboer, and Fred Paas. 1998. “Cognitive architecture and instructional design.” Educational Psychology Review 10 3: 251–96. doi:10.1023/A:1022193728205.
  29. Thorndike, Edward L. and Robert S. Woodworth. 1901. “The influence of improvement in one mental function upon the efficiency of other functions; II. The estimation of magnitudes.” Psychological Review 8 4: 384–95. doi:10.1037/h0071280.
  30. Van Boxtel, Carla, Jos Van der Linden, and Gellof Kanselaar. 2000. “Collaborative learning tasks and the elaboration of conceptual knowledge.” Learning and Instruction 10 4: 311–30. doi:10.1016/S0959-4752(00)00002-5.
  31. Van Gog, Tamara, Fred Paas, Nadine Marcus, Paul Ayres, and John Sweller. 2009. “The mirror neuron system and observational learning: Implications for the effectiveness of dynamic visualizations.” Educational Psychology Review 21 1: 21–30. doi:10.1007/s10648-008-9094-3.
  32. Van Gog, Tamara and Nikol Rummel. 2010. “Example-based learning: Integrating cognitive and social-cognitive research perspectives.” Educational Psychology Review 22 2: 155–74. doi:10.1007/s10648-010-9134-7.
  33. Van Merriënboer, Jeroen J. G. 1997. Training complex cognitive skills. Englewood Cliffs, NJ: Educational Technology Publications.
  34. Van Merriënboer, Jeroen J. G. 2013. “Perspectives on problem solving and instruction.” Computers and Education 64:153–60. doi:10.1016/j.compedu.2012.11.025.
  35. Van Merriënboer, Jeroen J. G., Richard E. Clark, and Marcel B. M. De Croock. 2002. “Blueprints for complex learning: The 4C/ID-model.” Educational Technology Research and Development 50 2: 39–64. doi:10.1007/BF02504993.
  36. Van Merriënboer, Jeroen J. G. and Anique B. H. De Bruin. 2014. “Research paradigms and perspectives on learning.” In Handbook of research on educational communications and technology. 4th ed., edited by J. M. Spector, M. D. Merrill, J. Elen, and M. J. Bishop: pp. 21–29. New York: Springer. doi:10.1007/978-1-4614-3185-5_2.
  37. Van Merriënboer, Jeroen J. G., Marcel B. M. De Croock, and Otto Jelsma. 1997. “The transfer paradox: Effects of contextual interference on retention and transfer performance of a complex cognitive skill.” Perceptual and Motor Skills 84 3: 784–86. doi:10.2466/pms.1997.84.3.784.
  38. Van Merriënboer, Jeroen J. G., Otto Jelsma, and Fred Paas. 1992. “Training for reflective expertise: A four-component instructional design model for complex cognitive skills.” Educational Technology Research and Development 40 2: 23–43. doi:10.1007/BF02297047.
  39. Van Merriënboer, Jeroen J. G. and Liesbeth Kester. 2005. “The four-component instructional design model: Multimedia principles in environments for complex learning.” In The Cambridge handbook of multimedia learning, edited by R. E. Mayer: pp. 71–93. New York: Cambridge University Press. doi:10.1017/CBO9780511816819.006.
  40. Van Merriënboer, Jeroen J. G. and Liesbeth Kester. 2008. “Whole-task models in education.” In Handbook of research on educational communications and technology. 3rd ed., edited by J. M. Spector, M. D. Merrill, J. J. G. Van Merriënboer, and M. P. Driscoll: pp. 441–56. Mahwah, NJ: Erlbaum/Routledge.
  41. Van Merriënboer, Jeroen J. G. and Paul A. Kirschner. 2013. Ten steps to complex learning. 2nd Rev. Ed. New York: Routledge.
  42. Van Merriënboer, Jeroen J. G., Paul A. Kirschner, and Liesbeth Kester. 2003. “Taking the load of a learner's mind: Instructional design for complex learning.” Educational Psychologist 38 1: 5–13. doi:10.1207/S15326985EP3801_2.
  43. Van Merriënboer, Jeroen J. G., Paul A. Kirschner, Fred Paas, Peter B. Sloep, and Marjolein C. J. Caniels. 2009. “Towards an integrated approach for research on lifelong learning.” Educational Technology Magazine 49 3: 3–14.
  44. Van Merriënboer, Jeroen J. G. and Dominique A. Sluijsmans. 2009. “Toward a synthesis of cognitive load theory, four-component instructional design, and self-directed learning.” Educational Psychology Review 21 1: 55–66. doi:10.1007/s10648-008-9092-5.
  45. Van Merriënboer, Jeroen J. G. and John Sweller. 2005. “Cognitive load theory and complex learning: Recent developments and future directions.” Educational Psychology Review 17 (2): 147–77. doi:10.1007/s10648-005-3951-0.
  46. Van Merriënboer, Jeroen J. G. and John Sweller. 2010. “Cognitive load theory in health professional education: Design principles and strategies.” Medical Education 44: 85–93. doi:10.1111/j.1365-2923.2009.03498.x.
  47. Vygotksy, Lev S. 1978. Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.
  48. Willoughby, Teena, Eileen Wood, Serge Desmarais, Suzanne Sims, and Michelle Kalra. 1997. “Mechanisms that facilitate the effectiveness of elaboration strategies.” Journal of Educational Psychology 89 4: 682–85. doi:10.1037/0022-0663.89.4.682.
  49. Winne, Philip H. and Allyson F. Hadwin. 2008. “The weave of motivation and self-regulated learning.” In Motivation and self-regulated learning: Theory, research, and application, edited by D. H. Schunk and B. J. Zimmerman: pp. 297–314. New York: Routledge.
  50. Zimmerman, Barry J. 1998. Self-regulated learning: From teaching to self-reflective practice. New York: The Guilford Press.
  51. Zimmerman, Barry J. and Dale H. Schunk (eds). 2001. Self-regulated learning and academic achievement. Mahwah, NJ: Erlbaum.
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