Do Learners Really Know Best? Urban Legends in
Education (Kirschner & Merriënboer)
Three legends as variations on the theme that it is the learner who knows best and that she or he
should be the controlling force in her or his learning.
Digital natives who form a generation of students knowing by nature how to learn from new
media, and for whom ‘old’ media and methods used in teaching/learning no longer work.
Learners have specific learning styles and education should be individualized to the extent
that the pedagogy of teaching/learning is matched to the preferred style of the learner.
Learners ought to be seen as self-educators who should be given maximum control over what
they are learning and their learning trajectory.
Peoples beliefs and convictions are usually second-hand (Twain, 1910/2010).
An urban legend: a story that is held to be true, sounds plausible enough to be believed, is based
primarily on hearsay, and is widely circulated as true.
In this article are urban legends three contemporary beliefs about learners and their learning. These
beliefs, though both popular and pervasive in education and educational policy, do not really concur
with the body of research in educational psychology.
Learners as Digital Natives
Children for whom ‘learning is playing’, ‘school is for meeting friends rather than learning’, and who
have the ‘skill to construct meaningful knowledge from discontinued audio-visual and textual
information flows’.
These children are creative problem solvers, experienced communicators, self-directed learners, and
digital thinkers who have developed the ability to do many different things at the same time that
require conscious thought processes.
These assumptions are all grounded in the belief that children are highly effective at managing their
own interactions with the technological world and should be trusted to be in control of these
interactions.
Digital native: group of young people who have been immersed in technology all their lives, giving
them distinct and unique characteristics that set them apart from previous generations and who have
shophisticated technical skills and learning preferences for which traditional education is unprepared
(Prensky, 2001/2006).
He had rationalized the phenomena that he had observed. Not based upon research.
It assumed that (a) they really understood what they were doing, (b) were using them
effectively and efficiently, and (c) it is good to design education where they can do this.
Homo zapiens: a new generation of learners who, unlike their predecessors, learn in a significantly
different way (Veen & Vrakking, 2006). It can be compared to a a generation that does not know a
world without computers etc.
Children develop – on their own, without instruction – the metacognitive skills necessary for
inquiry-based learning, discovery-based learning, networked learning, experiential learning,
collaborative learning, active learning, self-organization and self-regulation, problem solving,
and making their own implicit (i.e., tacit) and explicit knowledge explicit to others.
Does such an information technology savvy generation actually exist?
University students use a limited range of technologies for learning and socialization.
University students do not really have deep knowledge of technology, and what knowledge
they do have is often limited to basic office suite skills, e-mailing, text messaging, Facebook,
and surfing the Internet.
, Much professional commentary, popular writing and PowerPoint presentations overestimates
the impact of ICTs on the young, and that the ubiquitous presence of technology in their lives
has not resulted in improved information retrieval, information seeking or evaluation skills.
Butterfly defect: fluttering across the information on the screen, touching or not touching pieces of
information, quickly fluttering to a next piece of information, unconscious to its value and without a
plan. This leads to a very fragile network of knowledge.
young people’s engagement with digital technologies is varied and often unspectacular, in contrast to
popular portrayals of these so-called digital natives.
Multitaskers
S. Anderson (2009), in discussing what he called “the benefits of distraction,” opted that children
“growing up now might have an associative genius we don’t—a sense of the way ten projects all
dovetail into something totally new.
According to Small (Lin, 2008), exposure to digital media is rewiring the neural circuitry of children’s
brains, heightening skills like multitasking, complex reasoning, and decision making.
Multitasking: the simultaneous and/or concurrent performance of two or more tasks requiring
cognition or information processing.
The problem here is that human cognitive architecture and brain functioning only allows for
switching between different tasks rather than the simultaneous performance of tasks, even
though the performance seems subjectively to occur simultaneously. Human beings can do
more than one thing at any one time only when what they are doing is fully automated.
When task switching, first the individual shifts the goal and thus makes a “decision” to divert
attention from one task to another, and then the individual activates a rule so instructions for
executing one task are switched off, and those for executing the other are switched on. This so-called
multitasking involves dividing one’s attention between the tasks, and because each task competes for
a limited amount of cognitive resources, the performance of one interferes with that of the other.
What people really mean when they say that current generation is good at multitasking is that the
current generation has, through practice, seemingly developed the ability to quickly switch between
different tasks or different media. It leads to poorer learning results in students and poorer
performance of tasks.
It is not only the case that interrupting the completion of one task by another task leads to increases
in time spent on a task. Interruptions and distractions have also been found to be one of the most
common causes of pilot error.
there is strong evidence that multitasking and task switching impair performance and learning, and
there is no reason to expect positive effects of educational methods that require multitasking.
Learners and their learning styles
A second legend is that all learners are aware of their own personal learning style and that good
instruction requires diagnosing the learning style of each individual and aligning instruction
accordingly.
Most learning styles are based on types, meaning that they do not assign people scores on different
dimensions but classify people into distinct groups. There are at least three problems with putting
learners in pigeonholes: Many people do not fit one particular style, the information used to assign
people to styles is often inadequate, and there are so many different styles that it becomes
cumbersome to link particular learners to particular styles.
most differences and especially differences in cognition between people are gradual rather
than nominal. Many learning styles neglect this finding by using an arbitrary criterion (e.g., a
median or mean) to assign people to groups.
Almost without exception, self-report measures are used; however, the adequacy of such
self-reports for the assessment of learning styles is questionable. At least equally important is
the validity of the learning styles measures: Do they really measure what they aim to
measure? Again, the relationship between what people say about how they learn and how
, they actually learn is weak. Cognitive abilities are better predictors of how people learn most
effectively than their preferences. These abilities should be objectively measured on an
ordinal scale.
The mere number of reported learning styles is problematic. The truth might be that people
are different from each other on so many style dimensions, and for each dimension in so
many degrees, that it becomes totally impractical to take these differences into account in
instruction, even if the previous two problems did not exist!
Is What Learners Say They Prefer Good for them?
The learning-styles hypothesis: The required evidence for the learning-styles hypothesis is a crossover
interaction in which type A learners learn better with instructional method A, whereas type B
learners learn better with instructional method B.
In the learning styles literature, the theoretical basis for the formulation of crossover interactions is
typically based on a preferential model, also called the meshing hypothesis. The basic idea is that
instruction should be provided in the mode that best matches the learner’s style; thus, the learner is
assumed to “know” what is best for him or her.
the preferred way of learning, however, does not need to be the most productive way of
learning.
Learner preference was typically uncorrelated or negatively correlated to learning and learning
outcomes. That is, learners who reported preferring a particular instructional technique typically did
not derive any instructional benefit from experiencing it. Frequently, so-called mathemathantic (from
the Latin mathema = learning + thanatos = death) effects are found, that is, teaching kills learning
when instructional methods match a preferred but unproductive learning style.
a learning style that might be desirable in one situation might be undesirable in another situation due
to the multifaceted nature of complex skills.
The idea that learners with different learning styles should be taught with different instructional
methods is a belief for which very little, if any, scientific evidence exists. There are fundamental
problems with regard to the measurement of learning styles and the theoretical basis for the
assumed interactions between learning styles and instructional methods, and, last but not least,
substantial empirical evidence for the learning styles hypothesis is missing.
A focus on what learners have in common does not deny that there are individual differences; rather,
it helps to identify differences that really matter in education and to design instructional methods
that are practically feasible.
The expertise-reversal effect: indicates that learners with low prior knowledge learn more from
studying examples than from solving the equivalent problems and that this pattern reverses for
learners with higher prior knowledge.
Learners as Self-educators (on the Internet)
all that one needs to know and learn is “out there on the web” and that there is, thus, no need to
teach and/or acquire such knowledge any more.
self-educators can self-regulate and self-direct their own learning, seeking, finding, and making use of
all of the information sources that are freely available to them.
The idea that the present body of knowledge is rapidly becoming out of date or obsolete is far from
true. First, a distinction needs to be made with respect to the difference between knowledge
obsolescence and information growth. There is an enormous growth in the amount of information
available, but this does not mean that the existed prior to the Internet revolution is obsolete,
irrelevant, or no longer holds. Much is still valid and useful.
The set of activities and/or skills needed to adequately deal with this information generation and
dissemination is frequently referred to as information literacy or—when information and
communication technologies also play a key role—digital literacy activities/skills.
, solving information problems is for most students a major if not insurmountable cognitive endeavor.
Searching, finding, and processing information is a complex cognitive process that requires identifying
information needs, locating corresponding information sources, extracting and organizing relevant
information from each source, and synthesizing information from a variety of sources.
Learners not only have problems finding the information that they are seeking but also often trust the
first thing they see.
young children, teenagers, and adults are not capable of effectively choosing proper search terms,
selecting the most relevant websites, and questioning the validity of sources.
students lack regulatory skills and have difficulties defining the information problem; identifying what
they do not know.
students must learn to solve information-based problems and must learn transferable search and
evaluation strategies.
Unfortunately, in most cases students’ prior knowledge of the subject matter is minimal. From
research, it is known that low prior knowledge negatively influences the search process. (even though
it is on the internet, there is a need to teach it.)
On some accounts, student-directed education is to replace teacher-directed education (Gibbons,
2002). Hase and Kenyon (2000) called this the shift from andragogy (i.e., focusing on the best ways for
people to learn) to heutagogy (i.e., focusing on learning how to learn and learner self-direction).
Problems with the use of self-directed and self-determined learning in education:
learners are not always successful controlling their own learning, especially in computer-
based learning environments. Not all learners prefer nor profit from controlling the tasks and
forcing such control on them can be counterproductive. Learners do not have or do not know
how to utilize appropriate strategies when they are left to themselves to manage their
learning environment. learners often misregulate their learning, exerting control in a
misguided or counterproductive fashion and not achieving the desired result. This is due to
(a) not having the necessary standards upon which to judge their learning state, (b) not
having the necessary knowledge to monitor their own state in comparison with the
standards, and/or (c) not being able to initiate the proper processes to change their current
state when their behavior falls short of the standards.
learners often choose what they prefer, but what they prefer is not always what is best for
them.
Paradox of choice. . People appreciate having the opportunity to make some choices, but the
more options that they have to choose from, the more frustrating it is to make the choice. .
Shared control is a two-step process, in which the teacher or coach first selects from all
available learning tasks a subset of tasks with characteristics that fit the needs of the learner.
This should prevent overwhelming and frustrating the learner by letting him or her choose
from a very large set of tasks. Second, the learner selects from this subset one task to work
on. With learning tasks in the dietetics domain, shared control had superior effects on
student motivation. With ¨ learning tasks in the genetics domain, shared control over task
selection had superior effects on both student motivation and learning, provided that
learners had to choose from a subset of preselected tasks with features that were different
from the features of previous tasks. second-order scaffolding, in which there is gradually
decreasing support and guidance for self-directed learning skills. This second-order
scaffolding helps learners learn to select learning tasks, find relevant supportive information,
consult necessary procedural information, and identify useful part-task practice. The
instruction shifts gradually from traditional teacher or system controlled learning
environments, in which a teacher or another intelligent agent assesses a learner’s
performance and selects suitable further learning tasks, to on-demand education, in which
the learner self-assesses her or his performance and independently selects her or his own
learning tasks.
Education (Kirschner & Merriënboer)
Three legends as variations on the theme that it is the learner who knows best and that she or he
should be the controlling force in her or his learning.
Digital natives who form a generation of students knowing by nature how to learn from new
media, and for whom ‘old’ media and methods used in teaching/learning no longer work.
Learners have specific learning styles and education should be individualized to the extent
that the pedagogy of teaching/learning is matched to the preferred style of the learner.
Learners ought to be seen as self-educators who should be given maximum control over what
they are learning and their learning trajectory.
Peoples beliefs and convictions are usually second-hand (Twain, 1910/2010).
An urban legend: a story that is held to be true, sounds plausible enough to be believed, is based
primarily on hearsay, and is widely circulated as true.
In this article are urban legends three contemporary beliefs about learners and their learning. These
beliefs, though both popular and pervasive in education and educational policy, do not really concur
with the body of research in educational psychology.
Learners as Digital Natives
Children for whom ‘learning is playing’, ‘school is for meeting friends rather than learning’, and who
have the ‘skill to construct meaningful knowledge from discontinued audio-visual and textual
information flows’.
These children are creative problem solvers, experienced communicators, self-directed learners, and
digital thinkers who have developed the ability to do many different things at the same time that
require conscious thought processes.
These assumptions are all grounded in the belief that children are highly effective at managing their
own interactions with the technological world and should be trusted to be in control of these
interactions.
Digital native: group of young people who have been immersed in technology all their lives, giving
them distinct and unique characteristics that set them apart from previous generations and who have
shophisticated technical skills and learning preferences for which traditional education is unprepared
(Prensky, 2001/2006).
He had rationalized the phenomena that he had observed. Not based upon research.
It assumed that (a) they really understood what they were doing, (b) were using them
effectively and efficiently, and (c) it is good to design education where they can do this.
Homo zapiens: a new generation of learners who, unlike their predecessors, learn in a significantly
different way (Veen & Vrakking, 2006). It can be compared to a a generation that does not know a
world without computers etc.
Children develop – on their own, without instruction – the metacognitive skills necessary for
inquiry-based learning, discovery-based learning, networked learning, experiential learning,
collaborative learning, active learning, self-organization and self-regulation, problem solving,
and making their own implicit (i.e., tacit) and explicit knowledge explicit to others.
Does such an information technology savvy generation actually exist?
University students use a limited range of technologies for learning and socialization.
University students do not really have deep knowledge of technology, and what knowledge
they do have is often limited to basic office suite skills, e-mailing, text messaging, Facebook,
and surfing the Internet.
, Much professional commentary, popular writing and PowerPoint presentations overestimates
the impact of ICTs on the young, and that the ubiquitous presence of technology in their lives
has not resulted in improved information retrieval, information seeking or evaluation skills.
Butterfly defect: fluttering across the information on the screen, touching or not touching pieces of
information, quickly fluttering to a next piece of information, unconscious to its value and without a
plan. This leads to a very fragile network of knowledge.
young people’s engagement with digital technologies is varied and often unspectacular, in contrast to
popular portrayals of these so-called digital natives.
Multitaskers
S. Anderson (2009), in discussing what he called “the benefits of distraction,” opted that children
“growing up now might have an associative genius we don’t—a sense of the way ten projects all
dovetail into something totally new.
According to Small (Lin, 2008), exposure to digital media is rewiring the neural circuitry of children’s
brains, heightening skills like multitasking, complex reasoning, and decision making.
Multitasking: the simultaneous and/or concurrent performance of two or more tasks requiring
cognition or information processing.
The problem here is that human cognitive architecture and brain functioning only allows for
switching between different tasks rather than the simultaneous performance of tasks, even
though the performance seems subjectively to occur simultaneously. Human beings can do
more than one thing at any one time only when what they are doing is fully automated.
When task switching, first the individual shifts the goal and thus makes a “decision” to divert
attention from one task to another, and then the individual activates a rule so instructions for
executing one task are switched off, and those for executing the other are switched on. This so-called
multitasking involves dividing one’s attention between the tasks, and because each task competes for
a limited amount of cognitive resources, the performance of one interferes with that of the other.
What people really mean when they say that current generation is good at multitasking is that the
current generation has, through practice, seemingly developed the ability to quickly switch between
different tasks or different media. It leads to poorer learning results in students and poorer
performance of tasks.
It is not only the case that interrupting the completion of one task by another task leads to increases
in time spent on a task. Interruptions and distractions have also been found to be one of the most
common causes of pilot error.
there is strong evidence that multitasking and task switching impair performance and learning, and
there is no reason to expect positive effects of educational methods that require multitasking.
Learners and their learning styles
A second legend is that all learners are aware of their own personal learning style and that good
instruction requires diagnosing the learning style of each individual and aligning instruction
accordingly.
Most learning styles are based on types, meaning that they do not assign people scores on different
dimensions but classify people into distinct groups. There are at least three problems with putting
learners in pigeonholes: Many people do not fit one particular style, the information used to assign
people to styles is often inadequate, and there are so many different styles that it becomes
cumbersome to link particular learners to particular styles.
most differences and especially differences in cognition between people are gradual rather
than nominal. Many learning styles neglect this finding by using an arbitrary criterion (e.g., a
median or mean) to assign people to groups.
Almost without exception, self-report measures are used; however, the adequacy of such
self-reports for the assessment of learning styles is questionable. At least equally important is
the validity of the learning styles measures: Do they really measure what they aim to
measure? Again, the relationship between what people say about how they learn and how
, they actually learn is weak. Cognitive abilities are better predictors of how people learn most
effectively than their preferences. These abilities should be objectively measured on an
ordinal scale.
The mere number of reported learning styles is problematic. The truth might be that people
are different from each other on so many style dimensions, and for each dimension in so
many degrees, that it becomes totally impractical to take these differences into account in
instruction, even if the previous two problems did not exist!
Is What Learners Say They Prefer Good for them?
The learning-styles hypothesis: The required evidence for the learning-styles hypothesis is a crossover
interaction in which type A learners learn better with instructional method A, whereas type B
learners learn better with instructional method B.
In the learning styles literature, the theoretical basis for the formulation of crossover interactions is
typically based on a preferential model, also called the meshing hypothesis. The basic idea is that
instruction should be provided in the mode that best matches the learner’s style; thus, the learner is
assumed to “know” what is best for him or her.
the preferred way of learning, however, does not need to be the most productive way of
learning.
Learner preference was typically uncorrelated or negatively correlated to learning and learning
outcomes. That is, learners who reported preferring a particular instructional technique typically did
not derive any instructional benefit from experiencing it. Frequently, so-called mathemathantic (from
the Latin mathema = learning + thanatos = death) effects are found, that is, teaching kills learning
when instructional methods match a preferred but unproductive learning style.
a learning style that might be desirable in one situation might be undesirable in another situation due
to the multifaceted nature of complex skills.
The idea that learners with different learning styles should be taught with different instructional
methods is a belief for which very little, if any, scientific evidence exists. There are fundamental
problems with regard to the measurement of learning styles and the theoretical basis for the
assumed interactions between learning styles and instructional methods, and, last but not least,
substantial empirical evidence for the learning styles hypothesis is missing.
A focus on what learners have in common does not deny that there are individual differences; rather,
it helps to identify differences that really matter in education and to design instructional methods
that are practically feasible.
The expertise-reversal effect: indicates that learners with low prior knowledge learn more from
studying examples than from solving the equivalent problems and that this pattern reverses for
learners with higher prior knowledge.
Learners as Self-educators (on the Internet)
all that one needs to know and learn is “out there on the web” and that there is, thus, no need to
teach and/or acquire such knowledge any more.
self-educators can self-regulate and self-direct their own learning, seeking, finding, and making use of
all of the information sources that are freely available to them.
The idea that the present body of knowledge is rapidly becoming out of date or obsolete is far from
true. First, a distinction needs to be made with respect to the difference between knowledge
obsolescence and information growth. There is an enormous growth in the amount of information
available, but this does not mean that the existed prior to the Internet revolution is obsolete,
irrelevant, or no longer holds. Much is still valid and useful.
The set of activities and/or skills needed to adequately deal with this information generation and
dissemination is frequently referred to as information literacy or—when information and
communication technologies also play a key role—digital literacy activities/skills.
, solving information problems is for most students a major if not insurmountable cognitive endeavor.
Searching, finding, and processing information is a complex cognitive process that requires identifying
information needs, locating corresponding information sources, extracting and organizing relevant
information from each source, and synthesizing information from a variety of sources.
Learners not only have problems finding the information that they are seeking but also often trust the
first thing they see.
young children, teenagers, and adults are not capable of effectively choosing proper search terms,
selecting the most relevant websites, and questioning the validity of sources.
students lack regulatory skills and have difficulties defining the information problem; identifying what
they do not know.
students must learn to solve information-based problems and must learn transferable search and
evaluation strategies.
Unfortunately, in most cases students’ prior knowledge of the subject matter is minimal. From
research, it is known that low prior knowledge negatively influences the search process. (even though
it is on the internet, there is a need to teach it.)
On some accounts, student-directed education is to replace teacher-directed education (Gibbons,
2002). Hase and Kenyon (2000) called this the shift from andragogy (i.e., focusing on the best ways for
people to learn) to heutagogy (i.e., focusing on learning how to learn and learner self-direction).
Problems with the use of self-directed and self-determined learning in education:
learners are not always successful controlling their own learning, especially in computer-
based learning environments. Not all learners prefer nor profit from controlling the tasks and
forcing such control on them can be counterproductive. Learners do not have or do not know
how to utilize appropriate strategies when they are left to themselves to manage their
learning environment. learners often misregulate their learning, exerting control in a
misguided or counterproductive fashion and not achieving the desired result. This is due to
(a) not having the necessary standards upon which to judge their learning state, (b) not
having the necessary knowledge to monitor their own state in comparison with the
standards, and/or (c) not being able to initiate the proper processes to change their current
state when their behavior falls short of the standards.
learners often choose what they prefer, but what they prefer is not always what is best for
them.
Paradox of choice. . People appreciate having the opportunity to make some choices, but the
more options that they have to choose from, the more frustrating it is to make the choice. .
Shared control is a two-step process, in which the teacher or coach first selects from all
available learning tasks a subset of tasks with characteristics that fit the needs of the learner.
This should prevent overwhelming and frustrating the learner by letting him or her choose
from a very large set of tasks. Second, the learner selects from this subset one task to work
on. With learning tasks in the dietetics domain, shared control had superior effects on
student motivation. With ¨ learning tasks in the genetics domain, shared control over task
selection had superior effects on both student motivation and learning, provided that
learners had to choose from a subset of preselected tasks with features that were different
from the features of previous tasks. second-order scaffolding, in which there is gradually
decreasing support and guidance for self-directed learning skills. This second-order
scaffolding helps learners learn to select learning tasks, find relevant supportive information,
consult necessary procedural information, and identify useful part-task practice. The
instruction shifts gradually from traditional teacher or system controlled learning
environments, in which a teacher or another intelligent agent assesses a learner’s
performance and selects suitable further learning tasks, to on-demand education, in which
the learner self-assesses her or his performance and independently selects her or his own
learning tasks.
