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Learning as a Generative Activity: Eight Learning Strategies that Promote Understanding


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During the past twenty-five years, researchers have made impressive advances in pinpointing effective learning strategies (i.e., activities the learner engages in during learning that are intended to improve learning). In Learning as a Generative Activity: Eight Learning Strategies That Promote Understanding, Logan Fiorella and Richard E. Mayer share eight evidence-based learning strategies that promote understanding: summarizing, mapping, drawing, imagining, self-testing, self-explaining, teaching, and enacting. Each chapter describes and exemplifies a learning strategy, examines the underlying cognitive theory, evaluates strategy effectiveness by analyzing the latest research, pinpoints boundary conditions, and explores practical implications and future directions. Each learning strategy targets generative learning, in which learners actively make sense out of the material so they can apply their learning to new situations. This concise, accessible introduction to learning strategies will benefit students, researchers, and practitioners in educational psychology, as well as general readers interested in the important twenty-first-century skill of regulating one's own learning.
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is book is based on the idea that meaningful learning is a generative
activity in which the learner actively seeks to make sense of the presented
material. e study of generative learning has implications for the sci-
ence of learning, the science of assessment, and the science of instruc-
tion. Concerning the science of learning, generative learning takes place
when the learner engages in appropriate cognitive processing during
learning, including attending to the relevant information (i.e., selecting),
mentally organizing incoming information into a coherent cognitive
structure (i.e., organizing), and integrating the cognitive structures with
each other and with relevant prior knowledge activated from long-term
memory (i.e., integrating). Concerning the science of assessment, gener-
ative learning is demonstrated when students who learn with generative
learning strategies or generative instructional methods perform better
on transfer tests than students who learn from standard instruction.
Concerning the science of instruction, generative learning can be
p r o m o t e d t h r o u g h instructional methods aimed at designing instruc-
tion that primes appropriate cognitive processing during learning or
through learning strategies a i m e d a t t e a c h i n g s t u d e n t s h o w a n d w h e n t o
engage in activities that require appropriate cognitive processing during
learning. is book focuses on eight generative learning strategies that
have been shown to improve student learning: summarizing, mapping,
drawing, imagining, self-testing, self-explaining, teaching, and enacting.
e concept of generative learning has roots in the work of Wittrock and
others, continues as a dominant view of learning today, and shows prom-
ise of further development in the future.
Introduction to Learning as a Generative
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Learning as a Generative Activity
Chapter Outline
1. Getting Started
2. What Is Generative Learning?
3. Implications of Generative Learning for the Science of Learning
4. Implications of Generative eory for the Science of Assessment
5. Implications of Generative eory for the Science of Instruction
6. What Is the Past and Future of Generative Learning?
Getting Started
What Can You Do?
S u p p o s e y o u s i t d o w n t o r e a d a b o o k c h a p t e r, y o u a t t e n d a P o w e r P o i n t l e c -
ture, or you view an online multimedia presentation. You are pro cient at
reading and listening, so you can easily understand all the words. Yet, when
you are nished with the lesson, you are not able to apply what you have
learned to new situations or to use the material to solve problems. What
could you have done to help you understand the material rather than sim-
ply to process every word?
is book is concerned with exploring what the research evidence has
to say about answering this seemingly simple question. Our proposed
solution is that you could engage in generative learning strategies during
learning – activities that are intended to prime appropriate cognitive pro-
cessing during learning (such as paying attention to the relevant infor-
mation, mentally organizing it, and integrating it with your relevant prior
For example, you could try to summarize the material in your own words
(perhaps by taking summary notes), you could create a spatial summary of
the material as a matrix or network, you could make a drawing that depicts
the main ideas in the text, or you could just imagine a drawing. ese are all
ways of translating the lesson into another form of representation.
A l t e r n a t i v e l y , y o u c o u l d g i v e y o u r s e l f a p r a c t i c e t e s t o n t h e m a t e r i a l ( s u c h
as trying to answer some questions), you could explain the material aloud
to yourself during learning, you could explain the material to someone else,
or you could use concrete objects to act out the material in the lesson. ese
are all ways of elaborating on the material.
Exploring each of these eight kinds of generative learning strategies is
the primary goal of this book.
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Introduction 3
Try is
L e t s b e g i n w i t h a b r i e f a s s e s s m e n t o f y o u r v i e w o f l e a r n i n g . M o s t p e o p l e
have an implicit theory of learning, because we all have spent so much time
in school. Please place a check mark next to each item that corresponds
with your conception of how learning works.
Learning works by engaging in hands-on activity, so it is better for
you to learn by doing rather than by being told.
L e a r n i n g w o r k s b y b u i l d i n g a s s o c i a t i o n s , s o y o u s h o u l d p r a c t i c e
giving the right response over and over.
L e a r n i n g w o r k s b y a d d i n g i n f o r m a t i o n t o y o u r m e m o r y , s o y o u
should work hard to nd and memorize new material.
L e a r n i n g o c c u r s w h e n y o u t r y t o m a k e s e n s e o f m a t e r i a l y o u e n c o u n -
ter, so you should strive to relate new information with your prior
L e a r n i n g i s a s o c i a l a c t i v i t y , s o i t i s b e t t e r f o r y o u t o l e a r n w i t h o t h e r s
in a group than to learn alone.
If you checked the fourth item, your view of learning corresponds to the
conception of generative learning proposed in this book – which simply
shows you have the good common sense to agree with us. As you will see
in this book, the learner’s cognitive processing during learning is a major
contributor to what is learned.
I f y o u a r e l i k e m o s t p e o p l e , y o u m a d e s o m e o t h e r c h e c k m a r k s . e rst
item is appealing, but according to the generative learning view, it focuses
too much on behavioral activity and not enough on cognitive activity.
Doing things does not necessarily cause learning, but thinking about what
you are doing does cause learning. us, the rst item should be modi ed
to say, “Learning works by engaging in appropriate cognitive activity during
e second item also seems appealing and is consistent with the rst the-
ory of learning to emerge in psychology and education more than a century
ago – which can be called associative learning . However, according to the
generative learning view, learning by forming associations applies to a nar-
row band of learning situations – such as learning to give the right response
for a given stimulus. Associative learning is not wrong, but it is just too lim-
ited. It does not deal with learning by understanding, which allows people
to take what they have learned and apply it in new situations.
e third item may sound familiar because it seems consistent with some
common educational practices such as asking students to attend hours of
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Learning as a Generative Activity
lectures or read hundreds of textbook pages. What is wrong with this item,
however, is that humans do not work like computers. We do not simply take
in what was presented and put it into our memory. Instead, we interpret it,
we reorganize it, and we relate to what we already know, thereby changing
what is presented from information (which is objective) into knowledge
(which is personal).
F i n a l l y , t h e l a s t i t e m i s c o n s i s t e n t w i t h a n e m e r g i n g v i s i o n o f l e a r n -
ing based on the idea that generative learning occurs best within group
c o n t e x t s t h a t i s , w h e n y o u c a n i n t e r a c t w i t h o t h e r s d u r i n g t h e l e a r n -
ing process. However, research on group learning tends to show that all
group interactions are not equally helpful in promoting meaningful learn-
ing. us, generative learning theory indicated by the fourth item can
be expanded to include social activities that promote appropriate cogni-
tive processing during learning and to exclude social activities that do not.
Overall, the point of this little exercise is to help you understand how the
generative learning view is di erent from what might seem like some com-
mon-sense views of learning.
Turning Passive Learning Situations into Active
Learning Situations
S u p p o s e t h a t y o u a r e a b o u t t o r e a d a t e x t b o o k c h a p t e r o n t h e h i s t o r y o f
the U.S. postal service, attend a PowerPoint lecture on how a virus causes a
cold, or view an online narrated animation explaining how lightning storms
develop. Each of these activities reading a book, attending a lecture, or
viewing an online presentation – seems like a passive experience destined
to foster suboptimal learning.
Yo u m i g h t b e s u r p r i s e d t o l e a r n t h a t t h e r e a r e e ective techniques that
can be used to turn such seemingly passive learning situations into active
learning experiences that produce meaningful learning. is book presents
eight ways to help people learn based on a generative theory of learning –
the idea that meaningful learning occurs when people engage in generative
processing during learning. In particular, each of the techniques seeks to
encourage learners to relate the represented material to what they already
know, or reorganize the presented material into a coherent structure, or
distinguish what is important from what is not. In this chapter, we describe
what we mean by generative learning; explain how generative learning con-
tributes to the science of learning, the science of assessment, and the sci-
ence of instruction; and end with a brief review of the history of scholarship
on generative learning.
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Introduction 5
What Is generative Learning?
L e a r n i n g i s a g e n e r a t i v e a c t i v i t y . is statement embodies a vision of
learning in which learners actively try to make sense of the instructional
material presented to them. ey accomplish this goal by actively engag-
ing in generative processing during learning, including paying attention
to the relevant aspects of incoming material (which we call selecting ),
organizing it into a coherent cognitive structure in working memory
(which we call organizing ), and integrating cognitive structures with rel-
evant prior knowledge activated from long-term memory (which we call
integrating ).
A s y o u c a n s e e , t h e l e a r n e r s c o g n i t i v e p r o c e s s i n g p l a y s a c e n t r a l r o l e i n
generative learning. Learning is not simply a process of adding information
to memory, as in a computer. Instead, learning depends both on what is
presented and on the learner’s cognitive processing during learning.
Similarly, the learner’s prior knowledge plays a central role in genera-
tive learning. Prior knowledge includes schemas, categories, models, and
principles that can help guide what the learner selects for further process-
ing, how the learner organizes it, and how the learner links it with other
structurally similar knowledge. us, learning depends both on what the
instructor presents and what the learner brings to the learning situation.
is is why two learners can be exposed to the same learning scenario such
as attending the same lecture or viewing the same online presentation – and
come away with quite di erent learning outcomes.
As summarized in Table 1.1 , not all forms of learning are generative
learning – that is, learning by understanding, which results in meaningful
learning outcomes. Another common form of learning is rote learning
that is, learning by memorizing, which results in rote learning outcomes.
Finally, there is also associative learning t h a t i s , l e a r n i n g b y s t r e n g t h e n -
ing associations, which results in rapid responses to well-learned stimuli.
Although there are other forms of learning, in this book, we focus on
Table 1.1. ree kinds of learning situations
Learning situation What happens What is enabled
Generative learning Making sense of information Solving new problems
Rote learning Memorizing information Remembering what was
Associative learning Building associations Giving a response for a
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Learning as a Generative Activity
generative learning. In particular, we focus on ways to promote generative
learning because we are interested in helping students transfer what they
have learned to new situations.
O u r r a t i o n a l e f o r f o c u s i n g o n g e n e r a t i v e l e a r n i n g i s t h a t t h e t w e n t y - rst
century needs problem solvers and sense makers (Pellegrino & Hilton, 2012 ) .
e need for rote learning and associative learning is somewhat reduced
because we now have access to databases that can store vast amounts of
information or give answers to simple questions. e world needs people
who can select, interpret, and use information to solve new problems they
have not encountered before. In short, today’s focus on twenty- rst-century
skills such as creative problem solving, critical thinking, adaptability, com-
plex communication, and constructing evidence-based arguments can be
seen as a call for generative learning that helps people develop “transferable
knowledge and skills” (Pellegrino & Hilton, 2012 , p. 69).
Implications of Generative Learning
for the Science of Learning
e science of learning is the scienti c study of how people learn (Mayer,
2011 ) . is section examines the cognitive processes, memory stores, and
knowledge representations involved in generative learning, as well as the
motivational and metacognitive processes that support them.
Cognitive Processes in Generative Learning
H o w d o e s l e a r n i n g w o r k ? e basic premise of generative learning theories
is that learning occurs when learners apply appropriate cognitive processes
to incoming information. Figure 1.1 s u m m a r i z e s t h e SOI model of gen-
erative learning, w h i c h f o c u s e s o n t h r e e c o g n i t i v e p r o c e s s e s i n d i c a t e d b y
arrows – selecting, organizing, and integrating. As indicated by the arrow
from instruction t o sensory memory, i n s t r u c t i o n f r o m t h e o u t s i d e w o r l d
enters your cognitive system through your eyes and ears (or other senses)
and is brie y held in your sensory memory for a fraction of a second. If
you pay attention to some of this eeting information in sensory memory,
you transfer the attended material to working memory for further pro-
cessing (as indicated by the selecting a r r o w ) . I n w o r k i n g m e m o r y , y o u c a n
mentally reorganize the selected material into coherent mental representa-
tions (as indicated by the organizing a r r o w ) . Y o u c a n a l s o a c t i v a t e r e l e v a n t
prior knowledge from long-term memory and integrate it with incom-
ing material in working memory (as indicated by the integrating a r r o w ) .
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Introduction 7
e knowledge you build in working memory can be stored in long-term
memory for future use (as indicated by the arrow from working memory
to long-term memory ) a n d c a n b e u s e d t o s o l v e p r o b l e m s y o u e n c o u n t e r
in the outside world (as indicated by the arrow from working memory t o
performance ) .
A n i m p o r t a n t i n s t r u c t i o n a l i m p l i c a t i o n o f t h e S O I m o d e l i s t h a t t h e
instructor’s job is not only to present information but also to make sure his
or her students engage in appropriate processing during learning – including
selecting, organizing, and integrating. Similarly, the learners job is not to
memorize the information exactly as it is presented but to engage in appro-
priate cognitive processing during learning. Table 1.2 s u m m a r i z e s t h e t h r e e
cognitive processes in the SOI model of generative learning, which has been
continuously adapted to the study of learning strategies over the past thirty
Instruction Performance
figure 1.1. e SOI Model of Generative Learning.
Table 1.2. ree cognitive processes in generative learning
Cognitive process Description Arrow in SOI Model
Selecting Attending to relevant material Arrow from sensory memory
to working memory
Organizing Mentally organizing incoming
material into a coherent
cognitive structure
Arrow from working
memory back to working
Integrating Connecting cognitive structures
with each other and with
relevant material activated
from long-term memory
Arrow from long-tem
memory to working
memor y
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Table 1.3. ree memory stores in generative learning
Memory store Description Capacity Duration
Sensory memory Holds visual images and
sounds of what was presented
High Very short
Working memory Allows pictures and words to
be held and manipulated
Limited Short
Long-term memory Acts as permanent storehouse
of knowledge
High Lon g
years (Kiewra, 2005 ; M a y e r , 1988 , 1994 , 1996 , 2011 ; P e p e r & M a y e r, 1986 ;
Shrager & Mayer, 1989 ; Weinstein & Mayer, 1985 ) .
Memory Stores in Generative Learning
e SOI model of generative learning shown in Figure 1.1 contains three
memory stores, indicated by the boxes. Sensory memory h o l d s s e n s o r y c o p -
ies of the visual images you saw and the sounds you heard (and other input
from other senses) for a fraction of a second, so it has high capacity for
a very short duration. In working memory , p i e c e s o f i n f o r m a t i o n c a n b e
consciously held and manipulated, but the capacity of working memory is
quite limited so you can actively process only a few pieces of information
at any one time (and without active processing, information is lost within
about twenty seconds). Long-term memory i s y o u r p e r m a n e n t s t o r e h o u s e o f
knowledge, so it has high capacity and long duration.
A c c o r d i n g t o t h e S O I m o d e l s h o w n i n F i g u r e 1.1 , w o r k i n g m e m o r y i s a
sort of bottleneck in your cognitive system because it has limited processing
capacity (i.e., only a few elements can be actively processed at one time),
whereas sensory memory and long-term memory on either side of it each
have large capacities. An important instructional implication of this bot-
tleneck is that rapidly presenting a lot of information to a learner is likely
to overload the learner’s working memory and result in much of the infor-
mation being lost. e three memory stores in the SOI model of generative
learning are summarized in Table 1.3 .
Knowledge Representations in Generative Learning
I n a d d i t i o n t o u n d e r s t a n d i n g t h e b o x e s a n d a r r o w s i n F i g u r e 1.1 , i t i s
worthwhile to consider the kinds of external and internal representations
involved in generative learning. For example, consider what happens when
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Introduction 9
you attend a narrated slideshow lecture. We begin with the instructional
presentation involving spoken words, printed words, and graphics , which
become visual images and auditory sounds i n y o u r s e n s o r y m e m o r y , spatial
and verbal representations t h a t c a n b e m a n i p u l a t e d i n w o r k i n g m e m o r y ,
and semantic knowledge s t o r e d i n l o n g - t e r m m e m o r y . e conversion of
presented information (i.e., the external representation) into constructed
knowledge (i.e., the internal representation) is what happens when learners
engage in generative learning. ree important steps in the development
of knowledge in working memory are to select the pieces of information
for further processing, to build internal connections among them so they
form a coherent representation, and to build external connections with
other representations in a systematic way. Table 1.4 l i s t s t h e p r o g r e s s i o n o f
representations in generative learning.
Metacognition and Motivation in Generative Learning
G e n e r a t i v e l e a r n i n g r e q u i r e s t h a t l e a r n e r s a p p l y a p p r o p r i a t e c o g n i t i v e p r o -
cesses during learning, but how do learners know which processes to apply
and when to apply them? How do you know which information to select,
what kind of organization to build, and which aspect of prior knowledge
to activate? Monitoring and controlling your cognitive processes during a
cognitive task (such as learning from a lecture or from a book) is called
metacognition. us, an important task of generative learning theories is to
understand the workings of metacognitive strategies – that is, strategies for
monitoring and controlling cognitive processes.
E v e n i f y o u a r e s k i l l e d i n u s i n g t h e c o g n i t i v e p r o c e s s e s o f s e l e c t i n g , o r g a -
nizing, and integrating, and even if you possess the metacognitive strate-
gies for orchestrating them, you may still not engage in generative learning
because you just don’t want to. What causes people to initiate and maintain
generative processing at a high level during learning? Motivation i s d e ned
a cognitive state that initiates, energizes, and maintains goal-directed
behavior. In short, motivation drives the cognitive system, so it is crucial
Table 1.4. External and internal representations in generative learning
Representation Type Location
Printed words, spoken words, graphics External Instruction
Visual images and sounds Internal Sensory memory
Spatial and verbal representations Internal Working memory
Knowledge Internal Long-term memor y
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Learning as a Generative Activity
to incorporate motivational mechanisms into generative learning theory.
In particular, the learning strategies suggested in this book are intended
to motivate learners to engage in productive cognitive processing during
W e r e f e r t o m e t a c o g n i t i o n a n d m o t i v a t i o n a s t h e Mighty M’s because they
power the SOI model of generative learning shown in Figure 1.1 . W i t h o u t
the motivation to make sense of a lesson, generative learning would not be
initiated. Without the metacognitive skills to control cognitive processing
during learning, attempts at generative learning would not be e ective.
Implications of Generative Theory for the
Science of Assessment
e science of assessment is the scienti c study of how to determine what
people know (Anderson et al., 2001 ; M a yer, 2011 ; P e l l e g r i n o , C h u d o w s k y , &
Glaser, 2001 ) . I n t h i s s e c t i o n , w e d e s c r i b e t w o k i n d s o f t e s t i t e m s a n d t h r e e
kinds of learning outcomes.
Two Kinds of Test Items
T a b l e 1.5 s u m m a r i z e s t w o k i n d s o f t e s t i t e m s t h a t c a n b e u s e d t o a s s e s s
what students have learned, based on the classic distinction between reten-
tion a n d transfer . R e t e n t i o n i s t h e a b i l i t y t o r e c a l l o r r e c o g n i z e w h a t w a s
presented. us, retention items are used when the goal is to assess how
much of the presented material can be remembered. Transfer is the abil-
ity to apply what was learned to solve new problems. us, transfer items
are used when the goal is to assess how well someone understands the
presented material.
If we asked you to de ne retention , you could simply reproduce the sec-
ond sentence of the preceding paragraph, which is an example of a retention
Table 1.5. Two kinds of test items
Item Target Description Example
Retention Remembering Ability to recall or recognize
what was presented
What is the de nition
of retention?
Transfer Understanding Ability to apply what was
presented to solve new
Create a transfer item
for this lesso n.
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... One major implementation of retrieval practice can be seen in audience response systems (e.g., clicker questions), the frequent use of which has been shown to benefit student learning (e.g., McDaniel, Agarwal et al., 2011;. Of course, students can also use retrieval practice on their own, such as through flashcards, recitation, and answering adjunct questions (e.g., Dunlosky et al., 2013;Fiorella & Mayer, 2015Kornell & Bjork, 2007). ...
... For example, novices may benefit from strategies that guide the construction of new schemas, whereas experts may benefit from instruction that guides the retrieval and use of already acquired schemas (Kalyuga, 2014). Whereas the benefits of some learning strategies, like concept mapping and self-explanation, are greater for LPK students, the benefits of other strategies, such as imagining and enacting (and to a lesser extent -summarizing and drawing), seem to be greater for HPK students (e.g., Ambrose et al., 2010;Fiorella & Mayer, 2015McNamara, 2004). ...
... Retrieval practice can both directly and indirectly benefit learning and clearly generalizes over many different educationally-relevant factors (e.g., learning materials and contexts, Dunlosky et al., 2013), though educators and researchers alike continue to cite prior knowledge as a critical individual difference that must be explored in future research (e.g., Dunlosky & Rawson, 2019;Fiorella & Mayer, 2015Mayer, 2017;Murphy & Pavlik, 2018). Despite this need for additional research, the wide applicability of retrieval practice has sparked a number of articles recommending increased educational implementation (e.g., Agarwal et al., 2012;Dunlosky et al., 2013;Karpicke & Blunt, 2011;Karpicke & Grimaldi, 2012;Nunes & Karpicke, 2015;Roediger, Putnam, & Smith, 2011;Roediger & Pyc, 2012). ...
... Learning by explaining occurs when students generate a written or oral explanation of instructional material they are reading or viewing (Fiorella & Mayer, 2015. In a review, Fiorella and Mayer (2015) reported that in 44 of 54 experimental tests, students who were prompted to explain what they were reading or viewing performed better on a posttest than students who were not, yielding a median effect size of d = 0.61. ...
... Learning by explaining occurs when students generate a written or oral explanation of instructional material they are reading or viewing (Fiorella & Mayer, 2015. In a review, Fiorella and Mayer (2015) reported that in 44 of 54 experimental tests, students who were prompted to explain what they were reading or viewing performed better on a posttest than students who were not, yielding a median effect size of d = 0.61. This research includes studies involving reading static text lessons and studies involving viewing dynamic multimedia presentations. ...
... Generative learning activities, such learning by explaining, are inspired by generative learning theory, which posits that meaningful learning occurs when learners engage in appropriate cognitive processing during learning (Fiorella & Mayer, 2015Mayer, 2020;Wittrock, 1974Wittrock, , 1989. These cognitive processes for building structure in working memory include the following: ...
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Generative learning theory posits that learners engage more deeply and produce better learning outcomes when they engage in selecting, organizing, and integrating processes during learning. The present experiments examine whether the generative learning activity of generating explanations can be extended to online multimedia lessons and whether prompts to engage in this generative learning activity work better than more passive instruction. Across three experiments, college students learned about greenhouse gasses from a 4-part online lesson involving captioned animations and subsequently took a posttest. After each part, learners were asked to generate an explanation (write-an-explanation), write an explanation using provided terms (write-a-focused-explanation), rewrite a provided explanation (rewrite-an-explanation), read a provided explanation (read-an-explanation), or simply move on to the next part (no-activity). Overall, students in the write-an-explanation group (Experiments 2 and 3), write-a-focused-explanation group (Experiment 2), and rewrite-an-explanation group (Experiment 3) performed significantly better on a delayed posttest than the no-activity group, but the groups did not differ significantly on an immediate posttest (Experiment 1). These results are consistent with generative learning theory and help identify generative learning strategies that improve online multimedia learning, thereby priming active learning with passive media.
... The current literature offers extensive accounts of SDL and individual differences based on characteristics such as attribution (Schunk, 2016), self-efficacy and skill (Schunk & Rice, 1987), self-control and goal-setting (Schunk & Rice, 1989, 1991, self-determination (Deci & Ryan, 2000), intrinsic motivation (Corno, 2001), attitude toward growth (Dweck, 2006), and self-awareness (Vallerand et al., 1997); or based on teachers' providing supportive learning environments (Camahalan, 2006;Turner et al., 2002), paving proper learning roadmaps (Bandura, 1986;Schunk et al., 2008), aligning tasks to learners' zone of proximal development (Wiliam, 2018), formative assessment (Panadero et al., 2018), and providing training on learning strategies (Fiorella & Mayer, 2015;Lodico, et al., 1983;). This prior work provides a basis for deeper investigations of SDL in detailed classroom learning environments. ...
... Moreover, while controlled laboratory experiments hold much promise for empirically validating the effectiveness of SDLR, such an approach is not without challenges. First, learning is a generative activity (Fiorella & Mayer, 2015), where development of self-direction and self-regulation take time (Paris et al., 2001). Second, learning autonomy is rooted in contexts (Benson, 2001). ...
With the rapid changes in globalization and technology advancement, self-directed learning is argued repeatedly as a key competency needed to survive in the twenty-first century. In August 2019, the Taiwan Ministry of Education implemented the new Curriculum Guidelines for 12-Year Basic Education. Being Taiwan’s first official curriculum that promoted self-directed learning, this study contributed to the emerging knowledge of how the introduction of the curriculum guidelines affected students’ readiness for self-directed learning. Three cohorts of high school students from seven schools returned 10,020 valid surveys. The Self-Directed Learning Readiness Scale (SDLRS), a reputable instrument developed by Lucy Guglielmino (2000), was used in the study. The results provided a bird’s-eye view of evidence supporting our explanations for the positive, though slight, effect of implementing new curriculum guidelines in fostering self-directed high-school learners. Moreover, the progression of self-directed learning readiness appeared differently among schools and among different demographic associations. We suggest that future researchers both (a) qualitatively explore how specific latent variables were changed in different instructional interventions, and (b) conduct panel studies to advance our understanding of curriculum reform and learners’ self-direction.
... Les activités telles que la lecture, le débat, le dessin, ou la prise de note stimulent la motivation et permettent le développement des aptitudes de l'élève. Dans [FM15], les auteurs estiment que l'apprentissage est actif quand l'élève essaye activement de comprendre le matériel qui lui est proposé en s'impliquant dans un traitement cognitif de l'information. Ce traitement se compose de trois actions : ...
... Toujours dans [FM15], plusieurs stratégies sont proposées pour stimuler l'apprentissage actif, parmi lesquelles apprendre en dessinant, apprendre en résumant, apprendre en enseignant à un binôme, apprendre en s'expliquant à soi-même, ou apprendre en jouant un ...
Cette thèse s’inscrit dans le cadre du projet national « e-Fran » dénommé ACTIF et porte sur la conception du système tutoriel intelligent IntuiGeo pour l’apprentissage de la géométrie au collège sur tablette orientée stylet.Les contributions de cette thèse s'inscrivent dans deux axes.Le premier porte sur la conception d’un moteur de reconnaissance permettant l’interprétation à la volée de figures géométriques. Il est basé sur un formalisme grammatical générique, GMC-PC (Grammaire Multi-ensembles à Contraintes Pilotée par le Contexte). Le deuxième axe adresse l’aspect tutoriel du système. Nous définissons un mode auteur qui permet au tuteur de générer des exercices de construction à partir d'une solution dessinée par l'enseignant. La connaissance spécifique au problème est représentée par un graphe de connaissance. Cette modélisation permet au tuteur de s’affranchir de la procédure suivie par l’enseignant et d’évaluer la production de l'élève, en temps-réel, quel que soit la stratégie suivie. Nous définissons de plus un module expert, basé sur un environnement de planification, capable de synthétiser des stratégies de résolution des problèmes. Le système tutoriel est capable de générer des feedbacks de correction et de guidage adaptés à l'état de l'avancement de l'élève. Les résultats des expérimentations en classe démontrent l’impact pédagogique positif du système sur la performance des élèves, notamment en termes de transfert d’apprentissage entre support numérique et papier.
... Motivational aspects of learning are critical to learning environments which require a high level of engagement (Fiorella and Mayer, 2015;Buhr et al., 2019). Through the activities we proposed in our instructional design model, we paid greater attention to the self-efficacy cognitions (Bandura, 1997) and the three psychological needs, derived from Self Determination Theory -autonomy, control, and relatedness (Ryan and Deci, 2017). ...
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The COVID-19 pandemic brought many challenges in higher education. All teaching and learning activities were moved online. Universities had to provide adapted solutions to facilitate learning and maintain students' engagement. Online education implies creating new learning environments with the help of digital technologies. Beyond the process of acquisition of knowledge, teachers needed to facilitate cooperative learning, build positive relations, and reduce negative emotions. We provide some expert insights based on empirical observations on teaching and assessment practices connected with psychology models applied in education. The aim of the paper is to formulate specific learning design recommendations for developing effective didactic strategies and addressing the current worldwide critical issue: dealing with digitization of higher education in the immediate future. We propose a model of university classes aimed at bringing together our experience as teachers of psychology and didactics with evidence-based cognitive-educational theories and practices. The result is an example of an instructional work-model based on the complex dynamic between cognitive, emotional-motivational, and social aspects of learning in online settings. The effectiveness of university teaching in the post-digital era is strongly connected with the ability to create cognitive-transferable learning experiences, emotionally safe learning environments, while promoting an active autonomy-focused approach for self-regulated learning.
... The game's design followed several multimedia learning principles (Mayer 2009;Mayer 2014b): the segmenting principle (level segmentation, self-paced slides); the pre-training principle (main concepts introduced in the slides); the modality principle (narrated slides and hints during gameplay). The selftesting principle (Mayer, & Fiorella 2015) was also followed with the incorporation of two or three yes/no questions with explanatory feedback after level two, level four, and the last level. ...
The instructional effects of customization features in child learning games have rarely been examined. This value-added study addresses the existing gap with regards to user-initiated cosmetic customization of environment elements (i.e., non-avatar customization). Participants (N = 143; Mage = 9.41) studied a biological topic for about 20 min: either using the experimental version of a learning game with customization features, or from a control version without them. Null results were found as concerns between-group differences: both for motivation-related variables and learning outcome measures. These findings indicate that user-initiated cosmetic customization features can be omitted by game designers, especially in settings where children are assigned specific instructional materials from which to study. Lay Description What is already known • User-initiated cosmetic customization (the term user-initiated is afterwards left out of the text for brevity) refers to making choices about a game's visual or audio attributes without directly affecting the content of gameplay itself. • Studies with non-child learners showed that a sub-type of cosmetic customization referred to as avatar customization (i.e., customization of learners' game characters) can be potentially beneficial for learning. • The instructional efficiency of other sub-types of cosmetic customization (i.e., customization of other elements, such as environmental ones) in game-based learning for children is less clear. What this paper adds • Children (Grades 3–4) prefer a game with a cosmetically customizable environment over its non-customizable counterpart: but only when they can contrast these two versions (not when they cannot contrast them). • Children enjoy learning and learn equally from customizable and non-customizable games. Implications for practice • Game-based learning with cosmetic customization features of game's environment can be used for learning without risk of harming learning outcomes. • However, when children's autonomy in choosing instructional materials is restricted, there is little need to invest in the cosmetic customization of a game's environment elements. • Cosmetic customization of a game's environment elements may play a more notable role when children are able to choose learning materials.
A relatively new technology being used to deliver academic lessons is immersive virtual reality (IVR). This study examined whether IVR is a more effective instructional medium than other multimedia, such as a video on a computer monitor. Additionally, this study explored the underlying affective and cognitive mechanisms of learning in an immersive environment. Participants viewed a history lesson in IVR or a 3D interactive video display on a desktop monitor. The results showed that participants who viewed the video lesson outperformed those who viewed the IVR lesson on transfer tests. The IVR lesson caused higher emotional arousal based on self-report and heart rate measures, and lower cognitive engagement based on electroencephalogram (EEG) measures. The results suggest that immersive environments may create excessive positive emotions, which distract form the necessary cognitive processing during the lesson, thereby harming performance on subsequent tests of learning outcomes.
Knowing when and how to most effectively use writing as a learning tool requires understanding the cognitive processes driving learning. Writing is a generative activity that often requires students to elaborate upon and organise information. Here we examine what happens when a standard short writing task is (or is not) combined with a known mnemonic, retrieval practice. In two studies, we compared learning from writing short open-book versus closed-book essays. Despite closed-book essays being shorter and taking less time, students learned just as much as from writing longer and more time intensive open-book essays. These results differ from students’ own perceptions that they learned more from writing open-book essays. Analyses of the essays themselves suggested a trade-off in cognitive processes; closed-book essays required the retrieval of information but resulted in lower quality essays as judged by naïve readers. Implications for educational practice and possible roles for individual differences are discussed.
Teaching other students in a face-to-face manner has been shown to effectively foster both one’s own and their learning. This study experimentally investigated whether and how tutors and tutees academically benefit from three phases of face-to-face teaching: preparing-to-teach, initial-explanation, and interaction phases. Japanese undergraduates (n = 80) acted as tutors or tutees in peer tutoring. After studying with the expectation of teaching face-to-face or taking a test (the preparing-to-teach phase), tutor participants provided tutee participants with initial instructional explanations, without asking or answering questions (the initial-explanation phase), and then engaged in a question-and-answer period (the interaction phase). Tutor and tutee participants learned better by providing and receiving higher-quality explanations in the initial-explanation and interaction phases. Face-to-face teaching vs. test expectancy had no effects on the quality of tutor participants’ explanations or their learning outcomes. The results suggest that both the initial-explanation and interaction phases contribute to learning by teaching face-to-face, whereas the preparing-to-teach phase does not.
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Three forms of summarization instruction comprised the treatment and control conditions explored in this study of sixth graders. The two treatment groups received direct instruction in either a rule-governed approach to summarization or an intuitive approach. A control group simply received advice to find main ideas with no explicit modeling. Two dependent measures were used to judge the efficacy of the three instructional approaches to summarization: (a) a paragraph summary writing task and (b) a standardized test of paragraph comprehension. On both measures, treatment groups significantly outperformed the control group. The results are discussed from the perspective of a combined textlinguistic and direct instruction model of learning.
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The effects of student-generated prequestions and summaries were compared as reading study strategies for college-age subjects. Eighty-seven freshmen and sophomores from several sections of a developmental reading course were trained to use one of two study approaches: (a) phrasing and answering higher level questions while reading or (b) constructing and checking summary statements while reading. An additional number of students from the same population served as members of a control group. The results of three post-treatment tests–free recall, an objective test, and an essay test–were used as dependent measures. The results indicated that training in summary generation as an adjunct study activity significantly increased college students' free recall as well as performance on the objective test and, especially, on the essay test. Training in the interspersed prequestioning significantly facilitated students' performance on the objective test recall but not on free recall or the essay test. It was concluded that college students benefited from engaging in encoding strategies during reading and that specific strategies may be strongly related to posttest measures.
Before and after testing on the Wide Range Achievement Test (WRAT) over a four month period assessed gains for student tutors and their pupils in a rural school system. 13 tutored pupils (grades 2 to 5) showed a mean net growth advantage of from 3 to 5 months on WRAT subtests over 14 comparable control untutored pupils. 10 student tutors (grades 8 to 12) showed a mean 9 month edge over controls, a gain of 13 months achieve-over 10 comparable control non-tutors. On the three WRAT subtests all experimental means exceeded control means, but only the reading subtest was significant at the 5% level (tutors held a mean 9 month edge over controls, a gain of 13 months achievement in 4 months). Implications discussed include suggesting a much more institutionalized role reversal between teacher and student, student tutoring, and an educational cooperative with graduated salaries and personal involvement for all participants.
Twenty-four undergraduate students were asked to read a 167-word expository text about Dutch elm disease and to write a summary of the text. Five days later, they were asked to complete a sentence-recognition task and to verbalize components of a successful text summary. Efficiency of summarization (a proportion of number of judged-important ideas to total number of words) was assessed, and high-efficient and low-efficient summarizers were compared on recognition and verbalization performance. An important finding of the study was that high-efficient students "recognized" true-to-text synthesis statements, which did not appear in the original text, far more frequently than low-efficient students, but also failed to strongly reject statements inconsistent with low-importance, in-text information. It appeared, within the study, that these students not only summarized efficiently, but also stored information in memory efficiently (i.e., in a highly streamlined, condensed manner).
The purpose of this study was to increase the learning of economics among lower socioeconomic level public high school students by teaching them to use generative comprehension procedures in their economics classes’ cooperative learning groups. In a randomly assigned two-treatment design, it was predicted and found that generative learning procedures in cooperative learning classes increased (p < . 0001) the learning of economics by sizable amounts compared with a control procedure that used only cooperative learning methods and that produced smaller increases. Students’ confidence in the correctness of their answers increased (p < . 0001), and the level of misinformation decreased (p < . 0001) as a result of generative teaching procedures. These facilitative effects of generative teaching occurred for both males and females.
Many reading comprehension strategies have been proposed, but only some have proven potent with elementary school children. Strategies that are supported by research evidence are discussed, and, thus, a fairly small set of strategies is recommended. The research on summarization, representational- and mnemonic-imagery, story-grammar, question-generation, question-answering, and prior-knowledge activation strategies is reviewed here. Effective teaching of these strategies is also discussed, with particular emphasis on direct explanation approaches to strategy instruction. Thorough teaching of a few effective reading strategies can be defended based on available research evidence; this approach can be incorporated into ongoing content-based instruction, with development of reading comprehension strategies occurring throughout the school day and across the curriculum.
This study reports the effects of metacognitive strategy training in summarization on the ability of foreign language learners to comprehend and summarize expository texts. Results indicated that students made substantial progress after the training: they included significantly more ideas units in their recall protocols, improved their ability to use the summarization rules, included significantly more important information in their summaries and expressed it in a more succinct manner. The improved summary performance was maintained three weeks after instruction ended. These results suggest that explicit instruction in the rules of summarization is an effective tool for improving comprehension and summarization of foreign language texts.
Three studies were conducted to examine the extent to which mapping strategies used in conjunction with basal reader stories enhanced the comprehension and writing performance of fifth-grade students. In Experiment 1, significant differences on a reading comprehension measure were found in favor of a group receiving key concept mapping strategies when compared to a control group, but no differences were found in the overall quality of compositions produced (N =30). The key-concept mapping strategy was found to work particularly well for those types of reading selections that were factual/informative in nature. Experiment 2 expanded the previous study to 80 subjects drawn from eight classrooms. In this study, the stories mapped were restricted to factual/informative type selections. A detailed scoring guide was developed in an attempt to pick up any differences in writing produced by the students. Again, significant differences were found on the reading comprehension measure, but no differences were found in the compositions produced. Experiment 3 was designed to test the effectiveness of literary mapping strategies when used with narrative type reading selections. No differences were found between the mapping group and control group on either reading comprehension or writing produced. Conclusions are that key concept mapping is a particularly powerful tool for enhancing reading comprehension of factual/informative reading selections; literary mapping strategies may present a viable alternative to inject variety into the reading lesson; and, any improvement in writing performance may require more long term intervention or direct instruction.