BookPDF Available

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


Abstract and Figures

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.
Content may be subject to copyright.
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
Cambridge University Press
978-1-107-06991-6 - Learning as a Generative Activity: Eight Learning
Strategies that Promote Understanding
Logan Fiorella and Richard E. Mayer
More information
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.
Cambridge University Press
978-1-107-06991-6 - Learning as a Generative Activity: Eight Learning
Strategies that Promote Understanding
Logan Fiorella and Richard E. Mayer
More information
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
Cambridge University Press
978-1-107-06991-6 - Learning as a Generative Activity: Eight Learning
Strategies that Promote Understanding
Logan Fiorella and Richard E. Mayer
More information
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.
Cambridge University Press
978-1-107-06991-6 - Learning as a Generative Activity: Eight Learning
Strategies that Promote Understanding
Logan Fiorella and Richard E. Mayer
More information
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
Cambridge University Press
978-1-107-06991-6 - Learning as a Generative Activity: Eight Learning
Strategies that Promote Understanding
Logan Fiorella and Richard E. Mayer
More information
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 ) .
Cambridge University Press
978-1-107-06991-6 - Learning as a Generative Activity: Eight Learning
Strategies that Promote Understanding
Logan Fiorella and Richard E. Mayer
More information
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
Cambridge University Press
978-1-107-06991-6 - Learning as a Generative Activity: Eight Learning
Strategies that Promote Understanding
Logan Fiorella and Richard E. Mayer
More information
Learning as a Generative Activity
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
Cambridge University Press
978-1-107-06991-6 - Learning as a Generative Activity: Eight Learning
Strategies that Promote Understanding
Logan Fiorella and Richard E. Mayer
More information
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
Cambridge University Press
978-1-107-06991-6 - Learning as a Generative Activity: Eight Learning
Strategies that Promote Understanding
Logan Fiorella and Richard E. Mayer
More information
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.
Cambridge University Press
978-1-107-06991-6 - Learning as a Generative Activity: Eight Learning
Strategies that Promote Understanding
Logan Fiorella and Richard E. Mayer
More information
... For instance, Mayer's (1984Mayer's ( , 1996Mayer's ( , 2014 selectorganize-integrate model posits that meaningful learning draws on three cognitive processes: selecting relevant information, organizing the selected information into a coherent mental representation, and integrating the newly constructed representation with existing knowledge structures. Accordingly, generative learning strategies are those that encourage learners to meaningfully make sense of to-be-learned information through engaging in such cognitive processes (Fiorella & Mayer, 2015. Of particular interest, one such generative strategy is learning-by-teaching. ...
... Over the past decades, an explosion of research on retrieval practice-the act of testing oneself from memory-has robustly shown that it is a potent technique for enhancing durable and meaningful learning (Roediger & Karpicke, 2006a, 2006b; for recent reviews, see Adesope et al., 2017;Agarwal et al., 2021;Carpenter et al., 2022;Karpicke, 2017;Yang et al., 2021). Besides reducing mindwandering (Wong & Lim, 2022a), the generative learning strategy of retrieval practice prompts learners to selectively activate and retrieve relevant knowledge, organize it by strengthening connections among learned ideas, and integrate the learned information with their prior knowledge by building new connections (Fiorella & Mayer, 2015. For instance, the elaborative retrieval account (Carpenter, 2009(Carpenter, , 2011 posits that retrieval activates cue-related semantic information, which may become bound with the target information to yield a more elaborated memory trace that aids future recall. ...
... Likewise, concept-mapping is a generative learning strategy that involves actively making sense of incoming information (Fiorella & Mayer, 2015Karpicke & Blunt, 2011b). In conceptmapping, learners graphically organize to-be-learned material by selecting key concepts to be represented as nodes, while organizing them into a coherent structure using links that represent their relations, and integrating the information with prior knowledge by determining the overall hierarchical arrangement of concepts (Novak & Gowin, 1984). ...
Asking good questions is vital for scientific learning and discovery, but improving this complex skill is a formidable challenge. Here, we show in two experiments (N = 152) that teaching others—learning-by-teaching—enhances one’s ability to generate higher-order research questions that create new knowledge, relative to two other well-established generative learning techniques: retrieval practice and concept-mapping. Learners who taught scientific expository texts across natural and social sciences topics by delivering video-recorded lectures outperformed their peers who practiced retrieval or constructed concept maps when tested on their ability to generate create-level research questions based on the texts (Experiment 1). This advantage held reliably even on a delayed test 48 hr later, and when all learners similarly received and responded to poststudy questions on the material (Experiment 2). Moreover, across both immediate and delayed tests, learning-by-teaching produced a recall benefit that rivaled that of the potent technique of retrieval practice. In contrast, despite recalling more than twice the study content that the concept-mapping group did, learners who practiced retrieval were unable to generate more create-level research questions based on that content. Three supplemental experiments (N = 168) further showed that retrieval practice consistently did not improve higher-order question generation over restudying, despite yielding superior long-term retention. Altogether, these findings reveal that simply possessing a wealth of factual knowledge is insufficient for generating higher-order research questions that create new knowledge. Rather, teaching others is a powerful strategy for producing deep and durable learning that enables research question generation. To ask better questions, teach.
... Learning-by-teaching is a generative learning strategy in which students are asked to teach what they are learning to others (Fiorella & Mayer, 2015). In this study, college students watched a multimedia lesson on chemical synaptic transmission with instructions that afterward they would explain the materials by making a lecture video (teach-to-camera condition), explain to a student face-to-face (teach-to-student condition), or explain to seven students face-to-face (teach-to-group condition), and then they engaged in the corresponding teaching activity, respectively. ...
... learning (Brown et al., 2014;Dunlosky et al., 2013;Fiorella & Mayer, 2015;Miyatsu et al., 2018). This work yields a growing list of effective generative learning activities, that is, activities that students perform during learning or studying with the intention of improving their learning outcomes. ...
... The present study focuses on how to increase the effectiveness of one generative learning strategy that has shown promise: learning-by-teaching. Learning-by-teaching is a generative learning activity in which learners explain the material in a lesson to others after studying it (Fiorella & Mayer, 2015Mayer, 2021). In light of the increasing use of online instruction that may not prime generative processing in learners, we focus on how to implement learning-by-teaching involving an online multimedia lesson such as a narrated animation (Fiorella & Mayer, 2021). ...
... Cases situate learning and instruction in the real-world context of teaching and (Hatch et al., 2016;Csanadi et al., 2021;Syring et al., 2015;Gravett et al., 2017;Stark et al., 2011). In other words, it serves as a generative prompt and fosters the transfer of learning for contextual application (Stark et al., 2011;Syring et al., 2015;Hatch et al., 2016;Fiorella & Mayer, 2015). Third, we discuss the potential of collaborative learning for harnessing the instructional effectiveness and benefits of classroom cases. ...
... Learning can take different forms, such as rote, association, or generation (Fiorella & Mayer, 2015). Rote learning entails memorizing information, associative learning involves building association, while generative learning demands that the learner draw meaning from and make sense of the information they are learning (Fiorella & Mayer, 2015;National Research Council, 2012;National Research Council, 2004 Darling-Hammond, 2014). ...
... Learning can take different forms, such as rote, association, or generation (Fiorella & Mayer, 2015). Rote learning entails memorizing information, associative learning involves building association, while generative learning demands that the learner draw meaning from and make sense of the information they are learning (Fiorella & Mayer, 2015;National Research Council, 2012;National Research Council, 2004 Darling-Hammond, 2014). This practice-focused goal of teacher preparation necessitates the need to engage preservice teachers in generative learning experiences, which require them to actively make sense of the teaching cases they engage in during their learning and apply what they learn in new situations (Ball & Forzani, 2009;Santagata & Guarino, 2011;Shulman, 1992). ...
... The model is based on the current conceptualisation of learning as the construction of new beliefs, knowledge and skills (i.e., competences) and the reconstruction and/or suppression of previous ones (e.g., Chi, 2009;Fiorella & Mayer, 2015;National Academies of Sciences, Engineering, and Medicine, 2018). The model is presented in Figure 1. ...
... Second, student knowledge of a wide set of learning strategies-goaloriented activities used for acquiring, organising, and transforming information-and skills to use them adequately are vital for understanding and memorising learned material. These skills are particularly important when there is little support from adults (Dunlosky et al., 2013;Fiorella & Mayer, 2015). The value of organising learning materials, deep (vs. ...
The importance of students' learning to learn competence for academic achievement, as well as their well‐being at school and in life, is increasingly emphasised by educators and policy makers in national curricula and educational strategies. In an uncertain and complex world, learners need to become autonomous, be able to analyse challenges and apply knowledge in different contexts, address complex tasks, and create new knowledge. This article explores concepts and approaches to the development of students' learning to learn competence in the context of education in Estonia. First, the conceptualisation, model and dimensions of learning to learn competence are described and related challenges for teachers are analysed. Second, an overview of Estonian teachers' current practices, beliefs, knowledge, skills and occupational standards relevant to students' learning to learn competence is provided. We discuss how Estonian teacher education policy may enhance or inhibit the work of teachers when supporting students to develop learning to learn competence. Future directions for teacher educators and how to prepare teachers to support the development of students' learning to learn competence are suggested.
... Fig. 1 illustrates an anatomy sketch used as source material in class. The pedagogical foundation of this work lies in the concept of generative drawing [6], which stipulates that learning by drawing can enhance student performance and understanding of the course material. We are therefore interested in problem solving by drawing anatomy sketches that satisfy the constraints defined in the instruction. ...
Conference Paper
Full-text available
This paper presents the first works on IntuiSketch, a pen-based intelligent tutoring system for anatomy courses in higher education. Pen-based tablets offer the possibility to have pen and touch interaction, which mimics the traditional pen and paper setting. The objective here is to combine online recognition techniques, that enable to interpret the sketches drawn by the students, with tutoring techniques, that model the domain knowledge. IntuiSketch is able to analyze the student drawings relatively to a problem defined by the teacher, and generate corrective feedback. The online recognition is based on the bi-dimensional grammar CD-CMG (Context Driven Constraint Multiset Grammar) which models the document structure, coupled with a fuzzy incremental classifier, which is able to learn from few examples. The tutoring system is based on constraint modeling, which enables to define domain and problem knowledge, and to analyse the student production relatively to the constraints that have to be satisfied to solve the problem. In this paper, we present a new architecture for anatomy sketch targeted intelligent tutoring system that combines different techniques. We also present a qualitative study of the feedback that our first system version is able to generate on a case study.
... Besser ist es, dass die Lernenden ihr Wissen abrufen und in eigenen Worten notieren resp. sich gegenseitig erklären, ausgearbeitete Lösungsbeispiele (Worked Example) nachvollziehen oder beispielshafte Prüfungsfragen beantworten (siehe auch Zusammenstellung von lernförderlichen Lernstrategien inFiorella & Mayer, 2015).Der größte Unterschied zwischen dem Präsenzunterricht onsite im Vergleich zum Online-Lernen ist die mit der zeitlichen und räumlichen Distanz veränderte Interaktion. Synchrone Phasen sind beispielsweise in MOOCs oder Kursen der innerbetrieblichen Weiterbildung mit einer globalen Teilnehmerschaft aufgrund der unterschiedlichen Lernzeiten schwierig. ...
Full-text available
Im vorliegenden Beitrag wird die Konzeption einer explorativen Studie vorgestellt, die sich mit der Entwicklung, Wirksamkeit und Implementierung von Fördermaßnahmen zum kompetenten Umgang mit digitalen Rechtschreibhilfen beschäftigt. Im Fokus steht die Frage, wie sich diese Fördermaßnahmen, die in Form interaktiver Lernpfade umgesetzt werden, auf die sprachformale Textrevisionen von Lernenden der Primarstufe und Sekundarstufe I auswirken.
... Strategic readers process texts deliberately, they monitor and modify their strategy use as well as attempt to construct meaning from texts (Afflerbach, Hurt & Cho, 2020;Paris et al., 1983). Based on general learning strategies across domains, (Boekaerts, 1999;Fiorella & Mayer, 2015;Weinstein & Mayer, 1986) reading strategies explicitly refer to the field of text comprehension. They have a positive impact on comprehension and strategy application (Goldman et al., 2016;Souvignier & Mokhlesgerami, 2006) even for struggling readers (Edmonds et al., 2009). ...
Full-text available
Based on the assumption that reading strategies facilitate text comprehension and that they should differ regarding types of texts, this study aims at analysing which cognitive and metacognitive reading strategies are applied by university students (N = 54) for reading a narrative text compared to an expository text. To measure text-specific reading strategies, different channels of information were included such as highlighting of text segments qualitatively and quantitatively, qualitative and quantitative note-taking as well as the coherence of notes, and self-reported strategy use after reading. The findings show that students' highlighting of text segments and note-taking differ regarding the type of text in amount and depth of processing, indicating a greater depth of processing for narrative texts. The self-reported strategies for reading the two types of texts also reveal differences in terms of the frequencies of applying elaborative and metacognitive strategies. Moreover, correlation analyses show that there is more correspondence between the reading strategies in the narrative condition compared to the expository condition. In sum, the students adapt their reading strategies to the types of texts and it appears that narrative text was read in a more strategic and deeply oriented manner than the expository text.
... Generative learning is a promising pedagogy based on constructivism that encourages students to actively make sense of the material by reorganizing it and integrating it with their existing knowledge (Fiorella & Mayer, 2015). It involves the crucial process of taking incoming information and transforming it into usable information by selecting, organizing, and integrating it appropriately (Parong & Mayer, 2018). ...
Full-text available
Creative writing is a valuable skill that enables learners to become proficient writers. One reason students often struggle with creative writing is their lack of contextual experiences. Spherical video-based virtual reality (SVVR) has been argued to support students’ writing through immersive virtual experiences. However, what specific pedagogical practices can be developed and integrated with emerging technologies like SVVR to improve their effectiveness to support elementary school students’ creative writing needs further work. This study proposes an innovative approach that integrated the generative learning strategy (GLS) of imagining with SVVR to enhance elementary school students’ creative writing performance. To test the effectiveness of the proposed approach, a quasi-experiment was conducted in an elementary school writing class. The experimental group (N = 56) used the generative learning-based SVVR approach (GSVVR), while the control group (N = 55) used the generative learning-based conventional approach (GC). The results showed that the GSVVR group outperformed the GC group in terms of creative writing (F = 10.953, p < 0.01) with a medium effect size. Furthermore, we found a significant impact on students’ behavioural and emotional engagement as well as their learning persistence, particularly if they had engagement values below 4.3 before the intervention. These findings indicates that while the approach may have limited benefits for students who are already highly engaged (engagement values exceeding 4.3) with SVVR. It can also notably enhance the performance of relatively less engaged (engagement values below 4.3) students. There was a positive correlation between learning persistence and creative writing in the GSVVR group, with learning persistence being one of the significant predictors of student creative writing performance. The study is concluded with a discussion on the pedagogical and theoretical implications of the findings to support elementary school students’ creative writing.
Full-text available
MITCA (homework implementation method) was born with the purpose of turning homework into an educational resource capable of improving the self-regulation of learning and the school engagement of students. In this article, following the current theoretical framework, we evaluate the impact of the MITCA method on school engagement in students in the 5th and 6th years of Primary Education. While the control group of students who did not participate in the 12 weeks of MITCA (N = 431; 61% of 5th grade) worsened significantly in emotional, behavioral, and cognitive engagement, these pre-post differences do not reach significance for the group that has participated in MITCA, even observing a tendency to improve. After the intervention, the students who participated in MITCA (N = 533; 50.6% of 5th grade) reported greater emotional and behavioral engagement than the students in the control group. MITCA students showed positive emotions, were happier in school and were more interested in the classroom, paid more attention in class, and were more attentive to school rules. The conditions of the tasks’ prescription proposed by MITCA would not only restrain the lack of engagement but would also improve students’ emotional and behavioral engagement in school found in the last years of Primary Education. In the light of the results, a series of educational strategies related to the characteristics of these tasks, such as the frequency of prescription and the type of correction are proposed.
Full-text available
Finding effective ways to engage students in sense-making while learning is one of the central challenges discussed in mathematics education literature. One of the big issues is the prevalence of summative assessment tasks prompting students to demonstrate procedural knowledge only, which is a common problem at the tertiary level. In this study, in a large university classroom setting (N = 355), an instructional innovation was designed, developed, implemented and evaluated involving novel tasks–Knowledge Organisers. The tasks comprised prompts for students to generate examples/non-examples and construct a concept map of the key mathematical concepts in the course. The initiative's design was based on the current understanding of human cognitive architecture. A concept map is a visualisation of a group of related abstract concepts with their relationships identified by connections using directed arrows, which can be viewed as an externalisation of a schema stored in a learner's long-term memory. As such, we argue for a distinction between a local conceptual understanding (e.g., example space) versus a global conceptual understanding, manifesting through a high-quality concept map linking a group of related concepts. By utilising a mixed-methods approach and triangulation of the findings from qualitative and quantitative analyses, we were able to discern critical aspects pertaining to the feasibility of implementation and evaluate learners' perceptions. Students' performance on concept mapping is positively correlated with their perceptions of the novel tasks and the time spent completing them. Qualitative analysis showed that students' perceptions are demonstrably insightful about the key mechanisms that supposedly make the tasks beneficial to their learning. Based on the results of the data analyses and their theoretical interpretations, we propose pedagogical strategies for the effective use of Knowledge Organisers.
Full-text available
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.
Full-text available
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.