Learning strategies: a synthesis and conceptual model

Abstract

The purpose of this article is to explore a model of learning that proposes that various learning strategies are powerful at certain stages in the learning cycle. The model describes three inputs and outcomes (skill, will and thrill), success criteria, three phases of learning (surface, deep and transfer) and an acquiring and consolidation phase within each of the surface and deep phases. A synthesis of 228 meta-analyses led to the identification of the most effective strategies. The results indicate that there is a subset of strategies that are effective, but this effectiveness depends on the phase of the model in which they are implemented. Further, it is best not to run separate sessions on learning strategies but to embed the various strategies within the content of the subject, to be clearer about developing both surface and deep learning, and promoting their associated optimal strategies and to teach the skills of transfer of learning. The article concludes with a discussion of questions raised by the model that need further research.

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REVIEW ARTICLE OPEN
Learning strategies: a synthesis and conceptual model
John AC Hattie
1
and Gregory M Donoghue
1
The purpose of this article is to explore a model of learning that proposes that various learning strategies are powerful at certain
stages in the learning cycle. The model describes three inputs and outcomes (skill, will and thrill), success criteria, three phases of
learning (surface, deep and transfer) and an acquiring and consolidation phase within each of the surface and deep phases.
A synthesis of 228 meta-analyses led to the identification of the most effective strategies. The results indicate that there is a subset
of strategies that are effective, but this effectiveness depends on the phase of the model in which they are implemented. Further,
it is best not to run separate sessions on learning strategies but to embed the various strategies within the content of the subject, to
be clearer about developing both surface and deep learning, and promoting their associated optimal strategies and to teach the
skills of transfer of learning. The article concludes with a discussion of questions raised by the model that need further research.
npj Science of Learning (2016) 1, 16013; doi:10.1038/npjscilearn.2016.13; published online 10 August 2016
There has been a long debate about the purpose of schooling.
These debates include claims that schooling is about passing on
core notions of humanity and civilisation (or at least one’s own
society’s view of these matters). They include claims that
schooling should prepare students to live pragmatically and
immediately in their current environment, should prepare
students for the work force, should equip students to live
independently, to participate in the life of their community, to
learn to ‘give back’, to develop personal growth.
1
In the past 30 years, however, the emphasis in many western
systems of education has been more on enhancing academic
achievement—in domains such as reading, mathematics, and
science—as the primary purpose of schooling.
2
Such an emphasis
has led to curricula being increasingly based on achievement in a
few privileged domains, and ‘great’students are deemed those
who attain high levels of proficiency in these narrow domains.
This has led to many countries aiming to be in the top echelon
of worldwide achievement measures in a narrow range of
subjects; for example, achievement measures such as PISA (tests
of 15-year olds in mathematics, reading and science, across
65 countries in 2012) or PIRLS (Year-5 tests of mathematics,
reading and science, across 57 countries in 2011). Indeed, within
most school systems there is a plethora of achievement tests;
many countries have introduced accountability pressures based
on high levels of testing of achievement; and communities
typically value high achievement or levels of knowledge.
3
The
mantra underpinning these claims has been cast in terms of
what students know and are able to do; the curriculum is
compartmentalised into various disciplines of achievement; and
students, teachers, parents and policy makers talk in terms of
success in these achievement domains.
Despite the recent emphasis on achievement, the day-to-day
focus of schools has always been on learning—how to know, how
to know more efficiently and how to know more effectively. The
underlying philosophy is more about what students are now ready
to learn, how their learning can be enabled, and increasing the
‘how to learn’proficiencies of students. In this scenario, the
purpose of schooling is to equip students with learning strategies,
or the skills of learning how to learn. Of course, learning and
achievement are not dichotomous; they are related.
4
Through
growth in learning in specific domains comes achievement and
from achievement there can be much learning. The question in
this article relates to identifying the most effective strategies for
learning.
In our search, we identified 4400 learning strategies: that is,
those processes which learners use to enhance their own learning.
Many were relabelled versions of others, some were minor
modifications of others, but there remained many contenders
purported to be powerful learning strategies. Such strategies help
the learner structure his or her thinking so as to plan, set goals and
monitor progress, make adjustments, and evaluate the process of
learning and the outcomes. These strategies can be categorised in
many ways according to various taxonomies and classifications
(e.g., references 5–7). Boekaerts,
8
for example, argued for three
types of learning strategies: (1) cognitive strategies such as
elaboration, to deepen the understanding of the domain studied;
(2) metacognitive strategies such as planning, to regulate the
learning process; and (3) motivational strategies such as
self-efficacy, to motivate oneself to engage in learning. Given
the advent of newer ways to access information (e.g., the internet)
and the mountain of information now at students’fingertips, it is
appropriate that Dignath, Buettner and Langfeldt
9
added a fourth
category—management strategies such as finding, navigating,
and evaluating resources.
But merely investigating these 400-plus strategies as if they
were independent is not defensible. Thus, we begin with the
development of a model of learning to provide a basis for
interpreting the evidence from our meta-synthesis. The argument
is that learning strategies can most effectively enhance perfor-
mance when they are matched to the requirements of tasks (cf.
10
).
A MODEL OF LEARNING
The model comprises the following components: three inputs and
three outcomes; student knowledge of the success criteria for the
task; three phases of the learning process (surface, deep and
1
Science of Learning Research Centre, Graduate School of Education, University of Melbourne, Carlton, VIC, Australia.
Correspondence: JAC Hattie (jhattie@unimelb.edu.au)
Received 30 December 2015; revised 12 April 2016; accepted 23 May 2016
www.nature.com/npjscilearn
Published in partnership with The University of Queensland

transfer), with surface and deep learning each comprising an
acquisition phase and a consolidation phase; and an environment
for the learning (Figure 1). We are proposing that various learning
strategies are differentially effective depending on the degree to
which the students are aware of the criteria of success, on the
phases of learning process in which the strategies are used,
and on whether the student is acquiring or consolidating their
understanding. The following provides an overview of the
components of the model (see reference 11 for a more detailed
explanation of the model).
Input and outcomes
The model starts with three major sources of inputs: the skill, the
will and the thrill. The ‘skill’is the student’s prior or subsequent
achievement, the ‘will’relates to the student’s various dispositions
towards learning, and the ‘thrill’refers to the motivations held by
the student. In our model, these inputs are also the major
outcomes of learning. That is, developing outcomes in achieve-
ment (skill) is as valuable as enhancing the dispositions towards
learning (will) and as valuable as inviting students to reinvest
more into their mastery of learning (thrill or motivations).
The skill. The first component describes the prior achievement
the student brings to the task. As Ausubel
12
claimed ‘if I had to
reduce all of educational psychology to just one principle,
I would say this ‘The most important single factor influencing
learning is what the leaner already knows. Ascertain this and
teach him accordingly. Other influences related to the skills
students bring to learning include their working memory, beliefs,
encouragement and expectations from the student’s cultural
background and home.
The will. Dispositions are more habits of mind or tendencies to
respond to situations in certain ways. Claxton
13
claimed that the
mind frame of a ‘powerful learner’is based on the four major
dispositions: resilience or emotional strength, resourcefulness or
cognitive capabilities, reflection or strategic awareness, and
relating or social sophistication. These dispositions involve the
proficiency to edit, select, adapt and respond to the environment
in a recurrent, characteristic manner.
14
But dispositions alone
are not enough. Perkins et al.
15
outlined a model with three
psychological components which must be present in order to
spark dispositional behaviour: sensitivity—the perception of the
appropriateness of a particular behaviour; inclination—the felt
impetus toward a behaviour; and ability—the basic capacity and
confidence to follow through with the behaviour.
The thrill. There can be a thrill in learning but for many students,
learning in some domains can be dull, uninviting and boring.
There is a huge literature on various motivational aspects of
learning, and a smaller literature on how the more effective
motivational aspects can be taught. A typical demarcation is
between mastery and performance orientations. Mastery goals are
seen as being associated with intellectual development, the
acquisition of knowledge and new skills, investment of greater
effort, and higher-order cognitive strategies and learning
outcomes.
16
Performance goals, on the other hand, have a focus
on outperforming others or completing tasks to please others.
A further distinction has been made between approach and
avoidance performance goals.
17–19
The correlations of mastery
and performance goals with achievement, however, are not as
high as many have claimed. A recent meta-analysis found 48
studies relating goals to achievement (based on 12,466 students),
and the overall correlation was 0.12 for mastery and 0.05 for
performance goals on outcomes.
20
Similarly, Hulleman et al.
21
reviewed 249 studies (N= 91,087) and found an overall correlation
between mastery goal and outcomes of 0.05 and performance
goals and outcomes of 0.14. These are small effects and show the
relatively low importance of these motivational attributes in
relation to academic achievement.
An alternative model of motivation is based on Biggs
22
learning
processes model, which combines motivation (why the student
wants to study the task) and their related strategies (how the
student approaches the task). He outlined three common
approaches to learning: deep, surface and achieving. When
students are taking a deep strategy, they aim to develop
understanding and make sense of what they are learning, and
create meaning and make ideas their own. This means they focus
on the meaning of what they are learning, aim to develop their
own understanding, relate ideas together and make connections
with previous experiences, ask themselves questions about what
they are learning, discuss their ideas with others and compare
different perspectives. When students are taking a surface
strategy, they aim to reproduce information and learn the facts
and ideas—with little recourse to seeing relations or connections
between ideas. When students are using an achieving strategy,
they use a ‘minimax’notion—minimum amount of effort for
maximum return in terms of passing tests, complying with
instructions, and operating strategically to meet a desired grade.
It is the achieving strategy that seems most related to school
outcomes.
Success criteria
The model includes a prelearning phase relating to whether the
students are aware of the criteria of success in the learning task.
This phase is less about whether the student desires to attain the
target of the learning (which is more about motivation), but
whether he or she understands what it means to be successful at
the task at hand. When a student is aware of what it means to be
successful before undertaking the task, this awareness leads to
more goal-directed behaviours. Students who can articulate or are
taught these success criteria are more likely to be strategic in their
choice of learning strategies, more likely to enjoy the thrill of
success in learning, and more likely to reinvest in attaining even
more success criteria.
Success criteria can be taught.
23,24
Teachers can help students
understand the criteria used for judging the students’work, and
thus teachers need to be clear about the criteria used to
determine whether the learning intentions have been successfully
achieved. Too often students may know the learning intention,
but do not how the teacher is going to judge their performance,
or how the teacher knows when or whether students have been
successful.
25
The success criteria need to be as clear and specific
as possible (at surface, deep, or transfer level) as this enables the
Figure 1. A model of learning.
Learning strategies: a synthesis and conceptual model
JAC Hattie and GM Donoghue
2
npj Science of Learning (2016) 16013 Published in partnership with The University of Queensland

teacher (and learner) to monitor progress throughout the lesson
to make sure students understand and, as far as possible, attain
the intended notions of success. Learning strategies that help
students get an overview of what success looks like include
planning and prediction, having intentions to implement goals,
setting standards for judgement success, advance organisers, high
levels of commitment to achieve success, and knowing about
worked examples of what success looks like.
23
Environment
Underlying all components in the model is the environment in
which the student is studying. Many books and internet sites on
study skills claim that it is important to attend to various features
of the environment such as a quiet room, no music or television,
high levels of social support, giving students control over their
learning, allowing students to study at preferred times of the day
and ensuring sufficient sleep and exercise.
The three phases of learning: surface, deep and transfer
The model highlights the importance of both surface and deep
learning and does not privilege one over the other, but rather
insists that both are critical. Although the model does seem to
imply an order, it must be noted that these are fuzzy distinctions
(surface and deep learning can be accomplished simultaneously),
but it is useful to separate them to identify the most effective
learning strategies. More often than not, a student must have
sufficient surface knowledge before moving to deep learning and
then to the transfer of these understandings. As Entwistle
26
noted,
‘The verb ‘to learn’takes the accusative’; that is, it only makes
sense to analyse learning in relation to the subject or content area
and the particular piece of work towards which the learning is
directed, and also the context within which the learning takes
place. The key debate, therefore, is whether the learning is
directed content that is meaningful to the student, as this will
directly affect student dispositions, in particular a student’s
motivation to learn and willingness to reinvest in their learning.
A most powerful model to illustrate this distinction between
surface and deep is the structure of observed learning outcomes,
or SOLO,
27,28
as discussed above. The model has four levels:
unistructural, multistructural, relational and extended abstract.
A unistructural intervention is based on teaching or learning one
idea, such as coaching one algorithm, training in underlining,
using a mnemonic or anxiety reduction. The essential feature is
that this idea alone is the focus, independent of the context or its
adaption to or modification by content. A multistructural
intervention involves a range of independent strategies or
procedures, but without integrating or orchestration as to the
individual differences or demands of content or context (such as
teaching time management, note taking and setting goals with no
attention to any strategic or higher-order understandings of these
many techniques). Relational interventions involve bringing
together these various multistructural ideas, and seeing patterns;
it can involve the strategies of self-monitoring and self-regulation.
Extended abstract interventions aim at far transfer (transfer
between contexts that, initally, appear remote to one another)
such that they produce structural changes in an individual’s
cognitive functioning to the point where autonomous or
independent learning can occur. The first two levels (one then
many ideas) refer to developing surface knowing and the latter
two levels (relate and extend) refer to developing deeper
knowing. The parallel in learning strategies is that surface learning
refers to studying without much reflecting on either purpose or
strategy, learning many ideas without necessarily relating them
and memorising facts and procedures routinely. Deep learning
refers to seeking meaning, relating and extending ideas, looking
for patterns and underlying principles, checking evidence and
relating it to conclusions, examining arguments cautiously and
critically, and becoming actively interested in course content
(see reference 29).
Our model also makes a distinction between first acquiring
knowledge and then consolidating it. During the acquisition
phase, information from a teacher or instructional materials is
attended to by the student and this is taken into short-term
memory. During the consolidation phase, a learner then needs to
actively process and rehearse the material as this increases the
likelihood of moving that knowledge to longer-term memory.
At both phases there can be a retrieval process, which involves
transferring the knowing and understanding from long-term
memory back into short-term working memory.
30,31
Acquiring surface learning. In their meta-analysis of various
interventions, Hattie et al.
32
found that many learning strategies
were highly effective in enhancing reproductive performances
(surface learning) for virtually all students. Surface learning
includes subject matter vocabulary, the content of the lesson
and knowing much more. Strategies include record keeping,
summarisation, underlining and highlighting, note taking,
mnemonics, outlining and transforming, organising notes, training
working memory, and imagery.
Consolidating surface learning. Once a student has begun to
develop surface knowing it is then important to encode it in a
manner such that it can retrieved at later appropriate moments.
This encoding involves two groups of learning strategies: the first
develops storage strength (the degree to which a memory is
durably established or ‘well learned’) and the second develops
strategies that develop retrieval strength (the degree to which a
memory is accessible at a given point in time).
33
‘Encoding’
strategies are aimed to develop both, but with a particular
emphasis on developing retrieval strength.
34
Both groups of
strategies invoke an investment in learning, and this involves ‘the
tendency to seek out, engage in, enjoy and continuously pursue
opportunities for effortful cognitive activity.
35
Although some may
not ‘enjoy’this phase, it does involve a willingness to practice, to
be curious and to explore again, and a willingness to tolerate
ambiguity and uncertainty during this investment phase. In turn,
this requires sufficient metacognition and a calibrated sense of
progress towards the desired learning outcomes. Strategies
include practice testing, spaced versus mass practice, teaching
test taking, interleaved practice, rehearsal, maximising effort, help
seeking, time on task, reviewing records, learning how to receive
feedback and deliberate practice (i.e., practice with help of an
expert, or receiving feedback during practice).
Acquiring deep learning. Students who have high levels of
awareness, control or strategic choice of multiple strategies are
often referred to as ‘self-regulated’or having high levels of
metacognition. In Visible Learning, Hattie
36
described these
self-regulated students as ‘becoming like teachers’, as they had
a repertoire of strategies to apply when their current strategy was
not working, and they had clear conceptions of what success on
the task looked like.
37
More technically, Pintrich et al.
38
described
self-regulation as ‘an active, constructive process whereby learners
set goals for their learning and then attempt to monitor, regulate
and control their cognition, motivation and behaviour, guided and
constrained by their goals and the contextual features in the
environment’. These students know the what, where, who,
when and why of learning, and the how, when and why to use
which learning strategies.
39
They know what to do when they
do not know what to do. Self-regulation strategies include
elaboration and organisation, strategy monitoring, concept
mapping, metacognitive strategies, self-regulation and elaborative
interrogation.
Learning strategies: a synthesis and conceptual model
JAC Hattie and GM Donoghue
3
Published in partnership with The University of Queensland npj Science of Learning (2016) 16013

Consolidating deep learning. Once a student has acquired surface
and deep learning to the extent that it becomes part of their
repertoire of skills and strategies, we may claim that they have
‘automatised’such learning—and in many senses this automatisa-
tion becomes an ‘idea’, and so the cycle continues from surface
idea to deeper knowing that then becomes a surface idea,
and so on.
40
There is a series of learning strategies that develop
the learner’s proficiency to consolidate deeper thinking and to be
more strategic about learning. These include self-verbalisation,
self-questioning, self-monitoring, self-explanation, self-verbalising
the steps in a problem, seeking help from peers and peer tutoring,
collaborative learning, evaluation and reflection, problem solving
and critical thinking techniques.
Transfer. There are skills involved in transferring knowledge and
understanding from one situation to a new situation. Indeed,
some have considered that successful transfer could be thought
as synonymous with learning.
41,42
There are many distinctions
relating to transfer: near and far transfer,
43
low and high transfer,
44
transfer to new situations and problem solving transfer,
5
and
positive and negative transfer.
45
Transfer is a dynamic, not static,
process that requires learners to actively choose and evaluate
strategies, consider resources and surface information, and, when
available, to receive or seek feedback to enhance these adaptive
skills. Reciprocal teaching is one program specifically aiming to
teach these skills; for example, Bereiter and Scardamalia
46
have
developed programs in the teaching of transfer in writing, where
students are taught to identify goals, improve and elaborate
existing ideas, strive for idea cohesion, present their ideas to
groups and think aloud about how they might proceed. Similarly,
Schoenfeld
47
outlined a problem-solving approach to mathe-
matics that involves the transfer of skills and knowledge from one
situation to another. Marton
48
argued that transfer occurs when
the learner learns strategies that apply in a certain situation such
that they are enabled to do the same thing in another situation
when they realise that the second situation resembles (or is
perceived to resemble) the first situation. He claimed that not only
sameness, similarity, or identity might connect situations to each
other, but also small differences might connect them as well.
Learning how to detect such differences is critical for the transfer
of learning. As Heraclitus claimed, no two experiences are
identical; you do not step into the same river twice.
Overall messages from the model
There are four main messages to be taken from the model. First,
if the success criteria is the retention of accurate detail (surface
learning) then lower-level learning strategies will be more
effective than higher-level strategies. However, if the intention is
to help students understand context (deeper learning) with a view
to applying it in a new context (transfer), then higher level
strategies are also needed. An explicit assumption is that higher
level thinking requires a sufficient corpus of lower level surface
knowledge to be effective—one cannot move straight to higher
level thinking (e.g., problem solving and creative thought) without
sufficient level of content knowledge. Second, the model proposes
that when students are made aware of the nature of success for
the task, they are more likely to be more involved in investing in
the strategies to attain this target. Third, transfer is a major
outcome of learning and is more likely to occur if students are
taught how to detect similarities and differences between one
situation and a new situation before they try to transfer their
learning to the new situation. Hence, not one strategy may
necessarily be best for all purposes. Fourth, the model also
suggests that students can be advantaged when strategy training
is taught with an understanding of the conditions under which the
strategy best works—when and under what circumstance it is
most appropriate.
THE CURRENT STUDY
The current study synthesises the many studies that have related
various learning strategies to outcomes. This study only pertains
to achievement outcomes (skill, on the model of learning); further
work is needed to identify the strategies that optimise the
dispositions (will) and the motivation (thrill) outcomes. The studies
synthesised here are from four sources. First, there are the
meta-analyses among the 1,200 meta-analyses in Visible Learning
that relate to strategies for learning.
36,49,50
Second, there is the
meta-analysis conducted by Lavery
51
on 223 effect-sizes derived
from 31 studies relating to self-regulated learning interventions.
The third source is two major meta-analyses by a Dutch team of
various learning strategies, especially self-regulation. And the
fourth is a meta-analysis conducted by Donoghue et al.
52
based
on a previous analysis by Dunlosky et al.
53
The data in Visible Learning is based on 800 meta-analyses
relating influences from the home, school, teacher, curriculum and
teaching methods to academic achievement. Since its publication
in 2009, the number of meta-analyses now exceeds 1,200, and
those influences specific to learning strategies are retained in the
present study. Lavery
51
identified 14 different learning strategies
and the overall effect was 0.46—with greater effects for
organising and transforming (i.e., deliberate rearrangement of
instructional materials to improve learning, d= 0.85) and self-
consequences (i.e., student expectation of rewards or punishment
for success or failure, d= 0.70). The lowest effects were for imagery
(i.e., creating or recalling vivid mental images to assist learning,
d= 0.44) and environmental restructuring (i.e., efforts to select or
arrange the physical setting to make learning easier, d= 0.22).
She concluded that the higher effects involved ‘teaching
techniques’and related to more ‘deep learning strategies’,
such as organising and transforming, self-consequences,
self-instruction, self-evaluation, help-seeking, keeping records,
rehearsing/memorising, reviewing and goal-setting. The lower
ranked strategies were more ‘surface learning strategies’, such as
time management and environmental restructuring.
Of the two meta-analyses conducted by the Dutch team, the
first study, by Dignathet al.
9
analysed 357 effects from 74 studies
(N= 8,619). They found an overall effect of 0.73 from teaching
methods of self-regulation. The effects were large for achievement
(elementary school, 0.68; high school, 0.71), mathematics (0.96,
1.21), reading and writing (0.44, 0.55), strategy use (0.72, 0.79) and
motivation (0.75, 0.92). In the second study, Donker et al.
54
reviewed 180 effects from 58 studies relating to self-regulation
training, reporting an overall effect of 0.73 in science, 0.66 in
mathematics and 0.36 in reading comprehension. The most
effective strategies were cognitive strategies (rehearsal 1.39,
organisation 0.81 and elaboration 0.75), metacognitive strategies
(planning 0.80, monitoring 0.71 and evaluation 0.75) and manage-
ment strategies (effort 0.77, peer tutoring 0.83, environment 0.59
and metacognitive knowledge 0.97). Performance was almost
always improved by a combination of strategies, as was
metacognitive knowledge. This led to their conclusion that
students should not only be taught which strategies to use and
how to apply them (declarative knowledge or factual knowledge)
but also when (procedural or how to use the strategies) and why
to use them (conditional knowledge or knowing when to use a
strategy).
Donoghue et al.
52
conducted a meta-analysis based on the
articles referenced in Dunlosky et al.
53
They reviewed 10 learning
strategies and a feature of their review is a careful analysis of
possible moderators to the conclusions about the effectiveness of
these learning strategies, such as learning conditions (e.g., study
alone or in groups), student characteristics (e.g., age, ability),
materials (e.g., simple concepts to problem-based analyses) and
criterion tasks (different outcome measures).
Learning strategies: a synthesis and conceptual model
JAC Hattie and GM Donoghue
4
npj Science of Learning (2016) 16013 Published in partnership with The University of Queensland

In the current study, we independently assigned all strategies to
the various parts of the model—this was a straightforward
process, and the few minor disagreements were resolved by
mutual agreement. All results are presented in Appendix 1.
RESULTS: THE META-SYNTHESIS OF LEARNING STRATEGIES
There are 302 effects derived from the 228 meta-analyses from the
above four sources that have related some form of learning
strategy to an achievement outcome. Most are experimental–
control studies or pre–post studies, whereas some are correlations
(N= 37). There are 18,956 studies (although some may overlap
across meta-analyses). Only 125 meta-analyses reported the
sample size (N= 11,006,839), but if the average (excluding the
outlier 7 million from one meta-analysis) is used for the missing
sample sizes, the best estimate of sample size is between 13 and
20 million students.
The average effect is 0.53 but there is considerable variance
(Figure 2), and the overall number of meta-analyses, studies,
number of people (where provided), effects and average effect-
sizes for the various phases of the model are provided in Table 1.
The effects are lowest for management of the environment and
‘thrill’(motivation), and highest for developing success criteria
across the learning phases. The variance is sufficiently large,
however, that it is important to look at specific strategies within
each phase of the model.
Synthesis of the input phases of the model
The inputs: skills. There are nine meta-analyses that have
investigated the relation between prior achievement and sub-
sequent achievement, and not surprisingly these relations are
high (Table 2). The average effect-size is 0.77 (s.e. = 0.10), which
translates to a correlation of 0.36—substantial for any single
variable. The effects of prior achievement are lowest in the early
years, and highest from high school to university. One of the
purposes of school, however, is to identify those students who are
underperforming relative to their abilities and thus to not merely
accept prior achievement as destiny. The other important skill is
working memory—which relates to the amount of information
that can be retained in short-term working memory when
engaged in processing, learning, comprehension, problem solving
or goal-directed thinking.
55
Working memory is strongly related to
a person’s ability to reason with novel information (i.e., general
fluid intelligence.
56
The inputs: will. There are 28 meta-analyses related to the
dispositions of learning from 1,304 studies and the average effect-
size is 0.48 (s.e. = 0.09; Table 3). The effect of self-efficacy is highest
(d= 0.90), followed by increasing the perceived value of the task
(d= 0.46), reducing anxiety (d= 0.45) and enhancing the attitude
to the content (d= 0.35). Teachers could profitably increase
students’levels of confidence and efficacy to tackle difficult
problems; not only does this increase the probability of
subsequent learning but it can also help reduce students’levels
of anxiety. It is worth noting the major movement in the anxiety
and stress literature in the 1980s moved from a preoccupation on
understanding levels of stress to providing coping strategies—and
these strategies were powerful mediators in whether people
coped or not.
57
Similarly in learning, it is less the levels of anxiety
and stress but the development of coping strategies to deal with
anxiety and stress. These strategies include being taught to
effectively regulate negative emotions;
58
increasing self-efficacy,
which relates to developing the students conviction in their own
competence to attain desired outcomes;
59
focusing on the
positive skills already developed; increasing social support and
help seeking; reducing self-blame; and learning to cope with error
and making mistakes.
60
Increasing coping strategies to deal with
anxiety and promoting confidence to tackle difficult and
challenging learning tasks frees up essential cognitive resources
required for the academic work.
Figure 2. The average and the distribution of all effect sizes.
Table 1. Overall summary statistics for the learning strategies synthesis
No. of metas No. of studies No. of people Prorated people No. of effects ES
Skill 13 3,371 136,270 229,370 9,572 0.75
Will 28 1,304 1,468,335 1,601,335 5,081 0.48
Thrill–motivation 23 1,468 451,899 638,099 4,478 0.34
Managing the environment 24 1,056 157,712 330,612 3,928 0.17
Success criteria 41 3,395 57,850 416,950 5,176 0.55
Acquiring surface learning 26 935 26,656 226,156 2,156 0.63
Consolidating surface learning 71 3,366 7,296,722 7,921,822 6,216 0.57
Acquiring deep learning 14 1,066 1,314,618 1,367,818 2,582 0.57
Consolidating deep learning 58 2,885 96,776 602,176 7,196 0.53
Transfer 3 110 39,900 173 1.09
Total 301 18,956 11,006,839 13,374,239 46,558 0.53
Table 2. Meta-analysis results for ‘the skill’
Skill No. of metas No. of studies No. of people Prorated No. of people No. of effects ES
Prior achievement 9 3,155 113,814 193,614 8,014 0.77
Working memory 4 216 22,456 35,756 1,558 0.68
Learning strategies: a synthesis and conceptual model
JAC Hattie and GM Donoghue
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Published in partnership with The University of Queensland npj Science of Learning (2016) 16013

There has been much discussion about students having
growth—or incremental—mindsets (human attributes are
malleable not fixed) rather than fixed mindsets (attributes are
fixed and invariant).
61
However, the evidence in Table 3 (d= 0.19)
shows how difficult it is to change to growth mindsets, which
should not be surprising as many students work in a world of
schools dominated by fixed notions—high achievement, ability
groups, and peer comparison.
The inputs: thrill. The thrill relates to the motivation for learning:
what is the purpose or approach to learning that the student
adopts? Having a surface or performance approach motivation
(learning to merely pass tests or for short-term gains) or mastery
goals is not conducive to maximising learning, whereas having a
deep or achieving approach or motivation is helpful (Table 4).
A possible reason why mastery goals are not successful is that too
often the outcomes of tasks and assessments are at the surface
level and having mastery goals with no strategic sense of when to
maximise them can be counter-productive.
62
Having goals, per se,
is worthwhile—and this relates back to the general principle of
having notions of what success looks like before investing in the
learning. The first step is to teach students to have goals relating
to their upcoming work, preferably the appropriate mix of
achieving and deep goals, ensure the goals are appropriately
challenging and then encourage students to have specific
intentions to achieve these goals. Teaching students that success
can then be attributed to their effort and investment can help
cement this power of goal setting, alongside deliberate teaching.
The environment. Despite the inordinate attention, particularly by
parents, on structuring the environment as a precondition for
effective study, such effects are generally relatively small (Table 5).
It seems to make no differences if there is background music, a
sense of control over learning, the time of day to study, the degree
of social support or the use of exercise. Given that most students
receive sufficient sleep and exercise, it is perhaps not surprising
that these are low effects; of course, extreme sleep or food
deprivation may have marked effects.
Knowing the success criteria. A prediction from the model of
learning is that when students learn how to gain an overall picture
of what is to be learnt, have an understanding of the success
criteria for the lessons to come and are somewhat clear at the
outset about what it means to master the lessons, then their
subsequent learning is maximised. The overall effect across the
31 meta-analyses is 0.54, with the greatest effects relating to
providing students with success criteria, planning and prediction,
having intentions to implement goals, setting standards for
self-judgements and the difficulty of goals (Table 6). All these
learning strategies allow students to see the ‘whole’or the gestalt
of what is targeted to learn before starting the series of lessons.
It thus provides a ‘coat hanger’on which surface-level knowledge
can be organised. When a teacher provides students with a
concept map, for example, the effect on student learning is very
low; but in contrast, when teachers work together with students to
develop a concept map, the effect is much higher. It is the
working with students to develop the main ideas, and to show the
Table 3. Meta-analysis results for ‘the will’
Will No. of metas No. of studies No. of people Prorated No. of people No. of effects ES
Self-efficacy 5 140 27,062 53,662 143 0.90
Task value 1 6 13,300 6 0.46
Reducing anxiety 8 247 105,370 158,570 1,305 0.45
Self-concept 6 440 345,455 372,055 2,548 0.41
Attitude to content 4 320 957,609 970,909 782 0.35
Mindfulness 3 66 4,622 4,622 184 0.29
Incremental versus entity thinking 1 85 28,217 28,217 113 0.19
Table 4. Meta-analysis results for ‘the thrill’
Thrill–motivation No. of metas No. of studies No. of people Prorated No. of people No. of effects ES
Deep motivation 1 72 13,300 72 0.75
Achieving approach 1 95 13,300 95 0.70
Deep approach 1 38 13,300 38 0.63
Goals (Mastery, performance, social) 11 587 348,346 401,546 3,584 0.48
Mastery goals (general) 3 158 12,466 39,066 163 0.19
Achieving motivation 1 18 13,300 18 0.18
Surface/ performance approach 2 344 91,087 104,387 344 0.11
Surface/ performance motivation 3 156 39,900 164 −0.19
Table 5. Meta-analysis results for the environment
Management of the environment No. of metas No. of studies No. of people Prorated No. of people No. of effects ES
Environmental structuring 2 10 26,600 10 0.41
Time management 2 86 26,600 86 0.40
Exercise 8 397 30,206 96,706 2,344 0.26
Social support 1 33 12,366 12,366 33 0.12
Time of day to study 3 267 31,229 44,529 1,155 0.12
Student control over learning 4 124 7,993 34,593 161 0.02
Background music 1 43 3,104 3,104 43 −0.04
Sleep 3 96 72,814 86,114 96 −0.05
Learning strategies: a synthesis and conceptual model
JAC Hattie and GM Donoghue
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npj Science of Learning (2016) 16013 Published in partnership with The University of Queensland

relations between these ideas to allow students to see higher-
order notions, that influences learning. Thus, when students begin
learning of the ideas, they can begin to know how these ideas
relate to each other, how the ideas are meant to form higher order
notions, and how they can begin to have some control or
self-regulation on the relation between the ideas.
Synthesis of the learning phases of the model
Acquiring surface learning. There are many strategies, such as
organising, summarising, underlining, note taking and mnemonics
that can help students master the surface knowledge (Table 7).
These strategies can be deliberately taught, and indeed may be
the only set of strategies that can be taught irrespective of the
content. However, it may be that for some of these strategies, the
impact is likely to be higher if they are taught within each content
domain, as some of the skills (such as highlighting, note taking
and summarising) may require specific ideas germane to the
content being studied.
While it appears that training working memory can have
reasonable effects (d= 0.53) there is less evidence that training
working memory transfers into substantial gains in academic
attainment.
63
There are many emerging and popular computer
games that aim to increase working memory. For example,
CogMed is a computer set of adaptive routines that is intended to
be used 30–40 min a day for 25 days. A recent meta-analysis
(by the commercial owners
64
) found average effect-sizes
(across 43 studies) exceed 0.70, but in a separate meta-analysis
of 21 studies on the longer term effects of CogMed, there was zero
evidence of transfer to subjects such as mathematics or reading
65
.
Although there were large effects in the short term, they
found that these gains were not maintained at follow up
(about 9 months later) and no evidence to support the claim
that working memory training produces generalised gains to the
other skills that have been investigated (verbal ability, word
decoding or arithmetic) even when assessment takes place
immediately after training. For the most robust studies, the effect
of transfer is zero. It may be better to reduce working memory
demands in the classroom.
66
Consolidating surface learning. The investment of effort and
deliberate practice is critical at this consolidation phase, as are the
abilities to listen, seek and interpret the feedback that is provided
(Table 8). At this consolidation phase, the task is to review and
practice (or overlearn) the material. Such investment is more
valuable if it is spaced over time rather than massed. Rehearsal
and memorisation is valuable—but note that memorisation is not
so worthwhile at the acquisition phase. The difficult task is to
make this investment in learning worthwhile, to make adjust-
ments to the rehearsal as it progresses in light of high levels of
feedback, and not engage in drill and practice. These strategies
relating to consolidating learning are heavily dependent on the
student’s proficiency to invest time on task wisely,
67
to practice
and learn from this practice and to overlearn such that the
learning is more readily available in working memory for the
deeper understanding.
Acquiring deeper learning. Nearly all the strategies at this phase
are powerful in enhancing learning (Table 9). The ability to
elaborate and organise, monitor the uses of the learning
strategies, and have a variety of metacognitive strategies are the
critical determinants of success at this phase of learning. A major
purpose is for the student to deliberately activate prior knowledge
and then make relations and extensions beyond what they have
learned at the surface phase.
Consolidating deep learning. At this phase, the power of working
with others is most apparent (Table 10). This involves skills in
seeking help from others, listening to others in discussion and
developing strategies to ‘speak’the language of learning. It is
through such listening and speaking about their learning that
students and teachers realise what they do deeply know, what
they do not know and where they are struggling to find relations
and extensions. An important strategy is when students become
teachers of others and learn from peers, as this involves high
levels of regulation, monitoring, anticipation and listening to their
impact on the learner.
There has been much research confirming that teaching
help-seeking strategies is successful, but how this strategy then
Table 6. Meta-analysis results for success criteria
Knowing success criteria No. of metas No. of studies No. of people Prorated No. of people No. of effects ES
Success criteria 1 7 13,300 7 1.13
Planning and prediction 4 399 53,200 420 0.76
Goal intentions 2 81 8,461 21,761 190 0.68
Concept mapping 9 1,049 9,279 75,779 1,141 0.64
Setting standards for self-judgement 1 156 13,300 156 0.62
Goal difficulty 7 428 30,521 57,121 526 0.57
Advanced organisers 12 935 3,905 136,905 2,291 0.42
Goal commitment 3 257 2,360 28,960 266 0.37
Worked examples 2 83 3,324 16,624 179 0.37
Table 7. Meta-analysis results for acquiring surface learning
Acquiring surface learning No. of metas No. of studies No. of people Prorated No. of people No. of effects ES
Strategy to integrate with prior knowledge 1 10 13,300 12 0.93
Outlining and transforming 1 89 13,300 89 0.85
Mnemonics 4 80 4,705 31,305 171 0.76
Working memory training 4 191 11,854 25,154 1,006 0.72
Summarisation 2 70 1,914 15,214 207 0.66
Organising 3 104 39,900 104 0.60
Record keeping 2 177 26,600 177 0.54
Underlining and highlighting 1 16 2,070 2,070 44 0.50
Note taking 7 186 5,122 58,322 287 0.50
Imagery 1 12 991 991 59 0.45
Learning strategies: a synthesis and conceptual model
JAC Hattie and GM Donoghue
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Published in partnership with The University of Queensland npj Science of Learning (2016) 16013

works in classrooms is more complex. Teachers have to welcome
students seeking help, and there needs to be knowledgeable
others (e.g., peers) from whom to seek the help—too often
students left in unsupported environments can seek and gain
incorrect help and not know the help is incorrect.
68
Ryan and
Shin
69
also distinguished between adaptive help seeking (seeking
help from others, such as an explanation, a hint, or an example,
that would further learning and promote independent problem
solving in the future) and expedient help seeking (seeking help
that expedites task completion, such as help that provides the
answer and is not focused on learning). They showed that
adaptive help seeking from peers declines and expedient help
seeking increases during early adolescence. Further, increases in
expedient help seeking were associated with declines in
achievement but changes in adaptive help seeking were unrelated
to achievement. The key is for teachers to teach adaptive help
seeking, to ensure the help is dependable and correct and to see
this more of a student than a teacher skill. Help seeking needs to
be welcomed before it can have an effect.
Transfer. The transfer model promoted by Marton
48
seems to be
supported in that a key in teaching for transfer involves
understanding the patterns, similarities and differences in the
transfer before applying the strategies to new task (Table 11).
Marton argued that transfer occurs when students learn strategies
that apply in a certain situation such that they are enabled to do
the same thing in another situation to the degree that they realise
how the second situation does (or does not) resemble the first
situation. It is learning to detect differences and similarities that is
the key that leads to transfer of learning.
Table 9. Meta-analysis results for acquiring deep learning
Acquiring deep learning No. of metas No. of studies No. of people Prorated No. of people No. of effects ES
Elaboration and organisation 1 50 13,300 50 0.75
Strategy monitoring 1 81 13,300 81 0.71
Meta-cognitive strategies 5 355 1,203,024 1,216,324 781 0.61
Self-regulation 6 556 109,444 109,444 1,506 0.52
Elaborative interrogation 1 24 2,150 15,450 164 0.42
Table 10. Meta-analysis results for consolidating deep learning
Consolidating deep learning No. of metas No. of studies No. of people Prorated No. of
people
No. of effects ES
Seeking help from peers 1 21 13,300 21 0.83
Classroom discussion 1 42 13,300 42 0.82
Evaluation and reflection 1 54 13,300 54 0.75
Self consequences 1 75 13,300 75 0.70
Problem-solving teaching 11 683 15,235 121,635 1,820 0.68
Self-verbalisation and self-questioning 4 226 6,196 19,496 2,300 0.64
via becoming a teacher (peer tutoring) 15 839 18,193 164,493 1,272 0.54
Self-explanation 1 8 533 533 69 0.50
Self-monitoring 1 154 13,300 154 0.45
Self verbalising the steps in a problem 3 154 39,900 154 0.41
Collaborative/cooperative learning 18 512 35,921 168,921 1,074 0.38
Critical thinking techniques 1 117 20,698 20,698 161 0.34
Table 11. Meta-analysis results for transfer
Transfer No. of metas No. of studies No. of people Prorated No. of people No. of effects ES
Similarities and differences 1 51 13,300 51 1.32
Seeing patterns to new situations 1 6 13,300 6 1.14
Far transfer 1 53 13,300 116 0.80
Table 8. Meta-analysis results for consolidating surface learning
Consolidating surface learning No. of metas No. of studies No. of people Prorated No. of people No. of effects ES
Deliberate practice 3 161 13,689 13,689 258 0.77
Effort 1 15 13,300 15 0.77
Rehearsal and memorisation 3 132 0 39,900 132 0.73
Giving/receiving feedback 28 1,413 75,279 288,079 2,219 0.71
Spaced versus mass practice 4 360 14,811 54,711 965 0.60
Help seeking 1 62 13,300 62 0.60
Time on task 8 254 28,034 121,134 300 0.54
Reviewing records 1 8 523 523 84 0.49
Practice testing 10 674 7,147,625 7,227,425 1,598 0.44
Teaching test taking and coaching 11 275 15,772 148,772 372 0.27
Interleaved practice 1 12 989 989 65 0.21
Learning strategies: a synthesis and conceptual model
JAC Hattie and GM Donoghue
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npj Science of Learning (2016) 16013 Published in partnership with The University of Queensland

DISCUSSION AND CONCLUSIONS
There is much debate about the optimal strategies of learning,
and indeed we identified 4400 terms used to describe these
strategies. Our initial aim was to rank the various strategies in
terms of their effectiveness but this soon was abandoned. There
was too much variability in the effectiveness of most strategies
depending on when they were used during the learning process,
and thus we developed the model of learning presented in this
article. Like all models, it is a conjecture, it aims to say much and it
is falsifiable. The efficacy of any model can be seen as an
expression of its capacity to generate a scalable solution to a
problem or need in ways that resolve more issues than prevailing
theories or approaches.
70
The model posits that learning must be
embedded in some content (something worth knowing) and thus
the current claims about developing 21st century skills sui generis
are most misleading. These skills often are promoted as content
free and are able to be developed in separate courses (e.g., critical
thinking, resilience). Our model, however, suggests that such skills
are likely to be best developed relative to some content. There is
no need to develop learning strategy courses, or teach the various
strategies outside the context of the content. Instead, the
strategies should be an integral part of the teaching and learning
process, and can be taught within this process.
The model includes three major inputs and outcomes. These
relate to what the students bring to the learning encounter (skill),
their dispositions about learning (will) and their motivations
towards the task (thrill). The first set of strategies relate to teaching
students the standards for what is to be learned (the success
criteria). We propose that effective learning strategies will be
different depending on the phase of the learning—the strategies
will be different when a student is first acquiring the matters to be
learnt compared with when the student is embedding or
consolidating this learning. That is, the strategies are differentially
effective depending on whether the learning intention is surface
learning (the content), deep learning (the relations between
content) or the transfer of the skills to new situations or tasks.
In many ways this demarcation is arbitrary (but not capricious)
and more experimental research is needed to explore these
conjectures. Further, the model is presented as linear whereas
there is often much overlap in the various phases. For example, to
learn subject matter (surface) deeply (i.e., to encode in memory) is
helped by exploring and understanding its meaning; success
criteria can have a mix of surface and deep and even demonstrate
the transfer to other (real world) situations; and often deep
learning necessitates returning to acquire specific surface level
vocabulary and understanding. In some cases, there can be
multiple overlapping processes. A reviewer provided a clear
example: in learning that the internal angles of a quadrilateral add
up to 360°, this might involve surface learning, which then
requires rehearsal to consolidate, some self-questioning to apply,
some detection of similarities to then work out what the internal
angles of a hexagon might be, and spotting similarities to the
triangle rule. There may be no easy way to know the right
moment, or no easy demarcation of the various phases. The
proposal in this paper is but a ‘model’to help clarify the various
phases of learning, and in many real world situations there can be
considerable overlap.
We have derived six sets of propositions from our conceptual
model of learning and the results of our meta-synthesis of
research on learning strategies. The first set relates to the
differential role played by what students bring to and take from
the learning encounter—the inputs and outcomes. Second, there
are some strategies that are more effective than others—but their
relative effectiveness depends on the phase in the model of
learning in which they take place. Third is the distinction between
surface learning, deep learning and the transfer of learning. The
fourth set relates to the skills of transfer, the fifth to how the
model of learning can be used to resolve some unexpected
findings about the effectiveness of some strategies, and the sixth
set discusses the question ‘what is learning?’.
The intertwining role of skill, will, and thrill
Our first set of claims relates to the differential role of what
students bring to and take from the learning encounter. Rather
than arguing that many factors contribute to achievement (an
important but sometimes the only privileged outcome of
learning), we are promoting the notion that the skill, will and
thrill can intertwine during learning and that these three inputs
are also important outcomes of learning—the aim is to enhance
the will (e.g., the willingness to reinvest in more and deeper
learning), the thrill (e.g., the emotions associated with successful
learning, the curiosity and the willingness to explore what one
does not know) and the skills (e.g., the content and the deeper
understanding). The relation between the thrill, will and skill can
vary depending on the student and the requirements of the task.
Certainly, negative emotions, such as those induced by fear,
anxiety, and stress can directly and negatively affect learning and
memory. Such negative emotions block learning: ‘If the student is
faced with sources of stress in an educational context which go
beyond the positive challenge threshold—for instance, aggressive
teachers, bullying students or incomprehensible learning materials
whether books or computers—it triggers fear and cognitive
function is negatively affected.
71
Our argument is that learning
can lead to enhanced skills, dispositions, motivations and
excitements that can be reinvested in learning, and can lead to
students setting higher standards for their success criteria. When
skill, will, and thrill overlap, this should be considered a bonus;
developing each is a worthwhile outcome of schooling in its
own right.
It is all in the timing
Our second set of claims is that while it is possible to nominate the
top 10 learning strategies the more critical conclusion is that
the optimal strategies depend on where in the learning cycle the
student is located. This strategic skill in using the strategies at
the right moment is akin to the message in the Kenny Rogers song
—you need to ‘know when to hold ‘em, know when to fold ‘em’.
For example, when starting a teaching sequence, it is most
important to be concerned that students have confidence they
can understand the lessons, see value in the lessons and are not
overly anxious about their skills to be mastered. Providing them
early on with an overview of what successful learning in the
lessons will look like (knowing the success criteria) will help them
reduce their anxiety, increase their motivation, and build both
surface and deeper understandings.
To acquire surface learning, it is worthwhile knowing how to
summarise, outline and relate the learning to prior achievement;
and then to consolidate this learning by engaging in deliberate
practice, rehearsing over time and learning how to seek and
receive feedback to modify this effort. To acquire deep under-
standing requires the strategies of planning and evaluation and
learning to monitor the use of one’s learning strategies; and then
to consolidate deep understanding calls on the strategy of self-
talk, self-evaluation and self-questioning and seeking help from
peers. Such consolidation requires the learner to think aloud, learn
the ‘language of thinking’,
72
know how to seek help, self-question
and work through the consequences of the next steps in learning.
To transfer learning to new situations involves knowing how to
detect similarities and differences between the old and the new
problem or situations.
We recommend that these strategies are developed by
embedding them into the cycle of teaching rather than by
running separate sessions, such as ‘how to learn’or study skills
courses. There is a disappointing history of educational programs
Learning strategies: a synthesis and conceptual model
JAC Hattie and GM Donoghue
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Published in partnership with The University of Queensland npj Science of Learning (2016) 16013

aimed at teaching students how to learn.
30,73,74
Wiliam
75
made
this case for why teaching these learning strategies (e.g., critical
thinking) out of context is unlikely to develop a generic skill
applicable to many subjects. He noted that in a ‘mathematics
proof, critical thinking might involve ensuring that each step
follows from the previous one (e.g., by checking that there has not
been a division by zero). In reading a historical account, critical
thinking might involve considering the author of the account, the
potential biases and limitations that the author may be bringing to
the account, and what other knowledge the reader has about the
events being described. The important point here is that although
there is some commonality between the processes in mathe-
matics and history, they are not the same. Developing a capacity
for critical thinking in history does not make one better at critical
thinking in mathematics. For all of the apparent similarities, critical
thinking in history and critical thinking in mathematics are
different, and they are developed in different ways’. Many others
have noted that metacognition is not knowledge-free but needs
to be taught in the context of the individual subject areas.
76,77
Perkins
78
also noted that there is a certain art to infusing the
teaching of thinking into content learning. Sometimes, ‘teachers
think it is enough simply to establish a generally thoughtful
atmosphere in a classroom, with regular expectations for thinking
critically and creatively...teaching for know-how about learning to
learn is a much more time-consuming enterprise than teaching for
just learning the ideas... Building active know-how requires much
more attention’.
Another aspect to consider is the difference, identified in the
model, between being first exposed to learning and the
consolidation of this learning. This distinction is far from novel.
Shuell,
79
for example, distinguished between initial, intermediate,
and final phases of learning. In the initial phase, the students can
encounter a ‘large array of facts and pieces of information that are
more-or-less isolated conceptually... there appears to be little
more than a wasteland with few landmarks to guide the traveller
on his or her journey towards understanding and mastery’.
Students can use existing schema to make sense of this new
information, or can be guided to have more appropriate
schema (and thus experience early stages of concept learning
and relation between ideas) otherwise the information may
remain as isolated facts, or be linked erroneously to previous
understandings. At the intermediate phase, the learner begins
to see similarities and relationships among these seemingly
conceptually isolated pieces of information. ‘The fog continues to
lift but still has not burnt off completely’. During the final phase,
the knowledge structure becomes well integrated and functions
more autonomously, and the emphasis is more on performance or
exhibiting the outcome of learning.
Horses for courses: matching strategies with phases
The third set of claims relates to the distinction between surface,
deep, and transfer of learning. Although not a hard and fast set of
demarcations, surface learning refers more to the content and
underlying skills; deep learning to the relationships between, and
extensions of, ideas; and transfer to the proficiency to apply
learning to new problems and situations. During the surface
learning phase, an aim is to assist students to overlearn certain
ideas and thus reduce the needs of their working memory to work
with these new facts when moving into the deeper understanding
phase. Note, for example, that Marton et al.
80
made an important
distinction between memorising without understanding first and
called this rote memorisation (which has long term effect),
and memorisation when you have understood and called this
meaningful memorisation (which can be powerful). The evidence
in the current study supports this distinction.
It is when students have much information, or many seemingly
unrelated ideas, that the learning strategies for the deep phase are
optimally invoked. This is when they should be asked to integrate
ideas with previous schema or modify their previous schema to
integrate new ideas and ways of thinking. The key to this process
is first gaining ideas—a fact often missed by those advocating
deeper thinking strategies when they try to teach these skills prior
to developing sufficient knowledge within the content domain.
The students need to first have ideas before they can relate them.
The model does not propose discarding the teaching or learning
skills that have been developed to learn surface knowing, but
advocates the benefits of a more appropriate balance of surface
and deeper strategies and skills that then lead to transfer. The
correct balance of surface to deep learning depends on the
demands of the task. It is likely that more emphasis on surface
strategies is probably needed as students learn new ideas, moving
to an emphasis on deeper strategies as they become more
proficient.
Pause and reflect: detecting similarities and differences
The fourth set of claims relate to the skills of transfer, and how
important it is to teach students to pause and detect the
similarities and differences between previous tasks and the new
one, before attempting to answer a new problem. Such transfer
can be positive, such as when a learner accurately remembers a
learning outcome reached in a certain situation and appropriately
applies it in a new and similar situation, or negative, such as when
a learner applies a strategy used successfully in one situation in a
new situation where this strategy is not appropriate. Too many
(particularly struggling) students over-rehearse a few learning
strategies (e.g., copying and highlighting) and apply them
in situations regardless of the demands of new tasks. Certainly,
the fundamental skill for positive transfer is stopping before
addressing the problem and asking about the differences and
similarities of the new to any older task situation. This skill can be
taught.
This ability to notice similarities and differences over content is
quite different for novices and experts
81,82
and we do not simply
learn from experience but we also learn to experience.
83
Preparation for future learning involves opportunities to try our
hunches in different contexts, receive feedback, engage in
productive failure and learn to revise our knowing based on
feedback. The aim is to solve problems more efficiently, and also
to ‘let go’of previously acquired knowledge in light of more
sophisticated understandings—and this can have emotional
consequences: ‘Failure to change strategies in new situations
has been described as the tyranny of success’.
84
It is not always
productive for students to try the same thing that worked last
time. Hence there may need to be an emphasis on knowledge-
building rather than knowledge-telling,
85
and systematic inquiry
based on theory-building and disconfirmation rather than simply
following processes for how to find some result.
Why some strategies do not work
The fifth set of claims relate to how the model can be used to
resolve some of the unexpected findings about the impact of
various teaching methods. In Visible Learning,
36
it was noted that
many programs that seem to lead to developing deeper
processing have very low effect sizes (e.g., inquiry based methods,
d= 0.31; problem-based learning, d= 0.15). For example, there
have been 11 meta-analyses relating to problem-based learning
based on 509 studies, leading to an average small effect (d= 0.15).
It hardly seems necessary to run another problem-based program
(particularly in first-year medicine, where four of the meta-
analyses were completed) to know that the effects of problem-
based learning on outcomes are small. The reason for this low
effect seems to be related to using problem-based methods
before attaining sufficient surface knowledge. When problem-
based learning is used in later medical years, the effects seem to
Learning strategies: a synthesis and conceptual model
JAC Hattie and GM Donoghue
10
npj Science of Learning (2016) 16013 Published in partnership with The University of Queensland

increase. Albanese and Mitchell
86
claimed that increased years of
exposure to medical education increases the effect of problem-
based learning. They argued that lack of experience (and lack of
essential surface knowledge) leads the student to make more
errors in their knowledge base, add irrelevant material to their
explanations and engage in backward reasoning (from the
unknown to the givens), whereas experts engaged in forward
reasoning (also see references 87,88). Walker et al.
89
also noted
that novice problem-based learning students tended to engage in
far more backward-driven reasoning, which results in more
errors during problem solving and may persist even after the
educational intervention is complete. It is likely that problem-
based learning works more successfully when students engage in
forward reasoning and this depends on having sufficient content
knowledge to make connections.
Deep understanding in problem-based learning requires a
differentiated knowledge structure,
90
and this may need to be
explicitly taught—as there is no assumption that students will see
similarities and differences in contexts by themselves. There is a
limit to what we can reasonably expect students to discover, and it
may require teaching students to make predictions based on
features that were told to them and that they may not notice on
their own. Deliberate teaching of these surface features can offer a
higher level of explanation that would be difficult or time
consuming to discover. A higher level explanation is important
because it provides a generative framework that can extend one
understanding beyond the specific cases that have been analysed
and experienced. On the other hand, the problems need not be
too overly structured, as then students do not gain experience of
searching out conceptual tools or homing in on particular cases of
application.
78
Another example of the different requirements of surface and
deep learning is the effect of asking students to explore errors and
misconceptions during their learning. Using meta-analysis, Keith
and Frese
91
found that the average effect of using these strategies
when the outcome was surface learning was −0.15 and when the
outcome was deep learning and far transfer to new problems,
it was 0.80.
So: what is learning?
The sixth set of claims relate to the notion of ‘what is learning?’.
The argument in this article is that learning is the outcome of the
processes of moving from surface to deep to transfer. Only then
will students be able to go beyond the information given to ‘figure
things out’, which is one of the few untarnishable joys of life.
92
One of the greatest triumphs of learning is what Perkins
78
calls
‘knowing one’s way around’a particular topic or ‘playing the
whole game’of history, mathematics, science or whatever. This is
a function of knowing much and then using this knowledge in the
exploration of relations and to make extensions to other ideas,
and being able to know what to do when one does not know
what to do (the act of transfer).
Concluding comments
Like all models, the one proposed in this article invites as many
conjectures and directions for further research as it provide a basis
for interpreting the evidence from the meta-synthesis. It helps
make sense of much of the current literature but it is speculative
in that it also makes some untested predictions. There is
much solace in Popper's
93
claim that ‘Bold ideas, unjustified
anticipations, and speculative thought, are our only means for
interpreting nature: our only organon, our only instrument, for
grasping her. And we must hazard them to win our prize. Those
among us who are unwilling to expose their ideas to the hazard of
refutation do not take part in the scientific game.’Further research
is needed, for example, to better understand the optimal order
through the various phases; there may be circumstances where it
may be beneficial to learn the deeper notions before developing
the surface knowledge. It is highly likely that as one develops
many ideas and even relates and extends them, these become
‘ideas’and the cycle continues.
94
We know much, but we need to
know much more, and in particular we need to know how these
many learning strategies might be better presented in another
competing model. Such testing of a bold model and making
predictions from models is, according to Popper, how science
progresses.
Further research is needed that asks whether the distinction
between the acquisition and the consolidation of learning is a
distinctive difference, a melding from one to the other or whether
both can occur simultaneously. If there is a difference, then more
research on ascertaining the best time to move from acquisition to
consolidation would be informative. Similarly, there is no hard rule
in the model of a sequence from surface to deep to transfer. In
some ways, teaching the strategies of knowing what success looks
like upfront implies an exposure to both surface and deep
learning. Also, the many arguments (but surprisingly there is a lack
of evidence) for the popular notions of flipped classrooms could
be supported with more evidence of introducing the success
criteria upfront to students. A typical flipped lesson starts with
students accessing online video lectures or resources prior to
in-class sessions so that students are prepared to participate in
more interactive and higher-order activities such as problem
solving, discussions and debates.
95
The most needed research
concerns transfer—the variation theory of Marton,
48
the claims by
Perkins
78
and others need more focused attention and the usual
(and often unsubstantiated) claims that doing xwill assist learning
yshould come back as a focus of learning sciences.
We are proposing that it is worthwhile to develop the skill, will
and thrill of learning, and that there are many powerful strategies
for learning. Students can be taught these strategies (declarative
knowledge), how to use them (procedural knowledge), under
what conditions it may be more or less useful to apply them
(conditional knowledge) and how to evaluate them. It may be
necessary to teach when best to use these strategies according
the nature of the outcomes (surface and deep), according to the
timing of learning (first acquiring and then consolidating learning)
and to teach the skill of transferring learning to new situations. We
need to think in terms of ‘surface to deep’and not one alone; we
need to think in terms of developing dispositions, motivations and
achievement, and not one alone. This invites considering multiple
outcomes from our schools. Singapore,
96
for example, is now
committed to developing an educational system which will
produce young people who have the moral courage to stand
up for what is right; pursue a healthy lifestyle and have an
appreciation of aesthetics; are proud to be Singaporeans; are
resilient in the face of difficulty, innovative and enterprising; are
purposeful in the pursuit of excellence; are able to collaborate
across cultures; and can think critically and communicate
persuasively. Academic achievement is but one desirable learning
outcomes of many.
Another important message is that developing a few learning
strategies may not be optimal. The failure to change strategies in
new situations has been described as the tyranny of success;
84
and the current meta-synthesis suggests that choosing different
strategies as one progresses through the learning cycle (from first
exposure to embedding, from surface to deep to transfer)
demands cognitive flexibility. It may not be the best option for
students to use the same strategies that worked last time, as when
the context is changed the old strategies may no longer work.
ACKNOWLEDGEMENTS
The Science of Learning Research Centre is a Special Research Initiative of the
Australian Research Council. Project Number SR120300015. We thank the following
Learning strategies: a synthesis and conceptual model
JAC Hattie and GM Donoghue
11
Published in partnership with The University of Queensland npj Science of Learning (2016) 16013

for critiquing earlier drafts of this article: Dan Willingham, Jason Lodge, Debra
Masters, Rob Hester, Jared Horvath and Luke Rowe.
CONTRIBUTIONS
The authors contributed equally to the project and writing of this paper.
COMPETING INTERESTS
The authors declare no conflict of interest.
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