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4
Principles of Learning for
Instructional Design
Much is known from decades of research with children, college stu-
dents, and older adults about the conditions that affect cognition and
learning and how cognition and learning change across the life span. In this
chapter, we describe principles of learning that have sufficiently strong and
broad support to warrant their application to the design of instruction for
adolescents and adults. We draw on and update several recent efforts to
distill principles of learning from research for educators that include
• Organizing Instruction and Study to Improve Student Learning
(Pashler et al., 2007), an initiative of the Institute of Education
Sciences (IES) in the U.S. Department of Education.
• Lifelong Learning at Work and at Home (Graesser, Halpern, and
Hakel, 2007), an initiative of the Association of Psychological Sci-
ences (APS) and the American Psychological Association.
• How People Learn (National Research Council, 2000).
• What Works in Distance Learning Guidelines (O’Neil, 2005).
• e-Learning and the Science of Instruction and Multimedia Learning
(Clark and Mayer, 2003; Mayer, 2009).
These reports and hundreds of published studies inform the commit-
tee’s conclusions about the elements of instruction with potential to support
adult learning and the research that is needed to discover how to apply
these principles most effectively to improve the literacy skills of diverse
populations of adult learners.
There is substantial convergence between the conditions that facilitate
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PRINCIPLES OF LEARNING FOR INSTRUCTIONAL DESIGN
learning in general and the principles of effective literacy instruction for
typical and struggling learners presented in Chapter 2. This convergence
leads to having greater confidence in the findings and further indicates the
value of incorporating them into the design of instruction for other popula-
tions, such as adult learners. How to use the principles of learning and ef-
fective literacy instruction presented in this report to substantially enhance
the literacy of diverse populations outside school is an important question
for future research.
THE DEVELOPMENT OF EXPERTISE
The ideal culmination of successful learning is the development of ex-
pertise. Learners who achieve expertise tend to be self-regulated (Azevedo
and Cromley, 2004; Pintrich, 2000b; Schunk and Zimmerman, 2008;
Winne, 2001). They formulate learning goals, track progress on these
goals, identify their own knowledge deficits, detect contradictions, ask good
questions, search relevant information sources for answers, make inferences
when answers are not directly available, and initiate steps to build knowl-
edge at deep levels of mastery. The “meta” knowledge of language, cogni-
tion, emotions, motivation, communication, and social interactions that is
part of self-regulated learning is well developed. The expert learner forms
conceptually rich and organized representations of knowledge that resist
forgetting, can be retrieved automatically, and can be applied flexibly across
tasks and situations. The development of expertise has specific features:
1. Experts acquire and maintain skill through consistent and long-
term engagement with domain-relevant activities, deliberate prac-
tice, and corrective feedback (Ericsson, 2006).
2. Experts notice features and meaningful patterns in situations and
tasks that are not noticed by novices (Chase and Simon, 1973; Chi,
Glaser, and Rees, 1982; Rawson and van Overschelde, 2008).
3. Experts have content knowledge that is organized around core
mental models and concepts that reflect deep understanding
(Mosenthal, 1996; Vitale, Romance, and Dolan, 2006).
4. Experts have the metacognitive skills to think about and apply
strategies (Hacker, Dunlosky, and Graesser, 2009).
5. Expert knowledge is tuned and conditionalized, so it includes rep-
resenting the contexts in which particular knowledge, skills, and
strategies apply (Anderson et al., 1995).
6. Experts retrieve and execute relevant knowledge and skills auto-
matically, which enables them to perform well on complex tasks
and to free cognitive resources for more attention-demanding ac-
tivities (Ackerman, 1988).
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108 IMPROVING ADULT LITERACY INSTRUCTION
7. Experts approach tasks flexibly, so they recognize when more
knowledge is needed and take steps to acquire it while monitoring
progress (Bilalić, McLeod, and Gobet, 2008; Metcalfe and Kornell,
2005; Spiro et al., 1991).
8. Within certain physical limits of speed and endurance associated
with aging and health status, experts retain domain-related skills
through adulthood as long as they are practiced (Krampe and
Charness, 2006).
Expertise is usually difficult to achieve—and for a complex skill such
as literacy requires many hours of practice over many years—experts tend
to have 1,000-10,000 hours of experience in their field of expertise (Chi,
Glaser, and Farr, 1988). With respect to literacy expertise taught in schools,
an hour per day from kindergarten through twelfth grade amounts to about
2,000 hours in total, after taking out the inevitable days when no real
instruction occurs, which is at the low end of the range needed to gain ex-
pertise. Adult literacy learners can be assumed to have missed out on many
of these hours or to need substantially more practice. Adults bring varied
goals to adult literacy education, but it is clear that given the hours of
practice needed to develop literacy skills for functioning well in the realms
of work, family, education, civic engagement, and so on, instruction needs
to be designed to ensure that learning proceeds as efficiently as possible.
Efficiency is especially important considering that adolescents and adults
live in complex worlds with many competing demands (Riediger, Li, and
Lindenberger, 2006).
Learning involves being proficient with the tools needed to complete
the tasks to be mastered and so requires practice with using tools. Tools
can be anything from a physical tool (pen, computer, textbook, or graphic
organizer) to more abstract tools—such as the appropriate lexicon of a par-
ticular domain or knowledge of how people in a domain construct written
arguments or literature. Tools can contribute to the development of deeper
understandings of a concept or idea by presenting learners with varied ways
of representing the idea (Eisner, 1994; Paivio, 1986; Siegel, 1995).
The learning principles described in this chapter vary in their attention
to explicit and implicit teaching and learning. Both explicit and implicit
learning contribute to the development of expertise in complex skills, such
as reading and writing, as illustrated in previous chapters. The principles
also vary in their emphasis on promoting initial acquisition of knowledge
and skills over transfer and generalization of acquired knowledge and skills
to new situations. Initial acquisition involves attention to and encoding of
relevant material, so that it can be retrieved from memory or applied to
problems within short retention intervals. Transfer and generalization are
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PRINCIPLES OF LEARNING FOR INSTRUCTIONAL DESIGN
maximized when acquired knowledge and skills are successfully applied to
relevant new situations that differ from the initial context of acquisition
(Banich and Caccamise, 2010). It has been widely acknowledged in the
cognitive sciences for decades that transfer and generalization can be very
difficult or nearly impossible when the surface characteristics of the mate-
rial and context differ between training and transfer problems and when
the correspondences are not highlighted or recognized (Forbus, Gentner,
and Law, 1995; Gick and Holyoak, 1980; Hayes and Simon, 1977). For
example, Hayes and Simon’s classic study shows that college students
experienced zero transfer between successive problems that were solved
when the problems were structurally identical at a deep level but had dif-
ferent surface features (e.g., missionaries and cannibals versus monsters and
globes). Each of the learning principles can be analyzed from the standpoint
of ease of initial acquisition versus successful transfer and generalization.
However, the principles that favor the latter are far from settled (Banich
and Caccamise, 2010).
SUPPORTING ATTENTION, RETENTION, AND TRANSFER
Researchers have identified a number of factors that improve retention
of information and transfer of acquired knowledge to new situations. These
factors are important for educators and product developers to consider
when designing curricula, texts, materials, and technologies and selecting
or creating lesson plans for use in adult education programs. For adult
learners who have underdeveloped literacy skills, following these guidelines
is especially important for ensuring that new concepts are absorbed, even
though literacy skill is, to some extent, the ability to overcome the less-than-
optimal designs of information sources.
Present Material in a Clear and Organized Format
Novices or those working to further develop their knowledge and skills
often need help in attending to the parts of a task that are most relevant
to their learning goal. Adults of all ages benefit from a clear (Dickinson
and Rabbitt, 1991; Gao et al., 2011; Wingfield, Tun, and McCoy, 2005)
and organized presentation that helps them to learn and remember new
information (Craik and Jennings, 1992; Hess and Slaughter, 1990; Mor-
row et al., 1996; Smith et al., 1983). It is important to remove any irrel-
evant information, even if interesting, that could detract from learning to
minimize cognitive load and competing demands on attention (Kalyuga,
Chandler, and Sweller, 1999; Moreno, 2007; Van Merrienboer et al., 2006).
Seductive details that do not address the main points to be conveyed also
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risk consuming the learner’s attention and effort so that they miss the main
points. Visual displays that are hard to read or spoken presentations that
are presented in noisy environments can compromise learning because they
distract attention away from deeper semantic processing (Dickinson and
Rabbitt, 1991; Heinrich, Schneider, and Craik, 2008).
According to the coherence principle, learners need to get a coherent,
well-connected representation of the main ideas to be learned. Providing
structure and organization is important to help them understand concepts
and how they relate to one another. The particular method used to orga-
nize ideas depends on the relations to be depicted. Outlines can be used
to show structural hierarchies (Ausubel, 1968). Graphic organizers show
the structure of interrelated ideas pictorially, with ideas represented as
concepts in circles and relationships as lines that connect the circles (Vitale
and Romance, 2007). Tables can be used to organize ideas in two or three
dimensions, and diagrams can help to convey more complex relationships.
According to the contiguity principle, materials and lesson plans should
be organized so that the elements and ideas to be related are presented near
each other in space and time (Clark and Mayer, 2003; Mayer, 2005; Mayer
and Moreno, 2003). For example, the verbal label for a picture needs to be
placed spatially near the picture on the display, not on the other side of the
screen. An explanation should be given at the time a concept is depicted
rather than many minutes, hours, or days later. According to the segmenta-
tion principle, new material should be presented in discrete units so that
new learners are not overwhelmed with too much new information at once.
Use Multiple and Varied Examples
There is substantial evidence that knowledge, skills, and strategies
acquired across multiple and varied contexts are better generalized and
applied flexibly across a range of tasks and situations (Atkinson, 2002;
Catrambone, 1996; Paas and van Merrienboer, 1994; Schmidt and Bjork,
1992; Spiro et al., 1991). Memories are triggered by multiple cues so
knowledge is available when needed. Acquisition can be slower, but learners
retain and transfer their knowledge and skills better than if learned only in
one context (Swezy and Llaneras, 1997).
Present Material in Multiple Modalities and Formats
Information is encoded and remembered better when it is delivered
in multiple modes (verbal and pictorial), sensory modalities (auditory and
visual), or media (computers and lectures) than when delivered in only a
single mode, modality, or medium (Clark and Mayer, 2003; Kalchman
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PRINCIPLES OF LEARNING FOR INSTRUCTIONAL DESIGN
and Koedinger, 2005; Kozma, 2000; Mayer, 2009; Mayer and Moreno,
2003; Moreno and Mayer, 2007; Paivio, 1986). For example, it is effective
to combine graphics with text, graphics with spoken descriptions, speech
sounds with printed words, and other combinations of modalities. Graphic
depictions with spoken descriptions are particularly effective for subject
matter in science and technology (Mayer, 2009). Multiple codes provide
richer and more varied representations that allow more memory retrieval
routes.
However, implementation of this principle must be balanced against
Principle 1: the amount of information should not overwhelm the learner
to the point of attention being split or cognitive capacities being overloaded
(Kalyuga, Chandler, and Sweller, 1999; Mayer and Moreno, 2003; Moreno,
2007; Sweller and Chandler, 1991). There needs to be a careful selection
of the pictures, graphs, or other visual representations in order to be rel-
evant to the material being taught. Graphics do not have to be completely
realistic to be useful; sometimes a more abstract or schematic picture will
best illustrate a key idea, whereas a more photorealistic graphic may actu-
ally distract the learner with details that are irrelevant to the main point.
There is also substantial evidence that memory retention increases when
a person studies the material at deeper, semantic levels of processing than
exclusively at the surface levels of processing (Craik and Lockhart, 1972;
Kintsch et al., 1990).
There is some evidence that, with aging, learners can increasingly ben-
efit from the environmental support provided by augmenting the material
to be learned with multimodal presentations (Craik and Jennings, 1992;
Luo et al., 2007). However, multimodal presentations can be relatively less
effective for older adults if the information across modalities is difficult to
integrate (Luo et al., 2007; Stine, Wingfield, and Myers, 1990).
Teach in the Zone of Proximal Development
According to Vygotsky’s (1986) concept of the zone of proximal devel-
opment (ZPD), the effectiveness of a text, technology, tutor, or instructional
approach in promoting learning can be assessed by comparing performance
with and without the supports provided in the intervention. Does the in-
tervention allow the person to perform better than they would have been
able to without the particular material, tool, or approach to instruction?
There is moderate evidence that the answer depends partly on the selection
of learning goals, materials, and tasks, which should be sensitive to what
the student has mastered and be appropriately challenging—not too easy
or too difficult, but just right (Metcalfe and Kornell, 2005; VanLehn et al.,
2007; Wolfe et al., 1998).
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112 IMPROVING ADULT LITERACY INSTRUCTION
Consider a text used to help an adult learn about a medical procedure:
if the text is extremely easy and overlaps perfectly with what readers al-
ready know, then the text will not stretch their knowledge beyond what
they already knew without the text. Similarly, the adult will not gain much
medical knowledge by reading a text that is too complex and riddled with
technical jargon far beyond what he or she can handle. People will learn
most from a text that appeals to some of what they already know and ex-
pands knowledge in a way that is neither too challenging nor redundant.
Individualized student instruction is expected to be more effective when
it takes into account the ZPD of individual learners. The U.S Common
Core Standards for reading and writing have adopted the ZPD principle
by proposing that text assignments push the envelope on text difficulty, as
reflected in Lexile scores and other text characteristics, but not too much
beyond what the student can handle. Evidence is accumulating that reading
skills are acquired better when interventions consider the characteristics
of individual learners. This has been demonstrated for beginning reading
in children, in that some types of readers benefit from one instructional
method and other types of readers benefit from another (Connor et al.,
2007). In that research, Assessment to Instruction (A2i) web-based soft-
ware was used to compare students’ lexical decoding skills (i.e., letter
and word reading skills) and vocabulary. Instruction methods were dif-
ferentially effective depending on the readers’ starting skill levels on these
dimensions. Readers with low lexical decoding benefited most from explicit
teacher-managed code-focused instruction; this instruction was not helpful
to readers with higher lexical decoding skills but low vocabulary. Readers
with low vocabulary needed a combination of explicit teacher-managed
code-focused instruction and explicit meaning-focused instruction. Those
with high vocabulary benefited from explicit meaning-focused instruction
or independent reading. Indeed, students with high lexical decoding skills
and vocabulary would best be left alone to conduct independent reading
on topics they are interested in.
Several factors affect growth experienced in the ZPD. First, having
more knowledge about the domain to be learned can increase the effi-
ciency of learning (Beier and Ackerman, 2005; Miller, 2009; Miller, Cohen,
and Wingfield, 2006; O’Reilly and McNamara, 2007). During adulthood
(in contrast to childhood) knowledge is highly individualized (Ackerman,
2008), so instruction should first assess and then build on the knowledge
the learner already has. Finally, gradual age-related declines in speed of
processing, attentional control, associative binding, and working memory
may decrease learning efficiency (Hertzog et al., 2008; Myerson et al.,
2003; Park et al., 2002; Waszak, Li, and Hommel, 2010), so slower pac-
ing or more practice or both may be required to reach a given level of
performance.
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Space Presentations of New Material
It is better to distribute the presentation of materials and tests over
time than to concentrate the learning experiences within a short time span
(Bahrick et al., 1993; Bloom and Shuell, 1981; Cepeda et al., 2006; Cull,
2000; Rohrer and Taylor, 2006). When studying for an exam, it is better to
space the same amount of study over days and weeks than to cram it into
a single study session the night before the test. Spaced practice has been
shown to be advantageous for adults of a variety of ages (Kausler, Wiley,
and Philips, 1990; Kornell et al., 2010; Logan and Balota, 2009).
Reexposure to course material after a delay often markedly increases
the amount of information that students remember. Delayed reexposure
can be promoted through homework assignments, in-class reviews, quiz-
zes, and other instructional exercises (Pashler et al., 2007). Evidence for
this principle is primarily based on memory for isolated information units
(such as facts or vocabulary definitions). However, there is evidence that
rereading can enhance metacomprehension skills and long-term retention of
text material, especially if it is spaced and especially for low-ability students
(Griffin, Wiley, and Thiede, 2008; Rawson and Kintsch, 2005; Rawson,
Dunlosky, and Thiede, 2000).
Test on Multiple Occasions, Preferably with Spacing
There is substantial evidence that periodic testing helps learning and
slows down forgetting (Bangert-Drowns et al., 1991; Bjork, 1988; Butler and
Roediger, 2007; Dempster, 1997; Karpicke and Roediger, 2007; McDaniel,
Roediger, and McDermott, 2007; McDaniel et al., 2007; Roediger and
Karpicke, 2006). One indirect benefit is that regular testing, which can
be quite brief and embedded in instructional materials, keeps students
constantly engaged in the material and guides instructors or computers in
making decisions about what to teach (Shute, 2008). The precise frequency
of testing presumably depends on the nature of materials to be learned.
Students benefit more from repeated testing when they expect a final exam
than when they do not expect one (Szupnar, McDermott, and Roediger,
2007). Spacing retrieval has been shown to improve performance for adults
from a wide age range (Bishara and Jacoby, 2008).
Ground Concepts in Perceptual-Motor Experiences
There is substantial evidence that it is important to link concepts to be
read or learned to concrete perceptions and actions (Glenberg and Kaschak,
2002; Glenberg and Robertson, 1999; Glenberg et al., 2004; Piaget, 1952).
For example, when reading instructions on assembling a piece of furniture,
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it helps to be able to view and hold the parts while reading the instruc-
tions. Perceptual-motor experience is particularly important when there is a
need for precision of ideas and communication and when a concept is first
introduced. Some cognitive frameworks have emphasized the importance
of grounding comprehension and learning in perceptual-motor experience
(called embodied cognition), but there is a debate on the role of abstract
representations and symbols in comprehension in addition to the embodied
perceptual-motor representations (de Vega, Glenberg, and Graesser, 2008;
Glenberg, 1997). As noted below, there is some evidence that it is effective
to integrate abstract with concrete representations of concepts. It is critical
to keep in mind that new knowledge is built on and interpreted in light of
existing knowledge, and much knowledge comes from everyday activities.
Building atop barely learned and abstract ideas is much more difficult and
error-prone than building atop well-learned concepts that are experienced
daily.
Stories and other types of narrative are usually about everyday experi-
ences and create perceptual-motor memories similar to daily experience.
There is substantial evidence that stories are easier to read, comprehend,
and remember than other types of learning materials (Bower and Clark,
1969; Casey et al., 2008; Graesser and Ottati, 1996; Rubin, 1995). For
many millennia, the primary way of passing wisdom down from generation
to generation was through stories. Stories have concrete characters, objects,
locations, plots, themes, emotions, and actions that bear some similarity to
everyday experiences and are natural packages of knowledge (Bower, Black,
and Turner, 1979; Graesser, Olde, and Klettke, 2002).
It is interesting to note that active experiencing, a theatrical technique in
which dialogue is learned by acting out scenes with physical and emotional
expression, facilitates learning large passages of dialogue without explicit
memorization (Noice and Noice, 2006, 2008; Noice et al., 1999; Noice,
Noice, and Kennedy, 2000). This finding is consistent with the notion that
stories are easier to understand and remember partly because of the gen-
eration of perceptual-motor memories similar to the memories of everyday
experience. Perceptual-motor memory is well preserved, if not enhanced, in
adulthood (Dijkstra et al., 2004; Radvansky and Djikstra, 2007; Radvansky
et al., 2001) and performing actions related to material to be remembered en-
hances memory for adults in a wide age range (Bäckman and Nilsson, 1985;
Feyereisen, 2009). Thus, stories may be powerful tools for practicing and
building comprehension skills and developing and reinforcing background
knowledge across the life span.
At the same time, there also is a tendency for other genres than narra-
tives to be underused in literacy instruction, and literacy does require the
ability to handle a number of forms other than stories. In order to acquire
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the ability to read and write in other forms, practice on those forms will
be required.
SUPPORTING GENERATION OF CONTENT AND REASONING
Interventions are needed that encourage the learner to actively gen-
erate language, content, and patterns of reasoning rather than passively
processing the material delivered by the learning environment. Learning is
enhanced when learners have to organize the information themselves and
exert cognitive effort during acquisition or retrieval. Simply put, it is the
student who should be doing the acting, thinking, talking, reading, and
writing for learning. Encouraging learners to engage in deeper levels of
thinking and reasoning is especially helpful to adults needing to develop
these skills for education, work, and other purposes involving complex
materials and tasks.
Encourage the Learner to Generate Content
Learning is enhanced when learners produce answers themselves in-
stead of reading or recognizing them (Chi, Roy, and Hausmann, 2008;
National Research Council, 2000; Tulving, 1967). This fact explains why
free recall or essay tests that require the test-taker to generate answers with
minimal cues often produce better retention than recognition tests and
multiple-choice tests in which the learner only needs to be able to recognize
correct answers. It also explains why tutors learn more than tutees in peer
tutoring when students start out on an even playing field (Fuchs et al., 1994;
Mathes and Fuchs, 1994; Topping, 1996). Learner-generated content can
lack detail and contain misconceptions, however, that need to be monitored
to ensure adequate learning and to prevent learning incorrect information.
Strategies that require learners to be actively engaged with reading
material also produce better retention over the long term (McNamara,
2007a, 2007b; Pressley et al., 1998). Learners can, for example, develop
their own mini-testing situations as they review material, such as stat-
ing the information in their own words (without viewing the text) and
synthesizing information from multiple sources, such as from class and
textbooks (Bjork, 1994). Programs exist to help students learn to do this
(Beck and McKeown, 2006). Although the strategies require cognitive
effort, their use is important to encourage since they improve learning
and are underdeveloped in many children and adults (Pearson and Duke,
2002; Pressley, 2002; Snow, 2002). For complex and coherent bodies of
material, outlining, integrating, and synthesizing information produce
better learning than rereading materials or other more passive strategies.
There is evidence that adults from a wide age range benefit from con-
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tent generation to improve learning (Johnson, Schmitt, and Pietrukowicz,
1989; Mitchell et al., 1986; Taconnat et al., 2008). Past their 20s, learners
slowly may become less likely to spontaneously generate content that is
rich, elaborative, and distinctive if they are learning in a domain outside
their previous knowledge and experience; consequently, more contextual
support may be needed as the learner generates content to optimize the
benefits of generation (Dunlosky, Hertzog, and Powell-Moman, 2005; Luo,
Hendricks, and Craik, 2007).
Encourage the Generation of Explanations, Substantive
Questions, and the Resolution of Contradictions
There is substantial evidence that learning is facilitated by construct-
ing explanations and arguments (Ainsworth and Loizou, 2003; Anderson
et al., 2001; Chi et al., 1994; Magliano, Trabasso, and Graesser, 1999;
McNamara, 2004; McNamara and Magliano, 2008; Reznitskaya et al.,
2008; VanLehn et al., 2007). Explanations consist of causal analyses of
events, logical justifications of claims, and functional rationales for ac-
tions. Explanations provide coherence to the material and justify why
information is relevant and important. Students may be prompted to give
self-explanations of material by thinking aloud or answering questions
that elicit explanations connecting the material to what they already know.
The self-explanations of students can be improved by explicit instruction
on self-explanations and by setting up collaborations with a student or tu-
tor to help with the process of constructing useful explanation. Studying
good explanations facilitates deeper comprehension, learning, memory, and
transfer.
Explanations of material and reasoning are elicited by deep questions,
such as why, how, what-if, and what-if not, as opposed to shallow ques-
tions that require the learner to simply fill in missing words, such as who,
what, where, and when (Graesser and Person, 1994). There is substantial
evidence that training students to ask deep questions facilitates comprehen-
sion of material from text, classroom lectures, and electronic media (Beck
et al., 1997; Craig et al., 2006; Dillon, 1988; King, 1994; Pressley et al.,
1992; Rosenshine, Meister, and Chapman, 1996). The learner gets into the
mindset of having deeper standards of comprehension (Baker, 1985), and
the resulting representations are more elaborate.
One method of stimulating thought, content generation, and reasoning
is to present some challenges, obstacles, or contradictions that place the
learner in “cognitive disequilibrium.” The occurrence of cognitive disequi-
librium is anticipated by instructors who purposefully select topics, texts,
and questions that clash with the students’ knowledge, beliefs, or attitudes.
Cognitive disequilibrium is confirmed when students ask relevant questions
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of content, materials, and tasks that both prompt the student to provide in-
formation and deliver relevant information to achieve learning. This is well
documented for comprehension strategy instruction (McNamara, 2007b;
Pressley, 2000; Williams et al., 2009; Williams, Hall, and Lauer, 2004). The
instruction typically goes from simple to complex, with substantial practice
at each step. It incorporates meaningful and interactive tasks, as well as
clear templates that exhibit instruction points.
Strategies of solving mathematical problems can also be acquired by
observing experts solving example problems step by step or by interleaving
worked example solutions with problem-solving exercises. That is, stu-
dents learn more by alternating between studying examples of worked-out
problem solutions and solving similar problems on their own than they
do when just given problems to solve on their own (Catrambone, 1996;
Cooper and Sweller, 1987; Kalyuga et al., 2001; Pashler et al., 2007). Proce-
dural skills can be modeled effectively through modeling-scaffolding-fading
(McNamara, 2007a; Renkl, Atkinson, and Grosse, 2004; Renkl et al.,
2002; Rogoff, 1990; Rogoff and Gardner, 1984): the expert first models
the solution, then the student tries with periodic feedback and scaffolding
from the expert, and then the expert assistance eventually fades. Strategies
of argumentation can be developed from structured practice with argument
stratagems in collaborative reasoning that transfer to writing (Anderson
et al., 2001; Reznitskaya et al., 2008). One central question is how much
learning of knowledge, strategies, and skills can be acquired through in-
formation delivery and scripted exercises without the more flexible and
interactive scaffolding (Connor et al., 2007; McNamara, 2007b).
There is some evidence that adults from a wide age range can benefit
from instruction in memory monitoring strategies to improve memory per-
formance (Dunlosky, Kubat-Silman, and Hertzog, 2003). Mnemonic train-
ing, especially if embedded in otherwise valued classroom literacy activities,
may be more effective in augmenting the repertoire of memory skills of
adolescents and young adults than of children (Brehmer et al., 2007, 2008).
Although even older adults benefit, it is possible that age-related decreases
in fluid abilities may slow the acquisition of new strategies in later life
(Brehmer et al., 2007, 2008; Hertzog et al., 2008).
It is well documented that both children and adults can experience
serious limitations in metacognition (Hacker, Dunlosky, and Graesser,
2009)—their ability to understand, assess, and act on the adequacy of their
memory, comprehension, learning, planning, problem-solving, and decision
processes. One would expect children to have limited metacognitive knowl-
edge, but it is somewhat remarkable that adults also have limited metacog-
nitive proficiency after their years of experience. More specifically, the vast
majority of adults are not good at judging their own comprehension of text
(Dunlosky and Lipko, 2007; Maki, 1998). They also are not good at plan-
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ning, selecting, monitoring, or evaluating their strategies for self-regulated
learning (Azevedo and Cromley, 2004; Azevedo and Witherspoon, 2009;
Winne, 2001), inquiry learning (Graesser, McNamara, and VanLehn, 2005;
White and Frederiksen, 2005), or discovery learning (Kirschner, Sweller,
and Clark, 2006; Klahr, 2002). Therefore, explicit training, modeling, and
guided practice are needed before students acquire adequate strategies
of comprehension, critical thinking, metacomprehension, self-regulated
learning, and discovery learning (Dunlosky and Hertzog, 1998). Domain
knowledge can also enhance self-regulated learning (Griffin, Jee, and Wiley,
2009).
Combine Complex Strategy Instruction with Learning of Content
There is moderate evidence that strategy instruction should be deeply
integrated with subject-matter content rather than being lists of abstract
rules or scripted procedures that ignore the content (National Research
Council, 2000). For example, it is a good strategy for readers to be asking
the question “why” when reading texts because it encourages the student
to build explanations of the content. This strategy is ideally implemented
across the curriculum, so students ask such questions as why catalysts are
important when reading a chemistry text, why the Spanish-American War
was important in U.S. history, why an action of a character in a novel has
a particular motive, and why an author bothers to describe the layout of
a city. Substantial subject-matter knowledge is needed to effectively apply
many reading strategies because comprehension involves the integration of
prior knowledge and text.
Many reading researchers claim that comprehension skills and strate-
gies are facilitated when they are embedded in content areas (e.g., science,
history, social studies) (Duke and Pearson, 2002; Guthrie et al., 2004; Moje,
2008b; Neufeld, 2005; Pearson and Duke, 2002; Pressley, 2002; Williams
et al., 2009), although some claim that more evidence should be amassed to
have greater certainty (Lee et al., 2006). Comprehension can improve after
instruction on the structure of expository text, such as compare-contrast,
problem-solution, cause-effect, description, sequence, and other rhetori-
cal frames (Chambliss, 1995; Meyer and Poon, 2001; Williams, Hall, and
Lauer, 2004; Williams et al., 2005, 2009). Such structure training, which
is often contextualized in subject matter, can improve comprehension for
adults from a wide age range (Meyer and Poon, 2001; Meyer, Young, and
Bartlett, 1989).
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PRINCIPLES OF LEARNING FOR INSTRUCTIONAL DESIGN
FEEDBACK
Feedback affects learning in a number of ways that are well docu-
mented (Azevedo and Bernard, 1995; Kluger and DiNisi, 1996; Shute,
2008). Adults from young to old can take advantage of feedback to acquire
new skills (Hertzog et al., 2007; Stine-Morrow, Miller, and Nevin, 1999;
West, Bagwell, and Dark-Freudeman, 2005). Feedback helps learners fine-
tune their knowledge, skills, and strategies. It can be explicitly delivered by
people or computers (supervised learning), or it can be implicitly provided
in situations that are engineered to make knowledge and skill gaps evident
to the learner (unsupervised learning). The feedback may identify and pos-
sibly correct inaccurate skills (bugs) and misconceptions (errors of commis-
sion) or may identify missing information (errors of omission).
Accurate and Timely Feedback Helps Learning
There is substantial evidence that students benefit from feedback
on their performance in a learning task, but the optimal timing of the
feedback depends on the task (Pashler et al., 2005; Shute, 2008). Im-
mediate feedback has the advantage of maximizing contiguity of correct
information and of preventing elaboration of incorrect information. Just
as people learn correct information from accurate feedback, they also can
learn incorrect information. For example, when incorrect alternatives on
multiple-choice tests are presented, the wrong answers can be learned
instead of the correct answers (Butler and Roediger, 2007; Roediger and
Marsh, 2005; Toppino and Luipersbeck, 1993), and accuracy may be
compromised as a function of the number of distracters (Roediger and
Marsh, 2005). This may also occur for true-false tests (Toppino and
Brochin, 1989) and when misconceptions are planted in texts (Kendeou
and van den Broek, 2005). These effects can be reduced when learners
receive feedback immediately after a test (Butler, Karpicke, and Roediger,
2008; Kang, McDermott, and Roediger, 2007; Metcalfe and Kornell,
2007; Roediger and Marsh, 2005) or while performing an action in a pro -
cedure (Anderson et al., 1995; Ritter et al., 2007) or completing a task.
They can also be reduced by a rhetorical structure (Kendeou and van den
Broek, 2007) or critical stance (Wiley et al., 2009) that encourages the
learner to be skeptical or to refute the presented information.
Immediate feedback can be useful under many conditions, but it does
have potential liabilities. A learner’s motivation can be threatened when
there is a barrage of corrections and negative feedback. Frequent interrup-
tions of organized action sequences (such as reading a text aloud) can be
not only irritating but also counterproductive in the acquisition of complex
motor skills. Immediate feedback blocks the possibility of the students’
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122 IMPROVING ADULT LITERACY INSTRUCTION
correcting their own reading errors and regulating their own learning more
generally. The impact and timing of feedback differ for tasks that involve
memory, simple procedural skills, reasoning, problem solving, and complex
domains of knowledge that have entrenched misconceptions.
The optimal administration of feedback is a complex mechanism that
depends on timing, the nature of the knowledge or skill to be developed,
and characteristics of the student. It is unlikely that an instructor can track
all of these levels for 30 students in a class—or even a single student for
a tutor. As discussed further in Chapter 6, technologies can keep track of
the details that are beyond the horizon of human capacities. Computerized
learning environments are poised to provide adaptive feedback that is sensi-
tive to all of these constraints.
Qualitative Feedback Is Better for Learning
Than Test Scores and Error Flagging
There is moderate evidence that feedback should both point out er-
rors to the learner and explain why the information is incorrect instead of
merely flagging that an answer is incorrect or giving a student an overall
score that does not provide information about the nature of the needed
improvements (Aleven et al., 2003; Ritter et al., 2007; Roscoe and Chi,
2007; Shute, 2008). Much of this research is on subject-matter content
rather than literacy per se, but the principles are expected to apply univer-
sally. That said, more research is needed on the type of qualitative feedback
that is optimal for different types of material and different types of learners
(Shute, 2008). How specific should the feedback be (Ritter et al., 2007)?
At what point will a negative feedback frustrate or dispirit students, espe-
cially those with low self-efficacy (Graesser, D’Mello, and Person, 2009;
Lepper and Woolverton, 2002)? How can task-specific feedback produc-
tively guide subsequent learning (Hunt and Pellegrino, 2005; Shute, 2008)?
When should students have control over the nature and extent of feedback
they receive (Aleven et al., 2003)? Under what conditions is it appropri-
ate to have an open learning environment, in which the students have full
knowledge of their extent of mastering knowledge, skills, and strategies at a
fine-grained level (Bull and Kay, 2007)? As in much of instructional design,
there are a number of trade-offs and sensitivities to the nature of the knowl-
edge and skills being trained. Instructionally perfect feedback may be ex-
pensive to provide, but to the extent that technology can be recruited, costs
can decrease. Fine-grained feedback is best for specific well-defined skills,
but some modicum of feedback is also appropriate for general, ill-defined
skills. Excessive feedback also runs the risk of preventing the development
of self-regulated learning, and so a fading process is needed to gradually
shift control to the student.
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PRINCIPLES OF LEARNING FOR INSTRUCTIONAL DESIGN
There is some evidence among older learners that quantitative feedback
on skill acquisition is more effective if it is framed in terms of positive feed-
back (what is good about one’s performance) relative to goal attainment,
compared with a raw score (West et al., 2005). Also, adult learners with ini-
tially high levels of perceived control may benefit more from feedback than
those with lower levels of perceived control (Miller and West, 2010). (This
may be true for younger populations, although further data are needed.)
ADAPTIVE AND INTERACTIVE LEARNING ENVIRONMENTS
As previously discussed, training in complex strategies, metacognition,
and self-regulated learning may to some extent be accomplished by well-
engineered training materials that guide all learners through the same regi-
men in a scripted fashion. However, some researchers think that students
need to be guided by knowledgeable tutors, mentors, and computer learning
environments that adaptively interact in a fashion that is sensitive to the
characteristics of the learner, called the learner profile (Conley, Kerner, and
Reynolds, 2005; Connor et al., 2007; Graesser, D’Mello, and Person, 2009;
McNamara, 2007b; Woolf, 2009). In essence, human or machine intelli-
gence facilitates learning when it fits the needs of the particular student in
a context-sensitive fashion, particularly in the case of complex skills and
knowledge (see Chapter 6 for more on technology). This research is consis-
tent with sociocultural theories of learning positing that learning depends
on interaction with a more knowledgeable other (Lave and Wenger, 1991,
1998; Rogoff, 1990, 1993, 1995; Rogoff and Lave, 1984; Rogoff and
Wertsch, 1984; Scribner and Cole, 1981; Vygotsky, 1986; Wertsch, 1991).
Adaptive Learning Environments Foster
Understanding in Complex Domains
There is moderate evidence that learning of complex material requires
adaptive learning environments that are sensitive to the learner’s general
profile and to the level of his or her mastery at any given point in time. In-
deed, this assumption underlies research showing learning gains through in-
telligent tutoring systems and learning environments (Anderson et al., 1995;
Dodds and Fletcher, 2004; Doignon and Falmagne, 1999; Koedinger et al.,
1997; Lesgold et al., 1992; Ritter et al., 2007; VanLehn, 2006; Woolf, 2009)
and other reading systems that adapt to the learner, either computer systems
(Connor et al., 2007; McNamara, 2007a; Meyer and Wijekumar, 2007) or
human tutors (Palincsar and Brown, 1984; Rosenshine and Meister, 1994).
When the knowledge conveyed by a text is complex, fine-tuned diagnosis
and remediation may need to be sensitive to a large spectrum of learners’
states of knowledge, skills, and strategies, as well as how the presence or
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absence of various supporting knowledge and skills impacts other compo-
nents of effective performance (Connor et al., 2009).
At this point, researchers have not differentiated the contributions of
context-sensitive adaptive strategies from the content in the learning experi-
ence. Simply put, is it adaptive instruction or the content of the instruction
that matters? Individualized adaptive training has been used successfully
to build cognitive skills among older learners (Erickson et al., 2007; Jaeggi
et al., 2008; Kramer et al., 1999; Kramer, Larish, and Strayer, 1995). How-
ever, as for younger populations, there is a lack of experimentation that
isolates the adaptive nature of the instruction as a cause of learning gains.
Differentiating the two requires a precise mathematical treatment of the in-
formation delivered by the interventions. Such control over content is rarely
imposed in research investigations (although see VanLehn et al., 2007).
Computer environments, rather than human instructors, may have the
most promise in manipulating and controlling these complex interventions
because of the complexity of diagnoses and remediation mechanisms. For
example, accomplished human tutors have a difficult time being adaptive
to many aspects of the learner (Chi, Roy, and Hausmann, 2008; Chi, Siler,
and Jeong, 2004; Graesser, D’Mello, and Person, 2009). Examples of the
kinds of computer interventions that can be achieved include analysis of
reading times for segments of a text against models of the strategies that
would distribute time in a given way, followed by coaching of specific ways
to read more effectively. Even without any machine intelligence, it is pos-
sible to mark text segments according to the amount of time past readers
have spent on them and thus guide students to consider their efforts more
carefully.
Interactive Learning Environments Facilitate Learning
There is moderate evidence that learners benefit from instructional in-
teractions in which they receive fine-grained feedback (i.e., feedback specific
to the immediate momentary task at hand) with hints that prompt them
to generate knowledge (Ainsworth, 2008; Chi, Roy, and Hausmann, 2008;
Graesser, D’Mello, and Person, 2009; Graesser, Person, and Magliano,
1995; VanLehn et al., 2007). Various teaching methods include such in-
teractions: reciprocal teaching method, modeling-scaffolding-fading, the
Socratic method, refutation, and others. Efficacy studies are needed, how-
ever, to determine the effects on learning and if the effects vary for different
learners (see McNamara, 2007b).
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PRINCIPLES OF LEARNING FOR INSTRUCTIONAL DESIGN
Learning Is Facilitated in Genuine and Coherent Learning Environments
Learning is enhanced by opportunities to practice and use skills for a
purpose (Ford and Forman, 2006; Forman, Minick, and Stone, 1993; Lave
and Wenger, 1991; Rogoff, 1990; Street, 1984). There is some evidence
that anchored learning practices help learning (Bottge et al., 2007; Collins,
Brown, and Newman, 1989; Dede and Grotzer, 2009; National Research
Council, 2000). Anchored learning refers to developing knowledge and skill
while working on problems encountered in the real world. Students often
work in teams for several hours or days trying to solve a practical problem
that matters to them and that connects to their knowledge. The problem is
also challenging, so learners need to engage in problem solving and recruit
multiple levels of knowledge and skills. With coaching, these activities can
be organized coherently around solving the practical problem.
Examples of anchored learning are problem-based curricula in medical
schools, in which students work on genuine medical cases, and communities
of practice, in which students try to solve problems of pollution in their city.
The students may spend 2 weeks learning about ecology to explain why fish
are dying in a pond or how to save an eagle in a forest. Medical students
may spend days analyzing the cases of patients in a hospital for diagnosis
and treatment (Vernon and Blake, 1993).
Anchored learning has features that are likely to motivate struggling
adult learners who are sensitive to the value of their learning experience.
Yet much needs to be understood about how to design effective anchored
learning experiences to achieve goals related to literacy and learning. For
instance, for any particular topic, what learning goals do students pursue
and what material should be read to achieve the learning goals? When an
article is accessed, what do they read, how much do they read, and when do
they give up? How much of the information in an article gets incorporated
in messages to peers, documents they write, and behavior? What deficits
in reading components present barriers to effective participation in a com-
munity of learners? There is little or no empirical evidence on answers to
these fundamental questions about goal-based reading (McCrudden and
Schraw, 2007). More research is needed on the principles and dynamics of
how adults sift through and select material for focused study (Pirolli, 2005,
2007; Pirolli and Card, 1999).
LEARNING IS INFLUENCED BY MOTIVATION AND EMOTION
Motivation is inextricably bound to learning, and decades of research
have attempted to explain the relationship (Deci and Ryan, 2002a; Dweck,
2002; Lepper and Henderlong, 2000; Linnenbrink and Pintrich, 2002;
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126 IMPROVING ADULT LITERACY INSTRUCTION
Meyer and Turner, 2006). Chapter 5, on supporting persistence, reviews
in detail research findings related to motivation and distills principles for
creating learning environments to inspire and support persistence and en-
gagement. We note two important points here. First, the affective response
that learners have to the learning experience influences not only engagement
and persistence in a task, but also their capacity for cognitive processing.
It is well known that adults are more motivated when the learning experi-
ence and materials are consonant with existing interests and dispositions
(Ackerman and Rolfhus, 1999; Beier and Ackerman, 2001, 2003, 2005),
and when engaged in reading or writing for a real purpose. Engaging nar-
rative, expository, or procedural texts on topics that interest the learner and
deliver knowledge the learner values are more likely to sustain the attention
needed for learning (Hultsch and Dixon, 1983; Morrow et al., 2009; Stine-
Morrow et al., 2004).
Second, motivation among adults is also more likely to be enhanced
when instruction helps to build self-confidence and self-efficacy and de-
velops the student’s identity as a person who reads. Adults with literacy
problems often have experienced being stigmatized or marginalized, which
makes enhancing self-confidence especially important. Because past expe-
riences may have been very painful, interventions need to accommodate
the occurrence of negative emotions, such as frustration, anger, boredom,
and disengagement. Social support from peers, family members, tutors,
and mentors facilitate motivation and mitigate their dropping out of adult
literacy programs.
SUMMARY AND DIRECTIONS FOR RESEARCH
Research on cognition and learning shows elements to include in the
design of instruction (see Box 4-1). Some of these findings have emerged
from research on literacy. The principles are expected to generalize across
populations, but how to apply them to the development of effective literacy
instruction for diverse adult learners in various forms of adult education
and developmental instruction in college must be determined in future re-
search. Given the findings from research on learning, three questions should
guide this research.
1. There is a high level of complexity involved in the design of learn-
ing environments consistent with principles of learning (e.g., ideal
levels of information delivery, task difficulty, and feedback tailored
to the individual learner). This complexity must be considered in
the development of hypotheses and research designs. The research
must also determine the expertise required to flexibly deliver in-
struction consistent with the principles once developed. To what
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PRINCIPLES OF LEARNING FOR INSTRUCTIONAL DESIGN
extent can technology leverage and augment the literacy instruc-
tor’s expertise to provide the adaptive learning environments that
are optimal for the learner?
2. For adolescents and adults to invest the time required to develop
their literacy, the instruction they receive must provide valued
content knowledge and literacy skills (see Chapter 5 on motiva-
tion, engagement, and persistence). Thus, a promising direction for
practice and research that is consistent with principles of learning
and motivation is to discover how to build effective literacy instruc-
tion (curricula, practices, texts, and tools) that connects with the
personal interests of learners and delivers the knowledge they need
in content domains (e.g., electronics). To what degree is it possible
for reading and writing instruction to piggyback onto instruction
to develop content knowledge, instead of content knowledge being
secondary to the acquisition of reading and writing skills? In other
words, to what extent can content drive the development of adults’
literacy?
3. Similarly, certain skills are in demand in the 21st century for social
interaction and for success in college and in the workplace. To
what extent can reading and writing skills be developed as part of
developing these forms of literate practice? Given that most literate
practice in today’s world involves technologies, a goal for research
is to determine how to effectively integrate important technologies
into literacy instruction and practice to enable adults to function
effectively in their educational, work, and social environments.
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BOX 4-1
Summary of Principles of Learning for Instructional Design
Attention, Retention, and Transfer
• resent material in a clear and organized format. To facilitate learning, re-
P
move irrelevant information, even if interesting, to minimize distraction, provide
structure and organization (coherence principle), present related elements to
be learned near each other in space and time (continuity principle), and pres-
ent new material in units that do not overwhelm with information (segmentation
principle).
• se multiple and varied examples. Knowledge, skills, and strategies acquired
U
across multiple and varied contexts are better generalized and applied flexibly
across a range of tasks and situations,
• resent material in multiple modalities and formats. Information is encoded
P
and remembered better when it is delivered in multiple modes (verbal and picto-
rial), sensory modalities (auditory and visual), or media (computers and lectures)
than when delivered in only a single mode, modality, or medium.
• each in the zone of proximal development. Select learning goals, materi-
T
als, and tasks that are sensitive to what the student has mastered and that are
appropriately challenging. Scaffold learning with instructional interactions and
systematic selection and sequencing of content, materials, and tasks that are
both at the appropriate level of difficulty and provide prompts and information
needed to learn.
• pace presentations of new material. Learning is facilitated by the temporally
S
distributed presentation of materials and tests instead of concentrated learning
experiences within a short time span. Reexposure to course material after an
optimal amount of delay often markedly increases the amount of information that
students remember.
• est on multiple occasions, preferably with spacing. Periodic testing helps
T
learning and slows down forgetting. Regular testing, which can be quite brief and
embedded in instructional materials, keeps students constantly engaged in the
material and guides instructors or computers in making decisions about what to
teach.
• round concepts in perceptual-motor experiences. Learning of concepts is
G
facilitated with instruction that employs or evokes concrete perceptions and ac-
tions. Stories, for example, which generate perceptual-motor memories similar
to the memories of everyday experience, may be powerful tools for practicing
and building comprehension skills and developing and reinforcing background
knowledge. Consider using content presented in stories to scaffold learning from
other genres.
Generation of Content and Reasoning
• ncourage the generation of explanations, substantive questions, and the
E
resolution of contradictions. These active learning processes impart coher-
ence and meaning to the material to be learned, facilitates habitual generation
of complex representations of information, and result in deeper understanding.
Learner-generated content can lack detail and contain misconceptions that must
be monitored and corrected.
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PRINCIPLES OF LEARNING FOR INSTRUCTIONAL DESIGN
• onstruct ideas from multiple points of view and different perspectives.
C
Considering multiple viewpoints and perspectives contributes to understanding
a concept and to greater cognitive flexibility in accessing and using the concept
in a range of contexts.
Complex Strategies, Critical Thinking, Inquiry, and Self-Regulated Learning
• tructure instruction to develop the effective use of complex strategies.
S
Explicit training, modeling, and guided practice in the use of complex strategies
is especially important for those with serious limitations in metacognition (the
ability to understand, assess, and act on the adequacy of one’s memory, com-
prehension, learning, planning, problem-solving, and decision processes) and
difficulties with regulating their own strategy use.
• ombine complex strategy instruction with the learning of content. To fa-
C
cilitate learning and application of new knowledge in a subject domain, strategy
instruction should be integrated with subject-matter content.
Feedback
• ffective feedback is immediate, accurate, and timely. Feedback should not
E
contain too many corrections, too much negative feedback, or frequent inter-
ruptions of organized action sequences (such as reading a text aloud) because
these can be demotivating and counterproductive in the acquisition of complex
skills.
• ualitative feedback is better for learning than test scores and error flag-
Q
ging. Feedback is more effective if it points out errors and explains why the
response is incorrect. The type of qualitative feedback that is optimal for different
types of material and different types of learners requires further study.
Adaptive and Interactive Learning Environments
• daptive learning environments foster understanding in complex do-
A
mains. Adaptive learning environments are sensitive to the learner’s general
profile, and level of mastery at any given point in time can facilitate the learning
of complex material. The degree to which adaptive instruction from human in-
structors and computerized learning environments can facilitate and accelerate
learning requires further study.
• nteractive learning environments facilitate learning. Fine-grained feedback
I
provided while learners engage in a task with hints that prompt generation of
knowledge facilitates learning. Research is needed to evaluate the effectiveness
of specific interactive instructional approaches (e.g., reciprocal teaching method,
modeling-scaffolding-fading, the Socratic method, refutation).
• earning is facilitated in genuine and coherent learning environments.
L
Learning is enhanced by opportunities to practice and use skills for a purpose,
although the effectiveness of specific approaches consistent with this principle
remains to be tested.
Motivation and Emotion
• otivation is essential for learning. A learner's affective response to the
M
learning experience influences not only engagement and persistence in a task
but also the capacity for cognitive processing.
