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Overcoming Deceptive Clarity by Encouraging Metacognition in the Web-Based Inquiry Science Environment


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In our research we view metacognition and cognition as interacting pro- cesses that together promote coherent understanding. We propose that the use of the knowledge integration pattern to design instructional scaffolding encourages the interplay between these two processes. In this chapter, we present and discuss findings that indicate that instructional activities designed using the knowledge integration pattern promote student learning from dynamic visualizations by helping to overcome deceptive clarity.
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In our research we view metacognition and
cognition as interacting processes that together
promote coherent understanding. We propose
that the use of the knowledge integration pattern
to design instructional scaffolding encourages
the interplay between these two processes. In this
chapter, we present and discuss ndings that indi-
cate that instructional activities designed using
the knowledge integration pattern promote stu-
dent learning from dynamic visualizations by
helping to overcome deceptive clarity.
Typical instruction encourages learners to
focus primarily on the cognitive aspects of learn-
ing, such as adding new ideas or comprehending
explanations of phenomena. However, we believe
that designing curriculum that also supports stu-
dents in being metacognitive by, for example, dis-
tinguishing among ideas using generated criteria
or re ecting on potential alternatives as they
engage in these and other cognitive activities
results in overall greater bene ts for student learn-
ing and understanding. In fact, much research
points to the bene ts of incorporating metacogni-
tive activities to promote coherent understanding
(e.g., Aleven & Koedinger, 2002 ; A z e v e d o , 2005 ;
Graesser, McNamara, & VanLehn, 2005 ; Quintana,
Zhang, & Krajcik,
2005 ; White & Frederiksen,
1998 ) , especially with dynamic visualizations.
We nd t ha t ac tivi ti es t ha t pr om ot e me ta co gnit iv e
skills such as prompting self-monitoring and
J. L. Chiu (*)
Science, Technology, Engineering and Math (STEM)
Education , Curry School of Education, University
of Virginia , Bavaro Hall , Charlottesville ,
VA 22904 , USA
J. K. Chen M. C. Linn
Education in Mathematics, Science,
and Technology , University of California ,
Tolman Hall , Berkeley , CA 94720-1670 , USA
e-mail: ;
Overcoming Deceptive Clarity
by Encouraging Metacognition
in the Web-Based Inquiry Science
Jennifer L. Chiu , Jennifer King Chen,
and Marcia C. Linn
R. Azevedo and V. Aleven (eds.), International Handbook of Metacognition and Learning Technologies,
Springer International Handbooks of Education 26, DOI 10.1007/978-1-4419-5546-3_33,
© Springer Science+Business Media New York 2013
I n o u r r e s e a r c h w e v i e w m e t a c o g n i t i o n a n d c o g n i t i o n a s i n t e r a c t i n g p r o -
cesses that together promote coherent understanding. We propose that the
use of the knowledge integration pattern to design instructional scaffolding
encourages the interplay between these two processes. In this chapter, we
present and discuss ndings that indicate that instructional activities
designed using the knowledge integration pattern promote student learning
from dynamic visualizations by helping to overcome deceptive clarity.
518 J.L. Chiu et al.
supporting critique of one’s understanding can
help students to interpret and learn from visualiza-
tions more successfully. By incorporating meta-
cognitive activities into curricula featuring
visualizations, we can help learners develop coher-
ent, normative understanding that builds upon
their prior knowledge. This emphasis can also
help students develop important metacognitive
skills such as evaluating, distinguishing, and
re ecting upon their understanding (Aleven &
Koedinger, 2002 ; Azevedo, Moos, Greene,
Winters, & Cromley, 2008 ; White & Frederiksen,
2005 ) . O u r p e r s p e c t i v e i s c o n s i s t e n t w i t h r e s e a r c h
that advocates for less extraneous and more ger-
mane cognitive processing with dynamic visual-
izations (Wouters, Paas, & van Merrienboer,
2008 ) . Although research points to the need for
use of metacognitive skills and knowledge inte-
gration when learning from visualizations, few
studies have focused on promoting or capturing
students’ use of metacognition when interacting
with dynamic visualizations.
This chapter describes research conducted
by the Technology-Enhanced Learning in
Science (TELS) Center using the Web-based
Inquiry Science Environment (WISE). Our
work focuses on supporting student learning
from dynamic visualizations using instructional
scaffolding developed according to the knowl-
edge integration pattern (Linn & Eylon, 2006 ) .
Research reports varied levels of effectiveness
for instruction with visualizations (Hof er &
Leutner, 2007 ) . We present evidence that the
use of dynamic visualizations often results in
deceptive clarity —students’ overestimation of
their understanding of the visualization after
rote completion of the instructed steps or only a
brief inspection (Tinker, 2009 ) . The deceptive
clarity of visualizations is highly problematic,
in that it can short-circuit students’ consider-
ation of alternative ideas and limit more detailed
interrogation with the visualization, producing
the kinds of mixed results found in the litera-
ture. In this chapter we argue that instruction
developed with the knowledge integration pat-
tern that encourages both metacognitive and
cognitive processes can help to overcome decep-
tive clarity and support student learning. We
discuss how a variety of WISE instructional
supports, such as prompting students to make
predictions or asking them to explain their
understanding, can help students develop skills
to monitor their learning of complex science
from dynamic visualizations.
WISE and Dynamic Visualizations
The Web-based Inquiry Science Environment
(WISE; ) is a
free online environment supported by the
National Science Foundation (Fig. 33.1 ).
F i g . 3 3 . 1 WISE guides students inquiry investigations
through the use of a map of activities and various step types
and tools, such as explanation prompts and dynamic visu-
alizations. The WISE environment also offers tools for
teachers and researchers to monitor student work and give
feedback, as well as to author and customize instruction
51933 Metacognition and Wise
Projects created in WISE support effective
inquiry learning and use interactive, research-
based instruction in conjunction with engaging,
dynamic visualizations (Slotta & Linn,
2009 ) .
Other instructional tools used in WISE projects
in addition to dynamic visualizations include
re ection notes, predict and revise prompts,
student journals, drawing tools, online discussion
forums, and concept maps. Projects present
students with compelling inquiry questions
embedded within relevant topics such as global
climate change, airbag safety, and genetic
inheritance. By making science accessible, and
by providing instruction that encourages stu-
dents to make their thinking visible to them-
selves and others, WISE projects promote the
development of both integrated understanding
as well as skills for autonomous, lifelong learn-
ing (Linn, Eylon, & Davis, 2004 ) .
Dynamic visualizations refer to external
representations used for learning that display
processes of scienti c phenomena that change
over time. Basic forms of dynamic visualizations
include animations, which consist of sets of
frames that alter properties such as shape or size,
depict motion, or make objects appear and disap-
pear (Lowe, 2004 ; Moreno & Mayer, 2007 ) .
More sophisticated instructional simulations and
computational models enable students to interact
and experiment with phenomena on scales that
are not directly observable such as molecular
dynamics (Linn & Eylon, 2006 ) or with visual-
ized concepts such as force (White & Frederiksen,
1998 ) . These dynamic visualizations enable stu-
dents to alter variables or settings to see different
outcomes. Students can generate and test hypoth-
eses by experimenting and interacting with the
visualization, as well as synthesize and re ne
their hypotheses by re ecting upon observed out-
comes as well as the effectiveness of their experi-
mentation strategies for learning (Ertmer &
Newby, 1996 ) .
By incorporating dynamic visualizations,
WISE projects offer students the opportunity to
interact with complex scienti c phenomena in
ways that are dif cult or impossible with tradi-
tional forms of instruction, such as lecture or
text-based teaching. For instance, text-based
instruction of chemical reactions typically
focuses on rules or classi cation of chemical
reactions, such as single-replacement or com-
bustion reactions. With typical instruction, stu-
dents might read that increasing the temperature
of reactant molecules will increase the reaction
rate because of increased collisions. In order to
understand this concept more fully, however,
students must visualize atoms and molecules
interacting on a molecular scale. Thus a limita-
tion of text-based instruction is that it requires
students to rely solely on their own mental repre-
sentations, which may be awed or incomplete.
Instruction featuring dynamic visualizations
enables students not only to compare their exist-
ing mental representations with scienti cally
normative dynamic visualizations, but also to
consider new ideas gleaned from interacting
with the visualizations. Chemical Reactions , a
weeklong WISE project for high school chemis-
try students, uses dynamic molecular visualiza-
tions of chemical reactions. Students can change
parameters and variables and immediately
observe what happens on an atomic level. For
instance, students can add energy to reactants
and watch product molecules form on the screen
(Fig. 33.2 ). Similarly, the WISE Static Electricity
project (Shen & Linn, 2010 ) incorporates
dynamic visualizations of protons and electrons
that students can use to investigate how the
movement of electrons relates to the phenome-
non of receiving an electric shock. In the WISE
Birds of a Feather Evolve Together project, stu-
dents explore how different traits and environ-
ments impact the survival of species with a
dynamic visualization that models natural selec-
tion. The visualization enables students to exper-
iment with various species over multiple
Dynamic Visualizations and Deceptive
Research demonstrates that while dynamic visu-
alizations have signi cant overall impact on
learning (Hof er & Leutner, 2007 ) , students face
dif culties when using them (Tversky, Morrison,
520 J.L. Chiu et al.
& Betrancourt, 2002 ) . For example, students
tend to overestimate their own understanding of
visualized systems (Rozenblit & Keil, 2002 ) .
This kind of deceptive clarity can be particularly
detrimental for learning. For instance, Lowe
( 2004 ) investigated learning with animated
weather maps. Subjects tended to focus on per-
ceptually salient aspects of the visualization,
such as isolated spatial or temporal features.
Subjects had trouble building more coherent pre-
dictions of weather that integrated features across
the visualization. Videos of subjects during the
learning task revealed that learners did not know
they should be looking at other important fea-
tures of the visualization (i.e., “Do I have to do
all these lines as well?” , p. 268). Other research
reports similar illusions of understanding with
other visualizations (Lewalter, 2003 ; Schnotz &
2005 ) .
Interactivity can encourage students to engage
and revisit with visualizations, and can have a
large impact on learning effectiveness (Moreno
& Mayer,
2007 ) . Interactive features allow stu-
dents to pause, slow down, speed up or replay a
visualization, or to change variables and inputs
to observe different outcomes. Learners can
revisit visualizations and focus upon concepts or
aspects they may have missed upon initial inter-
rogations. However, even with interactive fea-
tures learners can fail to build integrated
understanding from visualizations (Kalyuga,
2007 ) . Students need to be aware of the impor-
tant concepts upon which to focus, and they also
need to know how to monitor their understand-
ing to appropriately manipulate the visualization
to address any gaps in knowledge (Lowe,
2004 ) .
For example, students using a chemical reaction
visualization can focus on the impact of heat on
molecular motion, manipulate settings to under-
stand that relationship, think they understand it
and move on to the next step. If students are not
aware of other important aspects (such as bond-
ing), or have a false sense of understanding, stu-
dents will not fully utilize the functionalities of
the visualization, such as replaying it or experi-
menting with other variables (Linn & Eylon,
Fig. 33.2 Chemical Reactions uses dynamic molecular visualizations and pedagogical tools such as embedded prompts
within WISE
52133 Metacognition and Wise
2011 ) . This kind of self-monitoring can have a
large impact on how students interact with and
how much students learn from dynamic visual-
izations (Azevedo, Guthrie, & Seibert,
2004 ) .
Research into self-regulatory learning (SRL)
in multimedia environments lends insight into
deceptive clarity and visualizations (Azevedo,
2005 ) . Self-regulated learning involves setting
goals, determining and using learning strategies
to meet those goals, monitoring one’s learning,
evaluating how well one is reaching those goals
and responding by changing strategies
(Zimmerman, 2008 ). Students interacting with
visualizations may set different goals than stu-
dents working with text. For example, visualiza-
tions may trigger students to set procedural goals
to complete steps or run the visualization instead
of setting learning goals to seek conceptual
understanding of the underlying concepts.
Students may then select strategies, monitor
effectiveness and evaluate outcomes based on
these different goals (Reiber, Tzeng, & Tribble,
2004 ). In this way, differences in how students
perceive the task of learning with visualizations
can result in students believing that they have
understood the targeted concepts.
Overcoming Deceptive Clarity Through
Knowledge Integration
The knowledge integration pattern builds upon
research that learners have rich, diverse, and often
con icting ideas about scienti c phenomena
from various contexts and experiences (Davis &
Linn, 2000 ; diSessa, 2000 ; Linn, Clark, & Slotta,
2003 ; Songer & Linn, 2006 ) . Students’ existing
ideas are viewed as fruitful starting points for
developing deep understanding.
The knowledge integration instructional pat-
tern consists of four interleaved processes:
Eliciting current ideas, adding new ideas, devel-
oping criteria for evaluating ideas, and sorting
out ideas (Linn & Eylon,
2006 ) . First, eliciting
current ideas recognizes the diverse backgrounds
and experiences that individual students bring
with them into the classroom and acknowledges
these experiences and ideas as rich starting points
for learning (Davis & Linn, 2000 ; diSessa, 2000 ;
Linn et al.,
2003 ; Songer & Linn, 2006 ) .
Prompting learners to become aware of their pre-
existing ideas prepares them to form connections
between these ideas and new ones. Second, add-
ing new ideas involves introducing normative
ideas for students to consider against their exist-
ing ones. The careful design of effective visual-
izations can serve as a fertile source of useful and
relevant ideas for students to evaluate and incor-
porate into their thinking. Third, supporting stu-
dents in developing criteria for evaluating ideas
helps them to readily distinguish between their
own ideas and new ones. Fourth, in sorting out
their ideas, learners are encouraged to re ect
upon their ideas by using their developed criteria
to evaluate, sort and consolidate their ideas into a
revised and more coherent understanding. Using
generated criteria to evaluate the connections
among their ideas can help students to re ne their
knowledge based on these evaluations. The pro-
cess of knowledge integration thus encourages
students to consider their current networks of
ideas, make judgments about their understand-
ing, and seek ways to improve their understand-
ing by going back and adding, sorting, or re ning
ideas. When learners sort out their ideas and use
evidence to support their thinking, they strengthen
their understanding. The knowledge integration
instructional pattern thus helps students to gain a
coherent, integrated understanding of a scienti c
Instruction that focuses on knowledge integra-
tion helps students overcome the deceptive clar-
ity of visualizations because students engage in
both cognitive and metacognitive processes that
help them to more productively engage in moni-
toring their learning with the visualizations. The
knowledge integration pattern encompasses a
spectrum of activities that can be considered as
more cognitive in nature (such as adding norma-
tive ideas) to processes that are more metacogni-
tive in nature (such as distinguishing and
re ecting upon ideas). Ideally, learners monitor
and re ect upon their knowledge, nd gaps or
discrepancies in their understanding, and act to
remedy these situations by building coherent net-
works of ideas.
522 J.L. Chiu et al.
Since traditional classroom instruction does
not typically focus upon metacognitive skill
development (Linn & Eylon, 2011 ) , we focus on
scaffolding to help students engage in the entire
knowledge integration pattern, with a special
focus on distinguishing, evaluating, and
re ecting upon ideas and connections.
Instructional prompting or scaffolds help learn-
ers develop more sophisticated skills and engage
in more complicated activities than they could
on their own (Bransford, Brown, & Cocking,
1999 ) . Appropriate scaffolding in classroom
environments can be especially challenging,
with many students starting at varying levels of
skills and knowledge. In order for scaffolding to
be most effective, it needs to fall in the target
zone of not giving too much or too little support
(Vygotsky, 1978 ) .
Prior research suggests that scaffolding in
computer-enhanced environments can help learn-
ers engage in metacognitive activities with
curriculum featuring visualizations (Azevedo
et al., 2008 ) . For instance, White and Frederiksen
( 1998 , 2000 ) used re ective prompts in their
ThinkerTools curriculum to encourage student
self-evaluation at the end of each inquiry cycle
during the project. Students were either prompted
to engage in re ective self-assessment or not
prompted to self-assess. Students with monitoring
support who self-assessed understood scienti c
inquiry better than those who did not self-assess,
and the support especially bene tted students
with lower prior knowledge.
Davis and Linn ( 2000 ) investigated the effect
of two different types of prompts—sentence
starters that were either activity-focused or self-
monitoring—on middle school students’ inte-
gration of heat and light energy knowledge while
working with the WISE Aliens on Tour a n d All
the News projects. These projects required stu-
dents to design houses and clothing for cold-
blooded aliens with different climate
requirements and critique news articles about
energy and thermodynamics. Students responded
to activity-focused prompts and self-monitoring
prompts. The activity-focused prompts were
aimed to help students think about the justi cation
required to demonstrate the quality of their
designs (e.g., “Our design will work well
because…” ) while the self-monitoring prompts
encouraged students to re ect on the quality of
their designs (e.g., “Our design could be better
if we…” ). Findings indicate that while the activ-
ity-focused prompts were effective for helping
students to nish their project activities, students
did not develop integrative knowledge. Self-
monitoring prompts, however, encouraged
knowledge integration by helping students to
plan their activities, reminding them to re ect on
their understandings, and encouraging them to
explain and justify their design decisions.
Additionally, students who used the self-moni-
toring prompts to evaluate their understanding
and identify places of confusion had greater
project scores (Davis & Linn, 2000 ) .
In addition, prompting students to predict,
distinguish, draw, or critique ideas also engages
them in the process of knowledge integration
(Linn, Chang, Chiu, Zhang, & McElhaney,
2010 ) . These generative activities can be espe-
cially bene cial for learning with visualizations
because learners can compare new ideas to their
prior knowledge or existing mental models of
phenomena (Chi, De Leew, Chiu, & Lavancher,
1994 ; Lombrozo, 2006 ) . This gives students the
opportunity to identify what they do not understand
(Rozenblit & Keil, 2002 ) or to revise their views
(Chi, 2000 ) . Engaging students in the process of
knowledge integration helps students refocus
their learning goals on conceptual understanding
and employ both cognitive and metacognitive
strategies to reach those goals (e.g., Zimmerman,
2008 ) .
Students may fail to revise their initial ideas
and proceed with isolated views of the visualiza-
tion if not prompted to distinguish ideas. For
example, Chi (
2000 ) describes how generating
self-explanations while reading text helped a sub-
ject revise her mental model of the circulatory
system. The student rst used self-explanations
to generate her existing network of ideas. During
a later segment, the student distinguishes ideas
when she comes across a piece of information
that con icts with her existing model of how the
circulatory system works (that blood ows into
the lungs). The student reveals her efforts to dis-
52333 Metacognition and Wise
tinguish ideas by making many monitoring state-
ments such as “I don’t understand.” Subsequently
she revised her understanding of the circulatory
system to include a loop from the heart to the
lungs. This example demonstrates how prompt-
ing to distinguish ideas can help students over-
come deceptive clarity. The prompts can help
students realize what they do not understand, rec-
ognize con icting ideas, and remedy these
con icts or gaps in their understanding.
Research suggests that distinguishing ideas
also bene ts those learning with visualizations.
For instance, Cromley, Azevedo, and Olson
2005 ) i n v e s t i g a t e d h o w p e o p l e s e l f - r e g u l a t e
when learning from a multimedia environment
that included animations of the circulatory
system. Learners engaged in relatively less self-
regulation with the animation than other forms of
instruction, supporting the notion of deceptive
clarity. However, if the learners summarized their
understanding as they watched the animation, the
participants learned more. Similarly, in the WISE
Orbital Motion project, students interact with a
set of three dynamic visualizations to help them
connect from their everyday ideas about projec-
tiles to a more sophisticated understanding of
orbital motion. In one visualization, students
experiment with launching a cannonball using
different initial horizontal speeds. Without the
proper instructional scaffolding, students may
interact with the visualization only brie y (such
as launching the cannonball using just a few arbi-
trarily selected speeds) and believe their under-
standing to be sound or unproblematic. However,
the use of predict and revise prompts before and
after the visualization encourages students to
re ect more carefully about their interactions
with the visualization in order to achieve impor-
tant key outcomes—such as determining appro-
priate launch speeds for getting the cannonball to
hit the ground, achieve orbit, or escape into space.
(King Chen, Tinker, & McElhaney, 2011 ) .
Distinguishing ideas can also help students
overcome deceptive clarity with visualizations
because they encourage students to identify gaps
in their understanding (Renkl & Atkinson, 2002 ;
Rozenblit & Keil, 2002 ) . For instance, Rozenblit
and Keil ( 2002 ) conducted a number of experi-
ments where subjects judged their understanding
of certain concepts, explained their ideas about
the concepts, and then re-rated their understand-
ing. Rozenblit and Keil ( 2002 ) found that prompt-
ing students to generate their own ideas about
concepts consistently helped subjects to recog-
nize what they did not understand, especially for
visualized phenomena. This approach was not as
helpful for recognizing gaps in understanding of
procedures, narratives, or facts. These ndings
suggest that if students fail to monitor their under-
standing while interacting with a visualization,
having them generate their own ideas can help
them to become aware of and identify what they
may have missed in the visualization.
Generating predictions can also help students
overcome deceptive clarity by identifying gaps in
understanding. In one study, subjects asked to
predict behavior of animated and static visualiza-
tions of devices had a better understanding than
those who did not predict (Hegarty, Kriz, & Cate,
2003 ) . The researchers suggest that predicting
behavior helped the subjects recognize what they
did and did not know about the mechanical
device. This aligns with other studies that nd
that predicting answers bene ts learning, even if
these predictions are incorrect (Kornell, Hays, &
Bjork, 2009 ) .
Similarly, other activities that encourage
students to articulate causal chains of events or
sequences of states can help learners to identify
and ll in gaps in their understanding. Examples
of these kinds of activities include generating
representations, drawing, or categorizing and
sequencing. Chang, Quintana, and Krajcik
( 2010 ) studied students creating animations
using Chemation , a program that enables stu-
dents to make ipbook-style dynamic visualiza-
tions of chemical phenomena. They found that
students had a better understanding of atom
rearrangement than students using static visual-
izations and media because the students had to
pay attention to the dynamic aspects of bonds
breaking and bonds forming when creating their
We report next on a collection of ndings that
point to the bene t of using the knowledge
integration pattern for learning with dynamic
524 J.L. Chiu et al.
visualizations. We present our results regarding
deceptive clarity in more detail to demonstrate
how generation activities can help students elicit,
add, distinguish, sort out, re ect, and re ne their
ideas with visualizations. These results describe
how the knowledge integration pattern encour-
ages both cognitive and metacognitive processes
and how this combination bene ts learning with
Empirical Findings
WISE provides a rich environment to research
how instruction focused on knowledge integra-
tion can support students’ development of self-
knowledge and self-monitoring skills while
interacting with dynamic visualizations. WISE
logs students’ work as they progress through the
unit. These data logs capture exactly when and
what students write, details about students’ nav-
igation through the project, and how students
interact with the visualizations. We use these
data logs combined with self-assessments,
embedded explanations, and pre- and post-test
measures to both research and promote stu-
dents’ self-monitoring in authentic classroom
Addressing Deceptive Clarity
with Explanations
The WISE Chemical Reactions project
exempli es how designing for and prompting
knowledge integration processes can help stu-
dents mindfully build, assess, critique and sort
out their ideas with dynamic visualizations.
Chemical Reactions guides students through an
investigation of how chemical reactions relate to
climate change. The project focuses on making
connections among symbolic, molecular and
observable levels of chemical reactions, as well
as balanced equations, stoichiometric ratios, and
limiting reagents. Students interact with visual-
izations of common hydrocarbon combustion
reactions that contribute carbon dioxide to the
atmosphere, use dynamic visualizations of
hydrogen combustion to investigate hydrogen as
an alternative fuel, and explore greenhouse visu-
alizations to learn how greenhouse gases trap
infrared radiation. Students re ect upon and
synthesize the information they learn through-
out the project in an electronic letter to their
In Chemical Reactions students are asked to
distinguish between two visualizations. One that
shows the addition of energy and the resulting
explosion and one that does not involve adding
energy. While distinguishing among the two
visualizations, the students’ connections among
existing ideas and ideas added from the visualiza-
tion become visible. Many students initially
notice that the atoms and molecules bounced
around the container faster after the spark is
added, but when asked to distinguish they real-
ized they did not know how the spark caused the
reaction to occur (i.e., “I think the spark caused
the molecules to move around faster, but I’m not
sure” ). Using the interactive dynamic visualiza-
tion, students can go back and test their current
ideas to ll in gaps in understanding or see if their
predictions or ideas are correct. They can inspect
the visualization in greater detail to see that the
spark caused the reaction to occur by adding
energy that breaks bonds, creating free radicals
that then form intermediate and product
To explore how students judged their
understanding of concepts with these visualiza-
tions, we prompted for judgments of learning
(Chiu & Linn, 2008 ) . We asked one group of stu-
dents to rate their understanding immediately
after the visualizations, and another group to rate
their understanding after writing an explanation
of their understanding. We found that students
judged themselves as more knowledgeable imme-
diately after working with the visualizations.
Students rated themselves as less knowledgeable
after writing an explanation of their understand-
ing. These results have replicated across later
groups of students. Analysis and observations of
students using the project revealed that when stu-
dents initially interacted with the visualizations,
they tended to focus on following the instructions
or making the visualization “blow up.” These
52533 Metacognition and Wise
kinds of interactions seemed to convince students
that they understood the concepts underlying the
visualizations simply by completing the visual-
ization steps without deeper interrogation.
Prompting students to explain helped students
realize gaps in their understanding. Analysis of
log les revealed that students were likely to
revisit the visualization after the explanation
prompt. For instance, students would “blow up”
the visualization, or make carbon dioxide and
water product molecules and then judge their
understanding of balanced equations as very
good. After being prompted to explain how the
balanced equation relates to the visualization,
student pairs often asked one another, “I don’t
know, how did it relate?” Students would subse-
quently revisit the visualization before writing
anything, in the middle of generating their expla-
nation. Students would also revisit the visualiza-
tions after they had nished writing their
explanation to check their work.
Prompting for explanations helped students
engage with the full knowledge integration pat-
tern, interacting with the visualizations in both
cognitive and metacognitive ways. Not only did
students successfully connect their existing ideas
of balancing equations to new ideas from the
visualizations, but they also developed more
accurate self-knowledge and criteria for their
ideas (as demonstrated by their realizations that
their understanding was not as good as they rst
thought). Explaining also supported students’
development of self-regulatory skills, such as
sorting out and re ning ideas and connections.
Students acted upon their judgments of learning
and revisited the visualization to repair gaps in
their knowledge or to resolve con icts with their
understanding of the concepts.
Addressing Deceptive Clarity
with Drawing
In addition to explanation, other generative activ-
ities such as drawing can promote knowledge
integration with dynamic visualizations. Drawing
can help students elicit and build upon their prior
ideas, as well as sort and re ne their existing
ideas and the new ones conveyed by the visual-
ization. In a progression of classroom studies,
Zhang (Linn et al., 2010 ; Zhang & Linn, 2008 )
investigated how generating drawings can help
students overcome the deceptive clarity of visual-
izations. In these studies, middle school students
used a similar chemical visualization to the one
in Chemical Reactions . Pilot testing indicated
that students failed to understand bonds breaking
and forming as part of the chemical reaction pro-
cess. To investigate how drawing can help stu-
dents learn from visualizations, Zhang compared
students who drew sequences of chemical reac-
tions to students prompted to spend more time
with the visualization. The students in the draw-
ing condition were asked to draw the reacting
molecules before the reaction began, right after
the reaction began, after the chemicals had
reacted for some time, and after the chemicals
had reacted for a very long time.
Zhang found that students in the drawing con-
dition learned more overall than the students who
only explored the visualization, as generating
drawings helped students distinguish their ideas
from the visualization. Prompting students to
draw stages of the reaction helped focus the stu-
dents on the chemical reaction in terms of bonds
breaking and forming. Students could revisit the
visualization and compare their drawings to the
visualization. Similar to generating explanations,
generating drawings helped students realize gaps
in their understanding, or concepts they may have
missed in their initial investigation of the visual-
ization. Having students create representations of
chemical reactions helped students become aware
of their limited understanding and spurred them
to sort out and re ne their thinking by revising
the visualization. These results resonate with
other research studies demonstrating the bene t
of having students create representations of
chemical reactions (Chang, Quintana, & Krajcik,
2010 ; Schank & Kozma, 2002 ) .
To further investigate the effect of generating
drawings, Zhang compared students that drew
pictures to students who selected screenshots of
the model to represent four stages during the pro-
cess of a chemical reaction (Linn et al., 2010 ) .
Zhang found that the students who generated
526 J.L. Chiu et al.
their own drawings outperformed students who
selected screenshots on posttest assessments,
controlling for prior knowledge. The drawing
group outperformed the selection group on
assessment items that called for selecting and
sequencing static pictures of chemical reactions,
as well as items that called for the students to use
their understanding in different contexts (i.e., dif-
ferent reactions). Zhang suggested that generat-
ing drawings helped students re ect and sort out
their ideas, whereas the selection activity failed
to encourage students to stop and re ne their
understanding. This suggests that activities such
as generating drawings and explanations can help
students overcome the deceptive clarity of visual-
izations and promote knowledge integration,
whereas other activities such as selection may not
be as bene cial because they do not encourage
learners to revisit and re ne their understanding
of the visualization.
The ndings we presented point to the effective-
ness of various instructional scaffolding tech-
niques that can promote student self-monitoring
and self-assessment. Engaging students in mak-
ing predictions, generating explanations, or cre-
ating drawings or representations provides
opportunities for them to identify weaknesses in
their thinking, to evaluate, sort, connect and re ne
new and old ideas, or to verify their understand-
ings. We view these types of scaffolding as exam-
ples of desirable dif culties —that is, instruction
that enhances learning by introducing bene cial
cognitive dif culties for the student to address
(Bjork & Linn,
2006 ) .
We believe the knowledge integration pattern
helps learners succeed using visualizations
because it promotes this blend of cognitive and
metacognitive interaction with the learning envi-
ronment. Indeed, recent research from various
sources points to the bene t of combining cogni-
tive and metacognitive support for instruction
with visualizations (Ainsworth,
2008 ; Aleven &
Koedinger, 2002 ; Azevedo, Winters, & Moos,
2004 ; Moos & Azevedo, 2008 ; Reiber et al.,
2004 ) . I f l e a r n e r s a r e n o t a w a r e o f g a p s i n t h e i r
understanding or aware of critical information to
focus upon within the visualization, students can
continue without further thought. Engaging stu-
dents in the knowledge integration pattern thus
helps students to interact with the visualizations
both cognitively and metacognitively—they not
only add ideas to their thinking but also revisit
the visualizations to re ne and sort out their
Other current research points to the impor-
tance of combining dynamic visualizations with
the full knowledge integration pattern.
Kombartzky, Ploetzner, Schlag, and Metz ( 2010 )
investigated how prompting learners to engage in
strategies to elicit, distinguish, and re ect upon
ideas could in uence learning with a dynamic
visualization about honeybees. Kombartzky et al.
( 2010 ) compared students in two groups: The
essay group interacted with visualizations and
then wrote an essay about what they learned; the
strategy group made predictions about the visual-
ization, explained the visualization, revisited the
visualization, drew their understanding, and
re ected upon their work. Students in the strat-
egy condition outperformed the students in the
essay condition. Similarly, recent work with
self-regulatory learning in hypermedia environ-
ments found that learners who make large con-
ceptual gains tend to engage in monitoring
strategies such as summarizing and making
inferences, and making judgments of under-
standing (Azevedo, 2005 ; Azevedo et al., 2004 ;
Greene & Azevedo,
2007 ) .
Design principles based on recent research
from various perspectives also indicate the bene t
of including knowledge integration activities
(Plass, Homer, & Hayward,
2009 ) . For instance,
design principles based on the cognitive-affective
theory of multimedia learning (CATLM) calls for
activating prior knowledge and providing oppor-
tunities for students to examine and repair their
understanding (Moreno & Mayer,
2007 ) . Plass
et al. (
2009 ) highlight the importance of aligning
interaction within visualizations with cognitive
as well as metacognitive goals.
52733 Metacognition and Wise
Current Challenges
For students to use visualizations most effectively,
careful design of instruction that supports the use
of metacognition is essential. However, there are
still several challenges for moving this area of
work forward. Examples of some outstanding
issues include: Conducting research in authentic
classroom settings, assessing students’ use of
metacognition, and investigating the role of col-
laboration in developing self-monitoring skills.
Conducting research in authentic classrooms:
Conducting design experiments in authentic
classrooms poses challenges for research on
metacognition. Although we can implement par-
ticular types of instructional support and measure
the impacts of those interventions on students, it
is very dif cult to identify, distinguish and assess
the use of metacognitive processes by students.
Research conducted within the real-life con-
straints of classrooms necessitates the careful
design of studies that not only focus on what is
best for the learner, but also will allow research-
ers to obtain useful and appropriate data that
addresses the research questions of interest.
Results from laboratory settings may or may not
transfer to classroom environments where the
concepts to be learned are integrated with the
overall course instead of presented as an unre-
lated experiment (Richland, Linn, & Bjork,
2007 ) .
Conducting classroom re nement studies will
help researchers nd what kinds of metacognitive
interventions work in classrooms, as well as con-
tribute to learning theory (i.e., Brown,
1992 ) . I n
general, more design experiments using visual-
izations in classrooms will help test and re ne
design principles and recommendations from
experimental settings.
For instance, recent studies show bene t for
iterative re nement of classroom instruction
with visualizations to support both cognitive
and metacognitive processes using the knowl-
edge integration pattern (Chiu, 2010 ; Linn et al.,
2010 ; T a t e , 2009 ) . I n Airbags: Too Fast, Too
Furious? , a WISE high school physics project
investigating motion and airbag safety, students
use a visualization that enables them to conduct
car collision experiments and explore relations
among speed, distance, and driver safety. Pilot
testing revealed that students needed support to
set goals and plan experimental trials before
interacting with the visualization. In a revised
version of the project, the visualization was
accordingly modi ed so that students could
select experimentation goals from a drop-down
menu with options such as “driver height,” “col-
lision speed,” “crumpling,” or “just exploring”
before proceeding to investigate the selected
goal with the dynamic visualization. As a result,
students could interact with and revisit the visu-
alization by focusing on different research goals,
helping students to elicit, integrate and re ne
their ideas in a more targeted manner
(McElhaney, 2010 ) .
Knowledge Community and Inquiry (KCI)
instruction builds upon inquiry-based learning
and knowledge communities approaches to
encourage inquiry-based knowledge construc-
tion within classrooms (Slotta & Peters, 2008 ) .
KCI studies have also found that the knowledge
integration pattern helps to re ne instruction
with visualizations. As part of an inquiry sci-
ence lesson, students created and annotated their
own wikipages using WISE visualizations of
climate change (Naja & Slotta, 2010 ) . As a
result of pilot testing, the researchers found that
students needed help making and re ecting upon
links from their co-constructed curriculum to
the visualizations. Subsequent revisions will
incorporate re ective self-assessments and
self-monitoring guidance to enhance learning
with the visualizations.
Assessing students’ metacognitive activities:
Students’ use of metacognition can only be indi-
rectly inferred by analyzing what the student
does. Consequently, it is extremely dif cult to
know with any degree of certainty if certain
actions are, from the student’s perspective, truly
cognitive or metacognitive. Research that can
make these kinds of delineations more accurately
usually occurs in lab settings with relatively small
numbers of learners (i.e., Hegarty et al., 2003 ;
528 J.L. Chiu et al.
Lowe, 2004 ; Moos & Azevedo, 2008 ; Reiber
et al.,
2004 ). These studies provide valuable,
ne-grained information about strategies and
self-regulatory techniques used with visualiza-
tions. Future research needs to nd ways to cap-
ture metacognitive processes accurately and
reliably in classroom settings.
Using the knowledge integration instructional
pattern provides particular utility in classroom
settings to promote and assess both cognitive and
metacognitive goals. In our studies with explana-
tion and drawing we use self-ratings and prompts
for explanation measures of learning and self-
assessment as well as scaffolds for self-monitor-
ing. We use the logging technologies of WISE to
determine how students navigate through the
environment. Although these measures may not
distinguish strictly metacognitive from strictly
cognitive activities, there are great learning
bene ts from using these tools in the classroom.
Supplemented by data log les, we have insight
into the actions that student pairs take during the
inquiry units. More tools that work ef ciently
and effectively in classrooms would greatly
bene t the eld.
Investigating the role of collaboration in prompt-
ing self-monitoring : The complexity of the class-
room limits the nuances that can be determined,
since students working with WISE projects typi-
cally work in pairs. The decision to have students
work in pairs is dictated by several factors,
including: (1) Evidence that students learn from
each other, (2) the limitations of classroom space
and computers for students (class sizes are
approaching 40 in the schools where we work),
and (3) the availability of computers for all stu-
dents. With students’ varying levels of prior
knowledge and skills, student work in pairs
makes it challenging to accurately determine and
distinguish cognitive and metacognitive actions
for individual students. There is relatively little
research that investigates collaborative learning
with scienti c visualizations, and the existing
research provides mixed results (Ainsworth,
2008 ) . More research is needed to explore how
students learn from each other when working
with visualizations.
T h e s e r e s u l t s s u p p o r t t h e i m p o r t a n c e o f c o m b i n -
ing cognitive and metacognitive activities to pro-
mote knowledge integration. This is particularly
evident in studies of student interactions with
visualizations. Cognitive activities such as adding
ideas are not suf cient to ensure that those ideas
are coherently understood. Interactive dynamic
visualizations can provide unique opportunities
for learners to deeply engage in thinking about
challenging scienti c phenomena when instruc-
tion emphasizes metacognition. Students’ inter-
actions with visualizations need to be carefully
scaffolded in order to support metacognitive
activities such as distinguishing ideas and
re ecting on alternative interpretations. Because
students often do not monitor their understanding,
they tend to incorrectly accept inaccurate inter-
pretations of visualizations. These results suggest
that metacognitive activities can strengthen the
educative impact of visualizations.
Instruction designed according to the knowl-
edge integration pattern can help learners to
overcome the deceptive clarity of visualizations.
This involves rst eliciting student ideas, a com-
mon outcome of prompts for self-explanations or
predictions. When students generate their own
ideas they are prepared to look for con rmatory
evidence and are often surprised when their
expectations are not met. The second element of
the pattern, adding ideas, is supported by interactive
visualizations. The third element, distinguishing
ideas, is often achieved by speci c activities such
as critique of alternatives, drawing ideas, select-
ing among alternatives, contrasting cases (such
as comparing the case of using a spark or no
spark in a chemical reaction visualization), or
conducting experiments. The nal element of the
pattern, re ecting and sorting out ideas, is essen-
tial for success of the instruction. This is often
accomplished by asking students to prepare a
presentation, report, or poster and to pay atten-
tion to the way their ideas communicate to
The knowledge integration pattern emphasizes
incorporating both cognitive and metacognitive
52933 Metacognition and Wise
activities into instructional scaffolding. When
combined, students’ learning from the cognitive
activities is enhanced by the self-monitoring
emphasized in the metacognitive activities.
Consequently, students are guided to think more
deeply about their interactions with a visualiza-
tion, to evaluate their thinking and identify gaps
in understanding, and to critique and revise their
A c k n o w l e d g m e n t s This material is based upon work
s u p p o r t e d b y t h e N a t i o n a l S c i e n c e F o u n d a t i o n u n d e r
grants No. ESI-0334199 and ESI-0455877. Any opinions,
ndings, and conclusions or recommendations expressed in
this material are those of the authors and do not necessarily
re ect the views of the National Science Foundation. The
authors appreciate helpful comments from the Technology-
Enhanced Learning in Science research group.
Ainsworth, S. (2008). How do animations in uence learn-
ing? In D. Robinson & G. Schraw (Eds.), Recent inno-
vations in educational technology that facilitate
student learning . Charlotte, NC: Information Age
Aleven, V., & Koedinger, K. (2002). An effective meta-
cognitive strategy: Learning by doing and explaining
with a computer-based Cognitive Tutor. Cognitive
Science, 26 , 147–179.
Azevedo, R. (2005). Using hypermedia as a metacognitive
tool for enhancing student learning? The role of self-reg-
ulated learning. Educational Psychologist, 40 , 1 9 9 2 0 9 .
Azevedo, R., Guthrie, J. T., & Seibert, D. (2004). The role
of self-regulated learning in fostering students’
conceptual understanding of complex systems with
hypermedia. Journal of Educational Computing
Research, 30 (1), 87–111.
Azevedo, R., Moos, D., Greene, J., Winters, F., & Cromley,
J. (2008). Why is externally-facilitated regulated
learning more effective than self-regulated learning
with hypermedia? Educational Technology Research
and Development, 56 (1), 46–72.
Azevedo, R., Winters, F. I., & Moos, D. C. (2004). Can
students collaboratively use hypermedia to learn about
science? The dynamics of self- and other-regulatory
processes in an ecology classroom. Journal of
Educational Computing Research, 31 , 215–245.
Bjork, R. A., & Linn, M. C. (2006). The science of learn-
ing and the learning of science: Introducing desirable
dif culties. APS Observer, 19 , 29.
Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.).
(1999). How people learn: Brain, mind, experience
and school . Washington, DC: National Research
Brown, A. L. (1992). Design experiments: Theoretical
and methodological challenges in creating complex
interventions in classroom settings. The Journal of the
Learning Sciences, 2 (2), 141–178.
Chang, H.-Y., Quintana, C., & Krajcik, J. (2010). The
impact of designing and evaluating molecular anima-
tions on how well middle school students understand
the particulate nature of matter. Science Education,
94 (1), 73–94.
Chi, M. T. H. (2000). Self-explaining: The dual process of
generating inference and repairing mental models. In
R. Glaser (Ed.), Advances in instructional psychology
(pp. 161–238). Mahwah, NJ: Lawrence Erlbaum
Chi, M. T. H., De Leew, N., Chiu, M.-H., & Lavancher, C.
(1994). Eliciting self-explanations improves under-
standing. Cognitive Science, 18 , 439–477.
Chiu, J. L. (2010). Supporting students’ knowledge inte-
gration with technology-enhanced inquiry curricula
(Doctoral dissertation). Dissertation and Theses
Database . (UMI No. AAT 3413337).
Chiu, J., & Linn, M. C. (2008). Self-assessment and self-
explanation for learning chemistry using dynamic
molecular visualizations. In international perspectives
in the learning sciences: Cre8ting a learning world.
Proceedings of the 8th International Conference of the
Learning Sciences (Vol. 3, pp. 16–17). Utrecht, The
Netherlands: International Society of the Learning
Sciences, Inc.
Cromley, J. G., Azevedo, R., & Olson, E. D. (2005). Self-
regulation of learning with multiple representations in
hypermedia. In C.-K. Looi, G. McCalla, B. Bredeweg,
& J. Breuker (Eds.), Arti cial intelligence in educa-
tion: Supporting learning through intelligent and
socially informed technology (pp. 184–191).
Amsterdam, The Netherlands: IOS Press.
Davis, E. A., & Linn, M. C. (2000). Scaffolding students’
knowledge integrations: Prompts for re ection in KIE.
International Journal of Science Education, 22 (8),
d i S e s s a , A . ( 2 0 0 0 ) . Changing minds: Computers, learning
and literacy . Cambridge, MA: MIT Press.
Ertmer, P. A., & Newby, T. J. (1996). The expert learner:
Strategic, self-regulated, and re ective. Instructional
Science, 24 , 1–24.
Graesser, A., McNamara, D., & VanLehn, K. (2005).
Scaffolding deep comprehension strategies through
Pint and Query, AuthTutor and iSTRAT. Educational
Psychologist, 40 (4), 225–234.
G r e e n e , J . , & A z e v e d o , R . ( 2 0 0 7 ) . A d o l e s c e n t s u s e o f s e l f -
regulatory processes and their relation to qualitative
mental model shifts while using hypermedia. Journal
of Educational Computing Research, 36 (2), 125–148.
H e g a r t y , M . , K r i z , S . , & C a t e , C . ( 2 0 0 3 ) . T h e r o l e s o f
mental animations and external animations in under-
standing mechanical systems. Cognition and
Instruction, 21 (4), 325–360.
Hof er, T., & Leutner, D. (2007). Instructional animations
versus static pictures: A meta-analysis. Learning and
Instruction, 17 , 722–738.
530 J.L. Chiu et al.
K a l y u g a , S . ( 2 0 0 7 ) . E n h a n c i n g i n s t r u c t i o n a l e f c i e n c y o f
interactive e-learning environments: A cognitive load
perspective. Educational Psychology Review, 19 (3),
King Chen, J. Y., Tinker, R., & McElhaney, K. (2011).
Supporting student understanding of projectile and
orbital motion with dynamic models . Poster presented
at the Annual Meeting of the American Educational
Research Association, New Orleans, LA.
Kombartzky, U., Ploetzner, R., Schlag, S., & Metz, B.
(2010). Developing and evaluating a strategy for learn-
ing from animations. Learning and Instruction, 20 ,
Kornell, N., Hays, M. J., & Bjork, R. A. (2009).
Unsuccessful retrieval attempts enhance subsequent
learning. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 35 (4), 989–998.
Lewalter, D. (2003). Cognitive strategies for learning
from static and dynamic visuals. Learning and
Instruction, 13 (2), 177–189.
Linn, M. C., Chang, H.-Y., Chiu, J. L., Zhang, H., &
McElhaney, K. (2010). Can desirable dif culties over-
come deceptive clarity in scienti c visualizations? In
A. Benjamin (Ed.), Successful remembering and suc-
cessful forgetting: A Festschrift in honor of Robert A.
Bjork . New York: Routledge.
Linn, M. C., Clark, D., & Slotta, J. D. (2003). WISE
design for knowledge integration. Science Education,
87 , 517–538.
Linn, M. C., & Eylon, B.-S. (2006). Science education. In
P. A. Alexander & P. H. Winne (Eds.), Handbook of
educational psychology (2nd ed.). Mahwah, NJ:
Linn, M. C., & Eylon, B.-S. (2011). Science learning and
instruction: Taking advantage of technology to pro-
mote knowledge integration . New York: Routledge.
Linn, M. C., Eylon, B. S., & Davis, E. A. (2004). The
knowledge integration perspective on learning. In M.
C. Linn, E. A. Davis, & P. Bell (Eds.), Internet
environments for science education (pp. 73–83).
Mahwah, NJ: Erlbaum.
L o m b r o z o , T . ( 2 0 0 6 ) . T h e s t r u c t u r e a n d f u n c t i o n o f e x p l a n a -
tions. Tre nds in Cog nitive Sc ien ces, 10 (10), 464–470.
L o w e , R . ( 2 0 0 4 ) . I n t e r r o g a t i o n o f a d y n a m i c v i s u a l i z a t i o n
during learning. Learning and Instruction, 14 ,
McElhaney, K. W. (2010). Making controlled experimen-
tation more informative in inquiry investigations
(Doctoral dissertation). Dissertation and Theses
Database . (UMI No. AAT 3413549).
Moos, D. C., & Azevedo, R. (2008). Self-regulated learn-
ing with hypermedia: The role of prior knowledge.
Contemporary Educational Psychology, 33 , 270–298.
Moreno, R., & Mayer, R. (2007). Interactive multimodal
learning environments. Educational Psychology
Review, 19 , 309–326.
Naja , H., & Slotta, J. (2010). Analyzing equality of
participation in collaborative inquiry: Toward a
knowledge community. Proceedings of the 9th
International Conference of the Learning Sciences ,
Chicago, IL.
Plass, J. L., Homer, B. D., & Hayward, E. (2009). Design
factors for educationally effective animations and sim-
ulations. Journal of Computing in Higher Education,
21 (1), 31–61.
Quintana, C., Zhang, M., & Krajcik, J. (2005). A frame-
work for supporting metacognitive aspects of online
inquiry through software-based scaffolding.
Educational Psychologist, 40 (4), 235–2244.
Reiber, L. P., Tzeng, S., & Tribble, K. (2004). Discovery
learning, representation, and explanation within a
computer-based simulation: Finding the right mix.
Learning and Instruction, 14 , 307–323.
Renkl, A., & Atkinson, R. K. (2002). Learning from
examples: Fostering self-explanations in computer-
based learning environments. Interactive Learning
Environments, 10 , 105–119.
Richland, L. E., Linn, M. C., & Bjork, R. A. (2007).
Cognition and instruction: Bridging laboratory and
classroom settings. In F. Durso, R. Nickerson, S. Dumais,
S. Lewandowsky, & T. Perfect (Eds.), Handbook of
applied cognition (2nd ed.). New York: Wiley.
Rozenblit, L. R., & Keil, F. C. (2002). The misunderstood
limits of folk science: An illusion of explanatory
depth. Cognitive Science, 26 , 521–562.
Schank, P., & Kozma, R. (2002). Learning chemistry
through the use of a representation-based knowledge
building environment. Journal of Computers in
Mathematics and Science Teaching, 2 (3), 254–271.
Schnotz, W., & Rasch, T. (2005). Enabling, facilitating,
and inhibiting effects of animations in multimedia
learning: Why reduction of cognitive load can have
negative results on learning. Educational Technology
Research and Development. Special Issue: Research
on Cognitive Load Theory and Its Design Implications
for E-Learning, 53 (3), 47–58.
Shen, J., & Linn, M. C. (2010). A technology-enhanced
unit of modeling static electricity: Integrating scienti c
explanations and everyday observations. International
Journal of Science Education . doi:
S l o t t a , J . , & L i n n , M . C . ( 2 0 0 9 ) . WISE Science: Web-based
inquiry in the classroom . New York: Teachers College
Slotta, J. & Peters, V. (2008). A blended model for knowl-
edge communities: Embedding scaffolded inquiry.
Proceedings of the International Conference of the
Learning Sciences . Utrecht, Netherlands.
Songer, N., & Linn, M. C. (2006). How do students’ views
of science in uence knowledge integration? Journal
of Research in Science Teaching, 28 (9), 761–784.
Tate, E. (2009). Asthma in the community: Designing
instruction to help students explore scienti c dilem-
mas that impact their lives (Doctoral dissertation).
Dissertation and Theses Database . (UMI No. AAT
Tinker, R. (2009). In Visualizing to integrate science
understanding for all learners ( VISUAL ), NSF
Discovery Research K-12 grant proposal, #0918743.
53133 Metacognition and Wise
Tversky, B., Morrison, J. B., & Betrancourt, M. (2002).
Animation: Can it facilitate? International Journal of
Human Computer Studies, 57 (4), 247–262.
Vygotsky, 1. S. (1978). Mind in society: The development
of higher psychological processes. Cambridge, MA:
Harvard University Press.
White, B., & Frederiksen, J. (1998). Inquiry, modeling
and metacognition: Making science accessible to all
students. Cognition and Instruction, 16 (1), 3–118.
White, B., & Frederiksen, J. (2000). Has been deleted
from the text and substituted with the 2005 reference.
White, B., & Frederiksen, J. (2005). A theoretical frame-
work and approach for fostering metacognitive
development. Educational Psychologist, 40 (4),
Wouters, P., Paas, F., & van Merrienboer, J. J. G. (2008).
How to optimize learning from animated models: A
review of guidelines based on cognitive load. Review
of Educational Research, 78 (3), 645–675.
Zhang, Z., & Linn, M. C. (2008). Using drawings to sup-
port learning from dynamic visualizations. In
International perspectives in the learning sciences:
Creating a learning world. Proceedings of the 8th
International Conference of the Learning Sciences
(Vol. 3, pp. 161–162). Utrecht, The Netherlands:
International Society of the Learning Sciences, Inc.
Z i m m e r m a n , B . ( 2 0 0 8 ) . I n v e s t i g a t i n g s e l f - r e g u l a t i o n a n d
motivation: Historical background, methodological
developments, and future prospects. American
Educational Research Journal, 45 , 166–183.
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... An essential part of the learning-teaching method in the course under discussion was based on ICT use. While many researchers (Dori & Belcher, 2005;Park, Khan, & Petrina, 2009) claim that using ICT in STEM subjects might promote learners' conceptual understanding, others (Chiu, Chen, & Linn, 2013;Jimoyiannis, 2010) point to difficulties in learning with ICT and criticize the ICT's techno-centric and tokenistic use in science and technology. ...
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The current article about pre-service teachers (n=60) describes the implementation and evaluation of an innovative curriculum for teaching sound, waves and communication systems (SWCS). The study sought to investigate pre-service teachers' successes and difficulties in learning a science, technology, engineering and mathematics (STEM)-based program, to examine the program's influence on students' interest and self-efficacy beliefs about learning science and technology, and to explore the factors that affected their achievements and motivation. The participants were two groups of pre-service teachers who learned the course within their studies towards a B. Ed. degree in science teaching. Quantitative and qualitative data were collected by a final exam, a retention exam, a Motivation Questionnaire, final projects, class observations and interviews with the students. The findings revealed that the pre-service teachers successfully learned the new subject and were motivated in learning the course. The integration of science and technology studies, engaging the students in hands-on lab work and the use of ICT tools played a crucial role in promoting meaningful learning. However, the PBL part of the course contributed relatively little because the learners encountered difficulties in learning new subjects independently.
... A review of 37 comparison studies on inquiry instruction conducted between 1996 and 2006 found that teacher guidance for inquiry added value over unguided student inquiry (Furtak et al., 2012). Teachers' guidance enabled students to more fully experience reform-oriented inquiry activities, whereas student-led inquiry often leads to "deceptive clarity," in which students are engaged but formulate superficial understanding (Chiu, King Chen, & Linn, 2012). The teacher creates a balance between helping students integrate ideas and giving students the necessary space to flounder and sort out ideas on their own (Engle & Conant, 2002). ...
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Advances in technology, science, and learning sciences research over the past 100 years have reshaped science education. This chapter focuses on how investigators from varied fields of inquiry who initially worked separately began to interact, eventually formed partnerships, and recently integrated their perspectives to strengthen science education. Advances depended on the broadening of the participants in science education research, starting with psychologists, science discipline experts, and science educators; adding science teachers, psychometricians, computer scientists, and sociologists; and eventually including leaders in cultural studies, linguistics, and neuroscience. This process depended on renegotiating power structures, deliberate funding decisions by the National Science Foundation and others, and sustained, creative teamwork. It reflects a growing commitment to ensure that all learners are respected and that all students learn to address the complex scientific dilemmas they face in their lives. This chapter traces the evolution of research on science education in the United States with a focus on 5- to 17-year-olds. It highlights trends in the view of the learner, the design of instruction, the role of professional development, and the impact of technology. The chapter closes with recommendations designed to realize the full potential of these advances.
... An enhanced instructional role during lessons incorporating computer simulations may be necessary to help students learn collaborative and communication skills (Grant & Hill, 2006), prevent students from "mindlessly" clicking through a simulation (Wecker et al., 2007), and help students critically examine visualizations (Chiu & Linn, 2012). As a result, computer simulation professional development needs to not only familiarize teachers with the instructional tools, but also prepare them to implement them effectively to overcome students' habitual technology use patterns and facilitate critical engagement (Chiu, Chen & Linn, 2013;Lawless & Pellegrino, 2007;NRC, 2011;Trotter & Zehr, 1999;Wecker et al., 2007). Therefore, research is warranted to identify professional development elements that not only increase the number of teachers who incorporate simulations into science instruction but also improve the quality of computer simulation implementation. ...
The purpose of this study is to examine the effects of different ways to use visualizations on high school students’ electrochemistry conceptual understanding and motivation towards chemistry learning. Expanding upon a model-based learning approach (Khan, 2007), we adopted a VGEM sequence (View, Generate, Evaluate, and Modify) to create three instructional conditions. All conditions involved the viewing, evaluating, and modifying phases, whereas there were variations in the generating phase: (1) finishing worksheets (V group), (2) generating drawings (VD group), and (3) generating animations (VA group). Three intact classes with 109 eleventh graders from a public high school were randomly assigned to the three groups. A test of conceptual understanding was used as the pretest, posttest, and delayed posttest to assess respectively initial understanding, changes, and retention of understanding up to 6 weeks later. A questionnaire to measure students’ motivation to learn chemistry was administered before and after the instruction. Statistical results of the within-group comparisons revealed that all three instructional conditions could support students to develop a significantly better conceptual understanding of electrochemistry and that in the three groups, students’ understanding was retained after 6 weeks. Regarding the overall motivation before and after the instruction, only the VA group showed motivational benefits for chemistry learning. Furthermore, the between-group comparisons indicated no significant differences between the means of the three groups in the posttest and delayed posttest, and suggested that the three groups developed and retained a similar level of conceptual understanding after the instruction. Similarly, different uses of visualizations made no difference to students’ chemistry learning motivation. This study advances the understanding of how to develop effective instructional activities with visualizations for chemistry learning, and suggests possible conceptual and motivational benefits of viewing and generating visualizations.
To understand the longitudinal effect of a technology-enhanced learning (TEL) environment with reflective guidance on sixth-grade students’ learning in a photosynthesis unit in Shanghai, we designed a quasi-experimental longitudinal study with a pre-test and three post-tests, complemented by an examination of students’ log data. Our findings show that TEL is useful in improving students’ conceptual understanding and their retention of factual knowledge. Moreover, the regression analysis and the case study suggest that the time the students spent reflecting on ideas led to different levels of learning gains. In conclusion, these findings highlight the potential benefits of using TEL environments and illustrate the need to identify students who need extra guidance to engage in in-depth learning.
This study aimed at improving the decision-making (DM) skills of 11th graders by incorporating a DM framework, visualisation tools, collaboration, and metacognitive guidance into a socioscientific issue context. Two classes, the experimental group (embedded metacognitive guidance, N = 42) and the comparison group (no metacognitive guidance, N = 32), were involved in the implementation of the experimental methodology. An open-ended test and worksheets were developed to assess the students’ DM skills. The results indicated that the two versions of the DM learning modules had similar effects on the improvement in the students’ DM skills, but there were significant differences in their overall skills in DM (Z = −6.410, p < .001), generating criteria (Z = −6.956, p < .001), and evaluating DM results (Z = −2.533, p < .011) based on the student responses on the worksheets. These findings indicate that further studies need to explore the mechanism of metacognitive guidance for students with different socioscientific issue DM skills in e-learning environments.
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Major educational policy groups call on learners to engage in inquiry-based activities. With a growing amount of information now available online, there is an increased focus on online inquiry where learners ask research questions; search digital libraries and other information sources; and read, assess, and synthesize that information. Metacognitive skills are important for engaging in complex practices like online inquiry, but those skills are weak in novice learners. In this article, we propose a framework that describes the types of metacognitive problems that learners exhibit during online inquiry and the ways that software can serve a scaffolding function to address those problems and support learners through those metacognitive issues. Specifically, we consider how three categories of metacognition relate to online inquiry—task understanding and planning, monitoring and regulation, and reflection—and the issues learners face within each. Our framework describes different types of scaffolding features from a range of software tools that can support learners with the identified metacognitive issues in different ways, such as by describing online inquiry task structures for better task understanding, making planning an explicit activity, helping learners see online inquiry task spaces and strategies to monitor and regulate work, and describing to learners the important aspects of online inquiry that they should reflect on during their work. A common thread with these scaffolding features is the fact that they make different aspects of metacognition, which tends to be implicit to learners, more explicit so learners can engage in the metacognitive activity that they otherwise tend to bypass.
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It is well-documented that most students do not have adequate proficiencies in inquiry and metacognition, particularly at deeper levels of comprehension that require explanatory reason- ing. The proficiencies are not routinely provided by teachers and normal tutors so it is worth- while to turn to computer-based learning environments. This article describes some of our re- cent computer systems that were designed to facilitate explanation-centered learning through strategies of inquiry and metacognition while students learn science and technology content. Point&Query augments hypertext, hypermedia, and other learning environments with ques- tion-answer facilities that are under the learner control. AutoTutor and iSTART use animated conversational agents to scaffold strategies of inquiry, metacognition, and explanation con- struction. AutoTutor coaches students in generating answers to questions that require explana- tions (e.g., why, what-if, how) by holding a mixed-initiative dialogue in natural language. iSTART models and coaches students in constructing self-explanations and in applying other metacomprehension strategies while reading text. These systems have shown promising results in tests of learning gains and learning strategies.
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Many students leave high school chemistry courses with profound misunderstandings about the nature of matter, chemical processes, and chemical systems. The ChemSense project is addressing this problem through a multidisciplinary program of research and development to examine the impact of representational tools, chemical investigations, and discourse on chemistry learning and teaching in high schools and colleges. This work intersects several theoretical approaches to learning, including collaborative project-based investigations, representational competence, knowledge building, and the design of chemistry curriculum. The ChemSense Knowledge Building Environment allows students and instructors to collaborate in the investigation of chemical phenomena, collect data, build representations of these phenomena, and participate in scaffolded discourse to explain these phenomena in terms of underlying chemical mechanisms. Research indicates that ChemSense is effective in supporting student representational use and chemical understanding. In this article, the authors present their theoretical approach, describe the ChemSense learning environment in the context of actual use by high school students, summarize the research findings, and discuss the implications of these findings for future work.
Science Learning and Instruction describes advances in understanding the nature of science learning and their implications for the design of science instruction. The authors show how design patterns, design principles, and professional development opportunities coalesce to create and sustain effective instruction in each primary scientific domain: earth science, life science, and physical science. Calling for more in depth and less fleeting coverage of science topics in order to accomplish knowledge integration, the book highlights the importance of designing the instructional materials, the examples that are introduced in each scientific domain, and the professional development that accompanies these materials. It argues that unless all these efforts are made simultaneously, educators cannot hope to improve science learning outcomes. The book also addresses how many policies, including curriculum, standards, guidelines, and standardized tests, work against the goal of integrative understanding, and discusses opportunities to rethink science education policies based on research findings from instruction that emphasizes such understanding.
The effects of computer animations and mental animation on people's mental models of a mechanical system are examined. In 3 experiments, students learned how a mechanical system works from various instructional treatments including viewing a static diagram of the machine, predicting motion from static diagrams, viewing computer animations, and viewing static and animated diagrams accompanied by verbal commentaries. Although students' understanding of the system was improved by viewing both static and animated diagrams, there was no evidence that animated diagrams led to superior understanding of dynamic processes compared to static diagrams. Comprehension of diagrams was enhanced by asking students questions that required them to predict the behavior of the machine from static diagrams and by providing them with a verbal description of the dynamic processes. This article proposes that predicting motion from static diagrams engages students' mental animation processes, including spatial visualization, and provides them with information about what they do and do not understand about how the machine works. Verbal instruction provides information that is not easily communicated in graphics and directs students' attention to the relevant information in static and animated diagrams. The research suggests that an understanding of students' mental animation abilities is an important component of a theory of learning from external animations.
This study examines the role of self-regulated learning (SRL) in facilitating students' shifts to more sophisticated mental models of the circulatory system as indicated by both performance and process data. We began with Winne and colleagues' information processing model of SRL (Winne, 2001; Winne & Hadwin, 1998) and used it to examine how students regulated their own learning when using a hypermedia environment to learn about the circulatory system. Undergraduate students (N = 24) were trained to use a hypermedia environment to learn about the circulatory system. Pretest, posttest, and verbal protocol data were collected to measure the shifts in conceptual understanding (from pretest to posttest) and the SRL variables associated with shifts in conceptual understanding. We used a median split to divide the sample into two groups of learners—high-jumpers and low-jumpers (i.e., students who either showed large gains in conceptual understanding or showed relatively little or no gain in their conceptual understanding). Findings revealed that the high-jumpers shifted an average of 4.5 mental models, while the low-jumpers had an average shift of less than one mental model from pretest to posttest. The verbal protocol data were coded for various self-regulating variables to examine which SRL variables differentiated the low-jumpers from the high-jumpers. High-jumpers were much better at