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Enabling people who are blind to experience science inquiry learning through sound-based mediation


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This paper addresses a central need among people who are blind, access to inquiry-based science learning materials, which are addressed by few other learning environments that use assistive technologies. In this study, we investigated ways in which learning environments based on sound mediation can support science learning by blind people. We used NetLogo, a multi-agent programmable modeling environment that is widely used for learning about complex systems. In order to provide blind people with access to such models, we used a component that supports sound-based mediation. The sound-based mediation provided real-time information regarding objects' speed, location, and interactions with other objects. We examined blind peo-ple's learning about a chemical system of contained gas particles. The study employs a pre-test intervention–post-test design. Four adults participated individually in the study. They achieved most referent-representation connections; their scientific conceptual knowledge became more specific and aligned with scientific knowledge; and their systems reasoning showed greater discrimination and relation between components. Discussion addresses learning with sound-based mediation in broader terms and suggests further research into the potential of this unique type of low-cost learning environment to assist blind people in their science learning.
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Enabling people who are blind to experience
science inquiry learning through sound-based
mediationjcal_457 1..15
S.T. Levy* & O. Lahav†
*Faculty of Education, University of Haifa, Mount Carmel, Haifa, Israel
†School of Education, Tel-Aviv University, Tel-Aviv, Israel
Abstract This paper addresses a central need among people who are blind, access to inquiry-based
science learning materials, which are addressed by few other learning environments that use
assistive technologies. In this study, we investigated ways in which learning environments based
on sound mediation can support science learning by blind people. We used NetLogo, a multi-
agent programmable modeling environment that is widely used for learning about complex
systems. In order to provide blind people with access to such models, we used a component that
supports sound-based mediation. The sound-based mediation provided real-time information
regarding objects’ speed, location, and interactions with other objects. We examined blind peo-
ple’s learning about a chemical system of contained gas particles. The study employs a pre-test
intervention–post-test design. Four adults participated individually in the study. They achieved
most referent-representation connections; their scientific conceptual knowledge became more
specific and aligned with scientific knowledge; and their systems reasoning showed greater dis-
crimination and relation between components. Discussion addresses learning with sound-based
mediation in broader terms and suggests further research into the potential of this unique type of
low-cost learning environment to assist blind people in their science learning.
Keywords blindness, human–computer interaction, learning environments, simulations, special
In exploratory learning of science, people who are
blind need to employ compensatory channels to access
visual information. One way of doing this is to make use
of auditory information. To this end, we have developed
‘Listening to Complexity’1(L2C), a sound-based repre-
sentation to support learning about complex systems.
The sound-based mediation provides real-time infor-
mation regarding objects’ speed, location, and interac-
tions with other objects. We investigate inquiry learning
by people who are blind through a structured explora-
tion of a computer model based on auditory representa-
tions. The learning activity focuses on a system of
contained gas particles, a core topic in chemistry
The study is based on the assumption that providing
appropriate information through compensatory sensory
channels may contribute to learning by students who are
blind. The short-term goal of this research is to investi-
gate ways in which learning environments based on
sound mediation can support the learning of chemistry
by people who are blind. The long-term goal is to
support students who are blind by providing equal
Accepted: 9 September 2011
Correspondence: Sharona T. Levy, Faculty of Education, University of
Haifa, Mount Carmel, Haifa 31905, Israel. Email: stlevy@construct.
doi: 10.1111/j.1365-2729.2011.00457.x
Original article
© 2011 Blackwell Publishing Ltd Journal of Computer Assisted Learning 1
access to science, technology, engineering, and math-
ematics (STEM) learning, allowing them to interact
with exploratory materials, independently collect data,
and function as participants and contributors in a
research group.
Literature review
This literature review features science education and
auditory information technologies for people who are
blind and who are understanding and learning about
complex systems and chemistry through a complexity
Science education for students who are blind
In many countries around the world, specifically in
Europe, the USA, and Israel, students who are blind
have been integrated into public schools for over 60
years and are required to complete the same curriculum
and examinations as sighted students. However, as
many STEM education resources are based on the
visual mode, they are unable to access information
first hand (Beck-Winchatz & Riccobono 2008). Several
manuals have been written on how to teach science to
students who are blind and to those who are visually
impaired (Hadary & Cohen 1978; Willoughby & Duffy
1989; Kumar et al. 2001). Few learning environments
based on assistive technologies have been created to
support science learning, but one example is the use of
a force-feedback mouse to study physics (Farrell et al.
2001; Wies et al. 2001). Going beyond existing tech-
nologies, in the current research, the user interacts with
dynamic and multiple objects that are computed in real
time, providing a sense of reality while learning about
complex scientific phenomena.
Auditory information technologies for people who
are blind
People who are blind learn by gathering information
with perceptual and conceptual tools. The L2C environ-
ment harnesses the auditory mode to transmit dynamic
complex information through use of technologies. The
choice of an auditory display results from three consid-
erations: (1) the auditory mode transmits information
that changes both in space and time, similar to the visual
mode and different from the haptic mode; (2) the
auditory mode easily interfaces with large bandwidths
at fine frequency-discrimination and intensity-
discrimination thresholds (Capelle et al. 1998); and (3)
the auditory system is capable of dealing with complex
and rapidly changing sound patterns (Hirsh 1988).
Sonification is the presentation of information using
non-speech sound (Kramer 1994). Nees and Walker
(2009) present a classification of auditory sonified
computer-based representations: (a) alerts; (b) object
and status indicators, auditory menus; (c) data represen-
tation; (d) spatial audio displays; (e) soundscapes and
background sound; and (f) arts and entertainment.
Classes (a) through (d) are used in the L2C environ-
ment. Research into the impact of sound components
on auditory perception has shown that increasing the
number of audio channels beyond three causes degrada-
tion in comprehension (Stifelman 1994) and that a
greater frequency separation between sound streams
results in better stream segregation (Bregman 1990).
Over the years, several auditory technologies have been
developed for people who are blind. Some examples
from the field of orientation and mobility include
Sonicguide (Warren & Strelow 1985); Kaspa (Easton &
Bentzen 1999), Palmsonar (Takes Corporation 2007),
Talking Signs (Crandall et al. 1995); activated audio
beacon using cellphone technology (Landau et al.
2005); vOICe (Meijer 1992), virtual sound display
(Loomis et al. 2007), sound-based virtual environment
systems (Sánchez et al. 2008), auditory graphs (Walker
& Mauney 2010) and virtual environments for spatial
learning based on audio and haptic feedback (Lahav &
Mioduser 2004; Lahav et al. 2008).
The few systems developed to support STEM educa-
tion among students who are blind have been based
on audio and 2D tactile materials for learning math-
ematical and science diagrams. One example is the Line
Graphs technology, which employs auditory and haptic
feedback, and is geared to learning mathematics
(Ramloll et al. 2000).
Understanding and learning about complex systems
Complex systems are composed of many elements,
which interact among themselves and self-organize in
coherent global patterns (Bar-Yam 1997). The field of
complex systems contributes to our understanding of a
wide range of systemic phenomena (Nicolis & Prigog-
ine 1989; Barabasi & Bonabeau 2003; Turchin 2003). It
2S.T. Levy & O. Lahav
© 2011 Blackwell Publishing Ltd
provides a framework for representing and compre-
hending the structure and dynamics of systems, focus-
ing on generating global patterns from local behaviours
and interactions.
Complex systems challenge our understanding.
Several biases frequently sway people’s reasoning
about systems, such as assuming central control
(Resnick 1994), assigning the behaviour of one level to
another, presuming deterministic rather than stochastic
behaviours (Wilensky & Resnick 1999), focusing on
the system’s structure at the expense of function and
mechanism (Hmelo-Silver & Pfeffer 2004), and tending
to view causal relations as a consecutive chain of causes
and effects rather than as parallel concurrent interac-
tions (Chi 2005). Several innovative learning activities
have been designed to help people overcome these
biases and understand complex systems, such as con-
structing and exploring computer models (Wilensky &
Resnick 1999; Ioannidou et al. 2003; Levy & Wilensky
2009a,b) and participating in role-playing simulations
(Colella 2000; Klopfer et al. 2005). In this study,
systems reasoning is examined with a focus on dis-
tinguishing and connecting levels, and understanding
stochastic behaviours and interactions.
Understanding chemistry through a
complexity perspective
In chemistry education, one of the central frameworks
presented by Johnstone (1993) is that a well-developed
understanding of a chemical system relates three forms
of description, which map nicely onto a complexity
perspective: the submicroscopic level, the macroscopic
level, and representations. Previous research describes
several challenges in understanding chemical systems,
particularly with respect to gases, the focus of this
research. These include the lack of a particulate view of
matter, assigning macro-level behaviours to gas par-
ticles (Nussbaum 1985) and not considering random
particle motion in a gas or liquid (Novick & Nussbaum
1978; Westbrook & Marek 1991). Most of these prob-
lems result from a failure to distinguish among levels,
the particle level, and that of the whole group of many
particles. The challenge of understanding this distinc-
tion, as well as the idea of randomness, is directly
addressed in this study. In spite of these impediments
to understanding chemistry, well-designed educational
interventions that are based upon the use of computer-
based models with multiple and bridged representa-
tions, similar to those used in this study, have been
shown to circumvent the above-described difficulties
(Kozma 2000; van der Meij & de Jong 2006; Levy &
Wilensky 2009b). The present research examines con-
ceptual understanding of the scientific topic, the par-
ticulate nature of matter, and several physical principles
related to the behaviours and interactions of gas
The L2C learning environment
The L2C computer-assisted learning environment
supports students who are blind in an exploration
of simulated chemical systems using a sound-based
representation. L2C includes an agent-based computer
model, a recorded voice guide, and the interviewer.
The computer model
Agent-based modeling is a relatively new computa-
tional modeling paradigm, which simulates complex
dynamic systems by simulating each of their many
autonomous and interacting elements. NetLogo (Wilen-
sky 1999a) is one such modeling environment. NetLogo
models are used in the GasLab (Wilensky 1999b)
and Connected Chemistry (Levy & Wilensky 2009a)
curricula. These agent-based computer models enable
learning about chemical systems at both the observable
macro- and molecular micro-levels. With Connected
Chemistry, one learns the gas laws – how observable gas
properties change under various conditions, e.g. how
inflating a bicycle tire increases its internal pressure;
kinetic molecular theory (KMT) – motion, force, and
energy of invisible gas particles, and how KMT may
generate observable phenomena. Research with sighted
participants using this approach has revealed significant
learning gains in understanding both the behaviours
of gas particles and how these relate to observable
phenomena (Levy & Wilensky 2009b).
The activity in this study is based on the first module
in Connected Chemistry and centred on the phenom-
enon of inflating a bicycle tire. It consisted of interac-
tion with a real bicycle tire, training with the individual
sounds, and a guided exploration of the model. In order
to make the environment accessible to people who are
blind, variables, locations, and events related to both a
single particle and to all the particles together were rep-
resented with sound. The sound at this stage of research
Learning through sound-based mediation 3
© 2011 Blackwell Publishing Ltd
is in mono audio format. Table 1 shows the sound-based
components of the model and compares them with the
visual information available to sighted individuals with
the original model. These sounds can be heard singly or
concurrently (first listening to a single particle’s proper-
ties, events and location and then attending to these fea-
tures when the bicycle tire is inflated). It is possible to
view and listen to a sample at
watch?v=c0EdRKdjfgk. Figure 1 displays part of the
model’s interface: a number of dots (particles) inside a
container (bicycle tire with a valve) that can be inflated.
Several issues were considered when designing
the sound-based data representations. These included
mapping (which sound attributes are used), polarity
(direction of change in the sound attribute), and scaling
(how much change in the sound is needed to convey a
given change in the referent); the structure of the data
generating the sound [noisy or random (in the current
study)]; constant, linearly ascending, discontinuous, or
interrupted (Pauletto & Hunt 2009) and detectability
and discriminability, interactivity, individual differ-
ences, training, concurrent sounds, and delivery of
audio through hardware and software (Nees & Walker
Recorded voice guide
Learning through exploring the model is supported
with recorded instructions, explanations, and questions.
Fig 1 NetLogo sonified model of gas par-
ticles in a container interface.
Table 1. Comparison of sonified and visual representations in the L2C computer model.
Events, location, variables Visual representation Sonified representation (MIDI based)
Events Collision among particles from
the perspective of one focus
Two dots move in straight lines,
meet at one location, and
move apart in straight lines
Cowbell sound upon collision at
one pitch
A particle hitting the wall of the
container (a square) from the
perspective of one focus
One dot hits the wall at an angle,
the wall become lighter at the
point of contact, and the dot
bounces off the wall
Clavi sound upon hitting a wall
Entry of new particles in the
A semi-circle spread of dots
starts from the box’s opening
Gong sound at constant pitch
Location The location of a focus particle as
it moves through the container
Full information is provided by
placing a halo on the focus
particle or have it leave a trail
Partial information is provided by
wall hitting events where each of
four walls has a different pitch
Variable Speed of one focus particle The dot is seen as moving faster or
slower, changes colour within
three speed ranges (blue – slow,
green – medium, red – fast)
Oboe sound with pitch a linear
function of particle speed
MIDI, Musical Instrument Digital Interface.
4S.T. Levy & O. Lahav
© 2011 Blackwell Publishing Ltd
Most of the questions are structured on the Predict–
Observe–Explain scheme of inquiry learning in science
(White & Gunstone 1992). Adetailed description of the
activity is provided in Table 2.
The interviewer
In these interactions, the interviewer operated the
model; however, in future designs, the user will have
control, using the keyboard.
There were two main research questions:
1What scientific conceptual knowledge is learned as a
result of interaction with the L2C environment by
people who are blind?
2What systems reasoning is learned as a result of inter-
action with the L2C environment by people who are
Four participants were recruited by snowball sampling.
They were adult English speakers who were totally
blind, their onset of blindness was at least 2 years prior
to the experimental period, and they were computer
users. The participants’ age range was 40–57 years.
Three of the participants were male and one female. All
were late blind and had used computers for over 20
years. They had learned science, specifically phases
of matter and KMT, in middle school and high school,
and some in college. None had used computer-based
The target population ranges from seventh grade
through undergraduate level. The discrepancy in age
of this study’s participants with respect to the target
population arises for two reasons. First, since this type
of environment presents a new approach to teaching
science, we wanted to explore learning interactions
without the shadow of recent negative experiences
in learning science that may have developed in the
past and which could cause the participants to reject
learning within this environment. Second, we chose
participants who were experienced computer users
and who could provide a broad range of feedback,
helping us to improve the learning environment. This
sample is small due to the exploratory nature of this
Two dependent variables were defined: scientific con-
ceptual knowledge – understanding of the physical
principles governing the particles’ behaviour as single
objects (KMT) or as a group of particles (gas laws),
based on accepted science literature and science educa-
tion research; and systems reasoning – general structure
of explanations with respect to stochastic interactions
between objects, distinction, and relation between
submicro- and macro-levels.
Data collection instruments
A research protocol
This protocol is based on the first activity in Connected
Chemistry and focuses on the phenomenon of inflating
a bicycle tire. It relates to the way the learning activity is
conducted and is composed of three sections (Table 2):
(1) physical interaction with the bicycle tire; (2) inter-
acting with the submicro-level in the model; and (3)
interacting with both the submicro- and macro-levels in
the model. The three sections comprised a sequence of
tasks and included 20 open questions before and after
each task.
A background questionnaire
The questionnaire comprised 17 questions establishing
personal information about the participants and their
background in science education and computer use.
A pre- and post-test
A content knowledge questionnaire assessed the learn-
ers’ understanding of the gas laws and KMT, concepts
addressed by L2C. The questionnaires were identical
and included three open questions and ten multiple-
choice questions. The open questions required model-
ing of a system similar to that found in the experimental
setting, The multiple-choice questions were a subset of
those used in previous large-scale research on students’
learning of similar topics (Levy & Wilensky 2009b),
in which issues of validity were addressed through a
systematic analysis and mapping between the curricu-
lum and the questionnaire, employing several items pre-
viously used in other studies and several rounds of
review by two research teams. All the visual images in
the pre- and post-test were recreated as tactile images
with special dimensional paint. The image size was
Learning through sound-based mediation 5
© 2011 Blackwell Publishing Ltd
Table 2. Design of the activity in terms of three main sections, the learning goals, and the guiding questions.
Section Sub-section: phenomenon and concepts Questions directing inquiry
1: Direct manipulation of
physical phenomenon
Pumping up a bicycle tire: physical
experience, pressure changes
1. Describe the important differences
between an inflated and a deflated tire.
2. What evidence would support the
argument that there is gas inside the
balloon or the bicycle tire?
2: Interaction with model
of simulated dynamics
of micro-level particles
Collisions of a single particle with container
walls: random straight-line motion,
hitting wall changes direction
3. What pattern do you think describes
how a particle moves through the box,
and more specifically – when it hits the
4. (not used)
5. Based on what you have heard, describe
how the particle moves through the
Speed of a single particle: changes at
random intervals
6. What do you think causes the change in
7. How do you know?
Particle collisions of a single particle 8. Can you find a pattern that describes
how often a single particle collides with
other particles?
9. Explain your answer.
Particle collisions and speed of a single
particle: collisions at random intervals,
speed change upon collision
10. What do you predict about the
relationship between a particle’s
collisions with other particles and its
11. What relationship can you conclude
between how the particle speed
changes and its collisions with other
12. What relationship can you conclude
between how the particle speed
changes and its collisions with other
Wall collisions and speed of a single
particle: hitting a wall does not change
speed but changes direction
13. What do you predict about the
relationship between a particle’s wall
hits and its speed?
14. What do you conclude regarding how
wall hits and speed changes relate?
15. What do you conclude regarding how
wall hits and speed changes relate?
3: Interaction with model
of simulated dynamics
of both macro phenomena
and micro-level particles
Particle collisions of a single particle upon
adding particles: increase in rate
16. Predict how adding particles into the
container may impact a single particle’s
collisions with other particles.
17. How do the particle’s collisions with
other particles change when there are
more particles in the box?
Speed of a single particle upon adding
particles: rate of speed changes increases
with gas density
18. Predict how adding particles into the
container may impact a single particle’s
19. How does the particle’s speed change
when there are more particles in the
Transfer question: changing volume:
decreased volume results in greater
density and rate of speed changes, same
rate of wall hits and average speed
20. What will happen if the container is
smaller for the same number of
6S.T. Levy & O. Lahav
© 2011 Blackwell Publishing Ltd
between 5 and 8 cm, a size that can be explored with two
hands at the same time.
Participants’ activity was video-recorded.
The research protocol’s guiding instructions and the
activity questions were recorded by an English speaker
and were made available to the participants as an audio
All participants worked individually with the inter-
viewer. The design is a pre-test, intervention, and post-
test. When using the model, each participant sat at a
desk and received the auditory information through the
computer’s speakers. The session was carried out in
four stages. In the first stage, the study was introduced,
consent was obtained, and background information
was recorded (15–20 min). The second stage entailed
administering the pre-test questionnaire (15–20 min).
The third stage was the intervention: (1) the participants
manipulated and inflated a physical bicycle tire and
answered several questions about this experience; (2)
assisted by the interviewer operating the model and by
recorded guiding instructions and questions, the partici-
pants interacted with the model to learn about a particle;
and (3) similarly, they then used the model to learn
about air pumping (45–90 min). The fourth stage
involved administering a post-test (15–20 min). No
feedback was provided on performance at any stage.
Following the post-test, the subjects were asked to
comment on their learning experience and provide ideas
for improvements. The sessions (questionnaires and
learning activities) lasted about 1.5–2.5 h, were video-
recorded, and were later transcribed and analysed.
Data analysis
Data analyses were based on participants’ verbal
answers to the questions presented in the questionnaires
and activities. These answers were coded for the depen-
dent variables: scientific conceptual knowledge and
systems reasoning.
With respect to scientific conceptual knowledge,
questions were coded based on previous coding of the
same questions (Levy & Wilensky 2009b). Closed
questions were coded as correct or incorrect, and open
questions were coded for the relevant correct scientific
principles they included. The two authors indepen-
dently coded all the questions. Inter-judge reliability
was 90%. Alternative conceptions or incorrect scientific
principles were analysed separately for emergent
explanatory categories. The participants’ answers
during the activity were charted with respect to the dis-
tinct stages in the activity, so that possible associations
between the structure of the learning environment and
the kind of conceptual understanding elicited and
learned could be surmised.
For systems reasoning, the open questions were
coded based on three central components (Wilensky &
Resnick 1999; Jacobson 2001) that described the
structure of the explanation: whether the system was
described at the submicro-level (particles, e.g. ‘. . .
when particles collide they slow down’), the macro-
level (system-wide properties, e.g. ‘when I push down
on it [the bicycle tire], it takes more weight to push it
down and then flatten it’), or both (e.g. ‘it will slow
down the particles’ speed because of the pressure’;
including interactions among submicro-level objects
(e.g. ‘I think that when they [the particles] collide, the
speed increases’); expressing the stochastic nature of
the particles’ behaviour (event probabilities, e.g. ‘and it
[the particle] is moving randomly within the box’).
For the pre- and post-test, descriptive statistics were
compared, and progressions of frequencies were com-
puted and related to the activity.
The findings from this study are presented with respect
to the research questions.
Research Question One: What scientific conceptual
knowledge is learned as a result of interaction with the
L2C environment among people who are blind?
It was found that the participants’ total score for
the pre-test and post-test questionnaires rose from 37%
(sd =10%) to 62% (sd =14%). A more discriminate
analysis of individual dimensions shows the following.
Initial descriptions of the bicycle tire were in terms of its
observable machinery but not the invisible air particles;
prior knowledge about gas particles was that the rate of
collisions among particles depends on their density and
that particles’ collisions result in speed and direction
change. The main learning gain (three out of four
Learning through sound-based mediation 7
© 2011 Blackwell Publishing Ltd
participants) was in understanding of gas particles as
randomly distributed in the container. Describing the
model’s target – inflating a bicycle tire – as including air
(two out of four), indicated a shift to a combined view of
macro- and submicro-levels. One concept the partici-
pants did not learn is that collisions among particles
and with the wall of a container are distinct in terms of
change in speed. In parallel, two out of four participants
described the particles’ behaviour both in physical
terms (for collisions, they described the particles’
changing direction and speed) and intentional terms (for
diffusion, they portrayed particles as ‘wishing’ to move
into empty spaces). One participant understood the
system as physical from the start, and one participant
shifted from an intentional to a physical description.
Each of the participants’ responses during the activity
was coded for the expressed scientific concepts, attend-
ing to the submicro-level rules. Prediction questions
are not included in the analyses, as they do not reflect
learning. Figure 2 describes the number of participants
who expressed each of these principles for the activity’s
successive questions.
Activity questions (Table 2) regarding which three or
four participants expressed a given principle are noted.
This analysis is based on fundamental science literature
and science education research.
Gas is made up of particles
Once the model of gas particles in a box was introduced
(Q3), all participants began to describe the system in
terms of particles, and continued to do so throughout.
Particles move in straight lines
This principle was articulated frequently for Q5 and
Q15. Both relate to a particle hitting four different
walls of the container, from which all the participants
surmised that particles bounce between the walls and
expressed the principle of straight-line motion.
Particles change direction upon hitting a wall
All participants articulated this principle for Q5, part of
an activity that involved a particle hitting the walls.
Particles change direction upon colliding with
another particle
This principle was not mentioned by the participants.
Particles do not change speed upon hitting a wall
In the post-test, no learning of this principle was
observed. However, three out of four participants articu-
lated it for Q14 during an activity that highlighted a
particle’s speed and wall hits.
Particles change speed upon colliding with
another particle
All participants articulated this principle; it was under-
stood from the start and was frequently expressed
for Q12, which centred on a particle’s speed and its
collisions with particles.
Particles move about randomly
In moving from pre-test to post-test, all the participants
expressed a better understanding of the random spatial
distribution of particles. This principle was articulated
at high frequencies for Q5, Q8, and Q14, which relate
to distinct phenomena: a particle hitting the walls,
collisions among particles, and relating speed and
wall hits.
As mentioned above, during the pre-test, post-test,
and activities, the participants expressed ideas that were
related to the activity’s learning goals. In addition, they
expressed three ideas that related to the particles’ direc-
tion of motion, an aspect that was not represented in the
model. For example, the sound of a particle hitting each
of the four walls of the container provided only partial
information regarding location. However, the partici-
pants went beyond this information surmising a full
path of motion: a ‘ricocheting’ or ‘zigzagging’ motion
across the space of the container. Finally, some of the
participants perceived an ‘average speed’of the particle
and even connected it to its actual value.
Besides the correct ideas presented above, the partici-
pants expressed alternative conceptions, which are sci-
entifically incorrect ideas. The alternative conceptions
included five groups: (1) walls absorb energy from
particles, provided four times by all four participants;
(2) walls transfer energy to particles, provided four
times by three of the participants; (3) collisions take up
energy, provided six times by all four participants; (4)
collisions impart energy unto particles, provided twice
by one participant; and (5) additional ideas (provided
six times). The number of alternative ideas expressed
8S.T. Levy & O. Lahav
© 2011 Blackwell Publishing Ltd
throughout the activity is presented in Fig 3. Conflicting
alternative conceptions were provided by three partici-
pants: two described both (1) and (2); one described
both (3) and (4).
To conclude, participants varied with respect to
expressing alternative, rather than correct, ideas. Alter-
native ideas were expressed more frequently in the first
half of the activity (14 times) than in the second half
(eight times), portraying an increased understanding of
the system under study. Most alternative ideas involved
energy transfer relationships. More frequent were ideas
of energy absorption through collisions. These ideas
were not consistent.
Research Question Two: What systems reasoning is
learned as a result of interaction with the L2C environ-
ment by people who are blind?
Fig 2 Progression of expressions of correct understanding of the micro-level rules regarding gas particles’ behaviour. X-axis is the ques-
tion number in the activity. Y-axis is the level of the answer as described in the text.
Learning through sound-based mediation 9
© 2011 Blackwell Publishing Ltd
The following graphs present progressions for each
of the participants (Fig 4). Participants’ reasoning in
terms of levels is described as providing no answer
(1 in the graph), focusing on the macro-level (2), the
submicro-level (3), or relating the two (4). One can
observe clear distinctions between the three sections
of the activity (Table 2). Section 1 elicits descriptions
mainly at the macro-level. In Section 2, most of the
responses highlight the submicro-level. Section 3 draws
several responses that connect the levels in the system.
Figure 5 presents the number of participants who
described interactions at the submicro-level. No inter-
actions were described in Section 1. In Section 2, most
of the participants described such interactions. In
Section 3, the number of participants who included
interactions in their description increased.
The third component, stochastic behaviour of the par-
ticles, is not presented as it is identical to that described
in the previous section regarding the randomness of
particles’ motion.
To conclude the section on understanding as framed
by a complex systems approach, the participants’
answers corresponded with the structure of the activity,
shifting from macroscopic descriptions that did not
include the particle level and its interactions, to careful
attention to the stochastic interactions at the sub-
micro-level, culminating with an increased attention to
submicro-level interactions and connecting these to the
macro-level of the observed phenomena.
This discussion addresses the concepts learned, interac-
tions with the sound-based model and the process of
learning, and implications for educational settings. It is
important to note two limitations in the design of this
Fig 3 Number of alternative ideas
expressed by each participant throughout
the activity.
Fig 4 Progression of explanations in
terms of levels. X-axis is the question
number in the activity. Y-axis is the level of
the answer as described in the text: no
answer (1), focusing on the macro-level
(2), the submicro-level (3), or relating the
two (4).
10 S.T. Levy & O. Lahav
© 2011 Blackwell Publishing Ltd
study. One is the small sample size. A second is that
testing was conducted with adults rather than middle
school children, for whom this environment is designed.
Future research will enlarge the sample to include
younger participants.
Interactions with the sound-based model
The participants perceived most, but not all, representa-
tions as their referents. They easily mapped each
separate representation and its referent. However, once
representations were combined, we found a limit to
what could be integrated. When one representation had
a single pitch and the other a changing pitch, they could
be combined and reasoned with. However, when the
two representations had several pitches (wall hits and
changing speed), this proved to be too much to process;
less learning was evidenced during the activity and no
residual understanding was detected at the end. This
failure of the design is reflected in the participants’ not
learning how a particle’s hitting the wall is distinct from
particles’ collisions in terms of speed and energy and
in their comments regarding information overload.
Further design and additional training and intervention
with the environment may prove more beneficial to
Regarding construction of a conceptual understand-
ing, several impressive results were seen. Starting with
the very first listening to the model, participants gener-
ated richly detailed descriptions of a single gas parti-
cle’s motion. These descriptions made use of both the
representations and additional assumptions, such as
the particles’ straight-line motion. The representations
highlighted the randomness of the particles’ motion and
interactions and accordingly this idea was incorporated
into several of the provided explanations.
Most remarkable was the perception of an ‘average
speed’ by some of the participants. A difficult invariant
to grasp – the constancy of average speed in face of its
rapidly changing value, expressing the basic concept of
dynamic equilibrium – was gleaned from listening to the
Learning of scientific concepts
Through interaction with the sound-based model, two
scientific concepts were learned: understanding the
random behaviour of gas particles and incorporating a
particulate view in making sense of a physical system.
In such a short activity, we find these learning gains
significant in terms of their centrality to understanding
both the science content (Johnstone 1993) and systems
(Bar-Yam 1997). In chemistry, relating molecular
descriptions with those of physical phenomena and
understandings gas particles’ random behaviours
are difficult for learners (Novick & Nussbaum 1981;
Nussbaum 1985; Westbrook & Marek 1991). In under-
standing a complex system, its stochastic nature and
‘thinking in levels’ are both challenging and central
to reasoning (Resnick 1994; Penner 2000; Jacobson
2001). As these two ideas proved significant and consis-
tent in the learners’ articulations during the activity, we
see a clear relationship between interactions within the
activity and this learning.
Process of learning the science content
The learning experience in this study was designed as
a progression of activities, each geared to particular
Fig 5 Number of subjects that des-
cribed interactions among objects at the
Learning through sound-based mediation 11
© 2011 Blackwell Publishing Ltd
learning goals. An analysis of the participants’ articula-
tions in terms of the scientific principles shows that
most of the learning goals were met. Two ideas were
expressed across several activities – the particulate
nature of the matter and the randomness of particles’
behaviours – and these were the very same concepts that
were learned and sustained. Thus, while an idea may be
learned in situ, its re-use in several contexts is related to
the stability of its understanding. Ideas that were under-
stood temporarily in a particular section, but were not
revisited or reframed, were not maintained.
Alternative conceptions regarding the particles’
behaviours compete with scientific ideas. The alterna-
tive ideas identified in this study are provided inconsis-
tently, correspond with those reported in the literature
and relate to energy transformations, where energy is
removed or added through collisions. Similar ideas have
been found in previous research by one of the authors
and colleagues with sighted participants (Levy et al.
2006) and in reports on undergraduate students’ views
that an increased rate of collisions causes particles to
become faster (Loverude et al. 2002). Most common
was the idea that collisions among objects consume
energy, which makes sense from everyday experience of
inelastic collisions. In future design, this concept will be
addressed and explored.
The activity started out with a physical-world experi-
ence, inflating and palpating a bicycle tire. The reason
for this choice is the centrality of connecting and
referring to real phenomena in model-based learning
(Schecker 2003; Levy & Wilensky 2009a). When
returning to the phenomenon through exploring the
model, we have seen detailed and careful reasoning
about both particles and the system-wide phenomena.
This shows how prior knowledge is elicited through the
physical experience and later serves to frame and relate
to new information, incorporating it into a multi-faceted
and deeper understanding.
One of the interesting findings is that principles relat-
ing to the particles’ direction of motion, which was not
represented in the model, were expressed during the
activity. It would seem that these spatially based prin-
ciples were harnessed from the participants’ prior
knowledge and incorporated into an enriched mental
model of the particles in motion. Using the wall repre-
sentation, the participants went beyond it and created
a full path of motion in the space of the container.
One may conclude that not all information needs to be
included in the computer model. Future research should
address this balance between representing enough criti-
cal information to support creating a detailed mental
model and yet not overloading the cognitive system.We
propose that this research be content-specific, as infor-
mation that does not necessitate cues is probably related
to prior knowledge.
The computer model’s representations focused on
properties and events relating to one particle. In terms of
systems reasoning, the results show that the participants
were quite capable of making a connection between
these local stochastic interactions of a single particle
with the macro-level phenomenon. This was a culmina-
tion of a progression that began with experiencing
the physical system and then carefully exploring its
submicro-level rules and behaviours. When the macro-
level was later introduced, not only did expressions of
local interactions not diminish but they also increased.
Macro-level properties and dynamics were described as
emerging out of the interactions at the submicro-level,
systems reasoning at its best.
Implications for educational settings
The results of this investigation have shown gains both
in learning the science content and in understanding
systems. The long-term practical benefits of this
research are likely to have an impact on STEM educa-
tion for students who are blind, as equal access to low-
cost learning environments that are equivalent to those
used by sighted users would support their inclusion in
the middle school to undergraduate academic curricu-
lum. While the current investigation concerns a particu-
lar topic in chemistry, such a representation can be
used for a wide range of STEM topics: physics (e.g.
free-falling bodies, pendulum oscillations), biology
(e.g. circulatory system, ecological systems), chemistry
(e.g. reactions), engineering (e.g. gear mechanisms),
and mathematics (e.g. graphs). As most students who
are blind are integrated into the regular educational
system, it would be necessary to support the science
teachers with ways of incorporating the L2C tools into
the class curriculum and guiding teachers in the use of
such environments.
We have garnered knowledge from problems that
arose in this study and from our participants’ sugges-
tions. Regarding sonification, we have learned about the
needs of limiting the amount of information that can be
12 S.T. Levy & O. Lahav
© 2011 Blackwell Publishing Ltd
integrated in a single hearing and of distinguishing
related sounds of a single variable more clearly. In terms
of learning supports, there is a need to revisit some of
the more problematic ideas identified in the study
through a variety of contexts, appropriate feedback, and
explicit instruction. Shifting control to the user is essen-
tial to making this a truly exploratory environment. The
role of sonification shows a learning advantage in more
interactive settings (Pauletto & Hunt 2009). In addition,
this environment uses both visual and sound-based rep-
resentations, thus providing an environment geared for
integrating students who are both sighted and blind in
collaborative learning. Future design and research will
support such interactions.
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... Accommodations for students who are blind or have low vision. Recent research relevant to accommodating the needs of learners with vision impairments has largely focused on methods for rendering graphical or three-dimensional con- tent accessible for blind students and those with low vision (Darrah, 2013;Goncu & Marriott, 2011;Hansen et al., 2016;Levy & Lahav, 2011;Sullivan, Sahasrabudhe, Liimatainen, & Hakkinen, 2014). The technologies tested in these studies include tactile or vibrotactile interfaces, haptic feed- back devices, and audio interfaces describing visual con- tent. ...
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O uso de laboratórios virtuais de aprendizagem é cada vez mais adotado como uma ferramenta para apoiar as aprendizagens, por permitir que os sujeitos possam experimentar diversas situações que favoreçam seu desenvolvimento. Ao referirmos aos estudantes com deficiência visual e/ou cegueira, estas práticas tornam-se desafiadoras.Diante disso, apresentamos uma Revisão Sistemática de Literatura (RSL) sobre o uso de Laboratórios Virtuais em aulas de ciências com a participação desses educandos. Foi utilizado o protocolo de RSL consolidado por Kitchenham (2004) como base metodológica. As informações e dados foram encontrados em periódicos científicos conforme os critérios de inclusão e exclusão pré-estabelecidos no protocolo de pesquisa. Localizamos 320 trabalhos, desses, 11 foram selecionados e analisados, de forma a responder as questões estabelecidas. Conclui-se que são incipientes as pesquisas sobre LV para apoiar o ensino de Ciências para o público considerado.
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Computer-based modeling tools have largely grown out of the need to describe, analyze, and display the behavior of dynamic systems. Recent decades have seen increasing recognition of the importance of understanding the behavior of dynamic systems—how systems of many interacting elements change and evolve over time and how global phenomena can arise from local interactions of these elements. New research projects on chaos, self-organization, adaptive systems, nonlinear dynamics, and artificial life are all part of this growing interest in system dynamics. The interest has spread from the scientific community to popular culture, with the publication of general-interest books about research into dynamic systems (Gleick 1987; Waldrop, 1992; GellMann, 1994; Kelly, 1994; Roetzheim, 1994; Holland, 1995; Kauffman, 1995).
Exploration of unknown spaces is essential for the development of efficient orientation and mobility skills. Most of the information required for the exploration is gathered through the visual channel. People who are blind lack this crucial information, facing in consequence difficulties in mapping as well as navigating spaces. This study is based on the assumption that the supply of appropriate spatial information through compensatory sensorial channels may contribute to the spatial performance of people who are blind. The main goals of this study were (a) the development of a haptic virtual environment enabling people who are blind to explore unknown spaces and (b) the study of the exploration process of these spaces by people who are blind. Participants were 31 people who are blind: 21 in the experimental group exploring a new space using a multi-sensory virtual environment, and 10 in the control group directly exploring the real new space. The results of the study showed that the participants in the experimental group mastered the navigation of the unknown virtual space in a short time. Significant differences were found concerning the use of exploration strategies, methods, and processes by participants working with the multi-sensory virtual environment, in comparison with those working in the real space.
This study investigated whether extended training in an acoustically rich environment could enhance the spatial updating ability of 12 adults who were congenitally blind. After training, the adults' distance perception from a home-base location and novel locations was superior to that of a sighted control group, whereas their direction perception was comparable.