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Using Space Time Visualization in Learning Environment Design

  • May 2017
DOI:10.1145/3027063.3048422
  • Conference: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '17).
  • At: Denver, CO, USA
Authors:
Ben Rydal Shapiro at Georgia State University
Ben Rydal Shapiro
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Abstract and Figures

Space-time visualization is an established area of research and design. However, a significant gap in this work is how space-time visualization supports learning environment design in particular conceptual domains. This paper introduces two new and generalizable types of interactive learning environment designs in different conceptual domains that use space-time visualization. The first is for museum studies and is designed for learners (typically museum curators, educators and designers) to learn about how museum visitors engage with exhibits in museum gallery spaces. The second supports social studies education. Findings and discussion show how a) space-time visualization can be a powerful means to support specific types of learning environment designs and b) such efforts also can produce new types and uses of space-time visualization in particular settings.
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Using Space Time Visualization in
Learning Environment Design
Abstract
Space-time visualization is an established area of
research and design. However, a significant gap in this
work is how space-time visualization supports learning
environment design in particular conceptual domains.
This paper introduces two new and generalizable types
of interactive learning environment designs in different
conceptual domains that use space-time visualization.
The first is for museum studies and is designed for
learners (typically museum curators, educators and
designers) to learn about how museum visitors engage
with exhibits in museum gallery spaces. The second
supports social studies education. Findings and
discussion show how a) space-time visualization can be
a powerful means to support specific types of learning
environment designs and b) such efforts also can
produce new types and uses of space-time visualization
in particular settings.
Author Keywords
Space-time visualization; learning environment design;
time geography; information design; interface design;
interaction geography; design research; learning
sciences; education
ACM Classification Keywords
H.5.m. Information Interfaces and Presentation (e.g.,
HCI): Miscellaneous; I.3.6 Methodology and
Techniques; J.4 Social and Behavioral Sciences; J.5
Arts and Humanities
Introduction
Space-time visualization is an established area of
research and design [1, 2, 3, 10]. Moreover, it is a
rapidly growing area due to new technologies [2, 6],
new forms of urban mobility [3], open data resources
[7] and the increasing need to gain insights and reveal
patterns from complex data [6]. However, a significant
gap in space-time visualization work is how space-time
visualization supports learning environment design in
particular conceptual domains.
Permission to make digital or hard copies of part or all of this work for
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Copyright is held by the owner/author(s).
CHI'17 Extended Abstracts, May 06-11, 2017, Denver, CO, USA
ACM 978-1-4503-4656-6/17/05.
http://dx.doi.org/10.1145/3027063.3048422
Ben Rydal Shapiro
Space, Learning & Mobility Lab
Vanderbilt University
Nashville, T N 37203, USA
benrydal@gmail.com
Student Research Competition
CHI 2017, May 6–11, 2017, Denver, CO, USA
178
This paper illustrates the design of two types of
learning environments in different conceptual domains
that use space-time visualization to support learning.
The first is an interactive learning environment that
visualizes and allows for multi-modal analysis of
museum visitor’s physical movement, conversation, use
of personal information devices and multi-perspective
audio and video (if available) over space and time. This
environment supports learners (typically museum
curators, educators and designers) to learn about how
museum visitors engage with exhibits in museum
gallery spaces. The second is an interactive learning
environment that allows learners to capture, compare
and re-contextualize their “daily rounds” and “personal
time-geographies” [5, 10]. This environment supports
social studies education.
This paper begins by reviewing relevant work in space-
time visualization and learning environment design.
Subsequently, it illustrates and discusses each learning
environment by 1) introducing the design context, 2)
illustrating the environment in use through a figure and
3) discussing findings from its use. This paper
concludes by synthesizing this work, discussing it’s
limitations and illustrating the need for future research
in this understudied area.
Relevant Work
Two bodies of research concerning space-time
visualization and learning environment design inform
this work. With respect to space-time visualization, this
work draws from time geography [5] and research
concerning the use and advancement of the space-time
cube and space-time cube operations [4]. Moreover, it
also draws from the visualization and analysis of
movement [3] and the use of flow maps by expert and
non-expert audiences [8]. With respect to learning
environment design, this work draws from a new genre
of learning called “learning on the move” that focuses
on how personal mobility and digital mapping
technologies can be used to support new types of
learning environment designs [10]. It also draws from
conversation and interaction analysis research methods
used in education [6] and design based research
methods in the learning sciences that provide
empirically driven ways to build, test and refine
learning designs.
Learning Environment 1: Museum Studies
Design Context: This learning environment uses the
Interaction Geography Slicer (IGS) to interactively
visualize and allow for multi-modal analysis of people’s
(here museum visitors) physical movement,
conversation, use of personal information devices and
multi-perspective audio/video (if available) over space
and time. This paper shows how this learning
environment supports museum professionals learn
about how their visitors engage with exhibits in
museum gallery spaces. The empirical data of the
learning environment (eg. visitor’s movement,
conversation and audio/video) was collected during IRB
approved research funded by the National Science
Foundation [9]. This research includes 1) 22 case
studies of complete museum visits that captured
continuous, multi-perspective audio and video records
of visitor mobility and interaction and 2) audio, video
and survey based data from design sessions with
professional museum curators, educators and
designers. Figure 1 below shows aspects of the learning
environment during one design session with museum
professionals. Subsequently, this figure is used to
discuss findings that advance space-time visualization.
Student Research Competition
CHI 2017, May 6–11, 2017, Denver, CO, USA
179
Figure 1: The bottom left image
of the figure shows a group of
museum curators, educators and
designers using the Interaction
Geography Slicer (IGS) to study
how their visitors engage with
exhibits in museum gallery
spaces. Different visitor groups
and gallery spaces can be
selected and compared. The large
top image provides a detailed
view of what they are looking at,
which is the simultaneous
movement of a family of 5 (a
mother, 2 sons, their sister and
her fiancé) in one museum
gallery space. Color designates
individual family members. The
timeline Y-axis corresponds to
the vertical dimension of the floor
plan. Line pattern corresponds to
the horizontal dimension of the
floor plan. The bottom right
image displays video (from a
camera worn by family member
Lily) selected by a museum
curator at a point in space and
time when the family is gathered
together at an exhibit dedicated
to Maybelle Carter. Software is
written and will be made
available by Ben Rydal Shapiro
using JavaScript & p5.js. Source:
Copyright © by Ben Rydal
Shapiro. Reprinted by permission.
Student Research Competition
CHI 2017, May 6–11, 2017, Denver, CO, USA
180
Findings: Interaction and conversation analysis of audio
and video records in addition to post surveys of people
using the Interaction Geography Slicer show that, as a
learning environment, it helps learners see museum
visitors in new ways and challenge idealized models of
museum visitors as relatively passive consumers of
intended design. For example, the previous figure
captures a moment where many learners realize that a
young child’s rapid movements across a gallery space
are not childish behavior as traditionally conceived but
rather very intentional efforts to learn. In this case, the
young child is 6-year-old Blake (the blue path in the
figure) who runs back and forth across the museum
gallery trying to lead Adhir who is standing at an
exhibit dedicated to Hank Williams (straight orange
path from minutes 0-5) on a tour of other exhibits.
Blake is finally successful at minutes 5-6 indicated by
their intertwined movement paths.
Likewise, the learning environment necessitated
advancements in space-time visualization that
contribute to human-computer interaction research. For
example, as shown in the previous figure space-time
visualization was used to interact with complex multi-
party audio and video (eg. by selecting and playing) in
ways that further existing research [4] and in a new
context (eg. a museum). There is not room in this
paper to fully illustrate these capabilities however, it is
important to note that this learning environment also
necessitated advancing space-time visualization to
visualize conversation and the use of personal
information devices. Figure 2 below shows one way
conversation is visualized. It extends the previous
figure by showing the conversation of the same family
(the Bluegrass Family) in the same gallery space (the
Bluegrass Gallery).
Figure 2: Space-time visualization of the Bluegrass Family’s
conversation in the Bluegrass Gallery. Conversation organizes
transcribed talk colored by speaker in space and time and
groups topically related talk into conversation “boxes”. Thicker
boxes on the floor plan show repeated conversations in the
same area of space. Source: Copyright © by Ben Rydal
Shapiro. Reprinted by permission.
Learning Environment 2: Social Studies
Design Context: This learning environment allows
learners to capture, compare and re-contextualize their
“daily rounds” and “personal time-geographies” [5, 10].
A central part of this learning environment is a set of
dynamic visualization tools that allow learners to
visualize, interact and layer digital maps, large-scale
data sets, imagery and personal mobility in multiple 2D
& 3D representational forms. Figure 3 below shows how
part of this learning environment is used in a university
social studies course with 21 undergraduate and
graduate students to teach and support conversations
about human geography, new digital mapping
technologies as well as student’s experiences with race
and diversity in their daily lives. Subsequently, this
figure is used to discuss specific findings that advance
space-time visualization research.
Student Research Competition
CHI 2017, May 6–11, 2017, Denver, CO, USA
181
Figure 3: The bottom left image
shows an in-class activity with
university students. At the front
of the classroom one group of
students analyze all 21 students
personal mobility (collected prior
to class using cell phones) over
the “racial dot map” where color
indicates race. The large top
image magnifies their screen and
shows students highlighting the
“University Bubble” that
conditions their daily lives and
“Highway I -40” that was built in
the 1960s through a vibrant
African American neighborhood
destroying a famous music row
known as Jefferson Street where
Jimi Hendrix and Ray Charles
once played. The bottom right
image magnifies the screen of
students using a “space-time
cube” to display their movement
on a map and as it extends
upwards in time over a period of
3 days. Software is written and
will be made available by Ben
Rydal Shapiro using the
Processing Programming
Language and Unfolding Maps
Library.!Source: Copyright © by
Ben Rydal Shapiro. Reprinted by
permission.
Student Research Competition
CHI 2017, May 6–11, 2017, Denver, CO, USA
182
Findings: The learning environment makes personal
and aggregate (eg. the whole class) daily rounds and
time geographies “experiential and relevant” [10] and
personalizes large scale data sets such as the racial dot
map in a manner that supports learning about issues of
power, storytelling and representation important to
social studies education. For example, the figure above
illustrates students exploring their lived experiences of
physical places (eg. the university bubble) in
comparison to the represented space of the racial dot
map. This allowed them to see the spatial distribution
of cultural assets important to human geography
perspectives. It also allowed them to see why they
rarely interact with particular neighborhoods and why
there were extreme differences between undergraduate
and graduate student experiences (eg. undergraduates
were bound to the university bubble). Second, the
environment helps learners learn about the possibilities
of new forms of digital mapping and data visualization
technologies and open data resources. For students
shown in the figure above (some of whom are pre-
service social studies teachers), it helps them learn
about how they can use these emerging technologies
and resources in their own school and university social
studies classrooms.
Conclusion & Future Work
Space-time visualization provides a powerful means to
support learning environment design. Likewise, learning
environment design is an exciting area of research that
can advance space-time visualization research. Future
research should address a number of limitations and
needs in this early work such as how learning activities
and visualization can integrate seamlessly and how this
work can be sustained in specific contexts.
References
1. W. Aigner, S. Miksch, H. Schumann, and C.
Tominski. 2011. Visualization of time-oriented data.
Springer, Berlin, Germany.
2. N. Andrienko and G. Andrienko. 2005. Exploratory
Analysis of Spatial and Temporal Data: A
Systematic Approach. Springer-Verlag New York,
Inc., Sankt Augustin, Germany.
3. N. Andrienko and G. Andrienko. 2013. Visual
analytics of movement: an overview of methods,
tools and procedures. Information Visualization 12,
1: 3-24.
4. B. Bach, P. Dragicevic, D. Archambault, C. Hurter,
and C. Sheelagh. 2014. A Review of Temporal Data
Visualizations Based on Space-Time Cube
Operations. In IEEE Eurographics Conference on
Visualization. Swansea, Wales, UK.
5. Torsten Hagerstrand. 1970. What about people in
regional science? Papers in Regional Science, 24, 1:
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6. Brigitte Jordan and Austin Henderson. 1995.
Interaction analysis: Foundations and practice. The
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7. Tamara Munzner. 2014. Visualization Analysis and
Design. CRC Press.
8. T. Nagel, E. Duval, and A. Vande Moere. 2012.
Interactive exploration of geospatial network
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'12). ACM, New York, NY, USA, 557-572.
9. Ben R. Shapiro and Rogers Hall. 2016. Engagement
within Interest Driven Learning Environments. In
Proceedings of the 2nd annual Terrains symposium.
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10. Katie H. Taylor and Rogers Hall. 2013. Counter-
mapping the neighborhood on bicycles: Mobilizing
youth to reimagine the city. Technology,
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Student Research Competition
CHI 2017, May 6–11, 2017, Denver, CO, USA
183
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Full-text available
  • May 2012
This paper presents a tabletop visualization of relations between geo-positioned locations. We developed an interactive visualization, which enables users to visually explore a geospatial network of actors. The multitouch tabletop, and the large size of the interactive surface invite users to explore the visualization in semi-public spaces. For a case study on scientific collaborations between institutions, we applied and improved several existing techniques for a walk-up-and-use system aimed at scientists for a social setting at a conference. We describe our iterative design approach, our two implemented prototypes, and the lessons learnt from their creation. We conducted user evaluation studies at the two on-location demonstrations, which provide evidence of the prototype usability and usefulness, and its support for understanding the distribution and connectivity in a geospatial network.
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Keynote speaker: Visualization analysis and design
Conference Paper
  • Apr 2016
Computer-based visualization (vis) systems provide visual representations of datasets designed to help people carry out tasks more effectively. Visualization is suitable when there is a need to augment human capabilities rather than replace people with computational decision-making methods. The design space of possible vis idioms is huge, and includes the considerations of both how to create and how to interact with visual representations. Vis design is full of trade-offs, and most possibilities in the design space are ineffective for a particular task, so validating the effectiveness of a design is both necessary and difficult. Vis designers must take into account three very different kinds of resource limitations: those of computers, of humans, and of displays. Vis usage can be analyzed in terms of why the user needs it, what data is shown, and how the idiom is designed. I will discuss this framework for analyzing the design of visualization systems.
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Visualization of Time-Oriented Data
Book
  • Jan 2011
Time is an exceptional dimension that is common to many application domains such as medicine, engineering, business, or science. Due to the distinct characteristics of time, appropriate visual and analytical methods are required to explore and analyze them. This book starts with an introduction to visualization and historical examples of visual representations. At its core, the book presents and discusses a systematic view of the visualization of time-oriented data along three key questions: what is being visualized (data), why something is visualized (user tasks), and how it is presented (visual representation). To support visual exploration, interaction techniques and analytical methods are required that are discussed in separate chapters. A large part of this book is devoted to a structured survey of 101 different visualization techniques as a reference for scientists conducting related research as well as for practitioners seeking information on how their time-oriented data can best be visualized.
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Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
Book
  • Jan 2006
Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appeal: it involves hypothesis generation rather than mere hypothesis testing. The authors describe in detail and systemize approaches, techniques, and methods for exploring spatial and temporal data in particular. They start by developing a general view of data structures and characteristics and then build on top of this a general task typology, distinguishing between elementary and synoptic tasks. This typology is then applied to the description of existing approaches and technologies, resulting not just in recommendations for choosing methods but in a set of generic procedures for data exploration. Professionals practicing analysis will profit from tested solutions - illustrated in many examples - for reuse in the catalogue of techniques presented. Students and researchers will appreciate the detailed description and classification of exploration techniques, which are not limited to spatial data only. In addition, the general principles and approaches described will be useful for designers of new methods for EDA. © Springer-Verlag Berlin Heidelberg 2006. All rights are reserved.
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