www.rcetj.org
Volume 4, Number 1
Spring 2008
Edited by:
Karen Swan
Editor
Alison Bland
Managing Editor
Special Issue: Learning While Mobile
Guest Editor:
Mark van ‘t Hooft
Kent State University
Research Center for Educational Technology
Journal of the Research Center for Educational Technology (RCET)
Vol. 4, No. 1, Spring 2008
Editor
Managing Editor
Karen Swan
Alison Bland
Advisory Board
Joseph Bowman, Ph.D.
State University at Albany
Cheryl Lemke
Metiri Group
Rosemary Du Mont
Kent State University
Robert Muffoletto, Ph.D.
Appalachian State University
Ricki Goldman, Ph.D.
NYU
Elliot Soloway, Ph.D.
University of Michigan
Aliya Holmes
St. John's University
Review Board
Kadee Anstadt, Perrysburg City Schools
Savilla Banister, Bowling Green State University
William Bauer, Case Western Reserve University
Sebastian Diaz, West Virginia University
Evelyn Goldsmith, Kent State University
Albert Ingram, Kent State University
Jan Kelly, Mogadore Local Schools
Annette Kratcoski, Kent State University
Mary MacKay, Wake County Public School System
Theresa Minick, Kent State University
Jason Schenker, Kent State University
Chris Simonavice, Murray State University
Karen Swan, Kent State University
Mark van 't Hooft, Kent State University
Maggie Veres, Wright State Universit
Yin Zhang, Kent State University
The Journal for the Research Center for Educational Technology is published twice a year by RCET
(http://www.rcet.org). It provides a multimedia forum for the advancement of scholarly work on the effects
of technology on teaching and learning. This online journal (http://www.rcetj.org) seeks to provide unique
avenues for the dissemination of knowledge within the field of educational technology consistent with new
and emergent pedagogical possibilities. In particular, journal articles are encouraged to include video and
sound files as reference or evidence, links to data, illustrative animations, photographs, etc. The journal
publishes the original, refereed work of researchers and practitioners twice a year in multimedia electronic
format. It is distributed free of charge over the World Wide Web under the Creative Commons License
(Attribution-Noncommercial-No Derivative Works 3.0 United States) to promote dialogue, research, and
grounded practice.
Journal of the Research Center for Educational Technology (RCET)
Vol. 4, No. 1, Spring 2008
Volume 4, Number 1
Spring 2008
A Message from the Guest Editor
Mark van ‘t Hooft
1
Bridging the Gap? Mobile Phones at the Interface Between Informal
and Formal Learning
Professor John Cook, Norbert Pachler, and Claire Bradley
3
Affordances of PDAs: Undergraduate Student Perceptions
Yanjie Song and Robert Fox
19
The Effect of Information Visualization and Structure on Mobile Learning
Hyungsung Park
39
Using Place as Provocation: In Situ Collaborative Narrative Construction
Matthew Schaefer, Deborah Tatar, Steve Harrison, and Alli Crandell
49
A Personalized Mobile Mathematics Tutoring System for Primary Education
Xinyou Zhao and Toshio Okamoto
61
Journal of the Research Center for Educational Technology (RCET)
Vol. 4, No. 1, Spring 2008
RCETJ 4 (1), 39-48
The Effect of Information Visualization and Structure on Mobile Learning
Hyungsung Park
Korea National University of Education
South Korea
Abstract
The purpose of this study was to examine information visualization and structured learning content in a
mobile learning environment. It compared learning from three different representations of content on a
PDA system – traditional text (non-structured, non-visual), structured text without visuals, and structured
text with visuals. Learner comprehension of the content was tested during the session. Results showed
that structured text with visuals was more effective in supporting the development of learner
understanding than either structured or non-structured text. The results suggest that to overcome the
limitations of learning with mobile devices, ways of structuring text and visualizing content are required.
An earlier version of this paper was presented at the 2007 NECC Conference in Atlanta, GA.
Introduction
Currently, the use of mobile devices is seamlessly integrated in many everyday activities. Mobile devices
offer mobility, wireless communication, and connectivity to information resources. They are primarily used
as mobile digital assistants and communication mediators. Thus, it is no surprise that various attempts to
use mobile applications for learning purposes have been reported either inside or outside of school (see
for example Roschelle, 2003).
A number of projects have tried to find out how these technological devices may be integrated into
academic settings (Chen, Myers, & Yaron, 2002; Danielsson, Hedestig, Juslin, & Orre, 2004; Lundby,
2002; Park, 2005; Pownell & Bailey, 2001; Roschelle, 2003; Roschelle & Pea, 2002; Sharples, 2007;
Sharples, Corlett, & Westmancott, 2002). Mobile learning can be both a complement and have conflict
with formal education processes. On the positive side, students can extend their classroom learning
activities to homework, field trips, and museum visits by reviewing teaching materials on mobile devices
or collecting and analyzing information using integrated digital cameras, recorders or handheld data
probes. On the downside, such mobile devices can also lead to disruption of the carefully managed
classroom environment. For example, students may bring their own multimedia phones or portable
gaming devices and conduct private conversations within and outside the schooling process (Sharples,
2003). Nevertheless, the integration of mobile communication tools and devices with various multimedia
capabilities offers opportunities to develop digital tools that will assist individuals and groups with teaching
and learning, anytime and anywhere (Sharples et al., 2002).
The purpose of this study was to examine the use of different representations of academic content on
mobile devices in order to determine if information visualization has an impact on learning. More
specifically, it compared learning from three different representations of content on a PDA system –
traditional text (non-structured, non-visual), structured text without visuals, and structured text with
visuals.
Mobile Devices as a Tool to Support Learning in Authentic Contexts
Mobile technologies now allow us to talk to anyone at anytime, and anywhere. In fact, we take it for
Journal of the Research Center for Educational Technology (RCET)
Vol. 4, No. 1, Spring 2008
39
granted. Accessing information, taking photographs, and recording our thoughts are now considered
standard features on a single device. Recent developments in mobile phone technologies also offer the
potential for rich multimedia experiences and location-specific resources (Naismith, Lonsdale, Vavoula, &
Sharples, 2005, p.1). Wireless two-way internet connections are considered to be an integral component
as well.
One important field in which mobile technology can make significant contributions is education. In the fast
pace of modern life, students and instructors can appreciate constructively utilizing their spare time in
order to work on lessons, even when away from offices, classrooms, and labs (Virvou & Alepis, 2005, p.
53). The mobile and interactive technologies provide opportunities to create learning environments that
actively involve students in problem solving and exploring.
The concept of mobile learning (or m-learning) used in this research is defined as a teaching-learning
interchange, accomplished with the use of mobile and wireless digital tools. Devices may include smart
phones, personal digital assistants (PDAs), portable gaming consoles, Ultra Mobile Personal Computers
(UMPCs) and similar handheld devices. There is still some debate about the inclusion of tablet and laptop
computers into this category.
The important features of mobile devices for learning include portability, immediacy, individuality,
connectivity, and accessibility which are bringing about a paradigm shift in learning models (Shotsberger
& Vetter, 2000). Mobile learning is unique in that it allows for a truly personal learning experience
anywhere and anytime. Such learning can also be used to enrich, enliven, or add variety to conventional
lessons or courses. In addition, content can and is increasingly customized to individual learners
(Abfalter, Mirski, & Hitz, 2004).
Personal learning starts with a learner in a social, cultural and technological environment. The act of
learning involves the artful deployment of the environment to solve problems and acquire new knowledge.
Learning is a constructive process of acting within an environment and reflecting upon it (Sharples, 2000).
The latest mobile technologies that support mobile learning, combined with easy access to content, allow
learners to experience new situations outside of the classroom (Sharples, 2007). With mobile
communication technologies, the time and physical boundaries of the traditional classroom are expanded
( Abfalter et al., 2004; van ‘t Hooft & Swan, 2007). A mobile learning environment provides students and
teachers with the opportunity to obtain any material on their mobile computers. Furthermore, mobile
learning is not simply learning through portable devices, but also learning across contexts (Sharples,
2007). Such contexts include resources, strong search capabilities, in depth interactions between users,
powerful technical support, and performance-based assessment. Therefore, based on the characteristics
of mobile devices and the nature of mobile learning, content needs to be easily accessible to students
with a wide range of academic abilities. Information visualization can be of aid in this endeavor.
Information Visualization of Content for Mobile Learning
Cognitive learning theory explains how mental processes transform information received by the eyes and
ears into knowledge and skills in human memory. Clark and Mayer (2004) have described how visual
lessons and auditory information are briefly stored in a visual and auditory sensory memory, then enter
the working memory, and are finally stored in permanent, long-term memory. Sweller (2002) stated that
there is a need to visualize information because human cognition includes a working memory of limited
capacity and duration with partially separate visual and auditory channels, and an effectively infinite longterm memory that holds many schemas that can vary in their degree of automation. Visual structures
contribute to ‘intactness’ of the information and is encoded into the memory organization of the map
without exceeding the capacity limits of our working memory (Larkin & Simon, 1987).
The importance of selecting and using information wisely increases gradually as we get older; methods of
displaying and sharing such information are changing rapidly as well. While we have evolved from using
Journal of the Research Center for Educational Technology (RCET)
Vol. 4, No. 1, Spring 2008
40
simple pictures in ancient times to expressing ourselves with a character-based system (text), more
recent developments in visual and auditory media seem to favor a shift to information that can be seen
and heard in image and sound. Wireless mobile devices and the Internet are important players in this
shift.
As a result, information visualization has become an important skill. Zhang (1996) has defined it as a
process of transforming large quantities of data and information, which are not inherently spatial, into a
visual form that allows users to visually perceive the meaning of the information instead of trying to figure
it out cognitively. Why must we consider this visual structure of content? The explanation is really very
simple. Our lives have become impacted with multiple forms of data processing: the ATM transaction, the
online registration of our new software, the credit card purchase at the mall, the cellular phone call, etc.
(Agutter & Bermudez, 2005).
Information visualization helps us deal with large amounts of information. When incorporated into the
learning process, information visualization can enable users to comprehend information better, to receive
information more quickly, and to make more reasonable and relevant decisions. For example, information
visualization strategies have been considered as a way to summarize textual data, so that learners can
comprehend huge amounts of data more efficiently and effectively. Also, Clark, Nguyen, and Sweller
(2006) have reported that visualization of information positively affects achievement abilities.
Research in information visualization focuses on helping people visualize abstract or conceptual
information by reducing its complexity (Zhang & Wolfram, 2001). Past studies on information visualization
(Keim, 2002; Morris, Yen, Wu, & Asnake, 2003; Zhang, 1995; Zhang & Wolfram, 2001) have analyzed
and classified methods of data processing and presentation and reporting on their advantages and
characteristics. Nevertheless, research is limited on the importance of information visualization for the
learner.
According to Gerson and Eick (1997), visualization links the two most powerful information processing
systems known—the human mind and the modern computer. This process transforms data, information
and knowledge into a visual form, exploiting a person’s natural strength in rapid visual pattern recognition.
Developing techniques that can help individuals understand and rearrange information systematically is
critical. Thus, the primary objective of information visualization is to provide support for users to easily
grasp large quantities of information from search results and other querying contexts. Therefore, interface
design of content (aesthetics and function) is of critical importance for interactive systems and the
learning process, as well as the overall desirability of the system.
Visualization of content in interfaces for providing a number of benefits including: (1) an understanding of
internal relationships among documents to help users make judgments about the relevancy of information
in a search; (2) a transparent search and analysis process; (3) a visual environment enriched with useful
information; (4) the potential for developing new methods of information processing; and (5) the
recognition capacity to either discover or display information (Zhang, 1999).
Mobile devices can be a powerful tool in this process. Jonassen, Peck, and Wilson (1999) have argued
that such technologies can foster the application of human knowledge to solve real-world problems,
support human needs, and expand an individual’s functional capacities. However, various technological
constraints need to be taken into consideration during the design of activities. One issue is small screen
size, which often hinders the presentation of all the information of interest. In addition, the lack of a full or
full-size keyboard creates constraints with regards to data entry (Hayhoe, 2001).
Given the context as described up to this point, the purpose of this study was to examine information
visualization and structured learning content in a mobile learning environment. It compared learning from
three different representations of content on a PDA system – traditional text (non-structured, non-visual),
structured text without visuals, and structured text with visuals.
Journal of the Research Center for Educational Technology (RCET)
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Methodology
Research Questions
•
Is there a difference in learners’ achievement levels when different representations of digital
information on mobile devices ( non-structured, non-visual text, structured text without visuals,
and structured text with visuals) are used?
Research Hypothesis
•
•
Hypothesis 1: There is a statistically significant difference in achievement levels for learners who
receive visualized, multimedia information on mobile devices as opposed to learners who receive
non-visualized, text-only information on mobile devices.
Hypothesis 2: There will be a statistically significant difference in achievement levels for learners
who receive structured text-based information on mobile devices as opposed to those who
receive non-structured text-based information on mobile devices.
Participants
The sample for this study was drawn from one elementary school located in Suwon, Korea. There were
120 students (56 males and 64 females). The average age of the participants was between 10 and 11
years, and based on the pre-test scores they were all of similar achievement levels (see Tables 1 and 2).
Three intact classes of 40 students each made up the three experimental groups. The first experimental
group received non-visualized, text-based, and non-structured learning content on a mobile device; the
second experimental group received non-visualized, text-based, and structured learning content on a
mobile device; and the third experimental group received visualized learning content on a mobile device.
Research Design
The study took place at the school of the participants. The instructional intervention was divided into three
phases; pre-testing, treatment, and post-testing. Treatments were carried out across five weeks and
based on the 4th grade social studies curriculum. Assessment methods were the same for all students
who participated in the three groups.
During the sessions, three experimental tasks were employed in order to evaluate the degree of
achievement through the learning of non-structured, text-only content, structured, text-only content, and
visualized content that was developed for learning on mobile devices. Each group saw only one content
type. The content was focused on social studies. Figure 1 shows examples of the three types of content.
Journal of the Research Center for Educational Technology (RCET)
Vol. 4, No. 1, Spring 2008
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Non-Structured Text-Only Content
Structured, Text-Only Content
Visualized Content
Figure 1: The Three Types of Mobile Content
Following the intervention, students were given a questionnaire. The questionnaire used questions based
on the 7th National Social Studies Curriculum of Korea. Additional attribution items were developed to
evaluate the effectiveness of information visualization on student learning, as based on their test
performance. The questionnaire consisted of 20 items and was field-tested with 20 unrelated students to
check for clarity and appropriateness. A reliability test for examination items that were used to determine
achievement levels yielded a Cronbach Alpha = 0.85.
Results
As shown in Table 1, the three groups were similar in achievement levels on the pre-test. One-way
ANOVA was conducted in order to test for statistically significant differences between the three groups.
As indicated in Table 2, the result of the analysis showed that the three groups did not differ significantly,
F (2,112) = .201, p = .818. In other words, the three groups were similar in achievement as measured by
the pre-test.
Table 1: Achievement Pre-Test Means and Standard Deviations
Source
Group 1 (Non-Structured)
Group 2 (Structured)
Group 3 (Visualization)
N
39
38
38
Mean
13.54
13.55
13.08
Standard Deviation
4.44
3.43
3.11
Table 2: ANOVA Summary Table Comparing Group Scores on Pre-Test
Source
Between Groups
Within Groups
Total
Sum of Squares
5.541
1541.850
1547.391
df
2
112
114
Mean Square
F
p
2.771
13.767
.201
.818
Table 3 provides the group means and standard deviations for the post-test. Group 3 (Visualization) had
the highest mean score (M=16.74), followed by Group 2 (Structured) (M=15.11). One-way ANOVA was
Journal of the Research Center for Educational Technology (RCET)
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conducted in order to test for a statistically significant difference between the three groups. As indicated in
Table 4, the result of the analysis showed that the three groups differed significantly, F (2, 112) = 22.381,
p =.000.
Table 3: Achievement Post-Test Means and Standard Deviations
Source
Group 1 (Non-Structured)
Group 2 (Structured)
Group 3 (Visualization)
N
39
38
38
Mean
13.54
15.11
16.74
Standard Deviation
2.23
1.80
2.23
Figure 2, shows that mean improvements of three information visualization groups. Information
visualization and structured information were given groups increased the mean square from the pre-test
result , while the non-structured information given group did not improve in the mean square of
comprehension test.
Figure 2: Pre-Post Test Score Differences of the Three Groups
Table 4: ANOVA Summary Table Comparing Group Scores on Post-Test
Source
Between Groups
Within Groups
Total
Sum of Squares
196.891
492.640
689.530
df
2
112
114
Mean Square
F
p
98.445
4.399
22.381
.000
In order to check exactly where the significant difference lay, a post-hoc Tukey test was performed.
Table 5: Result of Tukey Post-Hoc Multiple Comparisons
Group
Group 1 (Non-structured)
Visualization
Structured
Mean difference
3.198
1.567
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p
.000
.004
44
Group 2 (Structured)
Group 3 (Visualization)
Non-structured
Visualization
Non-structured
Structured
1.567
1.632
3.198
1.632
.004
.003
.000
.003
From table 5 it can be concluded that significant differences occurred between all three groups. The
largest difference occurred between the visualization and non-structured groups (3.198), followed by the
difference between the visualization and structured groups (1.632).
Conclusion
The advent of solutions for mobile wireless technologies tailored to the educational arena may mark a
revolution in distance and online education. This new form of education has been dubbed m-learning or
mobile learning. The term m-learning is commonly used for mobile education that includes mobile
learning, teaching, and the support services of an educational organization (Dye, Solstad, & Odingo,
2003). Mobile learning offers students and teachers the opportunity to interact and gain access to digital
educational materials using a wireless handheld device. However, m obile devices have limitations
inherent to the nature of the form factor, including relatively small screen sizes and the lack of a full (-size)
keyboard. Therefore, creating suitable mobile content for learning needs to take these limitations into
consideration. In the same vein, we need to develop mobile learning content that not only works on the
devices but is also supportive of the learning process, especially with regards to information visualization.
The purpose of this study was to examine information visualization and structured-learning content in a
mobile learning environment. It compared learning from three different representations of content on a
portable handheld device – traditional text (non-structured, non-visual), structured text without visuals,
and structured text with visuals.
The results of the study suggest that information visualization is the most effective type of learning for
mobile devices, followed by structured text. There was a significant difference between the achievement
levels among the groups that did and did not use visualized content. Consequently, Hypothesis 1 ( There
will be a statistically significant difference in achievement levels for learners who receive visualized,
multimedia information on mobile devices as opposed to learners who receive non-visualized, text-only
information on mobile devices ) was accepted. Hypothesis 2 (There will be a statistically significant
difference in achievement levels for learners who receive structured text-based information on mobile
devices as opposed to those who receive non-structured text-based information on mobile devices) was
also accepted, because the group using structured text scored higher on the post-test than the group
using the non-structured text.
Future mobile devices are undoubtedly going to have even more functions than the devices that are
available today. Third generation mobile telephone networks are currently being launched in several
countries, and even the current 2.5 G technology is sufficient for most users to access multimedia
information via smart phones and other mobile devices for several years to come (Andersson, 2003).
Mobile technology will probably become more and more important for learning in the future. However, for
now, the technology will probably mainly be used for accessing and learning small chunks of content as
the opportunity for learning arises (Strandvall, 2003).
This study provides preliminary findings about the importance of visualized information for such learning
content on mobile devices. While further research is needed, the current findings indicate that
visualization is important in the design of mobile user interfaces and information content structures for
mobile learning applications.
In fact, some are already arguing that future mobile learning interfaces may be more visual and gamelike. Prensky (2001) has stated that handheld computers are an important platform for digital game-based
Journal of the Research Center for Educational Technology (RCET)
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learning. This has been implemented in language learning and the management of medical conditions.
Therefore, it will be up to mobile hardware and interface designers to begin thinking about game-based
content that incorporates information visualization strategies and other ways to structure information,
while at the same time, addressing the limitations inherent in mobile devices.
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