=Paper=
{{Paper
|id=None
|storemode=property
|title=Constructing
Narrative Visualizations as a Means of Increasing Learner Engagement
|pdfUrl=https://ceur-ws.org/Vol-1210/datawiz2014_13.pdf
|volume=Vol-1210
|dblpUrl=https://dblp.org/rec/conf/ht/YousufC14
}}
==Constructing
Narrative Visualizations as a Means of Increasing Learner Engagement==
https://ceur-ws.org/Vol-1210/datawiz2014_13.pdf
Constructing Narrative Visualizations as a means of
Increasing Learner Engagement
Bilal Yousuf Owen Conlan
KDEG, Trinity College Dublin KDEG, Trinity College Dublin
Dublin, Ireland Dublin, Ireland
yousufbi@scss.tcd.ie Owen.Conlan@scss.tcd.ie
ABSTRACT research addressed in this paper presents a framework, VisEN
Increasingly visualization systems are using storytelling to present (Visual Exploration with Narrative), which aims to provide a
complex data. However, many approaches neglect enabling users novel way to extract knowledge and meaning from data. VisEN
to independently explore details within the story. The research supports users in the role of narrative composers to analyze
presented in this paper provides an overview of the potentially complex data through advanced web based interfaces
implementation and discusses the evaluation of a novel to construct narratives. The narratives include explorations paths
framework (VisEN), which aims to allow users to construct to facilitate data drill downs and viewing related data. The
narratives containing multiple exploration paths. The narratives narratives are automatically transformed into personalized visual
are told through dynamically generated visualization techniques, narratives for end users, who can analyze and explore sections of
which are personalized for individual end users, and where every the narrative through multiple interactive visualization techniques
visualization technique in the narrative can be further explored. and gain a deep understanding of the data.
The evaluation described assesses the role personalized visual This paper discusses the implementation overview, evaluation and
narratives had in increasing engagement of weaker students with preliminary results of two key components of the VisEN
an online database SQL course. It was found that weaker students framework: the Narrative Builder and the Visual Narrative
who regularly interacted with their personalized visual narratives Explorer. The aim of the Narrative Builder is to enable narrative
showed an improvement in engagement. composers to construct explorable narratives through an advanced
web–based interface, which enables the analysis of potentially
Categories and Subject Descriptors complex data without dealing with data complexity issues. The
H.3.5 [Online Information Services]: Web-based services; H.5.2 aim of the Visual Narrative Explorer is to personalize the visual
[User Interface] Graphical User Interface; H.5.4 [Hypertext/ narratives for end users and facilitate analysis and exploration of
Hypermedia]: Architectures these narratives. VisEN was deployed to the AMAS [20]
Personalized Learning Environment (PLE), to provide
personalized visual narratives to 108 students who participated in
General Terms an online SQL course. Two evaluations were completed with the
Design, Experimentation, Human Factors, Performance first analyzing how effective the AMAS course professor found
the user interfaces provided by the Narrative Builder to build
Keywords explorable visual narratives. The second evaluation focused on
Visualizations, Personalized Visual Narratives, Visual Interaction weaker students’ level of engagement (“participation in
and Exploration educationally effective practices” [17]). In particular, it analyzed
how effective the personalized visual narratives were in allowing
weaker students to extract meaning from their activity data, in
1. INTRODUCTION order to motivate them to engage with the course. The results of
Research in the field on Information Visualization has largely both evaluations were very encouraging and it was found that
been focused on visual analytics and exploration, whereas these learners were drawn to their visual narratives in order to
research in visual presentation and storytelling has recently started understand and improve their engagement with the course.
to gain momentum. Storytelling in information visualization, or
narrative as it is referred to in this work, can be defined as an The remainder of this paper is structured as follows: Section 2
ordered sequence of steps consisting of visualizations, which are discusses the VisEN framework approach. Section 3 presents a
linked or connected to make the communicated message more review of the related work. Section 4 describes an implementation
memorable [1]. Stories provide effective ways of highlighting overview of VisEN. Section 5 presents two use cases; the first
facts, making points and passing on information [16], while describing a domain expert using VisEN to construct visual
visualizations facilitate a simple means to understand digitized narratives, and the second describing a learner using her
data as they map data attributes to visual properties [6]. The personalized visual narratives to gain a thorough understanding of
her personal course log data. Section 6 evaluates effectiveness of
VisEN when deployed to a PLE and discusses preliminary results.
Finally, section 7 discusses conclusions and future work.
2. VISEN APPROACH
VisEN automatically transforms narratives into explorable visual
narratives. This transformation requires data characterization and
mappings to transform data to appropriate visualization selected data through a further visualization. However, with drill
techniques. Data characterization or data transformation [6] downs, users reach an end point where their exploration must.
involves analyzing data to facilitate automated mappings to Exploration paths provided by VisEN are linked to elements in
visualization techniques. To enable this mapping or visual the visual narrative and when these elements are clicked,
encoding [6], the affordances and characteristics of visualization visualization techniques are generated rendering a drill down view
techniques are required, for example, through a matrix. VisEN or a related data view of the element selected. Drill down views
narratives consist of data slices, which are constructed using data show the details surrounding a selected element, whereas related
fields, metadata, filters and aggregations. Data slices form the data views show data which shares relationships with the selected
chapters or sections of the narrative. element. When a user reaches the lowest point in a drill down, she
always has the option to view related data. Visualizations have
When a data slice is constructed, visualizations that can render the
been used in Technology Enhanced Learning (TEL) to present
data are automatically generated and presented to the narrative
student activity data and peer comparisons [11, 22] to motivate
composer as a set. The narrative composer decides which
students. However, these are not represented through visual
visualizations to keep in the set. This action introduces humans
narratives, where users can explore the data presented.
into the visual matching process. This results in a refined set of
visualizations for a data slice, and takes place before the narrative Personalized visual narratives can aid the process of
is transformed into a visual narrative. VisEN automatically understanding complex data as they can present personalized data
generates personalized exploration paths to allow end users to and provide visualizations that suit individual preferences. In
select elements within visualizations and view details or view Tableau Story, Tableau [26] selects the most suitable visualization
related data through other visualizations. The exploration paths for the story point and this can be changed by the analyst.
are generated based on users preferences and consists of Similarly Google Fusion Tables [10] uses a suitable visualization
visualizations showing details and related data to the narrative for the data. However, we find on many occasions, a number of
viewed. visualization techniques are suitable to render the same data. The
visualizations generated by these systems are not personalized to
To complete the narrative, the narrative composer connects the
end user preferences. In TEL, a number of systems [2, 3, 21]
data slices to each other in a chronological order and publishes it.
provide personalized visualization forming part of the learning
Figure 1 shows a simplified view of the process used by VisEN to
module. VisEN’s architecture consists of a Personalization
produce personalized explorable visual narratives.
Engine, which generates personalized exploration paths for end
users. User data preferences are stored in a user model, which are
used to personalize the exploration paths.
From the visualization tools that support visual interactions and
explorations, Spotfire [27] supports drill down explorations,
however, the exploration path is fixed and an end user has the
option to either view the details behind a data point or not. The
exploration is not independent of the path constructed by the
analyst. VisEN provides multiple exploration paths from each
Figure 1: VisEN Flow data slice, allowing end users to explore various tailored paths
through the data set. Hence the exploration is independent from
3. RELATED WORK one end user to another and this allows users to derive personal
Interaction, exploration and visual storytelling are important conclusions.
aspects of presentation in information visualization as they allow From the analysis above, it can be seen that VisEN progresses the
users to gain a deeper understanding of data. This section analyses state-of-the-art by introducing three novel factors which focus on
the state of the art to determine how adequately generating allowing end users to: 1) explore related data through exploration
dynamic visual narratives and enabling personalized visual paths; 2) view visual narratives; and 3) analyze tailored
explorations of these narratives have been addressed. exploration paths.
Visual narratives have been effectively used in journalism [9, 15,
24] to tell stories with data. These have ranged from presenting 4. IMPLEMENTATION OVERVIEW
several visualizations with annotations in one view to slides The VisEN architecture uses principles discussed in 1) the
containing interactive visualizations to tell a story. Contextifier visualization pipeline [6]; 2) the visual information seeking
[15] for example, provides visualizations embedded in news mantra [25]; 3) the Template Editor and Shelf Configuration
articles and provides visualizations of related articles allowing visual interface design approaches [13]; and 4) sequencing in
users to navigate and explore these. Tools such as Gapminder visual narratives [14] to generate explorable personalized visual
[22], GED Viz [8] and SketchStory [18] provide users with narratives. Figure 2 shows VisEN architecture, which consists of
interactive visual storytelling. However, the interactions are the Narrative Builder, the Visualization Engine and the Visual
limited to hovering the mouse over data points to reveal details Narrative Explorer components.
and filtering regions of the data. StoryFlow [19] allows users to
explore data in a second layer of the story through its bundling
operation, which reveals a level of detail beneath a bundled line. 4.1 Narrative Builder
Spotfire [27] provides users with data drill down capabilities, The Narrative Builder enables narrative composers to easily
where visual structures can be clicked by users and the system construct narratives from complex data. Visualizations are not
loads another visualization that also provides a drill down of the introduced into the narrative during the narrative building phase.
data. A user can choose to drill down further and view the
4.2 Visualization Engine
The Visualization Engine transforms narratives into visual
narratives by mapping data slices from the narrative to
visualization techniques.
4.2.1 Query Builder
The Query Builder uses the data and metadata provided by the
narratives composers in the data slices to generate and execute
SQL queries against the specified data sources. The query results
are formatted by data type, size (data sizes and number of series of
data) and coordinates (data points) to aid the Rules Engine in
selecting appropriate visualizations for the data slice.
4.2.2 Rules Engine
The Rules Engine uses the formatted query results and the data
slice metadata to determine appropriate visualization techniques
for each data slice of the narrative. Instead of building
visualizations, VisEN utilizes JavaScript visualization libraries to
Figure 2. VisEN Architecture source visualization techniques. Extensive research [4, 5, 7, 12]
has evaluated the affordances and characteristics of visualization
4.1.1 Data Connection Component techniques and compared the suitability of various techniques for
Narrative Composers use the Data Connection component to data sets. This research has been used by VisEN to allow
connect to heterogeneous data sources to construct narratives. developers to build matrices that specify the characteristics,
Data connections are established by selecting data sources or affordances and constraints of the supported visualizations. The
specifying connection parameters. Preconfigured data source matrices are stored as XML files and new visualizations can be
parameters are stored in configurations files and new data source seamlessly incorporated into the framework by creating a new
parameters supplied by narrative composers are also saved to XML file (matrix) and importing the JavaScript library.
these files.
4.2.3 Visualization Builder
4.1.2 Data Analysis Interface The current set of visualization techniques supported by VisEN
Data slices form the individual pieces of narratives and are requires data to be formatted as JSON objects. The Visualization
constructed by the narrative composers via the web based Data Builder creates JSON objects using the query results and metadata
Analysis Interface. In addition to constructing the data slice, the and populates the set of visualization techniques (currently nine
Data Analysis Interface allows narrative composers to analyze techniques are supported including: bar chart, bubble chart,
data sources. The interface consists of a number of buttons which gauge, line chart, pie chart, scatterplot, stacked bar chart, area
run general queries such as “select count..”, “select ..” chart and parallel coordinates). It also makes the populated set of
etc.; this simplifies the process of constructing narratives as the visualizations available to the narrative composer to view through
raw data values can be analyzed by narrative composers. The Data a web interface as a dropdown list, where visualizations can be
Analysis Interface uses the jQuery Accordion widget to show removed from the set. The remaining set is used for the visual
source tables and fields and uses the jQuery Draggable widget to narrative.
facilitate dragging and dropping of data fields to construct data
slices. The interface provides a canvas with panels for fields and
filters. The data fields from the Draggable widget can be dropped 4.3 Visual Narrative Explorer
onto these panels to construct data slices. The drag and drop The Visual Narrative Explorer personalizes the visual narratives
design approach has been used effectively in state of the art [26]. for end users by generating tailored exploration paths for each
When a field is dropped onto a filter panel, VisEN runs queries to narrative based on individual preferences. It provides a web-based
fetch data to allow narrative composers to specify which values to interface where end users can analyze visual narratives and view
use in the filter. exploration paths to understand data.
4.1.3 Encoded Exploration 4.3.1 Personalization Engine
An important and novel aspect of VisEN is exploration paths, The Encoded Exploration component generates derivatives from
which are automatically constructed and connected to data slices. data slices for exploration paths, which can be accepted or
Exploration paths consist of a series of visualizations linked to rejected by the narrative composer. Accepted derivate data slices
each data slice or section of the narrative. End users can view and and data slices related to the narrative are used to form
analyze exploration paths by clicking on elements in a data slice personalized exploration paths. The Personalization Engine
to drill down into sections of a narrative or explore related items personalizes the exploration paths using user data preferences, set
to obtain a deeper understanding of the data. Exploration paths in the user model. These preferences are set when end users asked
are constructed by VisEN using data slices that have common to select data tags (taken from data slice metadata) they are
elements or derivatives in the narrative. The narrative composer interested in exploring when viewing visual narratives. Selected
can view the automatically constructed exploration paths and can tags are stored in the VisEN user model and these are used to
remove and visualization to the path via the available add/remove personalize the exploration path.
options on the Data Analysis Interface.
4.3.2 Narrative Dashboard to predict how long it will take her to complete her next five
Published visual narratives are made available to end users activities. Michelle now feels motivated and determined to work
through the web based Narrative Dashboard. End users are hard and obtain a good grade. As she completes each activity, she
presented with the first data slice of visual narratives and the explores her visual narratives and estimates the time the next
remaining data slice can be access by clicking the titles at top of activity would take.
the interface. When an end user wishes to explore an element in
the data slice, she can click it and this generates the first 6. EVALUATION
visualization in exploration path, which is shown in a popup VisEN was deployed to the AMAS [20] PLE during the 2013-
window on the web browser. Clicking an element in the visual 2014 academic year to provide learners with personalized visual
narrative fires an AJAX request and the linked exploration path is narratives to allow them to analyze their engagement score, view
made available to the end user. At any point the end users can time spent on activities and analyze peer comparisons. AMAS
close the exploration path popup window and continue analyzing provides a dynamic and adaptive framework for composition and
the visual narrative or alternatively continue with the exploration. assignment of personalized learning activities [20]. It has been
used over the past three years to deliver an SQL database course
5. USE CASES to final year university students in Trinity College Dublin. Two
This section discusses two use cases; the first use case describes a evaluations were carried out in conjunction with the delivery of
university professor using VisEN to construct two narratives. The the AMAS SQL course. The first evaluation involved a university
second use case describes a student using personalized visual professor using VisEN to construct visual narratives for his
narratives to understand and improve her course engagement. students. The second evaluation involved participating students of
the course using personalized visual narratives in order to
5.1 Use Case One – University Professor understand their performance and engagement from their log data.
John is a Professor lecturing Database Management System to
final year university students. His students need to use the AMAS 6.1 Evaluating the Narrative Builder
[20] portal to study SQL. John understands the challenges In this evaluation, the professor whose students worked through
learners’ have engaging with online learning modules and wishes the AMAS activities, assumed the role of a narrative composer
to provide visual narratives to improve engagement by allowing and constructed narratives using the AMAS log data from the
them to visually analyze and explore their individual log data. 2012-2013 academic year. The aim of this trial was to evaluate the
John logs into VisEN and assumes the role of a narrative end to end tasks of the narrative composer: analyze a complex
composer. He connects to the AMAS data source containing data set; construct narratives with exploration paths; and critique
learner log data from the last time the course was run. This data the set of generated visualizations. The professor was provided
source consists of thousands of entries with all the interactions with a 15 minutes training session on how to use the Narrative
learners had with the course over a three months period. After Builder and then asked to construct the two narratives using the
analyzing the data he wishes to construct two narratives. He starts Narrative Builder (shown on the left of figure 3): 1) A narrative
constructing data slices by dragging data fields onto the Narrative showing learners’ engagement score and how it was calculated; 2)
Builder interface.. He clicks on the "Visualize Data" button and A narrative presenting the time learners spent on activities, and
views the set of visualizations for each data slice and also views allowing learners to compare activity times with their peers.
the automatically generated exploration paths. Finally he Exploration paths were automatically generated, which showed a
disassociates the narrative with the previous log data and connects breakdown of selected students' engagement score (drill down).
it to new data source (this consists of test entries as the course is The other exploration path showed engagement scores of similar
yet to commence) and publishes the narratives. students (related data). Once both narratives were completed
(which took 25 minutes with some assistance), the professor was
5.2 Use Case Two – Final Year Student asked to interact with the visual narratives, which were
Michelle is a final year Computer Science student and has automatically generated and analyze the data through exploration
received an average grade of below 50% each year during the first paths. During the analysis, he was asked to answer questions by
three years of her course. However, she is determined to improve exploring and interacting with the visual narratives, which he did
her grade in her final year. As part of one of her modules she with ease and answered all the questions.
needs to study SQL using the AMAS portal. During the first His final task was to critique the visualizations and the process of
month of the three month module, Michelle has occasionally used constructing the narrative through a questionnaire and interview.
the portal. At the end of this month she receives a notification The questionnaire focused on how useful the professor found the
from the portal informing her of her poor engagement with course process of constructing narratives and analyzing exploration
activities and advises her that in previous years the students who paths. For example, one of the questions asked: "When viewing
continued to engage at this level performed poorly. course engagement by activity, how useful was it to view students
Following on from this notification, Michelle wants to understand with similar engagement through an exploration path”. The
how she can improve her engagement and estimate how much questions also addressed how well the framework and
time she must commit to this module to perform well. She views visualizations met his needs, such as “Did the framework support
her personalized visual narratives and analyzes her engagement you in telling the story you wanted to tell” and “Where you ever
score and how it was calculated. She analyzes peer engagement frustrated with the limitations of the user interface”, to which he
comparisons using her visual narratives which allow her to offered useful suggestions such as providing tooltips and help
determine how to improve engagement. By analyzing peer options. From the feedback the professor found exploration paths
comparisons and exploring her visual narratives, Michelle is able very useful for gaining insight and was able to tell the story
Figure 3. Narrative Builder Interface (left) and two sample visualizations from a Personalized Student Visual Narrative (Right)
requested. He expressed that the data slices and resulting narratives), these learners frequently returned to view their
visualizations represented his needs quite well. In the interview, personalized visual narratives. Hence, it can be concluded that the
the professor expressed that he was able to follow and interact personalized visual narratives assisted these learners in gaining a
with the visualizations easily and expressed confidence in deeper knowledge of their performance data.
constructing data slices and building the narratives. Examining
The second study analyzed if there was a correlation between
the time taken to learn and construct the narratives, it was evident
weaker students interacting with their visual narratives and an
that the professor had a very positive experience constructing
improvement in engagement. The log data of the 17 weaker
narratives using the Narrative Builder.
students, who showed engagement improvement following a
below average engagement notification, was analyzed. It was
6.2 Evaluating Personalized Visual Narratives found that all of these learners showed a minimum of a 70%
One of the primary aims of AMAS is to support weaker students increase in interactions with their visual narratives during the
completing their course. The second evaluation focused on period in which their engagement improved. From this, it was
analyzing the impact the personalized visual narratives had on concluded that weaker students who increased in interactions with
supporting weaker learners to improve course engagement. The their personalized visual narratives showed an improvement in
right hand side of Figure 3 shows two visualizations from one of their course engagement level.
the narratives presented to learners. 108 students participated in
the AMAS SQL course; 22 of these were identified as weak
students as they had an average grade of below 50% for each of
7. CONCLUSIONS AND FUTURE WORK
This paper introduced VisEN as a framework to construct visual
the previous three years of their course.
narratives and facilitate personalized visual explorations by
During the course, AMAS sent fortnightly notifications to learners allowing end users to: 1) explore related data; 2) analyze visual
informing them of their engagement levels. The first study narratives; and 3) analyze personalized exploration paths.
analyzed the AMAS log data, (consisting of thousands of entries Two evaluations were carried out; the first evaluation involved a
for three months of interactions from 108 learners), and found that university professor analyzing the log data of his students' course
all of the weaker students had at some stage received a below activities and constructing visual narratives. The results of this
average engagement notification. The analysis of the log data of evaluation were positive, with the professor confidently creating
the 22 weaker students found that 17 of these students showed an data slices and narratives and positively commenting on his
improvement in engagement following this notification. It was experience of executing the tasks required. The second evaluation
found that 14 of these 17 learners were immediately drawn to involved analyzing the log data of weaker students who
their personalized visual narrative following a below average participated in an online SQL course. This evaluation found that
engagement notification. All of these 14 learners executed a the personalized visual narratives assisted these learners in
minimum of 45% of their total narrative interactions on the first understanding and improving their engagement and performance
day after reading the notification. Following this notification data.
(which did not explicitly direct them to their personalized visual
Preliminary results have been obtained from both evaluations. [12] Graham M. and Kennedy J. 2010. A survey of multiple tree
Further work is required to evaluate the Narrative Builder through visualization. Information Visualization 9, 4 (Dec. 2010),
qualitative and quantitative analysis using several users. In the 235–252. DOI= 10.1057/ivs.2009.29
2014 - 2015 academic year, it is intended to continue to provide [13] Grammel, L., Bennett, C., Tory, M., & Storey, M. A. 2013.
learners with personalized visual narratives and compare A Survey of Visualization Construction User Interfaces. In
engagement results with control groups, and quantify the increase EuroVis-Short Papers (pp. 19-23). The Eurographics
in engagement levels, and verify the statistical significance. Association.
[14] Hullman J1, Drucker S, Henry Riche N, Lee B, Fisher D,
8. ACKNOWLEDGMENTS Adar E. 2013. A deeper understanding of sequence in
This research is supported by the Science Foundation Ireland
narrative visualization. IEEE Trans Vis Comput Graph. 19,
(Grant 12/CE/I2267) as part of CNGL (www.cngl.ie) at Trinity
12 (Dec 2013), 2406-15. DOI= 10.1109/TVCG.2013.119.
College Dublin.
[15] Hullman, J., Diakopoulos, N. and Adar, E. 2013.
Contextifier: Automatic generation of annotated stock
9. REFERENCES visualizations. In Proceedings of the SIGCHI Conference on
[1] Austin, M. Evolution, Anxiety, and the Origins of Literature. Human Factors in Computing Systems (Paris, France April
University of Nebraska Press, 2011. 27-May 2). ACM, New York, NY 2707-2716. DOI=
[2] Brusilovsky, P., Ahn, J. W., Dumitriu, T. and Yudelson, M. 10.1145/2470654.2481374
2006. Adaptive knowledge-based visualization for accessing [16] Kosara R. and Mackinlay J. 2013. Storytelling: The next step
educational examples. In Proceedings of Tenth International for visualization. Computer 46, 5 (2013), 44–50.
Conference on Information Visualization (London, UK 5-7
July 2006). IEEE pp. 142-150. DOI= 10.1109/IV.2006.16 [17] Kuh, G. D. 2007. How to Help Students Achieve. Chronicle
of Higher Education. 53 (41), pp. B12–13.
[3] Brusilovsky, P. and Loboda, T. D. 2006. WADEIn II: A case
for adaptive explanatory visualization. ACM SIGCSE [18] Lee, B., Kazi, R. H., and Smith, G. 2013. SketchStory:
Bulletin. 38, 3, 48-52. ACM, New York, NY. Telling more engaging stories with data through freeform
sketching. Visualization and Computer Graphics, IEEE
[4] Chi, E. H.2000. A taxonomy of visualization techniques Transactions on, 19,12 (2013) 2416-2425.
using the data state reference model. In Proceedings of the
IEEE Symposium on Information Visualization (Salt Lake [19] Liu, S., Wu, Y., Wei, E., Liu, M., & Liu, Y. (2013).
City, Utah, USA, October 9-10 2000), IEEE, pp. 69–75. Storyflow: Tracking the evolution of stories. Visualization
DOI= 10.1109/INFVIS.2000.885092 and Computer Graphics, IEEE Transactions on, 19(12),
2436-2445. DOI=10.1109/TVCG.2013.196
[5] Carr, D. B., Littlefield, R. J., Nicholson, W. L., and
Littlefield, J. S. 1987. Scatterplot matrix techniques for large [20] O’Keeffe I. et al. 2012. Personalized activity based
N. Journal of the American Statistical Association, 82, 398 eLearning. In Proceedings of the 12th International
(1987), 424-436. Conference on Knowledge Management and Knowledge
Technologies (Graz, Austria, September 5-7, 2012), i-
[6] Card S. K., Mackinlay J. D., Shneiderman B. 1999. Readings KNOW ’12, ACM,New York, NY, Article 2.
in Information Visualization: Using Vision to Think. Ed.
Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, [21] Pierson, W. C., and Rodger, S. H. 1998. Web-based
1999. animation of data structures using JAWAA. In ACM
SIGCSE Bulletin, 30, 1, (1998). 267-271. DOI=
[7] Dias M. M. Yamaguchi J. K. Rabelo E. and Franco C. 2012. 10.1145/274790.274310
Visualization techniques: Which is the most appropriate in
the process of knowledge discovery in data base? InTech [22] Santos J., Govaerts S., Verbert K., and Duval E. 2012. Goal-
(September 2012). DOI=10.5772/50163 oriented visualizations of activity tracking: A case study with
engineering students. In Proceedings of the International
[8] Esche A. 2013. Ged viz Retrieved, November 10, 2013 from Conference on Learning Analytics and Knowledge, ACM,
http://viz.ged- project.de/ May 2012, pp. 143-152, DOI=10.1145/2330601.2330639.
[9] Gao, T., Hullman, J., Adar, E., Hecht, B., and Diakopoulos, [23] Rosling H. 2013. Gap minder viz Retrieved, March 1, 2014
N. 2014. NewsViews: An Automated Pipeline for Creating from http://gapminder.org.
Custom Geovisualizations for News. In Proceedings of the
Conference on Human Factors in Computing Systems [24] Segel, E., & Heer, J. 2010. Narrative visualization: Telling
(Toronto, Canada April 26–May 1, 2014). (CHI) ACM, New stories with data. Visualization and Computer Graphics,
York, 3005-3014. DOI= 10.1145/2556288.2557228 IEEE Transactions on, 16,6, (2010) 1139-1148.
[10] Gonzalez H. et al. 2010. Google fusion tables: Data [25] Shneiderman B. 1996. The eyes have it: A task by data type
management, integration and collaboration in the cloud. In taxonomy for information visualizations. In Proceedings of
Proceedings of the 1st ACM symposium on Cloud computing the IEEE Symposium on Visual Languages (1996), IEEE,
(Indiana, USA, June 6–11, 2010). ACM, New York 175-180. Washington, DC, USA, 336–343.
DOI= 10.1145/1807128.1807158 [26] Tableau software. 2013, Retrieved January 15, 2014 from
[11] Govaerts S., Verbert K., and Duval E. 2011. Evaluating the http://tableausoftware.com
student activity meter: Two case studies. In Proceedings of [27] Tibco spotfire.2013. Retrieved, October 5, 2013 from
the International conference on Advances in Web-Based http://spotfire.tibco.com
Learning. Springer, Dec. 2011, pp. 188-197.