Creating Effective Data Visualizations for Online Learning

From E-Learning Faculty Modules


Contents

Module Summary

Data visualizations are a common part of online learning. Some are static, and others are dynamic and interactive. Data visualizations help illustrate concepts, show data relationships, and emphasize important points. This module describes data visualizations and proposes some ways to make these aspects more effective in an online learning context.

Takeaways

Learners will...

  • review common types of data visualizations used in online learning and consider their important roles
  • consider how data visualizations are created through both manual and computational means
  • review some common challenges to viewing and interpreting data visualizations; consider basic ways to improve data visualizations
  • explore how to create a sense of organization (structure) in data visualizations through sequencing and contextualization
  • review how to build interactive data visualizations and consider common types of interactivity in interactive data visualizations

Module Pretest

1. What are common types of data visualizations used in online learning? What are some roles of data visualizations in online learning?

2. How are data visualizations created? What are some basic drawing methods? What are some computer-created data visualizations?

3. What are some common challenges to viewing and interpreting data visualizations? What are some basic ways to improve data visualizations, so these are more easily viewed and analyzed?

4. What are some methods to creating a sense of organization in data visualizations through sequencing and contextualization?

5. What are interactive types of data visualizations, and how are these generally created? What are common types of interactivity in interactive data visualizations?

Main Contents

The main learning contents are hosted in this area.

1. What are common types of data visualizations used in online learning? What are some roles of data visualizations in online learning?

What are common data visualization types in online learning? In a general sense, there are the following: pie charts, bar charts, line graphs, scattergraphs, timelines, and maps.

Depending on the domain field, there may be more unique data visualization types. More complex maps are used in geography, political science, sociology, epidemiology, and others. Network graphs are often used in sociological and humanities studies. Theorized models are fairly common in education. Survival analyses or time-to-event analyses visualizations are common in the health sciences. A wide range of data visualizations are par for the course in statistics.

At core, data visualizations are created to convey information. The information may be sourced from various contexts: from conceptualizations, from theory, from empirical data, and others. (The source for the underlying information should always be specified in the data visualization, or in its near-surrounds / context.) The information may show various aspects of data: frequency counts, associations, complex interrelationships, time relationships, physical proximity, other data features, and other combinations of data features.

Essentially, data visualizations should convey information, not be decorative or just break up text or draw reader attention.


2. How are data visualizations created? What are some basic drawing methods? What are some computer-created data visualizations?

Data visualizations are created in two general ways.

  • One way is manual drawing, such as using a diagramming tool to express data relationships and information. This first approach is most common with conceptual data, for models, theories, frameworks, and so on. This first approach is highly freeform and creative, within the limits of the 2D space.
  • Another common way is to use computer software programs to output data visualizations. This latter approach is most common with empirical data, whether structured, semi-structured, or unstructured. The different data visualizations have different requirements for the visualizations to come out correctly.


3. What are some common challenges to viewing and interpreting data visualizations? What are some basic ways to improve data visualizations, so these are more easily viewed and analyzed?

By nature, data visualizations look benign and intuitive. They seem easy to understand and interpret. They seem like they would be easy to interpret and not particularly time-consuming. These light assumptions of data visualizations can be risky.

Some common challenges to viewing and interpreting data visualizations include the following:

  • Users of the data visualizations may spend little time viewing the data visualization and not make the effort to understand the issue with more fact-based depth
  • Users of the data visualizations may lack the background to fully understand the information
  • Users of the data visualizations may understand one data visualization and that facet of the issue but not understand how to contextualize that data in a larger view (This is a version of a cognitive limitation of what Dr. Daniel Kahneman calls "WYSIATI" or "what you see is all there is".)
  • Users of the data visualizations may not know how to analyze the information or to understand what is relevant
  • Users of the data visualizations may not be familiar with that data visualization type and not know how to read that data (for example, a viewer may not know how to process a dendrogram—or whether to start with the trunk or the leaves…; for another example, a viewer may have viewed linegraphs in the past but does not understand how to interpret a linegraph derived from a “survival analysis” or a “time-to-event analysis”)
  • Users of the data visualizations may be dazzled by the aesthetics or some other non-informational aspect of the data and miss more critical points

To truly understand a data visualization, it is important to understand how the research was conducted, where the data came from, how much confidence people may put into the data, how the data visualization was created, and reasonable takeaways from the data visualizations.

To that end, it is important to slow down learners’ consumption of data visualizations, so they can actually learn the information correctly and in-depth.

What are some basic ways to improve data visualizations, for lower cognitive load?

  • Create data visualizations with the proper data labels, axis labels, titles, and contextual information.
  • Ensure access to the underlying dataset, if necessary.
  • If information is complex, use multiple data visualizations to convey the information and to provide a larger sense of the full data.
  • Avoid ornateness, and ensure that every element of a data visualization adds informational value.
  • Include directions on how to view and interpret a data visualization. (Use cognitive scaffolding to strengthen the usability of data visualizations.)
  • Test data visualizations with target users, and redesign data visualizations if they are leading to misunderstandings.


4. What are some methods to creating a sense of organization in data visualizations through sequencing and contextualization?

This section summarizes two basic ways to create a sense of order within and around data visualizations. Before data is ready to be visualized, it has to be cleaned and processed. Outliers may have to be omitted. Missing data may have to be addressed. Once that is all done, though, it is important to ensure that other organizational levers be applied to the data before the actual visualization.

Some common organizational mechanisms are as follows:

  • alphabetization (letter order)
  • numerical order (positioning in a list, rank, others)
  • simple to complex, complex to simple (complexity)
  • smallest to largest, largest to smallest (size)
  • chronological date order, reverse chronological data order (date)
  • top to bottom, bottom to top, left to right, right to left, outside in, inside out (spatial)
  • most to least, least to most (amount)
  • categorization (type) (Hai-Jew, 2017)

In terms of contextual approaches, data visualizations would benefit from the following: the conducted research, research methodology, researcher names, dates of the research, limits to the data, how to read or interpret the data visualization(s), and so on. The contextual data should be designed to the target audience receiving the data visualization.


5. What are interactive types of data visualizations, and how are these generally created? What are common types of interactivity in interactive data visualizations?

Online, there are various types of interactive data visualizations. Generally, such data visualizations simulate some in-world phenomenon (whether theorized or actual)…or they show some data relationships over time…or they provide some systems dynamics. Interactive data visualizations enable users to input particular parameters or change up some aspect of the visualization for a different visualization and sometimes, outcomes.

Technically, some of the older interactive data visualizations built on Shockwave Flash (.swf). For agent-based modeling, NetLogo is a free tool that shows system effects over time based on simple modeling rules. Many modern interactive data visualizations are created in various authoring tools and exported in HTML5. Some interactive data visualizations are created using light scripting and apps in R and in Python, and these are often deployed with the underlying datasets also available (to enable the visualizations). Some simulations are made in the Wolfram Language (such as the Wolfram Demonstrations Project at http://demonstrations.wolfram.com/).

One note: In the same way that static data visualizations show only a facet of complex underlying data, interactive data visualizations present simulations that only show particular dynamics of complex systems.

Examples

Please refer to the slideshow link below to see dozens of common data visualizations from actual real-world data. The slideshow is “Creating Effective Data Visualizations for Online Learning” and is hosted on SlideShare.

How To

The specific “how to” directions depend on a number of factors: the research context, the learning context, the data, the software, the learners, and others.

If there is a simple “how to,” it would be to test the data visualizations with the target recipients to fully understand how they engage with and interpret the data visualizations. If learners have to be fully “read into” a situation in order to understand the data visualizations, then the instructor has the responsibility to make sure that the learners’ learning needs are met. In general, data visualizations should be as easy-to-understand as possible (without extraneous cognitive load) and should be designed to work against misinterpretation.

Possible Pitfalls

Data visualizations are not right for every circumstance, of course. They are designed for particular information-sharing contexts, and they are designed for audiences with some level of data sophistication. Data visualizations should be designed to lessen the cognitive load for understanding the contents, on the one hand, and consumers of data visualizations need to actually engage with the data visualizations to understand them fully.

Please review the slideshow in the References section to review the legal considerations for the creation of data visualizations. Those requirements were not directly addressed here.

Module Post-Test

1. What are common types of data visualizations used in online learning? What are some roles of data visualizations in online learning?

2. How are data visualizations created? What are some basic drawing methods? What are some computer-created data visualizations?

3. What are some common challenges to viewing and interpreting data visualizations? What are some basic ways to improve data visualizations, so these are more easily viewed and analyzed?

4. What are some methods to creating a sense of organization in data visualizations through sequencing and contextualization?

5. What are interactive types of data visualizations, and how are these generally created? What are common types of interactivity in interactive data visualizations?

References

Hai-Jew, S. (2017, Apr. 1). Creating Effective Data Visualizations for Online Learning. SlideShare. https://www.slideshare.net/ShalinHaiJew/creating-effective-data-visualizations-for-online-learning.

Extra Resources