Data Visualization in Online Educational Research

Data Visualization in Online Educational Research

Xue Wen, Xuan Wang
Copyright: © 2020 |Pages: 26
DOI: 10.4018/978-1-7998-1173-2.ch012
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This chapter presents a general and practical guideline that is intended to introduce the traditional visualization methods (word clouds), and the advanced visualization methods including interactive visualization (heatmap matrix) and dynamic visualization (dashboard), which can be applied in quantitative, qualitative, and mixed-methods research. This chapter also presents the potentials of each visualization method for assisting researchers in choosing the most appropriate one in the web-based research study. Graduate students, educational researchers, and practitioners can contribute to take strengths from each visual analytical method to enhance the reach of significant research findings into the public sphere. By leveraging the novel visualization techniques used in the web-based research study, while staying true to the analytical methods of research design, graduate students, educational researchers, and practitioners will gain a broader understanding of big data and analytics for data use and representation in the field of education.
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Advances in technology have created numerous opportunities for research in online education. With the vast needs of resources for a diversity of learners, various online learning platforms have developed free online courses for online learners to acquire new skills, advance their careers, and deliver quality educational experiences at scale (Chen, Chen, Liu, Shi, Wu, & Qu, 2016). Massive open online courses (MOOCs), a popular online learning platform in recent years, have emerged and attracted a remarkable amount of public attention (Hollands & Kazi, 2019). The term MOOCs—massive open online courses, was first used in the educational community in 2008 by Stephen Downes and George Siemens (MAUT- McGill University, 2018). Educators intended to explore the possibility for interactions between a wide variety of participants made possible by online tools that provide a more productive learning environment than traditional tools would allow (Chen et al., 2016). Learners from all over the world can enroll in more than 1,000 courses, and the number of registrants has reached 10 million (Rollins, 2018). Because of its volume and complexity, MOOCs often generate large, heterogeneous datasets comprising clickstream data, contributions to discussion forums, and various performance metrics (Vieira, Parsons, & Byrd, 2018). Thus, to study MOOCs or related online learning platforms, educational researchers would be required to master visual learning analytics, educational data mining, and visual analytics to capture rich data about students and their online learning behaviors.

From this perspective, Vieira et al. (2018) define a new term called visual learning analytics, which can be defined as an integration of learning analytics, educational data mining, and visual analytics, to illustrate how designers and researchers can employ data visualization approaches for analyzing educational data. To help readers systematically review the field of data visualization in online educational research, the authors present a brief agenda for the field of visual learning analytics in an educational context in this chapter. Also, based on the unique characteristics of MOOCs’ environment, this chapter focuses on illustrating three data visualization techniques that help readers understand which types of educational data can be customized and how to visualize the educational data in MOOC scenarios. Visual learning analytics is a discipline that shows significant promise in helping users gain insight into data visualizations (Vieira et al., 2018). This term integrates data analysis, visual representations, and user interactions to leverage the strengths of technology and humans. In the context of the web-based environment, visual learning analytics use computational tools and methods for understanding educational phenomena, such as students’ learning paths, the effectiveness of learning materials, and different approaches that students use for a given task through visualization discourse.

Key Terms in this Chapter

Word Clouds: A visualization of text in which the more frequently used words are adequately emphasized by occupying more prominence in the representation.

Tableau: A powerful and fast-growing data visualization tool that is used in the industry of business intelligence. This tool helps to simplify raw data into a straightforward, understandable format.

Python Jupyter Notebook: A data analysis program that produces a standard correlational heatmap visual for demonstrating the potential relationship among the factors by applying Python Programming.

Wordle: A web-based platform that demonstrates a fast and rich visual to enable researchers to obtain a basic understanding of the data (textual information). This platform produces word-cloud analyses of the spoken and written responses of informants in research projects.

Correlational Heatmap: A visual data visualization tool that demonstrates correlational statistics by applying color-coding to represent the relationship between data values. It is advantageous to explore the two-dimensional data.

Big Data: A large volume of data that may be analyzed computationally and systematically to reveal characteristics, patterns, trends, and associations in various fields.

Dashboard: A web-based program that apprehends and visualizes traces of learning activities, to facilitate awareness, reflection, and sense-making, and to enable learners to identify goals and track progress towards these goals.

Visualization: A data analysis technique that emphasizes the external representation of abstract or concrete ideas to help people understand the meaning of the expressed information, such as images, diagrams, and animations.

MOOCs: Also knowns as Massive open online courses, MOOC was first used in 2008 in the educational community by Stephen Downes and George Siemens.

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