Social Media-Based Data Collection and Analysis in Educational Research

The past decades have brought many changes to education, including the role of social media in education. Social media data offer educational researchers first-hand insights into educational processes. This is different from most traditional and often obtrusive data collection methods (e.g., interviews and surveys). Many researchers have explored the role of social media in education, such as the value of social media in the classroom, the relationship between academic achievement and social media. However, the role of social media in educational research, including data collection and analysis from social media, has been examined to a far lesser degree. This study seeks to discuss the potential of social media for educational research. The purpose of this chapter is to illustrate the process of collecting and analyzing social media data through a pilot study of current math educational conditions.

Using Wikis in Educational Research

A wiki is a user-created website that can be accessed and modified by multiple users. As a Web 2.0 technology, wikis can be used to produce collaborative, co-created information and knowledge. The use of wikis can harness a group's collaborative and creative energy and allow the group to produce shared knowledge that benefits all members. Wiki also serves as a powerful tool for educational research. In this chapter, the author discusses the use of wikis in education and presents the results of a case study which explores factors that impact perceptions among faculty and students of the value of wiki technology in legal education, as well as the benefits of using wiki technology in educational research.

Data Visualization in Online Educational Research

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.

Web-Based Data Collection for Educational Research

Web-based surveys and web-based interviews are useful techniques to collect data through the web in educational research. In addition, web activities such as blogging, searching, and web mining have become quite convenient to collect and extract data from the web for research purposes. The purposes of this chapter are to describe and discuss techniques and tools for collecting and extracting data from the web for educational research purposes. First, a survey and a web-based or online survey are described and explained with examples. Second, web-based or online interviews, which are often similar to the face-to-face interview protocols are discussed and exemplified. After presenting the synchronous and asynchronous online interview tools, the selection criteria of the online interviewing tools are discussed. Lastly, this chapter describes and discusses web activities such as blogging, searching, and web mining to collect and extract data from the web.

Educational Data Mining

This chapter presents a comprehensive discussion of educational data mining and its potential for educational research. The origins of data mining and the emergence of educational data mining are discussed. The variety of data generated in education (e.g., text, speech, performance, etc.) are described and the challenges of mining these data for useful information are identified. Techniques for mining these data are discussed. Software used to mine these data are noted and issues of theory and ethics are considered. Examples from published literature are cited throughout the chapter and recommendations for educational researchers are offered.

Using Geographic Information Systems in Educational Research

The chapter introduces education researchers to geographic information systems (GIS) and the significant value of incorporating a geospatial perspective within research. The GIS approach to studying and presenting data incorporates geographic location and uses maps to visualize relationships for spatial and nonspatial variables, both of which enhance education research by visualizing local geographies. This chapter unfolds as a step-by-step guide that prepares researchers to identify the data needed for a GIS exercise, to collect or retrieve the data, clean and upload the data to ArcMap, georeferenced and symbolize the data, and interpret and present the results in a manuscript. After completing the exercise, researchers will have a basic understanding of ArcMap functionality and how integrating a geospatial perspective in educational research offers insights that may have otherwise been overlooked when using quantitative research methods alone.

Researching Mobile Learning

m-Learning (mobile learning) is an exciting research field but few studies address the potential effect of human-computer interactions on users' learning experience. A well-designed mobile learning study should consider the potential confounding variables of user context and usability. The purpose of this chapter is to inform readers of current research in m-learning and mobile HCI (human-computer interactions). The chapter will provide an example of a study that was designed to address the important factors of user context and usability.

Amplifying Participant Voices Through Text Mining

Text mining presents an efficient, scalable method to separate signals and noise in large-scale text data, and therefore to effectively analyze open-ended survey responses as well as the tremendous amount of text that students, faculty, and staff produce through their interactions online. Traditional qualitative methods are impractical when working with these data, and text mining methods are consonant with current literature on thematic analysis. This chapter provides a tutorial for researchers new to this method, including a lengthy discussion of preprocessing tasks and knowledge extraction from both supervised and unsupervised activities, potential data sources, and the range of software (both proprietary and open-source) available to them. Examples are provided throughout the paper of text mining at work in two studies involving data collected from college students. Limitations of this method and implications for future research and policy are discussed.

Applying Critical Theories to Social Media Mining and Analysis

To add to the limited higher education research that seeks to explore the riches of social media as a space for data collection, this chapter provides an example for how to use social media mining in combination with critical theories as an exploratory tool. This study is designed to apply critical theories to social media mining techniques in order to examine how membership organizations have engaged in discourse around racial issues and social inequities in higher education. This chapter will examine how associations engage particular social media contexts for the purpose of influencing educational research and praxis and provide future directions for using social media to expose social injustices.

Linguistic Analysis of Science Teachers' Narratives Using AntConc Software

This chapter presents the linguistic analysis of science teachers' narratives regarding their worldviews in the digital age and their views of technology. The analysis was performed using Laurence Anthony's software AntConc, which is suitable for analyzing large data corpora. The language behavior of the following groups of teachers was analyzed by exploring three distinctive linguistic markers: personal pronouns to study participants' foci of attention; emotion words, to measure the extent of their emotional immersion in the discourse; and semantic fields of specific word collocations. The results, based on the variations in the language behavior, indicated differences between the three groups of teachers' worldviews. In addition, the examination of the degree of descriptive elaboration, expressed through the use of sense, motion, and exclusion words, revealed similar levels of truthfulness in all three groups. The linguistic analysis, enhanced by various computational linguistic technologies available through the AntConc software, made it possible to identify implicitly conveyed thoughts and feelings, thereby affording a better understanding of complex education-related processes and phenomena.