2.1. Researches about Teaching and Learning in Higher Education
A past or present, a critical issue to higher education lies in teaching and learning. Accordingly, different agendas from a wide variety of perspectives in higher education are progressively evolving. However, we need to revisit the basic principals and focus on the essential subjects of teaching and learning (Biggs, 2001; Churchill, 1982; Jarvenoja, Lalley, Houston, & Gasteen, 2018; Naykki, & Tormanen, 2019; Park, 2009; Pitan & Muller, 2019; Price & Kirkwood, 2014; Shen & Ho 2020; Yilmaz & Keser, 2016; Wood, Galloway, Sinclair, & Hardy, 2018; Zainuddin & Halili, 2016). Traditionally, education has focused on finding the diverse matters surrounding the roles of students and teachers, respectively, to examine the learning effects and the factors that influence it, that is, to find cause and effect (causality) in teaching and learning (Elton, 1977; Elton & Laurillard, 1979; Entwistle, 1973). This has progressed into an in-depth discussion of how psychological factors, such as students' perceptions, feelings, or relationships with teachers, influence learning outcomes (Jarvenoja, Naykki, & Tormanen, 2019; Martin, Wang, & Sadaf, 2018; Xing, Tang, & Pei, 2019). What is more, many studies examined how various demographical factors such as gender, race, and income level of families affect students' learning or its outcomes (Chevalier, 2011; Lalley, Houston, & Gasteen, 2018; Kelly, O'Connell, & Smyth, 2010; Strauss & De La Maisonneuve, 2009). In addition to this approach, attention was paid to the teachers' point of view to find out more effective ways for teaching and what new teaching methods were being developed and used and how those methods worked. Flipped learning, blended learning, online learning, or interactive learning using various technology tools or simulation game: these are the most recently adopted teaching methods (Foster, 2019; Heilstra, & Siguroardottir, 2017; Hilliard, & Stewart, 2019; Iniguez, 2015; Lomer & Anthony-Okeke, 2019; Wood, Galloway, Sinclair, & Hardy, 2018).
Other endeavors have been made to find general trends in teaching and learning with a holistic perspective (Deng, Benckendorff, & Gannaway, 2019; Elton & Laurillard, 1979; Foster, 2019; Guri-Rozenblit, 1991; Henderson, Selwyn & Aston, 2015; Kim, 2015; Maxwell, 2019; Nikitina & Lapina, 2017; Nomuoja, 2010). Deng, Benckendorff, & Gannaway (2019) focused on identifying trends and categorizing the study on Massive Open Online Courses in teaching and learning. Elton & Laurillard (1979) sought to find research trends in students'' learning and discover new research paradigms. They analyzed the trends to uncover the determinants of how humans learn, the differences among individuals in human learning, how content elements affect learning, and how contextual factors affect learning. Guri-Rozenblit (1991) reviewed and analyzed four books that can use to examine trends in learning. Based on this, he defined the definitions of distance education and open education. He covered a wide range of free public/distance systems, course design, advanced technology, and delivery systems, student support and survival issues, and lastly, inter-university and inter-institutional collaboration issues. Henderson, Selwyn & Aston (2015) studied students'' perceptions of useful digital technologies in teaching and learning in the university, which has an online education or interactive education through an online system. It is attracting attention as research that captures the transforming the nature of university education. Nikitina & Lapina (2017) proposed that recent business education trends were organized into three categories: a curriculum that meets the desire of society and business, partnership & networking, and a modern and flexible teaching method in their research. Besides, new forms of teaching and learning, including blended learning, interactive learning, and flipped learning, have addressed by many scholars (Heilstra, & Siguroardottir, 2017; Hilliard, & Stewart, 2019; Lomer & Anthony-Okeke, 2019; Wood, Galloway, Sinclair, & Hardy, 2018). Besides that, a large number of studies have mainly concentrated on the numerous factors or trends affecting educational development and management (Foster, 2019; Kim, 2015; Maxwell, 2019; Nikitina & Lapina, 2017; Nomuoja, 2010). For instance, Nomuoja (2010) studied the current trends emerging in business schools of higher education. Consequently, career awareness, risk management, people-oriented strategy and its management, and skills-based curriculum were mainly discussed. Moreover, there are interviews results from global top MBA schools to discover major MBA trends such as 'growing trend of double degrees', 'growth acceleration of online or technology-based education and blended learning in business education (Iniguez, 2015; Foster, 2019; Maxwell, 2019). Accordingly, a huge amount of research work has been done with broad and varied perspectives on teaching and learning. However, most of them were independent studies, which are investigated based on a specific situation or context rather than grasping the educational flow or trends. Moreover, there is still a lack of study that looks at the global direction of such research more objectively and quantitatively using big data. Thus, this study began to fill in the gap of these existing studies.
2.2. Semantic Network Analysis using big data of the unstructured text
We live in an era where all aspects of our lives are uncertain and rapidly changing (Levine, 2019; Kim & SNU Consumer Trend Analysis Center, 2019; Park, 2019). The best way to cope with this uncertain and unknown future is to predict and prepare for the future based on a variety of historical big data by reducing this prediction error. In this regard, people focus on using big data to read trends and prepare for the unknown future. This substantial phenomenon is well represented in diverse and separate research fields as well. Many scholars in a very different area are working actively to discover insights into big data using various data mining techniques (Algarni, 2016; Doerfel, 1998; Kharlamov, Gradoselskaya, & Dokuka, 2018; Miner, Elder, Fast, Hill, & Nisbet, 2012; Shneiderman & Aris, 2006; Srivastava & Srivastava, 2014; Steyvers & Tenenbaum, 2005; Park & Alenezi, 2018; Park, 2019; Yoon & Park, 2007; Yun & Park, 2018). Due to the breakthrough technology, we can deal with big data or data sets, which are too complicated or broad to be dealt with by traditional data-processing approaches. In particular, it became possible to analyze a large amount of unstructured text data through text mining, one of the data mining techniques, as linguistic techniques have developed and applied to diverse areas (Bose, 2009; Miner et al., 2012; Wright, 2018).
A morphological or semantic network analysis deals with dividing a sentence into the smallest meaningful unit of language, namely, morphemes by importing unstructured text data such as speeches, comments, or posting in social media like Twitter, Instagram, or any bibliographic information (for example, books, scholarly articles, records, interviews, etc.) (Doerfel, 1998; Drieger, 2013; Kharlamov, Gradoselskaya, & Dokuka, 2018; Nulty, 2017, Park, 2019; Yun & Park, 2018). It automatically extracts words in sentences, paragraphs, and documents to make it simple to construct a word-to-word network according to the degree of nearness or adjacency between words (Atteveldt, 2008; Kharlamov, Gradoselskaya, & Dokuka, 2018). Based on that, network structures provide intuitive and beneficial illustrations for modeling semantic inference and knowledge (Steyvers & Tenenbaum, 2005). Through this, we can comprehend the relationship among words or understand their association by combining topics through proper interpretations in a given text (Rice & Danowski, 1991; Cyram NetMiner, 2019; Steyvers & Tenenbaum, 2005). The more commended, the larger the size of the morpheme or word. Then, it can be seen at first sight, as it were, to visually stress major issues or agendas such as keywords in unstructured documents to extract critical attributes, mainly in big data that manages a large amount of information (Lambert, 2017). Nodes in a semantic network mean words, and links are word-to-word adjacency relationships (Nulty, 2017). Until recently, network analysis required data structured by nodes and ties ahead of time, and the subsequent processes were performed by individual programs, which required plenty of human efforts and time. However, for now, with the development of state-of-the-art technology, natural language processing is built into data mining programs, which can directly enter unstructured text data and extract words (nodes) in morphological units and create network data encompassing words. This broadens the horizon of network analysis with massive unstructured text data (Cyram NetMiner, 2019; Kim, Choi, & Youm, 2017). Accordingly, a large number of scholars has ripened into a semantic network analysis as a powerful tool of text mining in numerous ways since Rice & Danowski (1991) built a basic framework for network analysis (Doerfel, 1998; Monge & Eisenberg, 1987; Rice & Danowski, 1991; Stohl, 1993).
The purpose of analyzing text using text mining is very diverse. It is possible to comprehend between the lines in which the document intends to deliver by reassembling the text. Also, by visually grasping the relationship between the main concepts and other keywords in the text, it is easy to understand various types of networks. Through this, it is achievable to analyze the roles of words and their relationships by recognizing the word associations. One of the most significant advantages of text mining is to analyze the terms both qualitatively and quantitatively (Bose, 2009; Miner et al., 2012; Mishra et al., 2017).
Additionally, it uses to visualize or illustrate the relationship between objects or people in text and topic modeling as well (Kharlamov, Gradoselskaya, & Dokuka, 2018; Nulty, 2017). For this, a large amount of information can efficiently and effectively utilize to generate more comprehensive and extended knowledge, analytical reasoning, and even explorative analysis, which is the final goal of text analysis (Bose, 2009; Cyram NetMiner, 2019; Drieger, 2013; Miner et al., 2012; Mishra et al., 2017; Venkatesh, Balasubramanian, & Kaliappan, 2019). With those benefits of this approach, many scholars have discussed various topics with different perspectives using big data. Many scholars and observers have found huge opportunities and tremendous potentials of semantic network analysis with recognizing centrality indicators between words and sub-network structures of words (Lee, Choi, & Kim, 2010; Rice, 2005; Wasserman & Faust, 1994). Many of those studies exhibit the possibility of the ongoing development of the semantic networks as a powerful research tool emerging with the advent of the big data era. In particular, semantic network analysis is used in research to study teaching and learning in higher education. Shen & Ho (2020) showed the importance of technology-enhanced learning (TEL) through a semantic approach as an inspired way to improve the outcomes of teaching and learning in high education. Kim (2015) determined the study trends of music education using the semantic network analysis, and Lee (2016) analyzed the research trends in the area of journalism, pursuing the key words of the abstract of research articles published in 2005-2015 through semantic network analysis, then, finally established knowledge system as a result. Besides that, Kim, Choi, & Youm (2017) applied semantic network analysis to draw a significant agenda of the opinions on nursing care service by extracting data from online news and social media data. Recently, Park (2019) took the data of news media and social media to compare the trends from the two different kinds of big data sources to predict the sustainability of leading Korean companies.
Based on those previous studies, this research aims to investigate the most recent research issues and latest trends of teaching and learning in higher education through semantic network analysis. Using a large amount of unstructured text data, we can effectively and efficiently discover trendy insights and directions of future education in teaching and learning and in research (Doerfel, 1998; Shneiderman & Aris, 2006; Srivastava & Srivastava, 2014; Steyvers & Tenenbaum, 2005). Accordingly, it expects to generate subsequent development of knowledge and intuition to comprehend a new paradigm of future education in general, which is just around the corner. It would be very constructive and beneficial to educators, researchers, and even academic leaders and administrative leaders in higher education.
2.3. Proposed research framework
To pinpoint major agendas and trends in teaching and learning of higher education, semantic network analysis, which is a data mining technique, was used in this study. Accordingly, there is no theoretical framework with hypotheses in this study as the data-driven approach is used in this paper. This data-driven methodology became an extraordinarily capable and promising area. A massive amount of information reserved in electronic and digital records on the internet brings tremendous opportunities and impacts remarkably for knowledge discovery, information extraction, and analytical reasoning in education fields (Doerfel, 1998; Monge & Eisenberg, 1987; Wright, 2018). Thus, this empowers one to extract important algorithmic properties that give powerful intuitions and insights into the structure of networks and graphs (Miner et al., 2012; Srivastava & Srivastava, 2014; Steyvers & Tenenbaum, 2005; Zaki & Meira, 2014). As previous literature shows, a researcher can collect big data from various sources and platforms. For example, news channels (such as BBC, CNN, ABC, etc.), social media (such as Facebook, Twitter, Instagram, YouTube, etc.), web or internet search engines (such as Google, Bing, Yahoo, AOL, Journal databases or publishers’ databases, etc.), other financial reports (such as financial statements, press releases, conference calls regarding earnings and related information, etc.), and so on. (Baker, 2010; Bose, 2009; Mishra et al., 2017; Srivastava & Srivastava, 2014; Park, 2019). In this study, the data gathered for analysis from several distinguishable publishers (Sage Publications, Taylor & Francis, and Elsevier BV)’ web platforms through search engines. Figure 1 shows the proposed framework of this study with a holistic approach.
This study attempts to determine the most recent research agendas or trends of the leading higher education journals about teaching and learning in 2018 and 2019 through semantic network analysis. As the global trend is changing very fast, this study emphasizes teaching and learning in the last two years. For this purpose, this study establishes the following research questions.
(1) What are the main trends or agendas of teaching and learning in higher education in the last two years?
(2) What are the critical attributes of teaching and learning in higher education, and what are the implications of this?
(3) How are the specific sub-domains (topic modeling) of teaching and learning in higher education categorized as future education strategies?