Identifying emerging research fields: a longitudinal latent semantic keyword analysis

Revisiting five decades of educational technology research: A content and authorship analysis of the British Journal of Educational Technology

“…However, this was refuted by Courtial (), who pointed out that “words are not used as linguistic items to mean something, but as indicators of links between texts” and that co‐word analysis “computes network patterns and is consequently able to show that, at a certain time period, [a word] appears at a centre or strategic place within the co‐word network” (p. 98). Therefore, a combination of computer‐assisted analysis and manual content analysis was undertaken (Weismayer & Pezenka, ), providing qualitative examples of key emerging research topics, shown through “concept paths,” in order to illustrate the representativeness of the concept map terms and topics in each map.…”

“…This study also seeks to gain an overview of how research has developed across the past five decades through a content analysis, investigating how technology has influenced the shape of teaching and learning, as reported in BJET . There is a growing recognition of the importance of undertaking research into the development of research fields (Weismayer & Pezenka, ), including the field of educational technology (Bodily, Leary, & West, , this issue; Bond & Buntins, ; Rushby & Seabrook, ; Tamim, Bernard, Borokhovski, Abrami, & Schmid, ), and this contribution seeks to add to this body of work, by analysing BJET —recognised as one of the most influential journals in the field (Ritzhaupt, Sessums, & Johnson, )—across its 50‐year history. Whilst BJET has been the subject of previous authorship and content analyses, both as a single journal in focus (Hawkridge, ; Latchem, ; Mott, Ward, Miller, Price, & West, ) and in comparison to other educational technology journals (eg, Baydas, Kucuk, Yilmaz, Aydemir, & Goktas, ; Bodily et al , , this issue; Hsu et al , ; Hsu, Hung, & Ching, ), this contribution enables a deeper overview of key topics and issues published across 50 years, and offers a deeper understanding of its identity.…”

Self-Healing Asphalt: A Systematic Bibliometric Analysis for Identification of Hot Research Topics during the 2003–2018 Period

“…The author keywords highlight the main focus of the research presented in a scientific document. An analysis of these keywords can be employed as a quantitative content breakdown to identify the most important topics and trends in different research fields [ 61 , 62 ]. Since an analysis of the most frequently used author keywords provides valuable information, this work applied this approach.…”

From waifus to whales: The evolution of discourse in a mobile game-based competitive community of practice

“…LDA is a versatile method that has been used in past studies of textual corpuses, including more restrictive social media platforms such as Facebook and Twitter on which contributions are relatively short, offering sparse data with which to build a model (see Jelodar et al, 2019, for a detailed review). It has likewise been used to identify temporal shifts in longitudinal text corpora (e.g., Stavarache et al, 2015;Weismayer & Pezenka, 2017) as well as other social media data, such as in De Choudhury and De's (2014) study of mental health discourse on Reddit, and it has been applied to other game-related discourse (e.g., Saga & Kunimoto, 2016). Thus, LDA, which has been used to assess longitudinal changes in topic representations, can be applied to social media data including Reddit discussions, and is effective for modeling game-related conversations-all of which are key components of the present project-is well suited to this study.…”