Australasian Journal of Educational Technology, 2018, 34(4). 168 An analysis of the Australasian Journal of Educational Technology 2013-2017 Melissa Bond University of Oldenburg, Germany Katja Buntins University of Duisburg-Essen, Germany The Australasian Journal of Educational Technology (AJET) changed its editorial policy in 2013, to focus on higher education research and on improving journal submissions. This study analyses all articles (n = 256) in AJET from 2013-2017 to determine if there has been any change in research topics, methodologies, citations, and authorship since this editorial change, and compares findings to the analysis by Hadlock et al. (2014). The present analysis revealed that the percentage of combined methods research has doubled, although the top 10 most cited articles continue to be predominantly interpretative and inferential. Research has become more student-centred and focused particularly on online collaborative learning environments, and teacher skill and knowledge development, although research gaps exist in mobile learning and gamification. The results also highlight a lack of international collaboration amongst authors, and this is an area for future research. Introduction In an era of increasing numbers of predatory journals and high publishing fees to authors and readers (Bacevic & Muellerleile, 2017), open access journals are an excellent option in which to publish (Anderson, 2013). Despite concerns by some educational technology academics over publishing in open access journals, research has shown that they do not adversely impact citations or perceptions of prestige (Davis, 2011; Zawacki-Richter, Anderson, & Tuncay, 2010). The Australasian Journal of Educational Technology (AJET) is a highly respected open access journal (Perkins & Lowenthal, 2016), which charges no fees to authors or readers. In 2014, Hadlock et al. conducted an analysis of AJET articles published between 2003 and 2012 in order to understand the impact that the journal had made on the educational technology field. However, in 2013 AJET changed its focus to higher education only research (Dalgarno, Bennett, & Henderson, 2015a), as well as shifting its editorial focus (Bennett, Kennedy, & Dalgarno, 2013) towards highlighting topics specifically written to assist authors better understand the publication process and thereby potentially strengthen the quality of research. This research seeks to understand what impact these changes have had on the focus, scope and reach of the journal, by analysing all articles from 2013-2017 (Table 1), in order to compare data from the analysis by Hadlock et al. (2014). This article will be of particular interest to researchers who are considering where they should publish their work, especially given the increasing conversations about open access publishing (e.g., Anderson, 2018; National Institute for Digital Learning, 2017; Panker, 2018). Investigating research trends in educational technology Having an in-depth understanding of any field requires an examination of research from a variety of perspectives (Carr-Chellman, 2006). The development of the educational technology field has been of growing interest, in order to identify “emerging technologies and paradigm shifts” (Baydas, Küçük, Yilmaz, Aydemir, & Göktaş, 2015, p. 710), as well as future research directions (e.g., Halverson, Graham, Spring & Drysdale, 2012). A number of bibliometric and content analyses of leading educational technology journals have been conducted in the past 5 years, including open access journals International Review of Research in Open and Distributed Learning (IRRODL) (Zawacki-Richter, Alturki, & Aldraiweesh, 2017), and the International Journal of Educational Technology in Higher Education (IJETHE) (Marin, Duart, Galvis, & Zawacki-Richter, 2018). There have also been a number of comprehensive analyses of multiple educational technology journals (e.g., Baydas et al., 2015; Halverson et al., 2012; Hsu et al., 2012; Küçük, Aydemir, Yildirim, Arpacik, & Göktaş, 2013; West & Borup, 2014), which seek to contrast and compare a range of data including trends in research topics, methodologies and methods, and citations across time. Australasian Journal of Educational Technology, 2018, 34(4). 169 In their analysis of the British Journal of Educational Technology (BJET) and Educational Technology Research & Development (ETRD) from 2002-2014, Baydas et al. (2015) found that learning approach, learning environments and online learning were the most frequent research topics. These were also found in the analyses of Computers & Education (CAE) (Zawacki-Richter & Latchem, 2018), Distance Education (Zawacki-Richter & Naidu, 2016) and the IJETHE (Marin et al., 2018). Whilst learning environments were also found to be a focus in the comprehensive analysis by Hsu et al. (2012), in addition emphasis was placed on teachers, with the pedagogical use of technology and the effectiveness of instructional design the most frequent research topic. Baydas et al. (2015) reported more quantitative research than any other methodology (39.5%). This was also found in Turkish research (Küçük et al., 2013) and in a study of ICT research in K-12, published in CAE (Perez-Sanagustin et al., 2017), which had 50% of studies using a quantitative methodology. However, in their analysis of 10 educational technology journals from 2001- 2010, West & Borup (2014) found that theoretical research was by far the most represented methodology. To this end, it would be interesting to contrast these results with the articles published in AJET, in order to see whether more Australasian-centred research (Atkinson & McLoughlin, 2009) in the field of educational technology has a methodological bias. The sample: Australasian Journal of Educational Technology (AJET) AJET was established in 1985 by the Australian Society for Educational Technology (ASET), under the title Australian Journal of Educational Technology (Australasian Journal of Educational Technology [AJET], 2017a). AJET is published six times a year by the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE) and its lead editors since 2012 have included Sue Bennett, Barney Dalgarno, Gregor Kennedy, Michael Henderson, Eva Heinrich, and Chwee Beng Lee (AJET, 2017a). It is indexed in many Australian and international indexes, including the Social Science Citation Index (SSCI), with a current 5-year impact factor of 1.460 and ranked 151 out of 235 journals in the field of education and educational research in the InCitesTM Journal Citation Reports® (JCR) of Web of ScienceTM (Thomson Reuters, 2017). AJET became an open access, online only journal in 2008, after merging with the International Journal of Educational Technology and the e-Journal of Instructional Science and Technology in 2007. The move towards open access reflected the desire of editors that the journal be “founded on truly open values” (Bennett, 2015, p. i), which “aims to promote research and scholarship on the integration of technology in tertiary education, promote effective practice, and inform policy” (Australasian Journal of Educational Technology [AJET], 2017b). With this move, AJET also sought to expand its reach and has widened its scope, seeking to become a journal that is internationally focused, aiming to champion the voice of non- English speaking researchers in the Asia-Pacific region in particular (Atkinson & McLoughlin, 2009; Hadlock et al., 2014). Table 1 Annual numbers of issues and articles in AJET (2013-2017) Year Issues Articles Year Issues Articles 2013 6 60 2016 6 45 2014 6 48 2017 6 57 2015 6 46 Total 256 Special Issues A number of special issues have been published in AJET (Table 2) since 2010. These are included in the sample, although the editors acknowledged that AJET was a “relatively late entrant into special issues” (Atkinson & McLoughlin, 2011, p. iii). Special issues enable publications to devote a substantial number of pages to specific themes that are considered particularly pertinent to this “rapidly moving field” (Girvin, Hennessy, Mavrikis, Price, & Winters, 2017, p. 5) and reflective of current research trends (Zawacki- Richter & Naidu, 2016). For example, one of the 2017 special issues (TPCK/TPACK Research and Development: Past, Present, And Future Directions) reflects a strong trend in research across educational technology journals in the past 5 years in particular, in order to understand the level of ICT knowledge and Australasian Journal of Educational Technology, 2018, 34(4). 170 skills of pre-service and practising teachers, and best prepare students to engage with twenty-first century tools (e.g., Hofer & Grandgenett, 2012; Tournaki & Lyublinskaya, 2014). However, this was the first themed journal to focus on this topic in one special issue (Harris, Phillipy, Koehler, & Rosenberg, 2017), which helps position AJET as a cutting-edge journal in the field of educational technology. Table 2 Special Issues in AJET (2010-2017) Year Volume Issue Topic 2017 33 6 Mobile Augmented and Virtual Reality 2017 33 3 TPCK/TPACK Research and Development: Past, Present, And Future Directions 2016 32 6 Brain, Mind and Educational Technology 2015 31 5 Educational Design Research 2012 28 6 Building the ICT capacity of the next generation of teachers in Asia 2012 28 3 Virtual worlds in tertiary education: An Australasian perspective 2011 27 8 ICT for accessible, effective and efficient higher education: Experiences of Southeast Asia 2011 27 5 Assessing students’ Web 2.0 activities in higher education 2010 26 8 Learning technology and organisations: Transformational impact? 2010 26 1 Interactive whiteboards: An Australasian perspective AJET in previous bibliographic studies AJET has previously been the subject of a bibliographic analysis by Hadlock et al. (2014). Their analysis of 470 articles from 2003-2012 included identifying the most frequently used keywords and key phrases, ascertaining which research methodologies were most popular, determining which were the most cited articles by year, as well as the number of authors published per year. The five most frequent subject keywords identified in the analysis were: foreign countries, educational technology, electronic learning, teaching methods, and computer assisted instruction. These reflected the growing “international focus of the journal” (Hadlock et al., 2014, p. iv). They were also similar to the AJET corpus analysis findings of McDonald and Loke (2016), which analysed articles from 1991-2014, and found that the top 10 most frequent words included: students, learning, technology, online, teachers, and teaching. The six most frequently mentioned phrases in Hadlock et al. (2014) were: e-learning, higher education, case study, pre-service, online learning, and learning environment. Their analysis also revealed a trend towards collaboration, with a reduction in the percentage of single-authored articles across time. Given the growing need for cross-institutional and international collaboration (Guri-Rosenblit, 2014), it would be interesting to see whether the number of multiple-authored articles continued to grow after 2012, as predicted by Hadlock et al. (2014). The analysis by Hadlock et al. (2014), also revealed that interpretative studies were the most published (32% of articles), reflecting a trend in increased qualitative research within distance education journals (Zawacki-Richter, Baecker, & Vogt, 2009). However, given that CAE - a highly influential educational technology journal (Perez-Sanagustin et al., 2017) - has recently identified a lack of qualitative research being published within their pages, and in those of other journals (Küçük et al., 2013; Twining, Heller, Nussbaum, & Tsai, 2017), it would be timely to analyse whether the number of qualitative studies published in AJET has continued to grow since 2012, and whether the keywords have changed. The AJET editorial team also provide yearly bibliometric analyses of the journal (e.g., Dalgarno, Heinrich, & Henderson, 2016), covering publication summaries, the most downloaded articles, submission and review statistics, and citation information. The most recent data (Heinrich, Henderson, & Redmond, 2018) shows that article submissions have grown, as have the AJET JCR 2 and 5-year impact factors. The number of total citations in the JCR SSCI also rose significantly in 2016. In order to ascertain a more concrete understanding of how AJET compares to other leading educational technology journals, it would be interesting to compare citation data between those that AJET editors have identified as leading (Dalgarno, Kennedy, & Bennett, 2014). Australasian Journal of Educational Technology, 2018, 34(4). 171 Therefore, this research seeks to answer the following questions: 1. How have article keywords, research methodologies, authorship and the most cited articles in AJET changed over time? 2. How do AJET journal metrics compare to other educational technology journals? Method This study analysed all articles published in AJET from 2013-2017 (n = 256), excluding editorials and book reviews, and used the methodology employed by Hadlock et al. (2014). It then compared the results of the 2003-2012 analysis of AJET (Hadlock et al., 2014) with data from 2013-2017, in order to ascertain whether changes to the approach of editorials in 2013 (Bennett, Kennedy, & Dalgarno, 2013) have resulted in any substantial changes of topic, methodology or authorship patterns. Keyword and abstract analysis In order to answer research question 1: How have article keywords, research methodologies, authorship and the most cited articles in AJET changed over time?, a keyword analysis of AJET articles (n = 256) published between 2013 and 2017 was conducted using the ERIC database, as used by Hadlock et al. (2014) in their analysis. Unfortunately, not all AJET articles were indexed there and the decision was therefore made to export full citation data from the Web of Science for the remaining articles (n = 36), and to manually add the keyword meta data, as listed on the AJET website (https://ajet.org.au/index.php/AJET/issue/archive). The records were then coded in EPPI Reviewer software (http://eppi.ioe.ac.uk/eppireviewer4/), where keywords were combined into categories following those used by Hadlock et al. (2014). The top 20 combined keywords were then identified. The article abstracts were also analysed, as per Hadlock et al. (2014), by using the online tool WriteWords (http://www.writewords.org.uk/phrase_count.asp). All abstracts were submitted to the tool, which searched for the most frequent two-word combinations. Combinations such as “of the” or “show that” were excluded. The phrases were then sorted as per the keyword analysis and all keywords/two-word combinations were then compared to those identified from the 2004-2012 analysis. Computer-assisted content analysis According to Nunez-Mir, Iannone, Pijanowski, Kong, and Fei (2016), automated or computer-assisted content analysis is superior to word clouds or simple word-frequency counts, such as WriteWords, as the software accounts for linguistic and semantic complexities. In order to confirm the results of this keyword and abstract analysis, all article titles and abstracts (n = 256) were uploaded into text-mining software Leximancer. The software automatically identified significant concepts and themes, producing concept maps that show frequency and connectedness (Smith & Humphreys, 2006), which were then analysed by the author. Leximancer reduces time and cost, as well as limiting human bias (Krippendorff, 2013), and is considered to be an appropriate method by which to map out a research domain (Fisk, Cherney, & Hornsey, 2012). Other journal analyses using Leximancer include Distance Education (Zawacki-Richter & Naidu, 2016), and the IJETHE (Marin et al., 2018). Analysis of article types and methodologies In order to ascertain whether the number of qualitative research published in AJET has increased since the analysis by Hadlock et al. (2014) and particularly since changes to editorial content was made in 2013 (Bennett, Kennedy, & Dalgarno, 2013), an analysis of articles published from 2013-2017 was conducted. Each article was coded following the method by Hadlock et al. (2014, p. iii) by methodology or type as follows: • Descriptive - research primarily relies on survey and questionnaire data collection methods where the statistics are analysed and reported descriptively. • Inferential/quantitative - articles employ experimental, quasi-experimental, or correlational types of methodologies that test hypotheses or validate instruments. https://ajet.org.au/index.php/AJET/issue/archive http://eppi.ioe.ac.uk/eppireviewer4/ http://www.writewords.org.uk/phrase_count.asp Australasian Journal of Educational Technology, 2018, 34(4). 172 • Interpretative/qualitative - studies focus on interpreting data to develop theory and often include interviews and case studies. • Content analysis - articles reduce data into specific pre-determined categories that are reported descriptively. • Combined methods - studies are faithful to the requirements of at least two previously mentioned methods. • Theoretical - articles are not data-based and include models, instructional approaches, and literature reviews. Where uncertainty over methodology existed, two research associates in the field of educational research were consulted and agreement was reached. Authorship analysis In order to determine whether the trend towards collaboratively authored articles continued after 2012, all author data were extracted from the Web of Science database and analysed by the software R-Package bibliometrix (Aria & Cuccurullo, 2017). This enabled an analysis of whether articles were single or multi- authored, as well as who the most prolific authors were. In order to identify author affiliation, articles were hand-searched and the data then input into the software package SPSS. Citation analysis In order to analyse how the most cited articles in AJET have changed over time, a Google Scholar search was conducted by year of publication in the software Publish or Perish (Harzing, 2007). The search was conducted in March 2018 therefore the data are reflective of citation counts at that time. Journal metric analysis A citation analysis of AJET and other leading educational technology journals (Dalgarno et al., 2014), was conducted using Thomas Reuters Journal Citation Reports® and the Google Scholar h-index, in order to answer research question 2: How do AJET journal metrics compare to other educational technology journals?. Whilst it is recognised that there is “no universally accepted system of journal ranking” (Milesi, Brown, Hawkley, Dropkin, & Schneider, 2014, p. 367), the journal impact factor published by Thomas Reuters Journal Citation Reports® (JCR) is one of the most popular indicators of journal quality (Dalgarno, Bennett, & Henderson, 2015b; Perkins & Lowenthal, 2016) and “sets the standard for providing easily accessible data (from 1972) on a journal, paper and/or author” (Fiala, Mares, & Sestak, 2017, p. 698). The Google h-index is used to measure productivity, by taking into account the number of publications and the number of citations per publication (Fiala et al., 2017). These two metrics were chosen, rather than more recent and immediate metrics, such as Altmetrics, as they have been regularly used by AJET editors to provide bibliometric data (e.g., Heinrich et al., 2018). However, Altmetrics are acknowledged as providing added value (Markusova, Bogorov, & Libkind, 2018) and is an area that warrants further investigation for AJET. Whilst citation analyses are “commonly used to explore the intellectual structure of a given discipline” (Liu & Wang, 2005, p. 308), it is important that no one journal metric alone is used to assess a journal’s quality (Dalgarno et al., 2014). Interpreting educational research quality by using impact factors has been identified as particularly problematic, due to a range of factors including skewing of metrics, editorial policy, the small number of journals indexed in the ISI (Lavie, 2009; Saxena, Thawani, Chakrabarty, & Gharpure, 2013; Staller, 2017; West & Rich, 2012), and author epistemological or methodological preferences (Ouimet, Bedard, & Gelineau, 2011). Therefore, the results of this analysis should be viewed as one measure of AJET’s quality. In order to triangulate this data, and to give a more rounded understanding of AJET’s structure and editorial influence, the framework of rigour, impact and prestige (West & Rich, 2012) is used in a forthcoming article (Bond, 2018). Australasian Journal of Educational Technology, 2018, 34(4). 173 Results and discussion Keyword and abstract analysis The results of the keyword and abstract analysis highlighted the shift in focus within AJET towards higher education-only focused research (Dalgarno et al., 2015a). The keywords “higher education” (Table 4) yielded the most number of hits and was the most frequent phrase in article abstracts (Table 5). The continued high frequency of the keywords “foreign countries” within articles echoes the analysis by Hadlock et al. (2014), and shows a sustained focus on publishing international research. There was, however, a slight drop in the percentage of articles, indicating that there has potentially been less international representation in AJET in the past 4 years, which will be explored in a forthcoming article (Bond, 2018), as it is beyond the scope of this paper. One major shift in focus during the past five years has been the focus in articles on “computer/technology uses in education” and “student learning” (Table 4), which echoes the findings by McDonald and Loke (2016) in their corpus analysis of AJET, and which has also occurred in the wider field of educational technology. Marin et al. (2018) analysed 221 articles from the IJETHE between 2010-2017 and found that research had moved from focusing on technology and tools, to focusing more on how they could be used to foster student learning and competencies, so too Zawacki-Richter and Latchem (2018) in their analysis of CAE. Zawacki-Richter et al. (2017) also found that the emphasis in research has become more student- centred, with learner characteristics the second most frequent research area in IRRODL between 2011 and 2015. Table 4 Comparison of subject keywords in articles ERIC subject keywords Percentage of articles that contain keyword 2013-2017 2004-2012 Total number of articles 256 438 Higher education 71% 15% Foreign countries 70% 83% Educational technology 62% 59% Computer/technology uses in education 45% 18% Questionnaires 44% 16% Student learning 42% - College/undergraduate students 35% 16% Student attitudes 34% 24% Teaching methods 34% 27% Interviews 23% 18% Online courses 21% - Statistical analysis 21% - Computer mediated communication 19% 17% Pre-service teachers 17% - Technology integration 17% 24% Learning environment 16% - K-12 15% - Qualitative research 15% - Electronic learning 14% 27% Instructional effectiveness 13% 19% Keywords that were not present in the 2014 analysis include “online courses” and “learning environment”, which reflects an international trend towards understanding how to create effective and collaborative online learning experiences (Marin et al., 2018; Zawacki-Richter et al., 2017; Zawacki-Richter & Naidu, 2016), and which also reflects the findings of McDonald and Loke (2016). The appearance of the keyword “pre- service teachers” in the top 20 also reflected the results of the abstract analysis (Table 5), with “pre-service” and “content knowledge” the second and third most frequent combination. Whilst an entire issue was devoted to TPACK in AJET (Harris et al., 2017), thereby increasing the instance of the keyword and phrase occurring, this also reflects a growing international research trend (e.g., Marin et al., 2018; Rosenberg & Australasian Journal of Educational Technology, 2018, 34(4). 174 Koehler, 2015). “Mobile learning” was also a new key phrase in the abstract analysis, which is not surprising given the rise of mobile learning internationally (Crompton, Burke, & Gregory, 2017; Crompton, Burke, Gregory, & Gräbe, 2016; Krull & Duart, 2017). However, “mobile learning” was listed explicitly as a keyword in only 4% of articles, with “handheld devices” accounting for 7% of articles; a trend also found in CAE (Zawacki-Richter & Latchem, 2018). Another keyword that was surprisingly under- represented in articles was “gamification”, present in only 5% of articles. Given the importance of mobile learning and gamification internationally (Dicheva, Dichev, Agre, & Angelova, 2015), this represents an opportunity for AJET to commission special issues, in order to address this research gap. As in the analysis by Hadlock et al. (2014), the keyword frequency supported the methodology analysis, showing that the interpretative method is most common. “Interviews”, “qualitative research”, and “questionnaires” were in the top 20 words, with the number of studies using questionnaires (44%) having risen dramatically. Although “case studies” was no longer in the top 20, it was still mentioned 31 times (12%). The appearance of “statistical analysis” (21%) and “qualitative research” (15%) in the top 20 keywords was also reflected in the methodology analysis, with double the number of studies using combined methods. Table 5 Comparison of key phrases in article abstracts Phrase Times listed 2013-2017 Times listed 2004-2012 Higher education 58 83 Pre-service 56 57 Content knowledge (TPACK) 39 - e-Learning 37 102 Mobile learning 35 - Computer-assisted content analysis The concept map in Figure 1 depicts the major topics covered in articles (n = 256), published in AJET across 5 years (2013-2017). The thematic summary reveals that “learning” has the most direct mentions with 464 (100% relative count), followed by “teachers” (46%), “knowledge” (26%), “system” (15%), “support” (12%), and “different” (11%). These results confirm the keyword and abstract analysis, indicating that research during this time focused on student learning in online environments (see: online, students, learning, environment) as well as to support teachers in using educational technology in their teaching (see: teachers, development, practice and teachers, technology, teaching, skills). The concept map also highlights the use of the TPACK framework to assist in this process (see: teachers, framework, content, knowledge, pedagogical). Interestingly, the word “mobile” did not feature on the ranked concept list, which could be due to the linguistic sensitivity that text-mining software allows (Roberts, 2000), and helps to explain the small number of articles with “mobile learning” in their keywords. This should serve as a warning to use simple word-frequency counts to explain research trends with caution. Analysis of article types and methodologies The results of the article analysis revealed that there has been a substantial change in the methodological approach of research published in AJET since 2013 (Table 6). Whilst interpretative research is still the most represented methodology alongside quantitative research, as opposed to other educational technology journals (e.g., Küçük et al. 2013; Perez-Sanagustin et al., 2017), there has been no growth in the amount of qualitative research published in AJET, having dropped 1%. There has also been a substantial reduction in the number of descriptive and theoretical articles published. One of the reasons for this could be due to editorial opinion that “the field is swamped by descriptive studies” (Henderson, Heinrich, & Lee, 2016, p. i) and that it often prevents generalisation, which limits understanding of how the educational technology or practice can be applied in other contexts. Australasian Journal of Educational Technology, 2018, 34(4). 175 Figure 1. Concept map of AJET titles and abstracts, 2013-2017 (n = 256) There has been extensive growth in the number of studies published that use combined methods, with double the percentage of articles published in the past 5 years than in the previous 10. This contrasts sharply to research published in BJET and ETRD (Baydas et al., 2015), where it represented 9% of methodological approaches on average in 2013 and 2014. It should be noted, however, that only 7% of articles list “mixed methods research” in their keywords, which might indicate that authors do not feel confident to label their article as truly mixed. Mixed methods research draws on the strength of both qualitative and quantitative methods in order to answer complex problems (Johnson & Onweuegbuzie, 2004; Mertens, 2015), and has been described as a method whose “time has come … to guide work within the field of educational technology” (Poth, 2018, p. 4). Indeed, Bennett, Dalgarno, and Henderson (2015) focused on mixed methods within their editorial in Issue 3, stressing that authors need to sequence methods clearly and create a coherent story. The trend of articles (Table 7) shows that numbers (and therefore quality) had reduced, with only 8 published in 2015, however this number increased to 14 in 2017. By providing methodological guidance, AJET editors are improving researcher understanding and therefore submission quality. Table 6 Comparison of total research methodologies used by number and percentage Year Descriptive Inferential/ quantitative Interpretative/ qualitative Theoretical Content analysis Combined methods Total 2013- 2017 14 (5%) 79 (31%) 79 (31%) 17 (7%) 6 (2%) 61 (24%) 256 2003- 2012 37 (8%) 136 (29%) 149 (32%) 78 (17%) 15 (3%) 55 (12%) 470 Australasian Journal of Educational Technology, 2018, 34(4). 176 Table 7 Articles by method and year Year Descriptive Inferential/ quantitative Interpretative/ qualitative Theoretical Content analysis Combined methods Total 2013 3 (5%) 17 (28%) 21 (35%) 3 (5%) 0 (0%) 16 (27%) 60 2014 3 (6%) 17 (36%) 13 (27%) 2 (4%) 1 (2%) 12 (25%) 48 2015 1 (2%) 16 (35%) 19 (42%) 1 (2%) 1 (2%) 8 (17%) 46 2016 3 (7%) 11 (24.5%) 10 (22%) 8 (18%) 2 (4%) 11 (24.5%) 45 2017 4 (7%) 18 (32%) 16 (28%) 3 (5%) 2 (4%) 14 (24%) 57 Total 14 (5%) 79 (31%) 79 (31%) 17 (7%) 6 (2%) 61 (24%) 256 Citation analysis The most cited articles in AJET by year of publication (Table 8) reflect the mixed methodological scope of the journal, with two interpretative, two theoretical and one inferential. They also reflect the keyword and abstract analysis, which highlighted: higher education, student learning, mobile learning, and pre-service teachers. These topics also strongly featured in the most cited articles published across 2013-2017 (Table 9). It is interesting to note that the article by Beckers, Dolmans, and van Merriënboer (2016) reflects a recent trend in educational technology towards conducting systematic reviews (e.g., Clark, Tanner-Smith, & Killingsworth, 2016; Crompton et al., 2017; Joksimovic et al., 2017). Table 8 Most cited articles by publication year, as listed in Google Scholar Year Citations Authors Paper 2013 189 Pegrum, Oakley, and Faulkner Schools going mobile: A study of the adoption of mobile handheld technologies in Western Australian independent schools 2014 66 Alammary, Sheard, and Carbone Blended learning in higher education: Three different design approaches 2015 30 Henderson and Phillips Video-based feedback on student assessment: Scarily personal 2016 13 Beckers, Dolmans, and van Merriënboer e-Portfolios enhancing students’ self-directed learning: A systematic review of influencing factors 2017 10 Chai, Tan, Deng, and Koh Examining pre-service teachers’ design capacities for web-based twenty-first century new culture of learning Table 9 Most cited articles published in AJET 2013-2017, as listed in Google Scholar Citations Year Authors Paper 189 2013 Pegrum, Oakley, and Faulkner Schools going mobile: A study of the adoption of mobile handheld technologies in Western Australian independent schools 88 2013 Pegrum, Howitt, and Striepe Learning to take the tablet: How pre-service teachers use iPads to facilitate learning 69 2013 Jimoyiannis, Tsiotakis, Roussinos, and Siorenta Preparing teachers to integrate Web 2.0 in school practice: Toward a framework for Pedagogy 2.0 66 2014 Alammary, Sheard, and Carbone Blended learning in higher education: Three different design approaches 63 2013 Mac Callum and Jeffrey The influence of students' ICT skills and their adoption of mobile learning 54 2013 Jang and Tsai Exploring the TPACK of Taiwanese secondary school science teachers using a new contextualized TPACK model 47 2013 Gedik, Kiraz, and Ozden Design of a blended learning environment: Considerations and implementation issues = 2013 Chai, Ng, Li, Hong, and Koh Validating and modelling technological pedagogical content knowledge framework among Asian preservice teachers Australasian Journal of Educational Technology, 2018, 34(4). 177 40 2013 Atkinson and Lim Improving assessment processes in Higher Education: Student and teacher perceptions of the effectiveness of a rubric embedded in a LMS 37 2013 Tondeur, Kershaw, Vanderlinde, and van Braak Getting inside the black box of technology integration in education: Teachers' stimulated recall of classroom observations 32 2014 Rambe Converged social media: Identity management and engagement on Facebook Mobile and blogs = 2014 Escobar-Rogriguez, Carvajal-Trujillo, and Monge-Lozano Factors that influence the perceived advantages and relevance of Facebook as a learning tool: An extension of the UTAUT = 2013 Koh A rubric for assessing teachers' lesson activities with respect to TPACK for meaningful learning with ICT Authorship analysis Of the articles published between 2013-2017, 50 were published by a single author, with only two articles published by the same author. Some 31.8% were published by two authors, 25.3% by three authors, 12.8% by four authors and 10.5% by five or more authors. Contrary to the findings by Hadlock et al. (2014), there was no ongoing trend towards more authors per article during these 5 years (Table 10), with the percentage of articles with three or more authors remaining relatively stable. Table 10 Authorship per year Year Authors Total articles % more than three authors % single authors 1 2 3 4 5 6 7 8 9 2013 10 20 16 7 6 0 0 0 1 60 50% 17% 2014 9 15 12 7 5 0 0 0 0 48 50% 18% 2015 13 14 12 6 1 0 0 0 0 46 41% 28% 2016 9 12 14 4 3 0 0 2 1 45 53% 20% 2017 9 20 11 9 5 2 1 0 0 57 49% 16% Total 50 81 65 33 20 2 1 2 2 256 49% 19% Percent 20% 32% 25% 13% 8% 0.8% 0.4% 0.8% 0.8% In this analysis, the 256 articles were written by 621 authors, which were spread relatively equally across the 5 years (see Table 11). Joyce Koh (Singapore), Chin-Chung Tsai (Taiwan) and Joke Voogt (Netherlands) were the most prolific authors, having co-authored six papers each. Interestingly, Joyce Koh had four papers published in 2013, Joke Voogt co-authored four in 2017, and Chin-Chung Tsai also published six times during 2003-2012, with five of them during 2012 (Hadlock et al., 2014). Of the articles that had more than one author, 101 came from one institution, 70 from two institutions, 22 from three institutions, a further 11 from four or more institutions, and 2 more had no affiliation. Only 39 of these collaborations were international, which warrants further investigation in future research. Table 11 Number of authors per year 2013 2014 2015 2016 2017 Total Number of articles published 60 48 46 45 57 256 Number of authors 160 127 101 131 152 Journal metrics analysis The Thomas Reuters Journal Citation Reports® (JCR) and the Google Scholar h-index are two of the most popular indicators of journal quality for authors and researchers (Dalgarno et al., 2015b). Despite a small dip in 2014, AJET’s 5-year impact factor has remained relatively stable (Table 12), which places it towards the bottom of the 18 leading educational technology journals (Table 13), as recognised by AJET editors Australasian Journal of Educational Technology, 2018, 34(4). 178 (Dalgarno et al., 2014). Interestingly, the h-index (Table 14), which considers both the quality and quantity of articles (Hirsch, 2005), places AJET above journals with a higher 5-year impact factor, such as Language Learning & Technology (2016 h-index = 29). This indicates that it is necessary to consult more than one citation metric when considering journal quality (West & Rich, 2012), which is further explored in an upcoming article (Bond, 2018). Table 12 Thomson Reuters JCR SSCI 5-yeari impact factor Year Impact factor 2012 1.540 2013 1.198 2014 1.006 2015 1.171 2016 1.460 Table 13 Journal citation report 5-year impact factors for leading educational technology journals Journal 2012 2016 Internet and Higher Education N/A 5.130 Computers & Education 3.305 5.047 Language Learning & Technology 2.205 3.306 Computer Assisted Language Learning N/A 2.902 ReCALL N/A 2.851 International Journal of Computer-Supported Collaborative Learning 2.904 2.808 IEEE Transactions on Learning Technologies 0.893 2.620 British Journal of Educational Technology 1.888 2.532 Journal of Computer Assisted Learning 2.095 2.415 International Review of Research in Open and Distributed Learning* N/A 2.139 Interactive Learning Environments 1.491 2.138 Distance Education N/A 2.056 Educational Technology & Society* 1.505 2.034 Learning Media and Technology N/A 2.000 Journal of Computing in Higher Education N/A 1.895 Educational Technology Research and Development 1.522 1.652 Australasian Journal of Educational Technology* 1.540 1.460 Journal of Educational Computing Research N/A 1.179 * Open Access journal Table 14 Comparison of Google Scholar h-5 index for educational technology journals, 2014-2016 Journal h5-index 2014 h5-index 2016 Computers & Education 81 94 British Journal of Educational Technology 44 53 Educational Technology & Society* 39 49 Internet and Higher Education 35 46 International Review of Research in Open and Distributed Learning* 34 41 Journal of Computer Assisted Learning 38 37 Educational Technology Research and Development 33 34 Australasian Journal of Educational Technology* 30 32 International Journal of Computer-Supported Collaborative Learning 25 26 IEEE Transactions on Learning Technologies 27 24 * Open Access journal Australasian Journal of Educational Technology, 2018, 34(4). 179 Limitations and future research Researchers interested in conducting journal content analyses should consider using a computer-assisted program such as Leximancer, rather than relying on human analysis alone, in order to eliminate human error or bias (Nunez-Mir et al., 2016). Whilst great care was taken in the extraction, analysis and interpretation of data, the human factor remains. The results of the abstract analysis using Leximancer revealed a more sensitive linguistic analysis than by using a word-frequency count method, as WriteWords only focused on two-word combinations, rather than looking at the semantic meaning of the text within two-sentence blocks, as in Leximancer (Smith & Humphreys, 2006). An analysis of what has caused the change in Impact Factor over time would be useful for authors and editors alike, however this is beyond the scope of the current paper. This article has also only focused on two journal metrics, for comparative purposes, however this should not be considered as the only measure of a journal’s quality (West & Rich, 2012), and therefore other measures will be further explored in a forthcoming article (Bond, 2018). Another prime area of interest that has arisen from this research has been the international collaboration of researchers within the field of educational technology. The authorship analysis revealed a strong tendency for authors to collaborate within their own countries, which is against the international trend towards openness and cross-border collaboration (e.g., Guri-Rosenblit, 2014); therefore, further exploration of this will also be the focus of future research. Conclusion and recommendations The analysis of articles published in AJET in 2013-2017 has confirmed a growing trend that educational technology research has become more student-centred (Marin et al., 2018; Zawacki-Richter & Latchem, 2018) since the Hadlock et al. (2014) analysis, and that there has been a rise in the number of studies researching online learning environments, and teacher knowledge and skill development. This reflects the journal shift towards higher education-only research, due in part to the widespread use of learning management systems by universities (Beer, Clark, & Jones, 2010). Despite the change in focus and scope of AJET, an even number of quantitative and qualitative studies continue to be published. However, the percentage of combined methods research has doubled in the past five years, which will likely continue to grow, as long as high quality mixed methods research skills are employed (Bennett et al. 2015; Poth, 2018). AJET is an open access journal, with a strengthening presence, and no particular methodological bias, thereby making it an attractive journal in which to publish. Opportunities exist for both authors and editors to broaden research in the areas of mobile learning and gamification, and the editors are encouraged to engage with Altmetrics as added value to authors, rather than rely on traditional citation metrics alone. 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An analysis of the Australasian Journal of Educational Technology 2013-2017. Australasian Journal of Educational Technology, 34(4), 168-183. https://doi.org/10.14742/ajet.4359 https://doi.org/10.1111/bjet.12081 https://doi.org/10.1007/s10755-012-9214-3 http://www.irrodl.org/index.php/irrodl/article/view/2806/4090 http://www.jofde.ca/index.php/jde/article/view/661/1170 http://www.irrodl.org/index.php/irrodl/article/view/741/1433 https://doi.org/10.1016/j.compedu.2018.04.001 https://doi.org/10.1080/01587919.2016.1185079 mailto:melissa.bond@uni-oldenburg.de https://doi.org/10.14742/ajet.4359