The document analyzes 2,997 articles from 6 educational technology journals between 2000-2010 using text mining. It identifies 19 clusters of research areas that were grouped into 6 categories and 3 domains. The top research areas included macro views of technology integration and online learning, instructional design, and learning interactions/collaborative learning. Productivity was highest for the US, England, Taiwan, Australia, and Netherlands. Trends showed rising attention in technology integration, online learning, and instructional design, while areas like learning styles received less attention. The analysis provides insight into trends to inform the field and facilitate future research directions.

1. Educational Technology Research Trends:
Examining Six SSCI-indexed Refereed Journals
Yu-Chang Hsu | Jui-Long Hung | Yu-Hui Ching
Boise State University

2. Introduction
Educational Technology
Year 2000-2010
6 Journals indexed in SSCI
2,997 Articles
Text Mining
International Research Trends

3. Prior “Review” Research
Selective article review: conducted by discussing author-selected articles.
Winn (2002) reviewed self-selected articles and defined the evolution of e-learning into the ages
of instructional design, message design, simulation, and learning environments. => scope, data
source, and article selection standards were usually not clearly defined.
Systematic content analysis
Shih, Feng, and Tsai (2008) investigated trends of cognition research in e-learning. The authors
collected 444 articles from five major SSCI educational technology journals during the period
from 2001 to 2005. => Require significant time and efforts on content analysis. Text mining can
be a relatively efficient alternative approach.
Systematic citation analysis
Ozcinar (2009) investigated 758 ‘‘instructional design’’ articles published between 1980 and
2008 and found that 87 % of those articles came from seven countries, with the countries in the
Far East being poorly represented.

4. Method: Text Mining (TM)
Text Mining adopts a set of algorithms for converting unstructured text into structured data
objects then analyze them with quantitative methods.
Characteristics/Advantages:
Large sample size:
• Sample size could range from hundreds to more than ten thousand
• Exhaustive search
• Traditional content analysis methods might focus mostly on selective
journals only, while TM enables researchers to extract all related articles
from the entire database.
• Data source:
• In order to control data noise and avoid obtaining trivial clusters, TM
studies usually utilize abstracts as the major data source.

5. Generic Text Mining Process

6. Tool
SAS Text Miner 4.2
Compared abstract similarity by using domain-related words
Grouped similar articles into clusters
All clusters were agglomerated into a hierarchical tree/taxonomy structure based on
agglomerative clustering algorithm
Additional variables (i.e., indicators commonly used in library science)
Publication Year
Journal Title
Number of Authors
Number of Pages
First Author’s Country
First Author’s Institution

7. Data Source
2,997 Articles (Abstracts) from the following 6 refereed journals:
British Journal of Educational Technology (BJET)
Computers & Education (C&E)
Journal of Educational Technology and Society (JETS)
Educational Technology Research & Development (ETR&D)
Innovations in Education and Teaching International (IETI)
Journal of Computer Assisted Learning (JCAL)
Year Range: 2000-2010

8. Data interpretation/evaluation
Examining the hierarchical taxonomy structure of research areas;
Sampling and reviewing articles in respective research areas;
Triangulating professional knowledge of research areas among the researchers of this study.

9. Results
2997 articles → 19 Clusters → 6 Groups → 3 Domains
Authors discussed, negotiated, gained consensus, decide on the “category” terms

11. Productivity by Country

12. TOP 5 Productive:
USA, England, Taiwan, Australia, and Netherlands
USA
• Led the productivity in ETR&D, and
• Ranked second in C&E, JCAL, and JETS
England
• Led productivity in BJET and IETI
Taiwan
• Ranked first in C&E, JCAL, and JETS

13. Top 5 Country: Productivity by Cluster
USA
• Macro View of Online Learning
• Learning Interactions and Online Collaborative Learning
• Instructional Design
England
• Macro View of Online Learning
• Educational Software and Simulation
• Learning Interactions and Online Collaborative Learning
Taiwan
Automated Instructional Systems
Learning Interactions and Online Collaborative Learning
Assessment in Technology-based learning Environments

14. Top 5 Country: Productivity by Cluster (continued)
Australia
Macro View of Online Learning
Learning Interactions and Online Collaborative Learning
Macro View of Technology Integration
Netherlands
Instructional Design
Learning Interactions and Online Collaborative Learning
Macro View of Online Learning

15. Productivity by Journal

16. Top Clusters by Journal
BJET
Macro View of Technology Integration (CL2),
Learning Interactions and Collaborative Learning (CL17)
Instructional Design (CL10)
CL2 > CL17 by 61.3 % in terms of the numbers of published articles
C&E
Learning Interactions and Collaborative Learning (CL17)
Educational Software and Simulation (CL14)
Macro View of Online Learning (CL2)
CL17 > CL14 by 29.3 %
CL17 > CL2 by 51.5 %
JETS
Macro View of Online Learning (CL2)
Learning Interactions and Collaborative Learning (CL17)
Instructional Design (CL10)
CL2 > CL17 by 23.3 %

17. Top Clusters by Journal (continued)
ETR&D
Instructional Design (CL10)
Learning Interactions and Collaborative Learning (CL17)
Macro View of Technology Integration (CL2)
CL10 > CL17 by 9.5 %
CL10 > CL2 by 53.3 %
IETI
Macro View of Online Learning (CL2)
Assessment in Technology-based Environments (CL12)
Instructional Design (CL10)/Learning Interactions and Collaborative Learning (CL17)
CL2 > CL12 by 308 %
CL2 > CL10/CL17 by 386 %
JCAL:
Macro View of Online Learning (CL2)
Learning Interactions and Collaborative Learning (CL17)
Educational Software and Simulation (CL14)
CL2 > CL17 by 9 %

18. Trends: Rising
Hot topics (i.e., averaged more than 15 articles published per year) include
Macro View of Technology Integration
Macro View of Online Learning
Instructional Design
Assessment in Technology-based Environments
Educational Software and Simulation
Automated Instructional Systems
Learning Interactions and Online Collaborative Learning

19. Trends: Stable
Stable topics (i.e., averaged no more than 15 articles published per year)
Educational Games
Technology-assisted Learning
Attitudes toward Technology
Learning Community
Online Discussions
Problem-solving

20. Trends: Low Attention
Cold topics (i.e., averaged no more than 5 articles published per year) include
Learning Styles
Technology Adoption
Learning Objects
Multimedia
Computer-Assisted Language Learning

21. Relatively New
E-learning in Higher Education
None between 2000 and 2003
2 articles published in 2004
19 articles published in 2010

22. Current Era of EDTECH Research
D1: Technology Integration & D3: Learning Environments have more rising clusters than D2:
Acceptance/Attitude of Emerging Technologies
Internet technologies constitute the major teaching/learning environments in EDTECH
Interaction and collaborative work contribute to the mainstream of instructional/learning
strategies
EDTECH research is the era with a focus on learning environment (Winn, 2002)
Instructional Design and simulation (focusing on interactions) are still hot topics in the field

23. Through the Lens of Critical Theory of Technology
Technology is a two-sided phenomenon that involves the operator and the object.
Through design, the operator can narrow the range of the object’s interests and concerns
(Feenberg, 2005).
Possible operators that could have potentially shaped the trends of EDTECH research:
Government
Journal
Technology Developer

24. Future Research
What do you hope to find out more about the EDTECH research trends?

25. Article Information
○ Hsu, Y. -C., Hung, J. -L., & Ching, Y. -H. (2013). Trends of Educational Technology research: More than a
decade of international research in six SSCI-indexed refereed journals. Educational Technology Research &
Development, 61(4), 685-705. doi:10.1007/s11423-013-9290-9
○ Abstract: This study applied text mining methods to examine the abstracts of 2,997 international research articles published
between 2000 and 2010 by six journals included in the Social Science Citation Index (SSCI) in the field of Educational
Technology (EDTECH). A total of 19 clusters of research areas were identified, and these clusters were further analyzed in
terms of productivity by country and by journal. The analysis revealed research areas with rising trends, stable status, and low
attention. This study also identified areas of research emphasis by journal and research strength by country. A discussion of
results through the lens of Critical Theory of Technology is also included. The authors hope to inform the EDTECH community
about the trends of EDTECH research on topics and regions of research contributions. The authors also believe that such
examination of trends can help facilitate fruitful discussions of directions for future research, and possible international
collaboration across various geographical regions.

26. Contact:
Dr. Yu-Chang Hsu
hsu@boisestate.edu