Where is the “theory” within the field of educational technology research?

Hew, Khe Foon; Min, Liang; Tang, Ying; Jia, Chengyuan; Lo, Chung Kwan

doi:10.1111/bjet.12770

Cited by 157 publications

(134 citation statements)

References 51 publications

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“…It is unlikely that such strong or general AI will exist in the near future. We are therefore dealing here with GOFAI ("good old-fashioned AI", a term coined by the philosopher John Haugeland, 1985) in higher educationin the sense of agents and information systems that act as if they were intelligent.…”

Section: Ai In Education (Aied)mentioning

confidence: 99%

“…The lack of theory might be a syndrome within the field of educational technology in general. In a recent study, Hew, Lan, Tang, Jia, and Lo (2019) found that more than 40% of articles in three top educational technology journals were wholly a-theoretical. The systematic review by Bartolomé et al (2018) also revealed this lack of explicit pedagogical perspectives in the studies analysed.…”

Section: Conclusion and Implications For Further Educational Researchmentioning

confidence: 99%

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Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Zawacki‐Richter

Marín

Bond

et al. 2019

Int J Educ Technol High Educ

1,292

468

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According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

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Section: Ai In Education (Aied)mentioning

confidence: 99%

Section: Conclusion and Implications For Further Educational Researchmentioning

confidence: 99%

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Zawacki‐Richter

Marín

Bond

et al. 2019

Int J Educ Technol High Educ

1,292

468

View full text Add to dashboard Cite

show abstract

“…Hence, purely deductive approaches in distance education tend to be relatively rare and more usually include a blend of both deductive and inductive approaches (Cohen et al 2011, p. 3). Beyond these, an abductive approach is required to construct theory, or generate 'suitable hypotheses', when current theories are insufficient, such as the case of much of education technology research (Hew et al 2019). This aligns with Peirce's original form of abduction as a pragmatic activity of exploration (Peirce 1955), perhaps best encapsulated by Frankfurt's summary of it as a process to construct "the admissibility of hypotheses to rank as hypotheses."…”

Section: Approach and Methodsmentioning

confidence: 99%

A longitudinal study of virtual design studio (VDS) use in STEM distance design education

Jones

Lotz

Holden

2020

Int J Technol Des Educ

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The use of virtual design studios (VDS) in practice-based STEM education is increasing but requires further research to inform understanding of student learning and success. This paper presents a longitudinal, large-scale study (3 years, 3000 students) of student behaviour in an online design studio used as part of a distance learning Design and Innovation qualification, within the School of Engineering and Innovation at The Open University (UK). The sample size and time period of the study is unprecedented and provides unique insights into student behaviours. Moderate correlations between overall VDS use and student success were identified in early stages of study but were weaker in later stages. Detailed results identify specific behaviour correlations, such as 'listening-in' (viewing other students' work) and student success, as well as behaviour shifts from 'passive' to 'active' engagement. Strong intrinsic motivations for engagement were observed throughout and selected social learning mechanisms are presented to explain the empirical results, specifically: social comparison, presence, and communities of practice. The contribution of this paper is the framing of these mechanisms as steps in the longitudinal development of design students in a distance setting, providing an informed basis for the understanding, design, and application of virtual design studios.

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“…Das TPACK-Modell von Mishra und Koehler (2006) berücksichtigt neben den drei von Shulman (1986Shulman ( , 1987 (Chai, Koh & Tsai, 2013;Hew, Lan, Tang, Jia & Lo, 2019;Voogt, Fisser, Pareja Roblin, Tondeur & van Braak, 2013;Willermark, 2018…”

Section: Das Tpack-modellunclassified