This week many politicians have been immersed in the debates about a broadband access in Australia. While the focus of this dispute has been on how many Australians (98% vs. 99%) will (should) have an access to a high-speed broadband network, it was a good motive to read and think about education and how educational research could help to embrace all this “fast stuff”. Thus, this entry is about innovation, education, educational research and e-research.
Back in 2004 the OECD’s Centre for Educational Research and Innovation published the study, which looked into how innovations occur in knowledge-intensive domains of the economy (such as biomedicine, pharmacy and microelectronic) and how these ideas could be used to drive innovations in the educational sector. Here some selected ideas from this study.
Forces of innovation in knowledge-intensive economies
Four main forces and sources of innovation are common in the knowledge-intensive domains:
- Science-based innovation. Most of innovations come through a formal basic research and development work, i.e. rigorous controlled scientific experiments.
- Horizontally organised user-doer networks. New types of innovators - “users and doers” - play important role in innovation processes. “Users and doers” make improvements, share them, learn from each other, apply what they learn in practice and work on improving their practices again.
- Modularity. Decentralised modular structures have possibilities and freedom to innovate independently in complex systems. These modular structures are coordinated from the centre and have shared interfaces. As a result, they can be joined together in a whole effective innovative system.
- ICT. General-purpose technologies provide opportunities to transform products, processes and organisations. Yet, these transformations happen only in those sectors where there is a willingness to change traditional ways of doing things and adopt new working practices.
Sources of innovation in education
In the report, these four general sources of innovation are mapped on to the educational sector:
- Science-based innovation. Experimental science has made limited impact and potentially could make more. “Education is not a field that lends itself well to experimentation” (p. 30), i.e. it is hard to replicate “science-enlightened” innovations. Additionally, there is a tension (ontological and epistemic - LM) between (a) those who emphasise tacit knowledge acquired through teaching practices and held by expert practitioners and (b) those who emphasise explicit knowledge derived from scientific experiments.
- User-doer based networks are probably the most embraced source of innovation in education. These networks typically appear in the form of “communities of practice”. However, teachers “do not fit the template of the modern knowledge-based communities” (p. 23). Teachers’ work involves “unstandardised materials” (i.e. students) and many pedagogical innovations are typically done at individual level. Thus, few innovations are actually passed on through these networks of practice.
- Modularity. Educational systems tend to be controlled from the centre (despite of numerous efforts to decentralize them - LM). Modularity is the usual way for organising education systems (typically hierarchical model vs. information assimilation or evolutionary connection, p. 54). Thus, potentially, multiple processes and independent innovation could be allowed. Nevertheless, there are too many obstacles to modular decentralised innovation (e.g., standard learning outcomes). For example, innovations might create not just “higher” standard outcomes, but “different” non-standard outcomes (p. 71). Moreover, ability to take risks and “fail intelligently” is a missing element in the educational system and culture. “Political demands for success create strong pressures not to fail” (p.72) (and sometimes not to innovate at all – LM).
- ICT. The ways in which ICT is used in education, haven’t transformed it yet. “It is one thing to assimilate ICT as a tool within the school, but another to use it to alter it fundamentally” (p. 56).
Some thoughts about recent developments and e-research
Educational research has changed significantly over the last few years. Trends towards design-based research, design experiments and other “what works”-type research have reduced some tensions between “laboratory” scientists and educational practitioners. Educators also started to “codify” their practical knowledge more often (e.g., action research, practice-based research, reflection-based practices), thus more of such tacit knowledge appear in various symbolic representations that can be stored, transferred via various media, accessed and analysed (e.g., see WWC).
As the Report notices, in the last 15 years, many advances in knowledge-intensive domains have been based on the possibility to research and compare facts and evidence from previous research. Over the last years, educational research has seen many new meta-analyses, too.
E-research potentially could offer new opportunities for innovation in education. Almost all present meta-analyses have been done on research results rather than on primary data sources. The range of questions that such meta-analyses could answer using already aggregated data is quite limited. If access to original datasets was available, qualitatively new types of data meta-analyses would be possible.
Thinking about present trends towards a decentralised “what works”-type research in education, e-research could open at least two new broad research opportunities:
- Vertical data meta-analyses - longitudinal research that is based on the results and data from the series of "what works"-type research. As an example, Extended-Term Mixed-Method Evaluation Designs (Chatterji, 2004).
- Horizontal data meta-analyses - research that is based on various combinations of data and results from several independent simultaneously conducted studies.
Nevertheless, a lot of still needs to be done not only developing methodologies for documenting, codifying and promoting practice researching (as the Report notices), but also documenting, storing and integrating raw data.
Innovation policy challenge
The OECD report states that “national specificities” could have a big impact on innovation. “The concept of a national innovation system helps to explain why certain clusters of institutions strongly influence innovation strategies and performance in each country. Such national innovation systems can be expected to exist in the education too” (p. 95).
This thought reminded about the earlier Australian Productivity Commission’s Issues Paper which defined the role of social science in since innovation in the following way: “The focus is thus on the physical and biological sciences, including engineering, with the social sciences (and the arts and humanities) excluded except to the extent they are relevant to innovation” (p. 5).
This formal national definition of the science innovation has changed recently to include HASS (see new PC Report). Education (specifically, math and science) has been emphasised as an important enabler of innovation. However, it looks as if the innovation in education per se stayed in the periphery of the national innovation agenda.
Selected readings about innovation in Australia
CHASS. (2006 July). CHASS Submission: Productivity Commission study on Science and Innovation. Council for the Humanities, Arts and Social Sciences. URL
Productivity Commission. (2007 March). Public Support for Science and Innovation. Research Report. URL
Productivity Commission. (2006 April). Public Support for Science and Innovation. Issues Paper. URL
ALP. (2007 April). New directions for innovation, competitiveness and productivity
New directions paper and a ten point plan for innovation in Australia. URL
OECD. (2004). Knowledge Management. Innovation in the Knowledge Economy: Implications for Education and Learning. OECD: OECD Publishing, Centre for Educational Research and Innovation, 10 May 2004, ISBN: 9789264105607. URL
Chatterji, M. (2004). Evidence on “What Works”: An Argument for Extended-Term Mixed-Method (ETMM) Evaluation Designs. Educational Researcher, 33 (9), 3-13.
What Works Clearinghouse. URL