More from the Network Learning Conference with Peter Jandric on Research Methods & the post-disciplinary challenge of network learning. Good research has an ‘itch to scratch’. In the case of network learning, there is a range of different methodological approaches. Raises the question on how to compare and synthesise different approaches in network learning?
The Rise of Disciplinarity: Ancient Greece had no discplinary borders but as knowledge became more complex so disciplines began to emerge eg, the seven liberal arts identified in the 7th century that still form the structure of humanities disciplines in western HE to the present day. The liberal arts articulated as the education of a gentleman by C19th (Parker 1890) implying other educations suitable for others, eg, vocational. So disciplinarity became linked to issues of class and culture.
Linking disciplinarity and technique – as human techniques develop, there is increased complexity and so we need more disciplines to cope. But this leads to fragmentation between disciplines as the restrictions of specialisation missing the bigger picture. But also disciplines must therefore, shape how we perceive the future possibilities.
Disciplinarity and the network: radical changes in science occurs through ‘blue skies research’ led by superstar scientists that is formally recognised. But what gets funded is applied research (STEM etc.).
New fields of research such as environmental science and network learning that is postdisciplinarity (see Buckler 2004). The diversity of the field requires diverse knowledge. This opens up large opportunities for forming connections between disciplines and research methods but faces large epistemological challenges.
Four postdisciplinary approaches:
1. multi-disciplinary learning, eg, through technology studies, through learning theory
2. interdisciplinarity seeks integrative results through different methods
3. transdisciplinarity seeks to inform and transform research through integrating disciplines
4. antidisciplinarity where disciplines abandoned entirely.
All these approaches opens up questions on the nature of inquiry on network learning. Points to the importance of being critically conscious of the way we inquire in to network learning.
Q. is antidisciplinarity feasible given strengths of disciplines but also if no disciplinary boundaries that is an interesting space to be in?
A. cites example of HIV AIDs as educationallisation of medicine eg, through preventative awareness raising.
Point made that network learning is a field which people bring their disciplines to.
Cathy Adams and Terrie-Lynn Thompson on materialities of posthuman inquiry. Have you considered the tools used in research may also shape that practice? These can be positive and negative on the research process. But academic expertise is bound up with technologies used daily and shapes that practice and their performative outcomes. Digital technologies are the encoded materialities of academic practice. Looking at the insights provided from Actor Network Theory and from phenomenology. Ingold explores the link between materiality and phenomena in correspondence. ANT subject-object separation are undermined through symmetry while in phenomenology, subject-object division becomes translucent.
Research practice assemblages of long lists of tools for diffusion, search engines, storage tools, visualisation software, etc… Enrolled in the research practice through digital traces including digital artefacts. So digital devices may participate and co-research in research storing, sharing and extending data. This deccentres the human expert in elicit and generate data and can be dynamic leading to movement and slippage. So the researcher is deskilled as research outsourced to digital tools and upskilled eg, in research data curation.
NViVo presented by QSR as a solution to the ‘problem’ of qualitative research but the software may configure and surcumscribe research practice (see Introna 2012). Research found NViVo enhanced the quality of data while reducing the tactility of research and enhancing the position of the technologist. Researchers found that demands of NVivo overtook the intent of the research. Researchers must subscribe to the methodological assumptions and structures of the software.
What are the implications of encoded research practices for researching networked learning. That the non-human actors should be treated as part of the research team – their views taken in to account.
Fluencies can be seen in
1. Agency as researchers is shared with encoded actors as entanglements
2. Research practices undergoing deskilling and upskilling including through the attraction of delegation
3. New enactments of data
4. Scale, mobility and scale of data reconfigured.
Points of friction: research defined by technologies; perceived as less objective is less techie; attraction of exotic tech; outsourcing of research tasks; increase in expectations of speed of research.
Q. push back on issue of symmetry and there is a qualitative difference between human and non-human in the research assemblage. That a telescope allows us to see the moon but is not a co-researcher
A. argues that the non-human component enhances the researcher. In the case of encoded technologies, the algorithm is too often black-boxed but its impact on the research process needs to be opened up.
And running out of steam now but worth following the tweets here