EARLI SIG14 keynote Littlejohn FINAL-2008242.pptx

This was the opening keynote presented by Allison Littlejohn at the EARLI SIG14 conference on 20th Aug 2024 in Jyvaskyla, FInland. It questions 'When humans (inter)work with technology �how are they affected?' using concepts of interwork, vulnerability, human agency, epistemic culture, professional identity negotiation and professional sacrafice.

1. This call may be recorded for training purposes: digital technology and professional learning EARLI SIG14 meeting Professor Allison Littlejohn,  Director, UCL Knowledge Lab, University College London Photo by  Markus Spiske  on  Unsplash

2. Photo by A lex Kotliarskyion Unsplash Digital presence & absence. Digital sensing. Digital decision-making. The digital panopticon. (Jeremy Bentham, UCL founder) The ‘Digital’ changes work

3. Title Machines assigns tasks to humans. ‘Gigs’ to gig workers. Diagnoses to surgeons. Hypotheses to scientists.

4. The capability of AI to generate hypotheses raises thought-provoking questions about the nature of creativity in the research process. However, although AI can identify patterns within data, the question remains: can they exhibit true creativity in proposing hypotheses, or are they limited to recognizing patterns within existing data? New relationship with ‘The Digital’

5. New relationship with ‘The Digital’ Cronin Lab, Department of Chemistry, University of Glasgow.

6. New relationship with ‘echo chamber’ Cronin Lab, Department of Chemistry, University of Glasgow.

7. Cronin Lab, Department of Chemistry, University of Glasgow. Knowledge production is social, not only technical. Nonaka, I., & Nishiguchi , T. (2001).  Knowledge emergence: Social, technical, and evolutionary dimensions of knowledge creation . Oxford University Press.

8. When humans (inter)work with technology how are they affected? Part 1: Interwork. Part 2: Agency . Part 3: Vulnerabilities. Part 4: Negotiations. Part 5: Sacrafice. Photo by Rodion Kutsaley on  Unsplash

9. Photo by  Markus Spiske  on  Unsplash Part 1: Interwork

10. Photo by Charanjeet Dhiman on on   Unsplash AI voice analytics detect if a customer is upset. Used to assist customer service agent in de-escalating calls by making real-time suggestions of phrases to use that only the employee can hear. Surveillance of activity and stress levels of the customer and service agent sent to service manager. Humans have to learn to collaborate with AI (and are being replaced by AI at information points). Bromuri , S., Henkel, A. P., Iren, D., & Urovi , V. (2021). Using AI to predict service agent stress from emotion patterns in service interactions. Journal of Service Management ,  32 (4), 581-611. Call centre voice analytics

11. Photo by john Schnobrich on Unsplash Rogan, J., Bucci, S., & Firth, J. (2024). Health Care Professionals’ Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis.  JMIR Mental Health ,  11 , e49577. Sensing technologies use AI to identify mental health risks . U ses smartwatch data to analyse speech characteristics, location, and activity (fatigue, sleep disruption, mood).  Aims to improve diagnostic accuracy , monitor patient trajectories and provide patient timely support. Professionals have concerns related to practice in terms of i ncreased workload, over-reliance on AI sensing, threat of ‘deskilling’ and data privacy. Main concern is the impact on t herapeutic relationships with patients and r isk to patient wellbeing. Mental health sensing technologies

12. Photo by A lex Kotliarskyion Unsplash Agency and forms of resistance

13. Photo by  Markus Spiske  on  Unsplash Part 2: Agency

14. Eteläpelto , A., Vähäsantanen , K., Hökkä , P., & Paloniemi , S. (2014). Identity and agency in professional learning.  International handbook of research in professional and practice-based learning , 645-672. Paivi Hökkä , Anneli Eteläpelto , and Helena Rasku-Puttonen , ‘The Professional Agency of Teacher Educators amid Academic Discourses’, Journal of Education for Teaching 38, no. 1 (February 2012): 83–102. Agency Agency is the response of each professional to change in Knowledge and practice . ( Etelapelto (20 14)

15. Agency Workplace ‘transformation’ involves new knowledge settings -arrangements, process, principles – that are constantly changing the workplace. Knorr- Cetina (2005) A gency mediates the relationship between professionals and the ‘transformed’ world. Workplace Cetina , K. K. (2005). How are global markets global? The architecture of a flow world.  The sociology of financial markets , 38-61.

16. Agency Epistemic culture Epistemic culture is a structural feature of societies that sustains or discourages epistemic outcomes . Knorr- Cetina (2005) ‘Transformation’ may engender ‘ epistemic injustice ’ when changes conflict with professional identity and ways of doing things. Perceived injustices may lead to resistance . Need for negotiation. Workplace

17. Identities negotiated at the intersection of the individual and the (collective) workplace Etelapelto, A., Vahasantanen , K., Hokka , P., Paloniemi , S. (2014)  Identity and Agency in Professional Learning. 

18. Photo by  Markus Spiske  on  Unsplash Part 3: Vulnerabilities

19. Durán del Fierro, F., Littlejohn, A., & Kennedy, E. (2024). Sociotechnical Imaginaries of Sharing and Emerging Postdigital Meaning-Making Practices in the Astronomy Community. Postdigital Science and Education.  https://doi.org/10.1007/s42438-024-00473-5 Case 1: Vulnerabilities of astronomers 18 interviews, May-June 2023.

20. Theme # 1 Concerns about changing valued systemic practices. ‘I feel that like when I when I did my first, you know research project as a master's student, it was very hands-on. It was a Here's an X-ray data set, and I did all of the computations for it, and came up with, you know, an answer, and that really felt like science. And now I feel most of the times like, maybe I'm more of like a technician, and then when I get the results eventually, then I can do some science. But the science that i'm doing at that point is more, you know, the kind of science that you could give if you give a like a beginning student right where you just say, here's a here's a final catalog , or here's a final image, and then do some science, and in some ways that's very nice, because I am a very busy person, and so it's nice to, you know. Have the hard bit taken out in the middle. But yeah, it also means that I feel sometimes more like I am a technician than a scientist, because I do have to have this whole’ Participant 1 ‘…’  I've spent so much time working on the data processing for a ska pathfinder. So, I know what goes into the data processing for LOFAR. I know how it works. I know what's being done. I've helped test a lot of it, but I've seen Phd . Students that just come along and use a pipeline at the end, and then they just get all these diagnostic pots, and they have, you know, very little understanding of what's happened if something goes wrong. And so, I think that's it. I think that the way people interact with data will change’ Participant 2 I don’t feel I’m doing the work of an astronomer.

21. Theme # 2 Fear of losing freedom/ control. ‘ you stop thinking, you can't any longer have your own personal way of doing things, of working with your own personal hardware. You've got to collaborate with others to do it because you got to use shared resources’‘‘ Participant 5 We either you the user or we, the people supporting you as a user, are going to have to be able to think of clever ways of dealing with data to avoid, you know, everything being really really slow and difficult’ Participant 7  honestly find this quite challenging myself [write research questions]. I mean, there's so much you could do. Where do you start? We always used to, you know, we broke it down into little incremental steps. We could, you know we could do this, we could get that. We could get a nice sample of objects and observe them this way, and then we'd be able to answer this specific question. But if it's already there, do you know what is the step you're going to do? What's the first? Which project should you pick? I think people will get used to this’ Participant 2  As areas of work are automated, I feel I have less freedom ‘‘

22. Theme # 3 Concerns over lack of recognition while working in new constellations. ‘when it's related to science, people like to have ownership of what they do, and they don't always like to give credit for where ideas fundamentally help them get where they are. Yeah, they don't like to give them. They don't like to give credit. They like to pretend they they've done it all’ Participant 3 As  I work in bigger networks, I find it difficult to show my contributions. (Especially early career!)

23. Theme # 4 Erosion of trust in distributed settings. ‘Because we are not going to be able to access or explore the data ourselves. So, I think that another thing that we really need also provided by the ska other teams and new software, etc.,is all the information of everything that has happened to the data before. What has been modified, what has been calibrated, all the changes to the data. And also, for example, what data has been flat and at which level all that information we'll need to know how to explore it because we will not be able to do that ourselves, so we will only be able to look at the reports of what has happened and what how the data was processed. So, to verify the quality of the data, we need to rely on processing that we have not done ourselves. So, I think that's also a change in the mentality that we need to that will be a hard thing to for astronomers in general to have to trust that someone else in this case observatory will have been processing the data, and we will not be able to access that’ Participant 2  As I work indirectly with people/ systems I am not   building trusted relationships.

24. Theme # 5 Difficulty with (verbal and non-verbal) communication in multidisciplinary settings. ‘‘expressing yourself to another scientist who is not an expert in your thing, is actually, I personally feel an even bigger challenge, because there are some things they do understand. You really don't need to dumb it down for them. And there are some things that you do need to like, skip over and understanding that what part they need to understand, what, how you explain it to them is actually going to be a challenge, because and I guess it's going to be the same for them. Participant 4  There's kind of been a bit of a divide ... mean, they've been very successful, because what they do is they co-locate the engineers and the scientists in the same building, the same place. And there's a lot of cross talk between them, but that doesn't always happen, and scientists can really can often look down on engineers. As you know, they're not as smart as us, or you know they don't know what's going on. They just, you know. They just come in and run the equipment. Participant 1 I work with people outside my discipline who  don’t understand my work.

25. Theme # 6 Unequal resource access. ‘‘‘If you're still trying to, you know by hand, collect your data when essentially it's already there, somebody is going to beat you to the publication (…) people who do understand the landscape of the datasets and the interaction with the data sets are gonna win over people who are still’ Participant 3  If it was me [an individual scientist]. I will be writing a scientific proposal. I will send it to the (…) They review it, and I will or won't get my time depending on the science quality of what I'm proposing. Then I will get services from the SRC, to provide the data. However, this comes into this bigger landscape of this allocation of the amount of time available. So, it isn't an open facility in the sense that you have to buy into it as a country. Participant  2 Not everyone has equal access to new resources. ‘‘As part of my transition to working with very large data, I've had to become familiar with all the other stuff that's out there. And a lot of people just haven't actually grasped how much there is that's already being done’ Participant 5

26. Example Roles 18 interviews with: Research Data Scientists Head of Data Management Digital Research Infrastructure Director Infrastructure Coordinator Senior Enterprise Fellow  Early Career Researcher Senior Academic Head of Data Governance Head of Profession for Research Data Stewardship Research Data consultant (ECR) Research Data Steward (ECR) Case 2: Vulnerabilities of data sharers Chisholm, L., Durán del Fierro, F., Littlejohn, A., Kennedy, E. (2024). FAIR Data Accelerator project report, UK Department for Innovation, Science & technology

27. Theme # 1 Concerns about changing systemic practices (data sharing is not a priority) ' The key to getting this right [data sharing] for me is all around collaboration between communities. I think if you can get those scientists excited about the idea of doing some interdisciplinary science using that data, then it's maybe a slightly different conversation, but if it's just that you should make your data available so other people can do that interesting stuff is perhaps not  gonna  work. So it is that  genuine collaboration that draws on the expertise from those that produce the data, and those that want to use the data , I think, is at the heart of making it easier to make progress with this  (…)' Participant 1 Everyone should be sharing their data (not just me!).

28. Theme # 2 Fear of losing control (over data) ' I think you can go through an entire academic training. You go through a degree master's and a Ph. D. without actually having to inquire about what data is (…) It's just the thing that you manipulate (…) And I think once people have a better understanding of what it is. Then I think they're able to say, well, actually, these are the things I need to know about the data (…) people talk about data literacy , which is always a good thing, not a bad thing. But I think perhaps it needs to be raised to a sort of slightly higher level, and that is to have data criticality .' Participant 7 'We need scientists that feel that sense of responsibility for sharing the data that they've produced' Participant 1 How do I maintain data criticality using new infrastructure?

29. Theme # 3 Concerns over lack of recognition (data sharing does not allow for recognition) ‘The old model, always used to be you take the data. That is your data. You've got a proprietary period. You can do what you want with them. If you have an idea nobody else is going to do that exact thing because you've made an observing plan. You've got a bunch of things and provided you don't just leave them there to go into the archive, you'll be able to do your work. What we have within the lofar survey community is, the data belongs to everybody. So, it belongs to everybody in the collaboration afterwards. It belongs to everybody in the world’ Participant 2 As  I work in bigger networks, I find it difficult to show my contributions.

30. Theme # 7 Fear of judgment ' I think if you’re taking to some of the things, and you're asking people to share more and more, they are opening themselves up and making it kind of vulnerable for criticism, and whether that's constructive scientific rigor, like being critical of other people's work and ensuring that you have done correctly, or potentially is a little bit more harmful, and I'm going to tell you that your work is rubbish without any basis for my work, which is obviously much like the negative side to criticism. I think, obviously having that community where people are like, respectful of other people's work (…) allowing criticism where it's valid and well thought or whatever but trying to not enable just all out. I don't know, slinging terrible fingers at each other. So, I think that's one of the major things' Participant 15 As my work becomes more visible, I worry about being judged. 

31. Theme # 8 Leadership not prioritising Sharing. Evidence that Principle Investigators may not want to share data until they have had an opportunity to fully analyse and publish findings. My manager does not value sharing data.

32. Types of Professional Development for professionals sharing data Acquisition of knowledge: Policies Guidelines Standards Frameworks Toolkits 3. Production of artefacts to develop knowledge and skills In person On demand / online General/ technical Discipline –specific 4. Collaboration/ practice: Data Challenges Hackathons Sandpits Data Dives 5. Community Engagement/ inquiry  Community of practice Research communities Facilitation approaches 6. Discussion of careers, recognition and rewards  Hidden REF Career pathways 2. Practical training: Data assets Software and workflows Computational storage & compute

33. Photo by  Markus Spiske  on  Unsplash Part 4: Negotiations

34. Source: https:// www.bbc.co.uk /news/health-46973641

35. UK aid programme, helping low and middle income countries to tackle antimicrobial resistance (AMR). The aim is to improve the surveillance of AMR and generate relevant data that is shared nationally and globally. https:// www.flemingfund.org / Global Programme

36. Professional Roles Country 1 Country 2 Country 3 Laboratory Professionals 9 10 5 Senior Laboratory Professionals 10 4 2 Clinical Services Professionals 1 - - Senior Management Staff in Clinical Services 1 - 1 Policymaker 3 6 4 AMR Community / Expert 1 1 2 Total 25 21 14 Method: field observations and interviews

37. Health professionals need to: Learn about new knowledge and practices relevant to AMR as these change. Understand their position and role within the AMR system as it changes (Theme 4). Adapt the workplace to apply new knowledge / practice (Theme 6).

38. Your role in an AMR surveillance network.  Tool 1 includes a number of activities that can help you and your colleagues reflect on your own roles and responsibilities, and the roles of other people. You can also identify gaps in existing roles within their own work setting (such as a local AMR network), understand the contribution of each role to the network and negotiate how you can work together more effectively. Theme 4 Trust concerns

39. Theme 4 Trust concerns

40. Theme 1 Concerns about practice Dealing with AMR data.  Tool 2 is designed to help you and your colleagues understand your contribution to data collection and management within AMR surveillance systems and identify areas for improvements in your workplace. By using this tool you will also have an opportunity to build on your understanding of bias and validity and the interpretation of data from AMR studies.

41. Theme 1 Concerns about practice

42. Theme 6 Unequal resource access Reflecting on your work and changing your workplace.  Tool 3 encourages you and your colleagues to develop strategies to apply your learning or what you know to day-to-day work. It also helps you to find ways to overcome barriers that delay or stop you as a team from applying your new learning or what you already know.

43. Theme 6 Unequal resource access

44. Learning Designer https://www.ucl.ac.uk/learning-designer/

45. Theme 3 Fear of judgement FAIR data Accelerator Workshop, Group 4 Learning Design on Researcher Development: Recognition for Data Sharing Workshop: Overcoming fear of judgement A imed at researchers who have concerns about sharing data. 2 day workshop, with some pre-prep and post- production. Aims to show how other researchers have overcome concerns, support researchers to discuss approaches, work on & peer review datasets.

46. Theme 3 Fear of judgement

47. Theme 8 Leadership FAIR data Accelerator Workshop, Group 4 Learning Design on Researcher Development: Recognition for Data Sharing Community of Practice The target group were Principal Investigators & senior Leaders. Stakeholders work together to identify, raise awareness of and resolve barriers to data sharing.

48. Theme 8 Leadership

49. Theme 3 Fear of lack of recognition FAIR data Accelerator Workshop, Group 4 Learning Design on Researcher Development: Recognition for Data Sharing Event Series: Improving Recognition for Data Sharing A researcher development activity that could be part of a series of events creating a community of practice with a structured learning path and opportunities for researchers to share experiences of data sharing. Within this, there is cohort-specific training and a culture of peer support. On-demand online training initiatives could be part of this.

50. Theme 3 Fear of lack of recognition

51. FAIR data Accelerator Workshop, Group 4 Learning Design on Researcher Development: Recognition for Data Sharing Workshop: Improving Recognition for Data Sharing A researcher development workshop aimed at senior staff who are in a position to recruit /promote researchers. First, discussion of the importance of data Sharing and what the institution is doing to recognise it. Then, p articipants take part in a collaborative activity around a narrative CV. Finally, participants produce guidance and notes for future job profiles.  Theme 3 Fear of lack of recognition

52. Theme 3 Fear of lack of recognition

53. Photo by  Markus Spiske  on  Unsplash Part 5: Sacrafice

54. At the 1887 International Congress, Astronomers from 16 countries discussed coordination of telescopes. To ensure the adoption of new research infrastructures, the levels of sacrifice demanded of the scientific collective were enormous: ”… the substitution of efficiency for painstaking precision, the monopolization of resources and personnel for long periods of routinized labor , the steadfast resistance to temptation to neglect old, collaborative commitments in pursuit of an exciting new discovery”. ( Dlston , 2023, p57-58 ) What are professionals willing to sacrifice for change? Daston , L. (2023).  Rivals: How Scientists Learned to Cooperate . Columbia Global Reports. Jones, D. (2000)The scientific value of the Carte du Ciel,  Astronomy & Geophysics , Volume 41, Issue 5, October 2000, Pages 5.16–5.20. The International Congress, Paris, 1887

55. Approaches used to stimulate negotiation 1. Change Lab 3. Studies of Socio-Technical Systems. 4. Ethics of Technology in the Workplace 5.  Policy Influence Studies 6. Science and Technology Studies 2 Participatory Design and User- Centreed Design .

56. Wenger, E., Trayner , B., & De Laat, M. (2011). Promoting and assessing value creation in communities and networks: A conceptual framework. Littlejohn, A., Kennedy, E., & Laurillard , D. (2022). Professional learning analytics: Understanding complex learning processes through measurement, collection, analysis, and reporting of MOOC data. In  Methods for Researching Professional Learning and Development: Challenges, Applications and Empirical Illustrations  (pp. 557-578). Cham: Springer International Publishing. Measuring value added

57. Photo by  Markus Spiske  on  Unsplash Call to action

58. Relationships between humans and machines are changing. Leads to empowerment (for some) and vulnerability (for others). Humans (not machines!) are vulnerable to epistemic injustices stimulated by transformation. May trigger forms of resistance . Call to action: work to help professionals to overcome vulnerabilities, creating more equitable workplaces.

59. Special thanks to the following collaborators… Dr Francisco Duran del Fierro, UCL Knowledge Lab Prof Eileen Kennedy,, UCL Knowledge Lab Dr Louise Chisholm, Joint-Director UK SKA Regional Centre Dr Koula Charitonos , Institute of Educational Technology, Open University