1. PhD Success in Qualitative Research Sten Ludvigsen InterMedia University of Oslo

2. PhD Success in Qualitative Research  Empirical contexts – InterMedia  Design experiments in schools (science, project work, social science, art history, etc)  Other naturalistic settings – workplaces (hospitals, computer engineering, software development – knowledge management system in action)  Video-ethnography –  observations – documents – video-recordings- interview – logs,

3. PhD Success in Qualitative Research  Rigor in methods, strategies, review and theory  Relevance – first and second order analysis  Members orientation  Systematic review

4. PhD success in …  Research design and analytic strategies  Design: theory, conceptual system, methods, analytic strategies, data, empirical results and findings

5. Design  Experiments  Quasi-experiments  Design experiments  Field trials  Ethnographic studies

6. Design  Theory-driven, but  Status of empirical data  Instruments-driven, but  Status of frames of interpretation  Explorative, hypothesis-testing, research question; theory based, empirical based

7. Analytic strategies  Coding, set of predefined categories  Structure and patterns  Emerging talk – categories  Processes  Relationships  Structure

8. Assumptions and core ideas  Framing  Turn to social practice  Social interaction  Tool  Materiality  Instruments

9. Analytic strategies  Research questions  How do participants talk about ……  Do content- or process-based prompts leads to most effective learning?  How do teachers organize the activities?  Which objects transform the activities  What's the relationship between the teachers actions and the students uptake?  What's the students orientations; social, epistemological, institutional …  Which concepts is used by students?

10. Analysing interactional data  Activity – interaction  Interviews  Observation  Video recorded data  Automatic generated data

11. Analysing interactional data  Theory as premises  Review  Empirical design  Data – how, what, ……  Unit of analysis  Levels of descriptions

12. The computer-based 3D models

13. The Situated and Historical Nature of CSCL………. Extract 1: Scientific concepts in flux Cornelia: I understood that we were going to build bricks and so on or build upward [in the 3D model]. I understood that and looking for all of these [amino acids]. I did not understand what insulin or a protein is … what a, why should we find these GTA and then it becomes Met and so on? That … I understood why we did that, but not why or what it means, and so on. Pat: No, neither did I. Cornelia: And then I didn’t think there was any point to building that thing [the 3D model of the protein] when we didn’t understand anything. Mark: I don’t understand anything. Fredric: Understand what? Mark: Well, what, what, what is it supposed to be good for? Fredric: What it is good for? You should help that guy! Because he... Mark: Why is it like that? Yes, why is it like that, so to speak? I will never understand that. Why is it like that? Pat: There should have been some links where it stood, so to speak, what you should do or what the different things meant. Teacher: Mmm. Pat: So that you understood it better. Fredric: Isn’t it just that way, so to speak...?

14. Model for analysing group interaction  Unfolding interaction with tools  Particularization and categorization  How to get a valid understanding  Multiplicity as starting point  Interconnectedness  Sensemaking (members orientation)  Dynamic understanding of context  Multiple layers of context  Sequences – but not only  Historical influence

15. Analysing interactional data  Step 1:  Overview over the corpus  Themes  Read many times – what do the participant do and what do they try to achieve

16. Analysing interactional data  Step 2:  Segments  Episodes  Time frames

17. Analysing interactional data  Step 3:  Intuitive  Contra intuitive  Usual – unusual  How do the participants orient themselves in relation to the others  The content of the talk  Specific terms, concepts,

18. Analysing interactional data  Step 4:  Introduction of a theme – closure  Thematic shifts –  Semiotic resources  Artifacts, language, history  Resources that gives directions – or conceptual

19. Analysing interactional data  Step 5:  Construction of time  Connection between types of data  Example: cut and paste – cognitive effort

20. Analysing interactional data  Step 6:  Key utterances – short sequences that create direction for the activities  Long sequences  Example: I do not understand (student)  Teachers interventions  Uptake over time – perspectives

21. The Situated and Historical Nature of CSCL………. Extract 1: Scientific concepts in flux Cornelia: I understood that we were going to build bricks and so on or build upward [in the 3D model]. I understood that and looking for all of these [amino acids]. I did not understand what insulin or a protein is … what a, why should we find these GTA and then it becomes Met and so on? That … I understood why we did that, but not why or what it means, and so on. Pat: No, neither did I. Cornelia: And then I didn’t think there was any point to building that thing [the 3D model of the protein] when we didn’t understand anything. Mark: I don’t understand anything. Fredric: Understand what? Mark: Well, what, what, what is it supposed to be good for? Fredric: What it is good for? You should help that guy! Because he... Mark: Why is it like that? Yes, why is it like that, so to speak? I will never understand that. Why is it like that? Pat: There should have been some links where it stood, so to speak, what you should do or what the different things meant. Teacher: Mmm. Pat: So that you understood it better. Fredric: Isn’t it just that way, so to speak...?

22. Analysing interactional data  Step 7:  Summary so far:  Data level  Data-data level  First order analysis – members categories and orientations

23. Analysing interactional data  Step 8:  Towards theory and analytic concepts  Orientations  Question, answers, summary, explanations, clarification, deepening, broadened, confrontations, elaboration, conclusion, ……

24. Analysing interactional data  Step 9:  Analytical concepts  Scaffolds, artifacts, resources, object, tensions, break downs, tools, history, community, rules, div. of labor, dialogue, ……..

25. Analysing interactional data  Step 10:  Back to research questions  Step 11  Interpretation based on the review  Step 12:  Interpretation based on theory – analytic concepts

26. Analysing interactional data  Step 13:  Discussion and conclusion  Second order analysis  Reliability  Validity  Type of generalizations (scale and scope)

27. Analysing interactional data  Step 14:  Levels of explanation:  Empirical data – and the main level of explanation  Ontogenesis  Micro genesis  Sociogenesis  Phylogenies 

28. Analysing interactional data  Step 15:  Institutional – historical – cognition  Premises – or outcome  To be shown

29. Analysing interactional data  Step 16:  The relationship between structure – and emerging talk

30. Analysing interactional data  Step 18:  In the family of socio-cultural perspective tension between structural- and phenomenological theories

31. PhD Success in Qualitative Research  Steps to be taken in a article  Data reduction  Data selection  Data analysis  Data presentation

32. PhD Success in Qualitative Research  Summary  Corpus  Transcripts ….  What it consist of  Zooming in – (Roth, 200x)  Zooming out

33. PhD Success in Qualitative Research  Summary  The phenomena – instruments – planning –  Variation – in depth analysis  Students engagement –  Everyday talk – more oriented towards concepts

34. PhD Success in Qualitative Research  Summary  Learning – metaphors  Change of ……..  Levels of explanation