phd success in qualitative research sten ludvigsen intermedia university of oslo
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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,
PhD Success in Qualitative Research
Rigor in methods, strategies, review and theory
Relevance – first and second order analysis Members orientationSystematic review
PhD success in …
Research design and analytic strategies
Design: theory, conceptual system, methods, analytic strategies, data, empirical results and findings
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
Analytic strategies
Coding, set of predefined categories Structure and patterns
Emerging talk – categories Processes Relationships Structure
Assumptions and core ideas
FramingTurn to social practice Social interaction Tool Materiality Instruments
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?
Analysing interactional data
Activity – interaction Interviews Observation Video recorded data Automatic generated data
Analysing interactional data
Theory as premises Review Empirical design Data – how, what, …… Unit of analysis Levels of descriptions
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...?
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
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
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,
Analysing interactional data
Step 4: Introduction of a theme – closure Thematic shifts – Semiotic resources
Artifacts, language, history Resources that gives directions – or conceptual
Analysing interactional data
Step 5: Construction of time Connection between types of data Example: cut and paste – cognitive effort
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
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...?
Analysing interactional data
Step 7: Summary so far:
Data level Data-data level
First order analysis – members categories and orientations
Analysing interactional data
Step 8: Towards theory and analytic concepts Orientations
Question, answers, summary, explanations, clarification, deepening, broadened, confrontations, elaboration, conclusion, ……
Analysing interactional data
Step 9: Analytical concepts Scaffolds, artifacts, resources, object,
tensions, break downs, tools, history, community, rules, div. of labor, dialogue, ……..
Analysing interactional data
Step 10: Back to research questions
Step 11 Interpretation based on the review
Step 12: Interpretation based on theory – analytic concepts
Analysing interactional data
Step 13: Discussion and conclusion
Second order analysis
Reliability Validity Type of generalizations (scale and scope)
Analysing interactional data
Step 14: Levels of explanation: Empirical data – and the main level of explanation
Ontogenesis Micro genesis Sociogenesis Phylogenies
Analysing interactional data
Step 15: Institutional – historical – cognition
Premises – or outcome To be shown
Analysing interactional data
Step 18: In the family of socio-cultural perspective
tension between structural- and phenomenological theories
PhD Success in Qualitative Research Steps to be taken in a article
Data reduction Data selection Data analysis Data presentation
PhD Success in Qualitative Research
Summary Corpus
Transcripts ….
What it consist of Zooming in – (Roth, 200x) Zooming out
PhD Success in Qualitative Research
Summary The phenomena – instruments – planning – Variation – in depth analysis Students engagement –
Everyday talk – more oriented towards concepts