Core Claim
Digital literacies research has evolved from technical competencies to encompass complex sociocultural practices, critical engagements, and material-technological entanglements. Ontological and epistemological assumptions underpin methodological choices—understanding these assumptions is essential for designing research and evaluating claims.
Historical Evolution
Phase 1: Functional Literacy (1980s–2000s)
Focus: "Digital literacy" as extension of computer/information literacy
Key characteristics:
- Emphasis on technical skills and cognitive processing
- Measurable competencies
- Individual acquisition model
- Gilster (1997) as foundational text
Underlying assumptions:
- Literacy as neutral skill
- Technology as tool to be mastered
- Context-independent competencies
Phase 2: Sociocultural Turn (2000s–2010s)
Focus: Shift toward New Literacy Studies frameworks
Key characteristics:
- Situated practices over decontextualized skills
- Identity construction
- Multimodal meaning-making
- Street (1995), Gee (1999) as foundations
Underlying assumptions:
- Literacy as social practice
- Context shapes meaning
- Multiple literacies for multiple contexts
Phase 3: Critical/Transformative Wave (2010s–Present)
Focus: Integration of equity theories and critical pedagogy
Key characteristics:
- Power imbalances in digital spaces
- Design justice principles
- Freire (1970) influence
- Attention to marginalization
Underlying assumptions:
- Literacy is political
- Research should serve transformation
- Justice as methodological concern
Phase 4: Post-Digital/Emergent (Emerging)
Focus: Material-technological entanglements, post-qualitative inquiry
Key characteristics:
- Human-nonhuman assemblages
- Algorithmic agency
- Speculative methods
- New materialism influences
Underlying assumptions:
- Agency distributed across human and nonhuman actors
- Categories like "digital" and "literacy" are unstable
- Research methods must evolve with phenomena
Research Paradigms
Positivist-Experimental Designs
Ontology: Single, objective reality exists and can be measured
Epistemology: Knowledge through observation, measurement, hypothesis testing
Methods:
- Randomized controlled trials
- Quasi-experimental designs
- Standardized assessments
- Large-scale surveys
Strengths:
- Generalizable findings
- Policy-relevant at scale
- Clear causal claims (when well-designed)
Limitations:
- May miss contextual nuance
- Privilege measurable over meaningful
- Risk of reductionism
Questions it answers well:
- Does intervention X improve outcome Y?
- What is the prevalence of skill Z?
- Are there significant differences between groups?
Interpretive Designs
Ontology: Multiple, constructed realities exist
Epistemology: Knowledge through understanding meaning-making
Methods:
- Ethnography
- Case study
- Narrative inquiry
- Phenomenology
Strengths:
- Rich contextual understanding
- Participant perspectives centered
- Captures complexity
Limitations:
- Limited generalizability
- Time-intensive
- Researcher positionality challenges
Questions it answers well:
- How do participants experience X?
- What meanings do people make of Y?
- What are the situated practices of Z?
Critical Designs
Ontology: Reality shaped by power structures that can be transformed
Epistemology: Knowledge is political; research should serve emancipation
Methods:
- Critical discourse analysis
- Participatory action research
- Critical ethnography
- Design-based research with equity focus
Strengths:
- Addresses power and inequality
- Connects to social change
- Centers marginalized perspectives
Limitations:
- May be dismissed as "advocacy"
- Tension between research and activism
- Complexity of power analysis
Questions it answers well:
- How do power structures shape X?
- Whose interests are served by Y?
- How can Z be transformed for justice?
Post-Qualitative Designs
Ontology: Reality as processual, relational, emergent
Epistemology: Knowledge as entangled, partial, always becoming
Methods:
- Diffractive analysis
- Speculative inquiry
- Assemblage mapping
- Artistic/creative methods
Strengths:
- Captures emergence and complexity
- Attends to nonhuman actors
- Methodological innovation
Limitations:
- Difficult to communicate to policy audiences
- Newer, less established
- Can seem abstract
Questions it answers well:
- What is emerging in this assemblage?
- How do human and nonhuman actors co-constitute X?
- What might become possible?
Choosing a Paradigm
Alignment Questions
-
What is the nature of the phenomenon?
- Stable and measurable → positivist
- Meaning-laden and contextual → interpretive
- Power-structured and changeable → critical
- Emergent and entangled → post-qualitative
-
What kind of claims do you want to make?
- Causal, generalizable → positivist
- Descriptive, contextual → interpretive
- Transformative → critical
- Speculative, generative → post-qualitative
-
Who is the audience?
- Policymakers → often positivist
- Practitioners → often interpretive
- Advocates/activists → often critical
- Scholars → any, depends on field
-
What are your commitments?
- Objectivity/neutrality → positivist
- Understanding → interpretive
- Justice → critical
- Experimentation → post-qualitative
Mixed and Integrated Approaches
Many digital literacies researchers combine paradigms:
- Sequential designs (qualitative exploration → quantitative testing)
- Concurrent designs (multiple methods simultaneously)
- Critical quantitative approaches
- Participatory mixed methods
The key is coherence—ontological and epistemological assumptions should align across the design.
Tensions in the Field
Functional vs. Critical
- Skills acquisition vs. power analysis
- Individual competence vs. systemic change
- Measurable outcomes vs. transformation
Local vs. Generalizable
- Rich case studies vs. scalable findings
- Context-sensitivity vs. transferability
- Depth vs. breadth
Present vs. Emerging
- Studying current practices vs. anticipating futures
- Established methods vs. methodological innovation
- Known phenomena vs. emergent possibilities
Open Questions
- How do we study phenomena (AI, algorithms) that are opaque and rapidly changing?
- What methods are appropriate for researching human-AI entanglements?
- How do we maintain critical edge while producing policy-relevant findings?
- What does "rigor" mean across different paradigms?
Key Formulations (Preserve These)
"Digital literacies research has evolved from technical competencies to encompass complex sociocultural practices, critical engagements, and material-technological entanglements."
"Ontological and epistemological assumptions underpin methodological choices."
"The key is coherence—ontological and epistemological assumptions should align across the design."
"From functional literacy to sociocultural practice to critical/transformative to post-qualitative—each wave builds on and challenges what came before."