Learning and Teaching with Digital Media: A Status Assessment of the Field

Introduction to Digitalization in Education

• Digitalization impacts all essential social areas and influences education in unprecedented ways.

• Digitalization is an object of education: Learners must be enabled to use digital media competently in daily and professional life to ensure social participation.

• Media competence includes:

  • Use of subject-independent digital offers (e.g., searching the internet for goal-relevant information).

  • Application of subject-specific digital tools (e.g., computer-based spreadsheet applications and simulations for mathematical problems).

• Media competence extends beyond technical operation to critical-reflective use concerning opportunities and risks.

• For educational staff, the expansion of educational goals around media competence implies:

  • Teachers must possess these competencies themselves.

  • Teachers must have the competencies to teach media competence.

• There is normative consensus (e.g., Standing Conference of the Ministers of Education and Cultural Affairs, $2016$) that media competence is a relevant educational goal in the $21^{st}$ century.

• Digitalization changes the design of teaching and learning processes: The focus is on using digital media to facilitate and improve the achievement of subject-related and interdisciplinary goals.

• The use of digital media is linked to the expectation of "added value" (Mehrwert) for teaching and learning.

Evidence for the Value of Digital Media (Meta-Analyses)

• Global assessments of computer-based learning media often show positive results:

  • Tamim et al. ($2011$): Summary of $25$ meta-analyses shows a small to medium effect in favor of computer-based media, though with strong fluctuations.

  • Chauhan ($2017$): Meta-analysis of $122$ studies in elementary education ($N = 32,096$) found a medium effect (Hedges’ $g = 0.55$).

    • Subject differentiation: Large effects for natural sciences, medium for languages and math, smallest for social sciences.

  • Hillmayr et al. ($2020$): Meta-analysis of $92$ studies shows the use of digital tools in math and science has a positive medium to large effect on learning performance (Hedges’ $g = 0.65$) compared to non-technology instruction.

• Conclusion: The question of added value is answered positively by research, though it is the implementation that matters.

Research Perspectives: Technology-Enhanced Learning (TEL) vs. Technology-Enhanced Teaching (TET)

• The contribution distinguishes between two research traditions:

• Technology-Enhanced Learning (TEL):

  • Established since the $1960s$.

  • Focuses on the individual use of computer-based learning environments.

  • Investigates learning processes and outcomes based on media features (e.g., multimedia, interactivity, feedback) and learner characteristics (e.g., prior knowledge, self-regulation).

  • Interaction occurs in a self-contained scenario; the teacher’s role is minimal (administration or constant).

  • Based in experimental teaching-learning research/laboratory settings.

• Technology-Enhanced Teaching (TET):

  • Emerged more recently with the availability of technology in schools (post-$2000$).

  • Views digital media as part of complex teaching-learning arrangements (digital and analog).

  • Focuses on the classroom context, teacher role, and the integration/orchestration of media.

  • Learning results are explained by overall teaching processes, not just the media itself.

• Integrated Approach: Gavriel Salomon ($1990$) argued for studying "the flute (media) and the orchestra (teaching context)."

Technology-Enhanced Learning (TEL): Effects and Affordances

• Early TEL was rooted in programmed instruction (Skinner, $1986$), focusing on practicing narrow skills with automatic feedback.

• Technical innovations expanded design to include:

  • Multimedia and immersive systems (e.g., Virtual Reality).

  • Dynamic and networked structures (e.g., simulations, hypermedia).

  • Constructive tools for learners to design their own environments.

  • Collaborative environments for sharing artifacts.

• The Clark-Kozma Debate:

  • Richard Clark ($1983$): Argued that media are "mere vehicles" (like a grocery truck delivering nutrition) and have no influence on learning achievement; only instruction methods matter.

  • Robert Kozma ($1991, 1994$): Argued that media have inherent functionalities or affordances for learning. Positive effects occur when instruction utilizes these functionalities to support cognitive processes that are impossible without the medium.

• Affordances:

  • Concept from ecological psychology (Gibson, $1977$) applied to digital design (Norman, $1988$).

  • Design features that "invite" specific interactions.

  • Example: Dynamic visualizations (animations) trigger the cognitive process of understanding change.

  • Meta-analysis on dynamic visualizations (Ploetzner et al., $2020$): Animations only outperform static images when the learning goal is specifically to understand process-related events.

Specific Applications in TEL: Intelligent Tutoring Systems (ITS)

• ITS represent a peak of TEL, adapting to individual learners via modeling:

  • Task Model: Specifies declarative and procedural knowledge required.

  • Learner Model: Generated from task solutions and error analyses.

  • Adaptive Loop: Continuous comparison of models to provide adaptive feedback.

• Meta-analysis by Ma et al. ($2014$) ($107$ effect sizes, $N = 14,321$):

  • ITS vs. regular classroom instruction: Hedges’ $g = 0.42$.

  • ITS vs. other computer applications: Hedges’ $g = 0.57$.

  • ITS vs. books/workbooks: Hedges’ $g = 0.35$.

  • Comparison with human tutors: No significant difference; ITS are functionally equivalent to one-on-one human tutoring (Corno, $2008$; Dumont, $2019$).

Effects "With" vs. "Of" Media

• Salomon and Perkins ($2005$) distinguish between:

  • Effects WITH media: Observed only during media use (e.g., better math solutions while using a calculator).

  • Effects OF media: Observable even after the medium is removed (e.g., sustainable knowledge acquisition or improved self-regulation skills).

• Negative possibilities: Learners might "unlearn" self-regulation if they become overly dependent on system feedback.

• Short-term media studies often fail to capture sustainable "effects of" media.

Technology-Enhanced Teaching (TET): Functional Models

• RAT Model (Hughes et al., 2006):

  • Replacement: Media replaces existing practice without changing goals/processes.

  • Amplification: Media increases the efficiency or effect of instruction.

  • Transformation: Media fundamentally changes instruction/goals.

• SAMR Model (Puentedura, 2006):

  • Substitution: Technology acts as a direct tool substitute with no functional change.

  • Augmentation: Technology acts as a tool substitute with functional improvement.

  • Modification: Technology allows for significant task redesign.

  • Redefinition: Technology allows for the creation of new tasks previously inconceivable.

• Critiques: Lack of valid operationalization to reliably distinguish levels; evidence cited by Puentedura ($2014$) was criticized for arbitrary study selection.

Classroom Quality and Process Quality in TET

• Lachner et al. ($2020$) suggest aligning TET research with traditional classroom research based on three dimensions:

  • Efficient Classroom Management: Maximizing learning time by reducing non-task behavior.

  • Cognitive Activation: High-quality/challenging tasks within the "zone of proximal development" (Vygotski, $1987$).

  • Supportive Climate: Climate where students feel valued and receive guidance.

• Empirical evidence: Students in tablet-based classes perceive instruction more positively, especially those with lower motivation or lower cognitive performance (Hammer et al., $2021$).

• The term Digitalität (Digitality) is increasingly used instead of digitalization to reflect the blurring lines between digital and analog, avoiding the implication of just "converting" analog predecessors.

Classroom Orchestration

• Orchestration refers to the process of integrating digital media into complex classroom activities (Dillenbourg, $2013$; Sharples, $2013$).

• Success depends on the synergy between learning media, learning goals, context, and social forms.

• Researchers refer to the "choreography" of teaching (Oser & Baeriswyl, $2001$).

• Example: Science Education.

  • Real experiments vs. virtual simulations.

  • Research shows highest efficacy when combined: Real experiments for familiarization with phenomena; virtual simulations for abstraction and detailed modeling (Wörner et al., $2021$).

Professional Competencies for Technology Integration

• Will-Skill-Tool Model (Knezek & Christensen, 2016):

  • Will: Positive attitude towards technology.

  • Skill: Required technical abilities.

  • Tool: Access to technology.

  • Explains $60\%$ of variance in intensity of media use (Petko, $2012$).

• Technology Acceptance Model (TAM; Davis, 1989):

  • Behavioral intention depends on Attitudes, Ease of Use, and Perceived Usefulness.

  • Meta-analysis ($45$ studies): TAM variables explain $39.2\%$ of teachers' usage intentions. "Perceived usefulness" has a direct effect.

• Second-order barrier: Motivation/belief is often a greater hurdle than equipment access (first-order barrier).

The TPACK Framework

• Developed by Mishra and Koehler ($2006$) based on Shulman ($1987$).

• Components:

  • Pedagogical Knowledge (PK)

  • Content Knowledge (CK)

  • Technological Knowledge (TK)

• Intersections:

  • PCK: Pedagogical Content Knowledge.

  • TCK: Technological Content Knowledge.

  • TPK: Technological Pedagogical Knowledge.

  • TPACK: The central intersection of all three.

• Critiques of TPACK:

  • Poorly defined boundaries between knowledge types.

  • TK is notoriously hard to define due to the evolving nature of tech.

  • FITness (Fluency of Information Technology): Understanding tech well enough to use it productively and adapt to changes.

  • Integrative view (sum of parts) vs. Transformative view (TPACK as a unique, independent construct).

Measuring Teacher Digital Competence

• Most research relies on self-reports, which measure self-efficacy rather than actual knowledge.

• PIAAC Data: Objective measurements of digital competency correlate poorly with self-reports (Hämäläinen et al., $2021$).

• ICILS 2018 (Germany results):

  • High confidence in basic tasks: $98.1\%$ can find materials online; $78.9\%$ can prepare lessons with tech.

  • Low specialized confidence: Only $49.3\%$ use tech for diagnostic purposes (intl. avg: $78.4\%$); only $33.6\%$ use Learning Management Systems (intl. avg: $58.8\%$).

• Objective Knowledge Tests:

  • Lachner et al. ($2019$): Tested TPK using vignettes; distinguished between levels of teacher experience.

  • Baier and Kunter ($2020$): Open items mapping technological affordances to pedagogical functions.

• Challenges in testing: Experienced teachers often have "encapsulated knowledge" (Boshuizen & Schmidt, $1992$) that is hard to verbalize but guides behavior.

Synergy Potential and Future Directions

• Potential for "task-sharing" between humans and machines.

• Dashboards: Providing teachers with learning process data from adaptive systems to support "data-based adaptive teaching" (Xhakaj et al., $2017$).

• Future research needs to bridge TEL and TET by:

  • Linking specific media affordances to classroom quality dimensions.

  • Validating knowledge tests against actual classroom performance.

  • Developing instructional concepts for resources like the Go-Lab collection ($Golabz.eu$).

  • Investigating how process data (Learning Analytics) can be made informative and useful for teachers in daily practice.