Digital Competence Measure

Abstract

The Digital Competence Measure (DCM), developed by Isabell Runge, Rebecca Lazarides, Charlott Rubach, Dirk Richter, and Katharina Scheiter (2023), is a psychometrically validated self-report instrument engineered to evaluate educators' competence-related beliefs regarding technology-enhanced instruction. Grounded explicitly in Area 5 (“Empowering Learners”) of the European Digital Competence Framework for Educators (DigCompEdu), the scale assesses teachers' subjective perceptions of their capability to implement learner-centered digital practices. The measure comprises 12 items systematically distributed across two correlated yet structurally distinct subscales: (1) Digital competence-related beliefs regarding differentiation (6 items) and (2) Digital competence-related beliefs regarding actively engaging learners (6 items). Respondents rate items along an authentic five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Psychometric evaluations were conducted on an empirical sample of in-service teachers in Germany. Structural equation modeling and extensive factor analyses—encompassing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM), and bifactor configurations—demonstrated that a bifactor CFA specification exhibits superior construct modeling, capturing a general dimension of learner empowerment alongside distinct subscale specificities. Reliability assessments demonstrate exemplary internal consistency, with McDonald's omega (ω) coefficients spanning .93 to .96 across subscales and the global latent composite. The DCM offers educational researchers, school administrators, and teacher training programs an empirically robust, theoretically sound diagnostic tool to assess digital self-efficacy, monitor professional development interventions, and clarify how educators' beliefs relate to observed and reported instructional quality in digital classrooms.

Keywords

Digital Competence Measure, Teacher Digital Competence, DigCompEdu, Empowering Learners, Differentiation, Actively Engaging Learners, Self-Efficacy Beliefs, Instructional Quality, Educational Technology, Structural Equation Modeling, Bifactor CFA, Teacher Professional Development

Authors

The Digital Competence Measure was conceptualized, operationalized, and psychometrically validated by a consortium of educational psychologists and empirical education researchers in Germany:

• Isabell Runge (ORCID: 0000-0002-4335-7451) – University of Potsdam, Department of Educational Sciences, Karl-Liebknecht-Straße 24-25, 14476 Potsdam, Germany. Email: [email protected] (Corresponding Author).
• Rebecca Lazarides (ORCID: 0000-0003-0392-4981) – University of Potsdam, Department of Educational Sciences, Potsdam, Germany.
• Charlott Rubach (ORCID: 0000-0003-0451-6429) – University of Rostock, Institute for School Pedagogy and Educational Research, Rostock, Germany.
• Dirk Richter (ORCID: 0000-0002-2384-1588) – University of Potsdam, Department of Educational Sciences, Potsdam, Germany.
• Katharina Scheiter (ORCID: 0000-0002-9397-7544) – University of Potsdam, Department of Educational Sciences, Potsdam, Germany.

Purpose

The primary purpose of the Digital Competence Measure (DCM) is to provide an empirically validated, fine-grained diagnostic questionnaire that captures teachers' competence-related beliefs regarding learner empowerment through digital technologies. While the integration of digital media into educational environments has accelerated globally, research consistently indicates that the mere availability of hardware and software infrastructure does not inevitably translate into high-quality instruction. Instead, the pedagogical agency of the instructor—governed centrally by their competence beliefs, digital self-efficacy, and pedagogical-technological orientations—serves as the pivotal catalyst determining whether technology is utilized for passive knowledge transmission or transformative, student-centered learning.

Existing instruments measuring educators' digital competencies often emphasize technical proficiencies, such as operating specific operating systems, managing learning management platforms, or executing basic digital administrative tasks. Such operational assessments frequently fail to capture the complex, pedagogical reasoning necessary for high-level didactic transformation. The DCM directly resolves this theoretical and empirical void by isolating Area 5 of the European Digital Competence Framework for Educators (DigCompEdu), titled “Empowering Learners”. This area addresses how educators leverage digital media to accommodate heterogeneous student capabilities, adapt pedagogical pacing, scaffold individual learning pathways, and foster cognitive and collaborative engagement.

In academic research, the DCM functions as an explanatory tool within structural equation models examining the determinants of teacher-reported and student-perceived instructional quality. By providing separate dimensions for differentiation and active learner engagement, the scale enables investigators to examine differential trajectories: for example, clarifying whether professional development interventions disproportionately boost teachers' confidence in motivating students (active engagement) while leaving adaptive assessment and personalization (differentiation) relatively unchanged. Furthermore, the scale serves longitudinal research paradigms tracking the developmental trajectory of pre-service teachers across their university preparation, practical induction, and ongoing professional development phases.

In practical and applied educational settings, the DCM serves as an evaluative benchmarking and needs-analysis tool for school leadership teams, instructional coaches, and educational policymakers. As school systems implement nationwide digital transformation strategies, administrators require reliable instruments to evaluate where instructional staff perceive significant professional hurdles. Rather than imposing one-size-fits-all digital training programs, educational authorities can use the DCM to identify specific deficits in differentiation or cognitive activation, thereby personalizing continuing professional development programs to meet teachers' pedagogical needs.

Psychological Construct

The construct operationalized by the Digital Competence Measure represents a specialized manifestation of teacher self-efficacy situated within technology-mediated teaching environments. In psychological and educational research, competence-related beliefs reflect an individual's judgment of their capabilities to execute specific pedagogical courses of action required to attain designated educational outcomes. Rather than capturing objective, externally tested procedural knowledge, the DCM quantifies the teacher's subjective conviction that they possess the pedagogical design skills, reflective capability, and technological fluency required to foster autonomous, individualized, and cognitively vibrant learning environments. The DCM delineates this broader construct into two distinct pedagogical subdimensions:

1. Digital Competence-Related Beliefs Regarding Differentiation

This dimension reflects an educator's perceived capability to harness digital technologies to address learner heterogeneity, personalize educational trajectories, and provide equitable learning opportunities. Modern classrooms are characterized by substantial variance in students' prior knowledge, working memory capacity, linguistic background, and learning speeds. Digital tools offer unprecedented possibilities to adapt task difficulty dynamically, offer individualized automated feedback, and present multimodal instructional representations. However, successfully implementing these strategies requires complex pedagogical-technological reasoning.

Teachers who score high on this subscale report strong confidence in their ability to select digital tools that allow students to progress at autonomous paces and distinct tiers of academic challenge (e.g., adaptive learning software, branching digital tasks). They feel capable of orchestrating digital diagnostic tools during the planning and assessment phases of instruction to align curricular demands with individual student profiles. Moreover, this construct encompasses advanced metacognitive and developmental competencies: highly competent educators do not merely consume existing software; they critically reflect upon whether a specific digital intervention genuinely supports personalization, modify their instructional designs based on diagnostic observations, and reconfigure or combine digital materials to optimize individualized learning trajectories.

2. Digital Competence-Related Beliefs Regarding Actively Engaging Learners

This subscale captures an educator's perceived capacity to utilize digital technologies to stimulate students' cognitive activation, emotional motivation, collaborative interaction, and autonomous knowledge construction. While digital media can inadvertently foster passive media consumption or surface-level distraction, pedagogically skilled teachers deploy digital technologies to foster active, exploratory, and self-regulated learning.

Educators endorsing high levels of this construct believe in their capability to deploy gamified elements, interactive response systems, and collaborative web platforms to generate sustained situational and individual interest. Beyond basic gamification, these beliefs capture the teacher's perceived ability to orchestrate complex, cognitively demanding tasks, such as collaborative digital project work, inquiry-based online simulations, and media-rich creative productions. High-scoring teachers perceive themselves as competent in reflecting on whether digital tools actively engage students at deep cognitive levels, demonstrating the agency to continuously innovate, refine, and adapt technology-enhanced strategies to foster student agency and self-regulated learning.

Theoretical Framework

The development of the Digital Competence Measure is grounded in two primary theoretical paradigms: Bandura's Social Cognitive Theory and the European Commission's Digital Competence Framework for Educators (DigCompEdu), integrated with contemporary models of generic and digital instructional quality.

Social Cognitive Theory and Teacher Self-Efficacy

According to Albert Bandura (1997), self-efficacy beliefs constitute the core mechanism of human agency. Individuals evaluate their capabilities to perform actions within specific domains, and these subjective assessments profoundly govern their goal selection, expenditure of effort, emotional resilience in the face of obstacles, and ultimate task performance. In educational contexts, teacher self-efficacy has been repeatedly demonstrated to predict instructional behavior, classroom management strategies, pedagogical innovation, and student academic achievement. Applying Bandura's tenets to technology integration, researchers emphasize that teachers who lack confidence in their digital pedagogical competence frequently avoid technology use or relegate it to superficial administrative duties, even when equipped with superior hardware. Conversely, teachers with robust competence beliefs view technological challenges as opportunities for instructional innovation.

The DigCompEdu Framework

To ground these self-efficacy beliefs within a coherent, policy-relevant educational taxonomy, Runge and colleagues (2023) utilized the DigCompEdu framework formulated by Redecker and Punie (2017) for the European Commission. The DigCompEdu model synthesizes educator-specific digital competence across six cumulative areas: (1) Professional Engagement, (2) Digital Resources, (3) Teaching and Learning, (4) Assessment, (5) Empowering Learners, and (6) Facilitating Learners' Digital Competence.

The DCM focuses specifically on Area 5: Empowering Learners. Within DigCompEdu, Area 5 is recognized as the pedagogical pinnacle of digital integration because it transcends operational software usage to emphasize learner-centered didactic methodologies. Area 5 is subdivided into three core competencies: Accessibility and Inclusion (5.1), Differentiation and Personalisation (5.2), and Actively Engaging Learners (5.3). Runge et al. operationalized competencies 5.2 and 5.3, synthesizing them with modern conceptualizations of generic instructional quality.

Integration with Three Basic Dimensions of Instructional Quality

In educational research, high-quality instruction is commonly structured along three overarching dimensions: (1) Classroom Management, (2) Supportive Climate, and (3) Cognitive Activation. The subscales of the DCM map directly onto these core dimensions. Specifically, differentiation serves as a foundational structural mechanism for establishing a supportive, individualized learning climate that adapts to student heterogeneity. Concurrently, actively engaging learners directly mirrors cognitive activation and constructivist learning theory, ensuring that educational technology facilitates deep-level conceptual understanding rather than passive memorization. By linking DigCompEdu directly with instructional quality frameworks, the DCM anchors self-efficacy beliefs within established pedagogical science.

Validity

Validity evaluation for the Digital Competence Measure was established through rigorous psychometric testing conducted by Runge et al. (2023). Although earlier measurement models often treated digital competence as a single uniform construct, the validation study of the DCM empirically tested complex structural alternatives to verify construct, convergent, and criterion-related validity.

Construct and Structural Validity

Construct validity was evaluated by comparing competing latent variable specifications within an empirical sample of German teachers. The authors systematically modeled: (a) a single-factor CFA, (b) a correlated two-factor CFA, (c) a bifactor CFA, (d) an exploratory structural equation model (ESEM), and (e) a bifactor ESEM. The results demonstrated that the two hypothesized dimensions—differentiation and actively engaging learners—possess empirical distinction while sharing common variance attributable to a overarching learner-empowerment capability. The bifactor CFA configuration yielded superior goodness-of-fit, confirming that researchers can examine the two specialized sub-constructs while simultaneously capturing a coherent overarching digital competence factor.

Convergent and Discriminant Validity

The items for both subscales demonstrated strong, statistically significant factor loadings on their designated latent dimensions (standardized loadings consistently exceeding .70 for positive markers), confirming convergent validity across indicators. Discriminant validity between the differentiation and actively engaging subscales was substantiated by the superior fit of multidimensional models over the unidimensional model; forcing all 12 items onto a single latent factor produced notable deterioration in model fit parameters, indicating that teachers systematically differentiate between the didactic challenge of tailoring learning paces (differentiation) and generating motivational activation (active engagement).

Criterion-Related and Predictive Validity

Criterion-related validity was established by examining the relationships between teachers' DCM scores and measures of instructional quality in technology-enhanced classrooms. Runge et al. (2023) demonstrated that teachers who reported stronger competence-related beliefs regarding differentiation and actively engaging learners reported significantly higher levels of cognitive activation, student support, and differentiated learning opportunities in their digital instruction. Furthermore, structural equation models revealed that these digital competence beliefs mediated the relationship between professional technology training and actual technology-mediated instructional practices, supporting the predictive utility of the scale.

Reliability

The reliability of the Digital Competence Measure was examined using internal consistency coefficients suitable for modern latent variable modeling. Traditional assessments of reliability relying exclusively on Cronbach's alpha (α) are frequently criticized in contemporary psychometrics for assuming tau-equivalence (equal factor loadings across all items) and underestimating or overestimating true scale reliability. Consequently, Runge et al. (2023) estimated McDonald's omega (ω) alongside conventional metrics.

Internal Consistency Estimates

The scale exhibited high internal consistency across all latent dimensions:

• Digital competence-related beliefs regarding differentiation: McDonald's ω values ranged between .93 and .95, indicating excellent measurement precision and minimal error variance among the six differentiation items.
• Digital competence-related beliefs regarding actively engaging learners: McDonald's ω values ranged between .94 and .96, confirming high internal consistency for the six activation items.
• Total Scale (Overall Learner Empowerment): When modeled as a composite latent structure, the total instrument yielded a McDonald's ω exceeding .95.

These values fall well above the recommended .80 cutoff for academic research and exceed the .90 threshold required for high-stakes individual diagnostics. The high reliability observed across both subscales indicates that the 12 items provide precise score estimates across varying levels of teacher digital self-efficacy.

Factor Analysis

The factorial architecture of the Digital Competence Measure was evaluated through comprehensive structural equation modeling procedures designed to resolve longstanding debates regarding the dimensionality of teacher digital competence. Runge et al. (2023) tested five distinct structural models:

1. One-Factor Confirmatory Factor Analysis (CFA): All 12 items loaded onto a single undifferentiated latent digital competence factor. This baseline model showed poor fit, demonstrating that digital competence in learner empowerment is not a monolithic construct.
2. Correlated Two-Factor CFA: Items were separated into their theoretical subscales (6 items on Differentiation, 6 items on Actively Engaging Learners), with the two latent factors allowed to correlate. This model exhibited significantly improved fit over the one-factor model, supporting the theoretical division between differentiation and engagement.
3. Exploratory Structural Equation Modeling (ESEM): An ESEM framework with target rotation was estimated to permit cross-loadings while retaining theoretical factor definitions. While fit was satisfactory, cross-loadings were relatively low, suggesting clean primary factor affiliations.
4. Bifactor ESEM: A model incorporating a general “Empowering Learners” factor alongside two specific orthogonal group factors with exploratory cross-loadings.
5. Bifactor CFA: Each item loaded simultaneously on a general target factor (representing generalized competence beliefs regarding empowering learners) and its designated specific factor (Differentiation or Actively Engaging Learners), with zero cross-loadings between specific dimensions.

Model Evaluation and Conclusions

The authors determined that the bifactor CFA model provided the most theoretically coherent and statistically rigorous representation of the data. The bifactor structure demonstrated strong goodness-of-fit indices (Comparative Fit Index [CFI] > .95, Tucker-Lewis Index [TLI] > .95, Root Mean Square Error of Approximation [RMSEA] < .06, and Standardized Root Mean Square Residual [SRMR] < .05). Standardized factor loadings on the specific group factors remained substantial even after partialling out the general factor, confirming that the subscales possess independent construct variance. This finding indicates that while educators possess an overall sense of digital empowerment efficacy, their confidence in implementing differentiation is distinct from their confidence in stimulating active engagement.

Instrument / Measurement Tool

The Digital Competence Measure is structured as follows:

• Instrument Name: Digital Competence Measure (DCM)
• Original Authors: Isabell Runge, Rebecca Lazarides, Charlott Rubach, Dirk Richter, and Katharina Scheiter (2023)
• Test Type: Standardized self-report inventory / rating scale
• Target Population: In-service and pre-service elementary and secondary school teachers (adults ≥ 18 years)
• Administration Format: Paper-and-pencil or digital/computer-assisted survey administration
• Estimated Completion Time: 4 to 7 minutes
• Total Item Count: 12 items
• Subscale Structure:
  • Digital competence-related beliefs regarding differentiation: 6 items (Diff01 through Diff06)
  • Digital competence-related beliefs regarding actively engaging learners: 6 items (Activ01 through Activ06)
• Authentic Response Scale: Items are rated on a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree).
  • 1 = Strongly disagree
  • 2 = Disagree
  • 3 = Neither agree nor disagree
  • 4 = Agree
  • 5 = Strongly agree
• Scoring and Transformation Rules:
  • Items Diff01 and Activ01 are formulated with negative phrasing and must be reverse-coded prior to computing score aggregates (i.e., recode: 1 → 5, 2 → 4, 3 → 3, 4 → 2, 5 → 1).
  • Subscale scores are derived by calculating the unweighted arithmetic mean of the six corresponding items, yielding a mean score ranging from 1.0 to 5.0 for each dimension.
  • A global composite score representing overall learner empowerment competence beliefs can be computed as the grand mean across all 12 items (after reverse-scoring). In structural equation modeling, latent bifactor or two-factor latent specifications are recommended.

Permissions & Fee and Test Year

The Digital Competence Measure was published in 2023 in Computers & Education. In accordance with open-science practices and academic conventions, the instrument may be utilized free of charge for non-commercial educational, scientific, and scholarly research purposes without formal written permission, provided that full bibliographic attribution is accorded to the original authors (Runge et al., 2023).

• Commercial Use: Prohibited without explicit, formal contractual authorization from the copyright holders.
• Access Fee: None (free for non-commercial academic research and university teaching).
• Original Language: German (empirically validated in German secondary and primary schools; authentic English translations provided by the original authors in the source publication).

References

Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman and Company.

Kunter, M., Klusmann, U., Baumert, J., Richter, D., Voss, T., & Hachfeld, A. (2013). Professional competence of teachers: Effects on instructional quality and student development. Journal of Educational Psychology, 105(3), 805–820. https://doi.org/10.1037/a0032583

Redecker, C., & Punie, Y. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union. https://doi.org/10.2760/159770

Runge, I., Lazarides, R., Rubach, C., Richter, D., & Scheiter, K. (2023). Teacher-reported instructional quality in the context of technology-enhanced teaching: The role of teachers’ digital competence-related beliefs in empowering learners. Computers & Education, 198, 104761. https://doi.org/10.1016/j.compedu.2023.104761

Items of the Scale