Next Article in Journal
ChatGPT for Science Lesson Planning: An Exploratory Study Based on Pedagogical Content Knowledge
Previous Article in Journal
The Impact of a MOOC: Long-Term Analysis of Teachers’ Learning Outcomes and Practices
Previous Article in Special Issue
From Gretel to Strudelcity: Empowering Teachers Regarding Generative AI for Enhanced AI Literacy with CollectiveGPT
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Education Sciences
Volume 15
Issue 3
10.3390/educsci15030337
education-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Promoting Digital Competencies in Pre-Service Teachers: The Impact of Integrative Learning Opportunities

by
Verena Köstler
1,* and
Monika-Sybille Wolff
2
1
Teacher Education Centre, University of Passau, 94030 Passsau, Germany
2
Faculty of Social and Educational Sciences, University of Passau, 94030 Passau, Germany
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(3), 337; https://doi.org/10.3390/educsci15030337
Submission received: 30 January 2025 / Revised: 21 February 2025 / Accepted: 3 March 2025 / Published: 9 March 2025
(This article belongs to the Special Issue Digital Literacy for Teaching Excellence: Empowering Educators in the Digital Age)
Versions Notes

Abstract

:
Offering learning opportunities for developing digital competencies in pre-service teacher education remains challenging despite its growing importance in preparing future educators. This study investigates the effectiveness of integrative learning opportunities, called “digitally enhanced courses”, which combine subject-specific and digital learning objectives. Implemented at a German university (2019–2023). These courses aimed to promote digital competencies required for technology-supported teaching. Using survey data from 312 pre-service teachers, the research examined students’ self-assessed digital competencies, technology acceptance, and value–cost assessments through multiple measurement instruments, including TPACK scales, the Technology Acceptance Model, and Expectancy–Value beliefs. Results revealed significantly higher self-assessed digital competencies in private contexts compared to teaching situations. While mere course participation showed no significant impact, both the frequency and number of attended courses positively correlated with higher self-assessed digital skills across all TPACK dimensions. Additionally, increased technology acceptance and higher success expectations were associated with enhanced teaching-related digital competencies. The findings emphasize that the effectiveness of digitally enhanced courses is contingent upon systematic implementation and student engagement, highlighting the need for structured curricular integration of digital competency development in teacher education through comprehensive, spiral-curriculum approaches rather than isolated interventions. However, this study’s reliance on self-reported data may introduce social desirability and subjective estimation bias, and its cross-sectional design limits causal interpretations. Future research should employ longitudinal approaches to examine competency development over time, incorporate objective performance-based assessments, and explore how instructional design and curricular integration influence digital competency acquisition.

1. Introduction

In today’s increasingly digital world, the integration of technology into education is no longer optional but essential. Teachers play a crucial role in fostering digital competencies among students, thereby equipping them with the necessary skills to navigate and succeed in modern societal and professional environments. However, the digital preparedness of pre-service teachers, i.e., students enrolled in university-based teacher education programs who have not yet entered formal post-graduate training phases such as the “Referendariat” in Germany, remains a significant challenge. Research highlights a gap between the demand for digital competencies in educational settings and the skills prospective teachers bring to the classroom (Malkawi & Khayrullina, 2021). Addressing this disparity is critical to ensuring the effective incorporation of technology in pedagogical practices.
Professional competencies for digitally enhanced teaching are fundamental for designing targeted, effective, and critically reflective institutional learning scenarios. Nevertheless, pre-service teachers often lack adequate preparation in this area. A review by Zhao et al. (2021) reveals that most university students and teachers possess a basic level of digital competence. However, student teachers are notably more reluctant to use digital media in educational contexts (U. Schmid et al., 2017). They also tend to employ digital tools less frequently compared to peers in other disciplines and assess their own competencies lower (Farjon et al., 2019; Senkbeil et al., 2020).
Germany has implemented various strategies to promote digital media integration in schools, including the “Education in the Digital World” strategy adopted by the Standing Conference of the Ministers of Education and Cultural Affairs in 2016. This strategic initiative mandates universities to ensure that prospective teachers acquire comprehensive media-didactic, media-ethical, and media-pedagogical skills during their training. Despite this, teacher education often lacks systematic and comprehensive approaches to fostering these competencies. Ideally, digital competencies should develop through a spiral curricular progression throughout teacher education. In practice, however, the absence of structured curricular offerings frequently places the developmental burden on individual students’ personal initiative (Reintjes et al., 2021). Consequently, the longitudinal development of digital skills during teacher training remains inconsistent (Bos et al., 2016; Johnson et al., 2023).
One promising approach to addressing these challenges involves integrative learning opportunities in teacher education curricula, which combine digital competency development with discipline-specific learning objectives. Research in self-regulated learning demonstrates that strategies are more effectively developed when integrated with domain-specific content rather than taught in isolation (Krapp, 1993; Dignath & Büttner, 2008). In accordance, standalone digital skill courses often fail to provide sufficient opportunities for domain-specific application, thereby limiting their overall effectiveness (Schuster, 2019). Integrative approaches offer the potential to contextualize digital skills within subject-specific pedagogical frameworks, enhancing both relevance and practical applicability.
In alignment with Baumert and Kunter (2006), digital competencies can be understood as professional competencies that encompass both cognitive and affective dimensions as prerequisites for managing profession-specific challenges. These aspects are captured in the Will-Skill-Tool (WST) model (Knezek & Christensen, 2008, 2016). While the model presents these three factors as discrete constructs, it simultaneously emphasizes their interconnectedness. For instance, teachers with a positive technological attitude (“Will”) and high digital capabilities (“Skill”) may still face challenges in integrating digital media in the classroom due to limited institutional resources (“Tool”). Research by Farjon et al. (2019) underscores the interrelated nature of these factors. Existing literature suggests a positive correlation between technological proficiency and attitudes toward technology utilization (Abbitt, 2011; Agyei & Voogt, 2011).
The WST model anticipates a high variance explanation (up to 90%, according to Knezek & Christensen, 2008). However, empirical findings have been inconsistent, with variance explanations ranging from 40% (Petko, 2012) to 60% (pre-service teachers; Farjon et al., 2019). A significant challenge in interpreting WST-related findings arises from the absence of a uniform understanding of the model’s individual aspects.
For operationalizing the cognitive aspect, the Technological Pedagogical Content Knowledge (TPACK) framework provides a valuable reference. This framework underscores the necessity of viewing technology integration in education as a unique construct that cannot be reduced to the sum of its parts (Scheiter, 2021; M. Schmid et al., 2020). TPACK develops independently of technological, pedagogical, and content knowledge and must therefore be promoted as a distinct competency. This transformative perspective also supports integrative approaches to be more effective for developing digital competencies than isolated interventions.
The Technology Acceptance Model (TAM; Davis, 1986) further explains how perceived usefulness and ease of use shape a teacher’s technological attitude, and their intention to use technology. Attitudes towards technology can be considered a motivational variable, mediating between the more cognitive-based variables of “perceived usefulness” and “perceived ease of use” and a behavioral outcome. “Perceived Usefulness” and “Perceived Ease of Use” can also directly predict the behavioral “Intention to Use.” (Scherer & Teo, 2019). Additionally, the Expectancy–Value Theory (Eccles, 2005; Eccles et al., 1983) provides insight into motivational factors influencing teachers’ technology acceptance. Achievement-related choices are driven by success expectations and the subjective value assigned to specific tasks. Task value is decomposed into four components: attainment value, intrinsic value, utility value, and associated costs.
Crucially, it would be erroneous to assume that merely providing digital infrastructure will automatically result in effective technological utilization by educators. Instead, cultivating digital competencies, including necessary attitudes and mindsets, requires targeted support initiatives to enable meaningful technology integration.
Against this background, the present study investigates the potential of integrative course designs that combine digital and discipline-specific learning objectives to enhance pre-service teachers’ digital competencies. By examining if digital competencies can be fostered through “digitally enhanced courses”, the research aims to inform teacher education program development. The primary objective is to provide insights into whether targeted, holistic approaches can better prepare future educators to confidently and effectively incorporate digital media into their pedagogical practices. Ultimately, this supports the broader goal of developing students’ digital competencies in an increasingly technology-driven educational landscape.

2. Materials and Methods

2.1. Digitally Enhanced Courses for Pre-Service Teachers

Between 2019 and 2023, digitally enhanced course concepts were developed and implemented through six interdisciplinary groups involving twelve academic disciplines across teacher education curricula. These courses were carried out with pre-service teachers, taking place in university classrooms designed for student-centered teaching and equipped with comprehensive media technology. Academic staff received support in planning, implementing, and reflecting on these course concepts through higher education resources. All courses systematically integrated subject-specific objectives with aspects of digitalization, aiming to equip student teachers with both pedagogical and technical skills for digitally supported teaching. Additionally, the concepts aimed at the development of competencies in critical media reflection, production, and utilization.

2.2. Study Design

Data collection was conducted during an eight-week period in the summer semester of 2023 using an online survey. It was part of the project-accompanying evaluation. Initially, emails were disseminated inviting all pre-service teachers to participate. An interim evaluation revealed the need for targeted follow-up surveys to achieve representativeness for individual demographic and program-specific characteristics. These follow-ups were conducted in selected courses under controlled conditions by trained project staff.

2.3. Sample

This study included responses from 403 pre-service teachers at a German university. To ensure data quality, only cases that had completed at least 50% of the survey items (excluding demographic information) were included in the analyses, resulting in a final sample size of 312 participants. The mean age was 22.74 years (SD = 4.68), with a range of 18 to 57 years. A majority of respondents (71%) identified as female.
A chi-squared test was conducted to compare the sample distribution with the broader population of pre-service teachers in terms of gender, degree program, teaching subject, and semester. The sample was representative in terms of gender, degree program, and teaching subject (χ2 (1) = 2.79, χ2 (5) = 4.56, χ2 (14) = 8.92, all p > 0.05). However, significant differences were observed for semester distribution (χ2 (11) = 40.93, p < 0.001), with students between the second and fourth semesters being overrepresented.

2.4. Measures

2.4.1. Digital Skills (Self-Evaluation)

Participants self-assessed their digital competencies using a global self-assessment scale and a Technological Pedagogical Content Knowledge (TPACK) scale. The global self-assessment included two items assessing digital skills in private and teaching contexts, rated on a 10-point scale (1 = “not competent” to 10 = “very competent”). The TPACK scale (M. Schmid et al., 2020) assessed students’ knowledge of subject matter and its application in digital contexts, based on a transformative view of TPACK development. This scale comprised seven subscales, each with four items (e.g., “I have the technical skills I need to use technology”). The subscales included: Pedagogical Knowledge (PK), Content Knowledge (CK), Technological Knowledge (TK), Pedagogical Content Knowledge (PCK), Technological Content Knowledge (TCK), and Technological Pedagogical Content Knowledge (TPACK). Items were rated on a five-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”), with internal consistency values ranging from α = 0.73 to 0.85 and ω = 0.73 to 0.85. The items were translated into German for the survey.

2.4.2. Attitudes and Beliefs Towards Technology

An adapted version of Gorovoj’s (2019) “Digital Technology Acceptance Scale” was employed to measure participants’ attitudes and acceptance of the use of digital media in the classroom. This scale consisted of 14 items encompassing four factors (e.g., “The use of digital media will improve my work as a teacher”). Internal consistency ranged from α = 0.71 to 0.87 and ω = 0.70 to 0.87. The items were answered on a five-point Likert scale, with 1 representing “strongly disagree” and 5 representing “strongly agree.” In addition, a scale adapted from the Expectancy–Value model (Wigfield & Eccles, 2000; Ranellucci et al., 2020; Rubach & Lazarides, 2019) was used to measure the perceived value, cost, and success expectations of digital media use. Five subscales with three items each (e.g., “I will be very successful in using digital media in the classroom”) were rated on a five-point Likert scale. Internal consistency ranged from α = 0.55 to 0.78 and ω = 0.70 to 0.79.

2.4.3. Familiarity with and Attendance at Digitally Enhanced Courses

Participants rated how well informed and how often they use digitally enhanced courses on four-point Likert scales (1 = “not at all” to 4 = “very well”; 1 = “never” to 4 = often). The internal consistency of these measures was α = 0.91 and ω = 0.90 for familiarity, and α = 0.80 and ω = 0.80 for frequency of use.

2.4.4. Self-Concept (Control Variable)

The “Core Self-Evaluations Scale” (Stumpp et al., 2010) was included to measure global self-concept as a control variable. This 12-item scale assessed self-esteem, self-efficacy, optimism, and internal locus of control (e.g., “I am confident in my abilities”). Items were rated on a five-point Likert scale, with internal consistency values of α = 0.87 and ω = 0.88.

2.5. Analysis

Data analysis was conducted using IBM SPSS 28. Group comparisons were evaluated using general linear models, and non-parametric tests (e.g., Wilcoxon, Mann–Whitney U, Kruskal–Wallis) were used when assumptions were violated. Predictive influences were examined through simple and multiple regression analyses. Missing values were minimal and addressed using multiple imputations with predictive mean matching. Mean scores were calculated for all scales, and a sum score was computed for the number of courses utilized per participant.

3. Results

3.1. Descriptive Statistics

The descriptive statistics on key variables provide insights into students’ self-perception of their digital competencies and their attitudes and beliefs about the use of digital technologies in the classroom. Table 1 summarizes the mean values, standard deviations, and other descriptive parameters.
Approximately 70% of the respondents reported participating in one or more of the digitally enhanced courses. Among these participants, a substantial proportion (around 47%) had utilized more than one offer. The mean number of digitally enhanced courses attended was 2.05 (SD = 2.36), with a median of 1.
Additionally, Appendix A presents a correlation matrix for all metric variables. Significant correlations indicate positive relationships across the variables. Overall, medium to high correlations were observed for most intercorrelations, with the highest correlations found between value beliefs and the acceptance of digital media (r = 0.36 to 0.77). Detailed correlation values and corresponding significance levels can be found in the table for further reference.

3.2. The Role of Technology Acceptance and Expectancy–Value in Digital Competencies

On average, students (N = 312) reported higher digital media competencies in the private context (global self-assessment) (M = 8.24, SD = 1.26; Md = 8) compared to the classroom context (M = 6.47, SD = 1.78; Md = 7). A Wilcoxon signed-rank test confirmed that this difference was statistically significant (z = −13.346, p < 0.001), with a large effect size (r = 0.76) based on Cohen’s (1992) criteria.
No significant differences in self-assessed digital competencies (measured by both the global self-assessment scale and the TPACK scale) were found when comparing students across degree programs, study progress, or teaching subjects.
When controlling for global self-concept students’ technology acceptance and Expectancy–Value beliefs significantly predicted their digital competencies (both global self-assessments and TPACK scales, p < 0.001). Within the regression model, “Perceived Ease of Use” (as measured by the Technology Acceptance Model, TAM scale) and “Expectations of Success” (as measured by the Expectancy–Value Theory, EVT scale) consistently made significant contributions to explaining variance (Table 2).

3.3. Differences Between Students Who Use Digitally Enhanced Courses and Those Who Do Not

A comparison of students who reported using digitally enhanced courses and those who had not revealed significant differences in their level of awareness regarding the available offerings. Students who utilized such courses demonstrated a substantially higher level of familiarity, indicating that they felt better informed. A Mann–Whitney U test confirmed the statistical significance of this difference (U = 13,758.50, z = 6.432, p < 0.001), with a correlation coefficient of r = 0.37 representing a medium effect size.
Regression analyses further showed that familiarity significantly predicted both the frequency of use (β =.619, p < 0.001; global self-concept: β = −0.024, p = 0.609; F(2, 293) = 90.283, p < 0.001, R2 = 0.377), and the number of offers utilized (β = 0.626, p < 0.001; global self-concept: β = −0.029, p = 0.526; F(2, 294) = 94.274, p < 0.001, R2 = 0.387), controlling for global self-concept.
In terms of self-assessed digital competencies (global, TPACK), no consistent differences were found between the groups. However, two TPACK subscales revealed statistically significant differences, albeit with small effect sizes. Specifically, subject-specific Content Knowledge (CK) showed a significant difference (U = 835.50, z = 2.158, p = 0.031, r = 0.13), as did Technological Pedagogical Knowledge (TPK) (U = 10,716.50, z = 1.983, p = 0.047, r = 0.12).
No significant differences were observed in students’ technology acceptance or their Expectancy–Value beliefs regarding the use of digital technology in the classroom.
Finally, the analysis found no notable discrepancies in any of the variables discussed above when comparing students across degree programs, academic disciplines, or academic terms.

3.4. Effects of Digitally Enhanced Courses on Pre-Service Teachers’ Digital Competencies

The predictive effect of participation in digitally enhanced courses on knowledge-related and affective-related competence aspects for teaching with digital media was analyzed using regression models, controlling for global self-concept. In addition to examining mere participation, the number and frequency of attended digitally enhanced courses were also taken into account.
The fact that students merely participated in digitally enhanced courses did not predict any significant changes in ratings of knowledge-related (global and TPACK) or affective-related (TAM and EVT) competence aspects for teaching with digital media.
However, results indicated that with each additional course attended, there was a statistically significant increase in global self-assessed digital competencies, with an average increase of 0.094 points (F(1, 295) = 4.784, p = 0.030, R2 = 0.013). Furthermore, the frequency of course attendance showed a positive relationship with global self-assessed digital competencies in the classroom (β = 0.142, p = 0.015; global self-concept: β = 0.47, p = 0.419; F(1, 293) = 3.379, p = 0.035, R2 = 0.016). The global self-concept was not included as a control variable in this analysis, as the effect was not found to be significant. Furthermore, the inclusion of this variable would have resulted in a non-significant model and a lower R2.
Moreover, analyses demonstrated that students who attended a higher number of digitally enhanced courses rated their knowledge across all TPACK dimensions significantly higher, with beta coefficients indicating small to medium effect sizes. (PK: β = 0.176, p = 0.002; CK: β = 0.164, p = 0.004; TK: β = 0.194, p < 0.001; PCK: β = 0.154, p = 0.008; TPK: β = 0.179, p = 0.002; TCK: β = 0.252, p < 0.001; TCPK: β = 0.157, p = 0.007). Similarly, the frequency of course attendance yielded significant effects on TPACK dimensions (with the exception of PCK, which showed no significant result: PCK: β = 0.097, p = 0.094) (PK: β = 0.144, p = 0.013; CK: β = 0.136, p = 0.018; TK: β = 0.204, p < 0.001; TPK: β = 0.171, p = 0.003; TCK: β = 0.255, p < 0.001; TCPK: β = 0.144, p = 0.013). These findings indicate that the more frequently students attended digitally enhanced courses the higher they rated their knowledge in the various TPACK dimensions.
Similarly, attendance at digitally enhanced courses alone did not significantly affect the subscales of either Expectancy–Value beliefs or technology acceptance. However, both the number and frequency of courses attended significantly predicted the TAM subscale “Perceived Ease of Use” and the EVT subscale “Expectancy for Success” (Table 3).

4. Discussion

The present study investigated the potential of integrative learning opportunities to enhance digital competencies among pre-service teachers. Implemented from 2019 to 2023 at a German university, these digitally enhanced courses aimed to align the Conference of Education Minsters’ “Education in the digital world” mandate, revealing critical insights into the challenges of digital competencies development in teacher education.
The large effect size observed in the difference between self-assessed private digital competencies and teaching-related competencies underscores a critical challenge in teacher education: The ability to use digital tools for personal purposes does not automatically translate into pedagogical application skills. This gap indicates that while students may be proficient in using technology in their daily lives, they often lack the structured training needed to effectively apply these tools in educational settings. Research on teacher training has consistently emphasized that pedagogical digital competence requires explicit, structured learning opportunities that go beyond informal, everyday technology use (e.g., Drossel et al., 2019; Tondeur et al., 2017). Future teacher education programs should, therefore, prioritize systematic integration of digital pedagogical applications into curricula rather than assuming that general digital literacy will naturally develop into effective classroom technology use.
Our findings align with research on self-regulated learning (Schuster, 2019), which suggests that integrative approaches, such as the digitally enhanced courses examined in this study, demonstrate significant potential to foster learning. The results suggest that the effectiveness of digitally enhanced courses largely depends on the frequency and intensity of student engagement. While no significant differences were found between students who generally participated in digitally enhanced courses and those who did not, a clear correlation emerged between competency development and the frequency of participation. Specifically, students‘ global digital competency scores (classroom) increased by an average of 0.094 points with each seminar attendance (F(1, 295) = 4.784, p = 0.030, R2 = 0.013). However, a key methodological observation is the relatively low variance explanation of observed effects, which likely stems from the project-based nature of the offerings and their limited systematic curricular integration.
Similarly, while participation in digitally enhanced courses did not directly influence technology acceptance or Expectancy–Value beliefs, the number and frequency of attended courses were significant predictors of Perceived Ease of Use (TAM) and Expectancy for Success (EVT). This suggests that a single course experience may not be sufficient to alter deep-seated beliefs about the usefulness and costs of digital technology in education. Instead, sustained exposure appears necessary to increase confidence and lower perceived barriers to technology use. This aligns with research indicating that habitual engagement with technology leads to higher technology self-efficacy over time (Teo, 2011). Future interventions should, therefore, focus not just on providing access to digital learning opportunities but on ensuring consistent engagement across multiple courses to facilitate deeper shifts in technology-related beliefs.
Furthermore, the differentiated effects across various TPACK dimensions underscore the importance of considering multiple factors influencing digital competency development in pre-service teachers, a challenge also highlighted by Drummond and Sweeney (2017) or Lachner et al. (2019). While participation in digitally enhanced courses correlated with improvements across all TPACK dimensions, its impact on Pedagogical Content Knowledge was less pronounced than on Technological Knowledge and Technological Content Knowledge. This suggests that merely integrating digital tools into coursework may not be sufficient—pedagogical integration requires additional instructional support and a structured, long-term engagement approach. These findings expand on Graham’s (2011) transformative perspective of TPACK, reinforcing the notion that TPACK is not merely a sum of its components but a distinct construct influenced by all subscale dimensions.
The findings of this study contribute to theoretical advancements by highlighting the interplay between motivational factors and digital competencies within the Will-Skill-Tool (WST) model. Our analysis suggests that the use of digitally enhanced learning opportunities predicts digital competencies not only directly but also indirectly through two critical mediating factors: perceived ease of use and confidence in handling digital media in educational settings. This indicates that the integration of digitally enhanced courses not only improves the ‘Skill’ component but also fosters positive attitudes and self-efficacy (‘Will’). The theoretical underpinnings of this finding are strongly supported by the Expectancy–Value Theory. When digital media usage is perceived as manageable and promising, students are more likely to engage with these technologies, subsequently leading to higher competency self-assessments. This suggests that digital competency development is deeply intertwined with motivational beliefs, thereby extending the original conceptualization of the WST model. Further research should employ mediation and moderation analyses to clarify the specific role of the “will” factor in the Will–Skill–Tool model.
From a practical perspective, the results underline the importance of a spiral curricular approach (Herzig & Grafe, 2007; Zylka & Müller, 2011) that provides consistent digital learning opportunities across all phases of teacher education. Isolated interventions are insufficient; instead, a systematic, longitudinal integration of digital competencies into the teacher training curriculum is necessary. Additionally, universities should provide targeted faculty development programs to ensure instructors are equipped to effectively teach and integrate digital tools into their courses.
This study’s findings must be interpreted in light of institutional and societal factors. One significant external influence is the COVID-19 pandemic, which coincided with the study period and may have affected students’ use of digital learning opportunities. The heightened accessibility of digital learning resources during the pandemic might have increased awareness and participation. However, it is also possible that specific student groups—particularly those with lower prior digital experience—were excluded due to a lack of targeted support measures. Furthermore, institutional structures, such as curricular rigidity and varying levels of institutional support, may have influenced students’ engagement with digitally enhanced learning opportunities.
While this study provides valuable insights, several limitations must be acknowledged. First, this study relies on self-assessment measures, which may not fully capture actual digital competencies. Self-perceptions are often influenced by biases such as overconfidence, underestimation, or social desirability effects, raising the question of whether the reported improvements reflect genuine skill acquisition or merely an increased sense of confidence in using digital tools. Future research would benefit from integrating objective assessment methods, such as performance-based tests or knowledge assessments, to validate self-reported data and provide a more comprehensive evaluation of digital competencies.
Second, this study was conducted at a single German university, which limits the generalizability of the findings to other teacher education contexts with different institutional structures or curricular designs. Comparative studies across multiple universities and educational systems could provide deeper insights into how institutional factors shape the effectiveness of integrative learning opportunities and help identify best practices that can be applied more broadly.
Third, this study’s cross-sectional design prevents definitive conclusions about causal relationships between course participation and fostering digital competencies. While the findings indicate a positive association between participation in digitally enhanced courses and increased self-assessed competencies, it remains unclear whether attending these courses directly fosters digital skills or whether students with higher pre-existing competencies are simply more likely to enroll. A longitudinal research design or experimental studies would be necessary to establish causal links and assess the long-term impact of these learning opportunities. Finally, an important methodological challenge lies in the selection effects of course participation. Since data were collected at the end of the project, there is a possibility that students with stronger digital skills were more inclined to take part in these courses, rather than the courses themselves driving competency growth. This suggests the need for targeted recruitment strategies and inclusive course designs that engage students with varying levels of digital proficiency. Future studies should investigate whether specific instructional approaches, scaffolding strategies, or differentiated learning pathways can help ensure that all pre-service teachers—regardless of their initial competency levels—benefit from digitally enhanced learning experiences.

5. Conclusions

The findings of this study underscore the critical importance of systematically integrating learning opportunities for digital competency development into pre-service teacher education curricula. While digitally enhanced courses provide valuable learning opportunities, their impact depends on consistent participation, structured curricular integration, and alignment with pedagogical objectives. The results demonstrate that isolated interventions are insufficient; rather, a comprehensive, strategically designed approach is necessary to ensure that digital competencies are not only acquired but also effectively applied in teacher education.
To achieve this goal, teacher education programs must move beyond treating technology as merely an additional skill and instead establish it as an integral component of professional preparation and pedagogical practice. This requires institutional commitment to integrating digital learning objectives across the curricula, as well as providing support to faculty in designing and implementing technology-enhanced instruction.
Ultimately, preparing future educators for digitally supported teaching is not only a curricular challenge but a fundamental necessity for modern education. A strategic, evidence-based approach is essential to equipping pre-service teachers with the competencies required to navigate an increasingly digitalized learning environment.

Author Contributions

Conceptualization, V.K.; methodology, M.-S.W.; software, M.-S.W.; validation, V.K. and M.-S.W.; formal analysis, M.-S.W.; investigation, M.-S.W.; writing—original draft preparation, V.K.; writing—review and editing, V.K. and M.-S.W.; visualization, V.K. and M.-S.W.; supervision, V.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research took place in context of the project “SKILL.de” funded by “Qualitätsoffensive Lehrerbildung”, Federal Ministry of Education and Research, grant number 01JA1624. This article was supported by University of Passau Open Access Publishing Fund.

Institutional Review Board Statement

There is no formal Ethics Committee or Institutional Review Board process available in our institutional context. The data collection described in our manuscript was conducted as part of a funded project’s evaluation component, which was reviewed and approved during the initial project application process.

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study. The study was conducted in accordance with the Declaration of Helsinki.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest. A self-assessment was conducted, which determined that this study does not pose a risk to individuals. No potentially hazardous technologies, procedures, or substances were involved in this study.

Appendix A

Correlation matrix for all metric variables; N = 312; * p < 0.05, ** p < 0.01, *** p < 0.001.
1234567891011121314151617181920
1DigComp_private
2DigComp_teaching0.480 ***
3CSES0.0250.060
4TPACK_PK0.200 ***0.386 ***0.083
5TPACK_CK0.263 ***0.300 ***0.176 *0.478 ***
6TPACK_TK0.496 ***0.634 ***0.122 *0.374 ***0.325 ***
7TPACK_PCK0.175 ***0.324 ***0.116 *0.649 ***0.522 ***0.365 ***
8TPACK_TPK0.235 ***0.419 ***0.0810.482 ***0.378 ***0.543 ***0.438 ***
9TPACK_TCK0.239 ***0.452 ***0.1030.428 ***0.422 ***0.554 ***0.381 ***0.477 ***
10TPACK_TPCK0.245 ***0.481 ***0.0530.508 ***0.359 ***0.604 ***0.520 ***0.779 ***0.484 ***
11TAM_Perceived Usefulness0.161 **0.291 ***0.0320.0980.154 **0.425 ***0.0290.419 ***0.298 ***0.427 ***
12TAM_Behavioral Intention to Use0.112 *0.225 ***0.0090.0230.0760.370 ***0.0170.336 ***0.199 ***0.372 ***0.699 ***
13TAM_Perceived Ease of Use0.421 ***0.571 ***0.124 *0.274 **0.269 ***0.637 ***0.289 ***0.524 ***0.367 ***0.602 ***0.446 ***0.319 ***
14TAM_Attitude Towards Usage0.231 ***0.371 ***0.0360.0980.138 *0.469 ***0.0890.415 ***0.289 ***0.468 ***0.677 ***0.681 ***0.479 ***
15EV_expectancy beliefs0.372 ***0.476 ***0.0890.237 **0.277 ***0.535 ***0.318 ***0.458 ***0.378 ***0.494 ***0.531 ***0.446 ***0.660 ***0.501 ***
16EV_utility_value0.162 **0.351 ***0.0290.160 **0.188 **0.433 ***0.129 *0.440 ***0.320 ***0.484 ***0.772 ***0.637 ***0.478 ***0.656 ***0.577 ***
17EV_attainment_value0.0740.284 ***0.0050.0820.0840.356 ***0.0470.378 ***0.205 ***0.386 ***0.704 ***0.696 ***0.356 ***0.655 ***0.500 ***0.762 ***
18EV_intrinsic value0.180 **0.280 ***0.0610.0710.0570.361 ***0.0420.336 ***0.1100.404 ***0.596 ***0.643 ***0.411 ***0.736 ***0.432 ***0.628 ***0.685 ***
19EV_cost_effort0.156 **0.206 ***0.0260.051−0.0140.249 ***0.0760.196 **0.0090.261 ***0.208 ***0.0820.409 ***0.228 ***0.295 ***0.243 ***0.202 ***0.341 ***
20DigCourses_information−0.0350.143 *0.0180.144 *0.140 *0.205 ***0.1000.172 **0.257 ***0.145 *0.1020.0580.119 *0.121 *0.139 *0.119 *0.0900.0500.38
21DigCourses_participation0.0450.150 *0.0880.166 **0.129 *0.216 ***0.160 **0.205 ***0.246 ***0.196 ***0.130 *0.0890.144 *0.1070.172 **0.0980.0570.049−0.0270.619 ***

References

  1. Abbitt, J. T. (2011). An investigation of the relationship between self-efficacy beliefs about technology integration and technological pedagogical content knowledge (TPACK) among preservice teachers. Journal of Digital Learning in Teacher Education, 27(4), 134–143. [Google Scholar] [CrossRef]
  2. Agyei, D. D., & Voogt, J. (2011). Exploring the potential of the will, skill, tool model in Ghana: Predicting prospective and practicing teachers’ use of technology. Computer and Education, 56(1), 91–100. [Google Scholar] [CrossRef]
  3. Baumert, J., & Kunter, M. (2006). Stichwort: Professionelle kompetenz von lehrkräften. Zeitschrift fur Erziehungswissenschaft: ZfE, 9(4), 469–520. [Google Scholar] [CrossRef]
  4. Bos, W., Lorenz, R., Endberg, M., Eickelmann, B., Kammerl, R., & Welling, S. (Eds.). (2016). Schule digital–der Länderindikator 2016: Kompetenzen von lehrpersonen der sekundarstufe I im umgang mit digitalen medien im bundesländervergleich. Waxmann. [Google Scholar]
  5. Cohen, J. (1992). A power primer. Quantitative Methods for Psychology, 112, 155–159. [Google Scholar] [CrossRef]
  6. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Sloan School of Management, Massachusetts Institute of Technology. [Google Scholar]
  7. Dignath, C., & Büttner, G. (2008). Components of fostering self-regulated learning among students. A meta-analysis on intervention studies at primary and secondary school level. Metacognition and Learning, 3(3), 231–264. [Google Scholar] [CrossRef]
  8. Drossel, K., Eickelmann, B., Schaumburg, H., & Labusch, A. (2019). Nutzung digitaler Medien und prädiktoren aus der perspektive der lehrerinnen und lehrer im internationalen vergleich. In B. Eickelmann, W. Bos, J. Gerick, F. Goldhammer, H. Schaumburg, K. Schwippert, M. Senkbeil, & J. Vahrenhold (Eds.), ICILS 2018 #Deutschland: Computer- und informationsbezogene Kompetenzen von Schülerinnen und Schülern im zweiten internationalen Vergleich und Kompetenzen im Bereich Computational Thinking (pp. 205–240). Waxmann. [Google Scholar] [CrossRef]
  9. Drummond, A., & Sweeney, T. (2017). Can an objective measure of technological pedagogical content knowledge (TPACK) supplement existing TPACK measures? British Journal of Educational Technology, 48(4), 928–939. [Google Scholar] [CrossRef]
  10. Eccles, J. (2005). Subjective task value and the eccles et al. model of achievement-related choices. In A. J. Elliot, & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 105–121). Guilford Publications. [Google Scholar]
  11. Eccles, J., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J., & Midgley, C. (1983). Expectancies, values and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motives. W. H. Freeman. [Google Scholar]
  12. Farjon, D., Smits, A., & Voogt, J. (2019). Technology integration of pre-service teachers explained by attitudes and beliefs, competency, access, and experience. Computers and Education, 130, 81–93. [Google Scholar] [CrossRef]
  13. Gorovoj, A. (2019). Technologieakzeptanz digitaler medien bei universitätsstudierenden verschiedener fächer und berufstätigen gleichen alters: Eine studie zu den psychologischen determinanten und hintergründen der akzeptanz digitaler medien auf der basis eines neu ausgerichteten messinstruments. Universitätsbibliothek Siegen. [Google Scholar] [CrossRef]
  14. Graham, C. R. (2011). Theoretical considerations for understanding technological pedagogical content knowledge (TPACK). Computers & Education, 57(3), 1953–1960. [Google Scholar] [CrossRef]
  15. Herzig, B., & Grafe, S. (2007). Digitale medien in der schule: Standortbestimmung und handlungsempfehlungen für die zukunft; Studie zur nutzung digitaler medien in allgemein bildenden schulen in Deutschland. Deutsche Telekom. [Google Scholar]
  16. Johnson, F., Schneider, C., & Müller, L. (2023). Zur Entwicklung digitalisierungsbezogener kompetenzen und einstellungen von lehramtsstudierenden im verlauf des bachelor of education. Unterrichtswissenschaft, 51(4), 605–622. [Google Scholar] [CrossRef]
  17. Knezek, G., & Christensen, R. (2008). The importance of information technology attitudes and competencies in primary and secondary education. In J. Voogt, & G. Knezek (Eds.), Springer international handbooks of education: Bd. 20. International handbook of information technology in primary and secondary education (Volume 20, pp. 321–331). Springer Science+Business Media. [Google Scholar] [CrossRef]
  18. Knezek, G., & Christensen, R. (2016). Extending the will, skill, tool model of technology integration: Adding pedagogy as a new model construct. Journal of Computing in Higher Education, 28(3), 307–325. [Google Scholar] [CrossRef]
  19. Krapp, A. (1993). Die psychologie der lernmotivation. Perspektiven der forschung und probleme ihrer pädagogischen rezeption. Zeitschrift für Pädagogik, 39, 187–206. [Google Scholar] [CrossRef]
  20. Lachner, A., Backfisch, I., & Stürmer, K. (2019). A test-based approach of modeling and measuring technological pedagogical knowledge. Computers and Education, 142, 103645. [Google Scholar] [CrossRef]
  21. Malkawi, E., & Khayrullina, M. (2021). Digital human skills from the corporate economy and business development. Ekonomicko-Manazerske Spektrum, 15(1), 64–74. [Google Scholar] [CrossRef]
  22. Petko, D. (2012). Teachers’ pedagogical beliefs and their use of digital media in classrooms: Sharpening the focus of the ‘will, skill, tool’ model and integrating teachers’ constructivist orientations. Computers and Education, 58(4), 1351–1359. [Google Scholar] [CrossRef]
  23. Ranellucci, J., Rosenberg, J. M., & Poitras, E. G. (2020). Exploring pre-service teachers’ use of technology: The technology acceptance model and expectancy–value theory. Journal of Computer Assisted Learning, 36(6), 810–824. [Google Scholar] [CrossRef]
  24. Reintjes, C., Porsch, R., & Brahm, G. (2021). Das bildungssystem in zeiten der krise. Empirische befunde, konsequenzen und potenziale für das lehren und lernen. Waxmann. [Google Scholar] [CrossRef]
  25. Rubach, C., & Lazarides, R. (2019). Heterogene digitale Kompetenzselbsteinschätzungen bei Lehramtsstudierenden. In Geschäftsstelle beim stifterverband (Ed.), Digitalisierung in studium und lehre gemeinsam gestalten (pp. 453–473). Springer Fachmedien Wiesbaden. [Google Scholar] [CrossRef]
  26. Scheiter, K. (2021). Lernen und lehren mit digitalen medien: Eine standortbestimmung [Technology-enhanced learning and teaching: An overview]. Zeitschrift fur Erziehungswissenschaft: ZfE, 24(5), 1039–1060. [Google Scholar] [CrossRef]
  27. Scherer, R., & Teo, T. (2019). Unpacking teachers’ intentions to integrate technology: A meta-analysis. Educational Research Review, 27, 90–109. [Google Scholar] [CrossRef]
  28. Schmid, M., Brianza, E., & Petko, D. (2020). Developing a short assessment instrument for Technological Pedagogical Content Knowledge (TPACK.xs) and comparing the factor structure of an integrative and a transformative model. Computers and Education, 157, 103967. [Google Scholar] [CrossRef]
  29. Schmid, U., Goertz, L., Radomski, S., Thom, S., Behrens, J., & Bertelsmann Stiftung. (2017). Monitor digitale bildung: Die hochschulen im digitalen zeitalter. Bertelsmann Stiftung. [Google Scholar] [CrossRef]
  30. Schuster, C. (2019). Transfer metakognitiver Strategien beim selbstregulierten Lernen [Doctoral Thesis, Ruhr-Universität Bochum, Universitätsbibliothek]. [Google Scholar] [CrossRef]
  31. Senkbeil, M., Ihme, J. M., & Schöber, C. (2020). Empirische arbeit: Schulische medienkompetenzförderung in einer digitalen welt: Über welche digitalen kompetenzen verfügen angehende lehrkräfte? Psychologie in Erziehung und Unterricht, 68(1), 4–22. [Google Scholar] [CrossRef]
  32. Stumpp, T., Muck, P. M., Hülsheger, U. R., Judge, T. A., & Maier, G. W. (2010). Core self-evaluations in Germany: Validation of a german measure and its relationships with career success. Applied Psychology, 59(4), 674–700. [Google Scholar] [CrossRef]
  33. Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers and Education, 57(4), 2432–2440. [Google Scholar] [CrossRef]
  34. Tondeur, J., van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2017). Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: A systematic review of qualitative evidence. Educational Technology Research and Development, 65(3), 555–575. [Google Scholar] [CrossRef]
  35. Wigfield, A., & Eccles, J. S. (2000). Expectancy–Value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. [Google Scholar] [CrossRef] [PubMed]
  36. Zhao, Y., Pinto Llorente, A. M., & Sánchez Gómez, M. C. (2021). Digital competence in higher education research: A systematic literature review. Computers and Education, 168, 104212. [Google Scholar] [CrossRef] [PubMed]
  37. Zylka, J., & Müller, W. (2011). Fundierung digitaler Medien im formalen Bildungswesen am Beispiel einer Fallstudie zu digitalen Medienkompetenzen. In T. Köhler, & J. Neumann (Eds.), Wissenschaftsgemeinschaften: Digitale medien—Öffnung und offenheit in forschung und lehre (pp. 250–260). Medien in der Wissenschaft. Waxmann. [Google Scholar] [CrossRef]
Table 1. Descriptive variables: self-concept, digital competencies, attitudes, and beliefs about digital technologies in the classroom (N = 312).
Table 1. Descriptive variables: self-concept, digital competencies, attitudes, and beliefs about digital technologies in the classroom (N = 312).
MSDMdMinMaxSEMcDonald ω
CSES3.470.643.5250.360.88
DigComp_private8.241.2683100.07–
DigComp_teaching6.471.7871100.10–
TPACK_PK3.790.593.752.2550.030.73
TPACK_CK3.810.673.75150.040.78
TPACK_TK3.670.763.751.2550.040.83
TPACK_PCK3.580.663.51.550.040.79
TPACK_TPK3.690.663.751.7550.040.81
TPACK_TCK2.810.872.75150.050.83
TPACK_TPCK3.530.753.5150.040.85
TAM_Perceived Usefulness3.780.743.75150.040.85
TAM_Behavioral Intention to Use3.810.694150.040.70
TAM_Perceived Ease of Use3.590.723.51.550.040.77
TAM_Attitude Towards Usage3.820.824150.050.87
EV_expectancy_beliefs3.790.583.671.6750.030.81
EV_utility_value3.860.634150.030.70
EV_attainment_value3.900.714150.040.79
EV_intrinsic_value3.990.784150.040.74
EV_cost_effort3.500.833.5150.050.70
Digitally enhanced courses:
level of information1.800.561.68140.030.90
participation1.330.431.2013.30.030.80
Table 2. Beta-coefficients of the factors, “Perceived Ease of Use“ (TAM) and “Expectation of Success“ (EVT), and regression models of TAM and EVT scales (N = 312; p < 0.001).
Table 2. Beta-coefficients of the factors, “Perceived Ease of Use“ (TAM) and “Expectation of Success“ (EVT), and regression models of TAM and EVT scales (N = 312; p < 0.001).
Dependent
Variable		βTAM ScaleβEVT Scale
TPCK0.478adj. R2 = 0.391; F(5, 306) = 41.0010.291adj. R2 = 0.298 F(6, 305) = 22.956
PK0.287adj. R2 = 0.071; F(5, 306) = 5.7710.240adj. R2 = 0.054 F(6, 305) = 3.978
CK0.229adj. R2 = 0.081; F(5, 306) = 6.4780.273adj. R2 = 0.104; F(6, 305) = 6.989
TK0.532adj. R2 = 0.442; F(5, 306) = 50.1920.392adj. R2 = 0.307; F(6, 305) = 23.956
PCK0.324adj. R2 = 0.092; F(5, 306) = 7.2900.371adj. R2 = 0.106; F(6, 305) = 7.143
TPK0.388adj. R2 = 0.305; F(5, 306) = 28.2740.287adj. R2 = 0.242; F(6, 305) = 17.558
TCK0.258adj. R2 = 0.149; F(5, 306) = 11.8570.331adj. R2 = 0.179; F(6, 305) = 12.264
GDCC *0.523adj. R2 = 0.336; F(5, 306) = 32.4370.403adj. R2 = 0.233; F(6, 305) = 16.767
* GDCC: global digital competencies in the classroom.
Table 3. Number and frequency of attended digitally enhanced courses predicted.
Table 3. Number and frequency of attended digitally enhanced courses predicted.
Dependent VariableNumber
Frequency		Regression Models
(N = 312; p < 0.05)
higher global digital competencies (classroom)β = 0.094
β = 0.142		F (1, 295) = 4.784 adj. R2 = 0.016
F (2, 293) = 3.379, adj. R2 = 0.016
higher scores in all TPACK knowledge dimensions0.154 < β < 0.252
0.136 < β < 0.255		0.025 < adj. R2 < 0.064
0.017 < adj. R2 < 0.068
higher expectancy for successβ = 0.141
β = 0.137		F (2, 294) = 3.943, adj. R2 = 0.019
F (2, 293) = 3.834, adj. R2 = 0.019
higher perceived ease of use for media integrationβ = 0.129
β = 0.117		F(2, 294) = 4.434, adj. R2 = 0.023
F (2, 293) = 4.031, adj. R2 = 0.020
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Köstler, V.; Wolff, M.-S. Promoting Digital Competencies in Pre-Service Teachers: The Impact of Integrative Learning Opportunities. Educ. Sci. 2025, 15, 337. https://doi.org/10.3390/educsci15030337

AMA Style

Köstler V, Wolff M-S. Promoting Digital Competencies in Pre-Service Teachers: The Impact of Integrative Learning Opportunities. Education Sciences. 2025; 15(3):337. https://doi.org/10.3390/educsci15030337

Chicago/Turabian Style

Köstler, Verena, and Monika-Sybille Wolff. 2025. "Promoting Digital Competencies in Pre-Service Teachers: The Impact of Integrative Learning Opportunities" Education Sciences 15, no. 3: 337. https://doi.org/10.3390/educsci15030337

APA Style

Köstler, V., & Wolff, M.-S. (2025). Promoting Digital Competencies in Pre-Service Teachers: The Impact of Integrative Learning Opportunities. Education Sciences, 15(3), 337. https://doi.org/10.3390/educsci15030337

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop