Digital teaching competence of higher education professors: self-perception study in an Ecuadorian university

Introduction

Technology plays a crucial role in contemporary and future society, with significant implications for the educational field.¹ The rapid pace of technological change has introduced profound challenges in teaching and learning.² As a result, educators must recognize the necessity of self-directed professional development to enhance their expertise, including achieving an adequate level of digital literacy. This proficiency is essential for success across various dimensions of education and training.³

As key institutions responsible for fostering the development of professional competence, universities must possess qualified resources, both in terms of materials and human capital, to prepare well-rounded professionals.^4–6 These professionals should be equipped with problem-solving skills, critical thinking abilities, and the right attitudinal disposition necessary to navigate the complexities of the digital age. However, despite the acknowledged importance of technology in education, there are significant gaps in research regarding how educators can effectively select and utilize technological tools within their teaching practices.

Therefore, it becomes crucial for educators to develop proficiency in selecting appropriate technological tools and seamlessly integrating them into their instructional approaches. By doing so, they can plan and implement activities that foster learning environments conducive to student success.⁷ Unfortunately, there is a dearth of comprehensive research that explores how educators can fully leverage the potential of technology to enhance teaching and learning in the classroom.

Hence, this study aims to address these research gaps by examining educators' self-perception of their digital competences and their capacity to effectively select and utilize technological tools in their teaching practice. By gaining a better understanding of the challenges and needs faced by educators in this context, more effective training and support strategies can be developed to foster their professional development and enhance the quality of education in the digital age.

The findings of this study will have significant implications for educational institutions, policymakers, and educators themselves. Understanding educators' self-perception of their digital competences will shed light on their level of confidence and awareness regarding the use of technology in education. Furthermore, identifying the challenges they face in selecting and utilizing technological tools will provide valuable insights into the barriers they encounter in integrating technology effectively into their teaching practice.

Based on these insights, tailored training programs can be developed to address the specific needs of educators, helping them acquire the necessary skills and knowledge to make informed decisions about technology integration. Additionally, the study will contribute to the development of guidelines and best practices for selecting and utilizing technological tools, benefiting both novice and experienced educators in their pedagogical endeavors.

Ultimately, the aim of this study is to improve the educational landscape by equipping educators with the tools and resources they need to navigate the ever-evolving digital terrain. By enhancing educators' digital competences and supporting their professional growth, the quality of education can be elevated, ensuring that students receive an education that prepares them for the challenges and opportunities of the digital age.

Methods

To explore the proposed objectives, a descriptive correlational study was designed, supported by a quantitative approach and a non-experimental design structure. The methodological choice was neither random nor incidental but was deliberately aligned with the overall goals of the research. The focus was on delving into the essential core of the studied phenomenon and the intricate relationships between predetermined variables.²⁶ It is noteworthy that the strategy adopted is based on a strictly observational model, excluding any form of direct intervention or manipulation of the subject matter.

Within this framework, the following key elements are established:

Results

This section presents the findings of this study.⁴⁰ First, a reliability test was conducted using Cronbach's alpha coefficient to assess the internal consistency of the survey items (see Table 3). Cronbach's alpha is a widely used measure to evaluate the reliability of a scale or questionnaire. It indicates the extent to which the items in the instrument measure the same underlying construct.

Table 3 displays the results of the reliability test. The coefficient value obtained provides an indication of the overall internal consistency of the instrument. A higher value suggests a greater degree of reliability, indicating that the items in the instrument measure the construct consistently. Conversely, a lower value may indicate inconsistency in the measurements.

The reliability test was conducted using statistical software, such as SPSS-21, which allows for the calculation of Cronbach's alpha coefficient. The test involved analyzing the responses to the 22 items included in the instrument. Reliability statistics provide valuable information about the robustness and stability of the instrument used in the study. By evaluating the internal consistency of the items, researchers can ensure that the instrument is reliable and yields consistent results.

Table 3 delineates the outcomes of Cronbach’s alpha reliability assessment for a range of competencies, segmented under various domains pertinent to professional endeavors. These domains encompass Professional Engagement, Digital Resources, Digital Pedagogy, Evaluation and Feedback, Empowering Students, and Facilitating Students' Digital Competence.

Upon meticulous examination, it becomes evident that all competencies under investigation exhibit remarkable reliability. This assertion is substantiated by Cronbach’s alpha values, which span between 0.946 and 0.949. Such values, particularly when surpassing the 0.7 threshold, are conventionally deemed to be emblematic of substantial internal consistency.

Transitioning our focus to the scale mean and variance in the event of item deletion offers a profound understanding of the potential fluctuations in these metrics should a specific competency be excluded. On average, the scale mean gravitates around 52.5 for the various competencies, insinuating a harmonious scale. In parallel, the scale variances, which oscillate primarily between the mid-120s and 130, display marginal variations among the competencies, reinforcing the notion that there are no pronounced disparities among them.

Moreover, an analysis of the correlations unveils a discernible linear association between individual competencies and the corrected total score. This score represents the cumulative value of all items, excluding the item currently under consideration. Notably, the correlation coefficients span from 0.500, evident in the Organizational Communication within the Professional Engagement domain, to 0.751 observed in Active Student Participation under Empowering Students. These consistently positive correlations imply that an uptick in scores for individual competencies is frequently associated with a surge in the overall score.

Lastly, by evaluating the column that depicts Cronbach's alpha in scenarios where an item has been excised, one gleans pivotal insights regarding the influence of individual competencies on the overarching reliability of the measurement tool. An attenuated value in this column, in contrast with the intrinsic Cronbach's alpha, intimates a potential decrement in reliability upon the removal of the said competency. Nonetheless, the relatively minute distinctions underscore that any singular competency's omission would unlikely induce a substantial shift in the tool's internal consistency.

Subsequently, a descriptive analysis was conducted to examine the participants' responses, considering the five intervals established by the Likert scale (Table 4). Descriptive analysis involves summarizing and interpreting the collected data to provide a comprehensive understanding of the participants' evaluations.

The results presented in Table 4 highlight the distribution of participants across different competence levels in each area. In the Professional Commitment competence, the highest levels are observed among experts and integrators, accounting for 38.99% and 36.46% respectively. On the other hand, the pioneer level has the lowest representation with only 0.72%, followed by the explorer, leader, and novice levels, which account for 16.97%, 3.61%, and 3.25% respectively.

Turning to the Recognizes, Evaluates and Empowers competency, Table 4 reveals that the integrator category holds the highest percentage with 39.35%, followed by the expert category with 35.38%. The explorer category represents 13.00%, while the novice, leader, and pioneer categories account for 2.53%, 9.03%, and 0.72% respectively.

In the Digital Pedagogy competence, the integrator category stands out with the highest representation at 40.07%, closely followed by the expert category at 39.71%. The explorer category accounts for 9.39%, while the leader, pioneer, and novice categories have percentages of 7.94%, 2.53%, and 0.36% respectively.

In the Evaluates and Provides Feedback competence, 46.21% of the surveyed professors position themselves in the integrator category, while 32.13% identify themselves as experts. Additionally, 9.03% consider themselves explorers, 7.58% as leaders, 3.68% as pioneers, and 1.44% as novices.

Regarding the Empowers Students competence, the results indicate that 42.60% of the respondents have an expert level, while 32.85% are classified as integrators. The leader, explorer, pioneer, and novice categories represent 9.03%, 8.30%, 4.69%, and 2.53% respectively.

Examining the Facilitates Competences competence, the majority of professors, 48.74%, are categorized as integrators, followed by experts at 36.82%. The explorer, leader, pioneer, and novice categories account for 5.05%, 3.61%, 3.25%, and 2.53% respectively.

These results provide insights into the distribution of participants across competence levels, highlighting the variations in self-perceived digital competences among the surveyed professors. Understanding these distributions is crucial for designing targeted interventions and support strategies to enhance digital competences and promote professional development in the educational context.

Table 5 presents the analysis of variance, which examines the sources of variation, the degrees of freedom, and the sum of squares for each numerical variable investigated. Analysis of variance (ANOVA) is a statistical technique used to determine the significance of differences between groups or categories.

When examining the sources of variation such as sex, academic profile, and age, as well as their interactions, significant differences are observed in the “pedagogy variable” due to the interaction effect between sex and academic profile. This suggests that the relationship between sex and academic profile influences the variability in pedagogical competence.

Furthermore, the relationship between academic profile and age accounts for approximately 31.892% of the variability in professional commitment. This indicates that the combination of academic profile and age significantly contributes to variations in the level of professional commitment.

Similarly, the confluence of sex, academic profile, and age has a notable impact, accounting for 11.755% of the variability in digital resources. This suggests that the interaction among these factors plays a significant role in determining the level of digital resource competency.

Table 6 provides a summary of the Chi-Square test results, which examines the independence of each factor against all categorical variables in each competence area. The “Pvalue” column indicates the significance of each test, with a significance criterion of α=0.05. If the test is not significant, it suggests that the variables are independent. However, for example, in the case of the “Evaluates and provides feedback” variable, a significant relationship with sex is found.

Regarding age, independence is observed in the “Evaluates and provides feedback” and “Empowers students” variables, suggesting that age does not significantly affect these competences. However, in the other variables, a significant relationship is found between age and the rest of the competences, indicating a non-independent effect of age on those variables.

For the academic profile, independence is found in relation to age, except in the “Empowers students” variable. This implies that the academic profile has a significant relationship with age in most competences, except in the case of empowering students.

These findings highlight the complex interplay of factors and their interactions in influencing the variations observed in different competence areas. Understanding these relationships is essential for tailoring educational interventions and designing targeted strategies to enhance specific competences based on sex, academic profile, age, and their combinations.

Within the possibilities of multidimensional scaling with the application of the multivariate technique, stress is presented as a measure of goodness of fit, whose indicator was ‘weak to poor’ with a value of 0.1828, which is reflected in the graph represented by a regression in Figure 1, where the degree of coupling of the data can be observed. However, in the Stress value, it was visualized that there was closeness between the items of the ‘Empowers’, ‘Evaluates’, ‘Facilitates Digital Competence’ competence areas. Therefore, the items of these areas of competence formed two compact groups of items, sharing items between one group and the other, but indicating that in general these items formed a construct.

Figure 1. Scaling of competences.

Source: Data provided by respondents, processed with SPSS-21 statistical software.

As it can be observed, it was verified that Professional Commitment and Digital Resources were left out of this group. In this way, it was observed that the most dispersed items were those of Professional Commitment. On the other hand, only “Reflective Practice” and “Professional Collaboration” were relatively close to each other, but very far from “Digital Training” and “Organizational Communication”. Likewise, the “Digital Resources” items were found to be close to each other. Consequently, it could be considered that the items of these two areas formed another construct.

Next, a two-stage factor analysis was performed.

The results of the factor analysis with the maximum likelihood method and the “Oblimin” oblique rotation method option showed a KMO value of 0.961 and Bartlett's test of sphericity was significant, indicating that the application of the factor analysis was adequate.

The lower part of Table 7 shows the characteristic values of the first two factors, and it was observed that with the first two factors a cumulative explained variance of 56.58% was obtained which was considered acceptable, suggesting a two-factor model. The first factor was made up of the competences that were mainly aimed at the achievement of empowerment by students of digital tools to apply them to the teaching-learning process.

The second factor generated a construct that described the professors’ personal attitudes toward their training and use of digital tools. That is, construct one referred to the entire pedagogical structure, and construct two to the professors’ personal training and use of it.

Similarly, Table 7 presents the factors and the loadings of each of the items within the factors. Thus, the rotation analysis made it possible to achieve what the literature suggests as a good result in factor analysis, in other words, that each factor is made up of variables with high values and some variables with values close to zero, and that each variable belongs to only one of the factors.

Therefore, variables with small values in the factors were omitted to avoid duplicities, taking as a criterion for the elimination of values lower than 0.35. For this reason, only two items could not be fully discriminated against, which were Professional Collaboration and Evaluation Strategies. However, Professional Collaboration was more important in factor 1, while Evaluation Strategies was more important in factor 2. Finally, it was observed that the item Digital Training was not part of any of the factors to obtain the scores.

It is clear that through the assisted clustering analysis, three groups were identified. These are shown in Figure 2.

Figure 2. Assisted clustering analysis of the constructs.

Source: Data provided by respondents, processed with SPSS-21 statistical software.

In this analysis, it was found that the grouping of participants was strongly influenced by factor 1, which pertained to the pedagogical aspect. Group 1 comprised professors who exhibited higher ratings on construct 1, indicating a strong emphasis on pedagogy. Group 2 represented an intermediate category, characterized by a balance between the two constructs. On the other hand, Group 3 consisted of professors whose ratings on construct 2 prevailed.

These groupings suggest that participants varied in their emphasis on different aspects of teaching and learning. Group 1 highlighted a strong focus on pedagogical approaches, while Group 3 demonstrated a greater emphasis on the second construct, which may be related to other dimensions or competences.

Understanding these groupings provides insights into the diverse perspectives and preferences of educators in relation to teaching practices and competences. It enables researchers and educators to identify specific areas where professional development and support can be targeted to enhance teaching effectiveness and promote a well-rounded approach to education.

To describe more precisely the constitution of the groups, the means corresponding to each of the variables that calculated the score in each area of competence were generated, the results of which are shown in Table 8.

To provide a more detailed description of the group composition, the means for each variable that contributed to the calculation of the score in each competence area were computed. The results of this analysis are presented in Table 8.

Table 8 displays the mean values for each variable within each competence area. These means offer a comprehensive understanding of the specific aspects or dimensions that contribute to the overall score in each competence area. By examining the means, researchers can identify the relative strengths and weaknesses of the participants in different areas of competence.

Analyzing the means enables a more nuanced interpretation of the group composition and allows for a deeper exploration of the participants' competences. It provides valuable insights into the specific areas where educators excel or require further development.

By considering these mean values, researchers and educators can design targeted interventions and strategies to enhance specific competences and address any areas of improvement identified. This information contributes to the overall goal of fostering professional growth and enhancing the quality of teaching and learning experiences.

Discussion

Upon examining the results, it is evident that the Integrator and Expert categories consistently present higher percentages in various competencies, such as PROFESSIONAL COMMITMENT, RECOGNIZE, EVALUATE AND EMPOWER, DIGITAL PEDAGOGY, EVALUATE AND PROVIDE FEEDBACK, EMPOWER STUDENTS, and FACILITATE COMPETENCIES. This indicates that teachers who perceive themselves as integrators and experts demonstrate a higher level of Digital Teaching Competence compared to explorers and novices. Additionally, leaders and pioneers, albeit fewer in number, show greater knowledge and abilities in these competencies.

These findings align with studies conducted by Espino-Díaz et al.,²⁷ and Hämäläinen et al.²⁸ which similarly highlight a pattern of behavior in competencies leaning towards a low to medium competence level. The study by Peled²⁹ also supports this idea, suggesting that there is a positive correlation between teachers' self-perception as experts or integrators and their level of Digital Teaching Competence.

In terms of demographic factors, Table 5 shows that sex and academic profile exhibit the highest values on the digital pedagogy variable. This suggests that, regardless of sex, educational teaching techniques are applied without gender bias, contributing to the development of Digital Teaching Competence. Furthermore, the lower values imply that sex is not a significant factor in acquiring commitments or fulfilling professional responsibilities in teaching.

These findings align with the discoveries of Guillén-Gámez et al.,³⁰ who emphasize the limited differences between sex and age in terms of self-perception of digital competencies. This corroborates that sex does not have a significant impact on learning and teaching abilities related to the use and management of digital tools. It also suggests the need to plan and implement strategies for integrating digital competencies into teacher training programs.

Regarding age, the percentages indicate that it is not perceived as a limitation for acquiring or fulfilling commitments related to the appropriation of digital resources. This finding aligns with Suárez and Colmenero,³¹ where no statistically significant association was found between age and competence level, indicating that there are no significant differences among different age groups. This result is also backed by the study of Gudmundsdottir and Hatlevik,³² suggesting that digital competence is not determined by age, but rather by attitude and training.

Table 6 examines the relationship between factors, including sex, age, and academic profile. It indicates that sex is independent of the variables, except for “Evaluate and provide feedback,” suggesting that sex influences the search for possibilities to identify and verify knowledge. Also, the “Evaluate and provide feedback” and “Empower students” variables show the lowest values, indicating that age does not determine processes involving communication with students.

Regarding the academic profile, the “Empower students” variable obtains the highest score, indicating that teachers strive to provide students with the necessary support and information to guide their development. Although some teachers possess basic or intermediate digital skills, it is essential that they empower their students through adequate training. This aligns with the study conducted by Colás-Bravo et al.,³³ emphasizing the need for teachers to contribute to developing critical, reflective, creative, and innovative thinking in students through proper training. Aidoo et al.³⁴ study supports this idea, suggesting that teachers need to have a deeper understanding of how to use digital tools to empower students and foster their autonomous learning.

The correlation analysis presented in Figure 1 highlights the interrelationship between items related to competencies such as “Empower”, “Evaluate”, and “Facilitate Digital Competence.” This suggests that as teachers set out to empower students, they contribute their own competencies to improve students' skills, thus demonstrating their commitment to professional performance and teaching praxis.

However, it is important to note that Professional Commitment and Digital Resources were not included in this construct, indicating that not all teachers share the same attitude towards student training, possibly due to resource limitations. Another construct formed by “Digital Training” and “Organizational Communication” suggests the existence of divided positions regarding empowerment practices.

Findings from List³⁵ support this idea, as they indicate that the management of digital identities in the educational context, specifically in the dimensions of communication and collaboration, is limited among teachers. This suggests that there is a need for further development of digital competencies among educators. Similarly, the study of Benitt et al.³⁶ concludes that teachers need more comprehensive training in digital competencies to effectively utilize technologies in teaching.

Table 7 provides an analysis of the factors, indicating that Professional Collaboration competencies, which contribute to the application of strategies in adapting virtual environments, have greater relevance in educational processes. Also, Evaluation Strategies is identified as a significant factor in determining the extent of learning and organizing improvements. This aligns with the study conducted by López-Belmonte et al.,³⁷ emphasizing the importance of promoting teachers' participation in training programs focused on developing digital competence and using emerging methodologies. These programs aim to adapt teaching practices to the new techno-pedagogical paradigms of a digitized society.

Figure 2 identifies three distinct groups of teachers. The first group (G1) emphasizes the importance of grades in pedagogical processes, while the second group (G2) lacks a clear definition. The third group (G3) expresses limited contributions to the topic. These findings are consistent with the conclusions drawn by Gil-Jaurena and Domínguez,²² who recommend incorporating more profound educational technology content into training programs to improve educational quality and enable appropriate teaching praxis for the digital age. Moreover, the findings of the study by Rapanta et al³⁸ support this recommendation, suggesting that a better understanding of educational technology can help teachers adapt their practices to students' needs and expectations in the digital environment.

Finally, Table 8 presents the mean values and standard deviations of the formed groups. It reveals that Group 1 (G1) shows the lowest proportional effect on the “DIG RESOURCES” variable, indicating that without adequate digital resources, teachers face limitations in providing quality education. On the other hand, Group 1 (G1) shows the highest weighted average on the “FACILITATESCO” variable, suggesting that the ability to facilitate required competencies largely depends on the interaction between teachers and their students. In Group 3 (G3), the “DIGRESOURCES” variable receives the lowest scale, underlining the importance of having digital resources to promote skills for effective use of digital environments. These findings align with the study conducted by Pozo-Sánchez et al.,³⁹ which associates higher digital competence scores with dimensions related to information and communication literacy and collaboration.

The study provides a comprehensive view of how teachers perceive themselves in terms of digital competence and how this perception relates to demographic variables such as gender, age, and academic profile. However, certain limitations and ambiguous areas suggest the need for further and more detailed research in the future.

Conclusions

The findings of the descriptive analysis shed light on the self-perception of digital teaching competence among UTM professors, revealing an overall trend towards the integrator and expert categories. Notably, competences such as “Facilitates Competences,” “Evaluates and Provides Feedback,” and “Digital Pedagogy” were frequently highlighted.

Furthermore, the study identified significant differences in the relationship between age, sex, and academic profile and professors' digital competence, particularly in the “Digital Pedagogy” domain. This indicates an inconsistent relationship between sex and academic profile, with most variables exhibiting independence, except for the “Evaluates and Provides Feedback” variable, which showed significance.

Regarding age, a relationship was found in the “Evaluates and Provides Feedback” and “Empower Students” variables, while independence was observed for the remaining variables. The study acknowledges certain limitations, primarily the lack of random sampling, which hindered the creation of a more balanced and homogeneous sample. Consequently, the generalizability of the findings may be limited.

Future research directions should focus on designing training programs tailored to address the specific digital teaching competence needs of professors in order to effectively tackle the challenges posed by the digital era. These programs should incorporate formative and procedural aspects that allow participants to actively produce and implement their learning, emphasizing practical application to meet real needs.