The Teaching Digital Competence of Health Sciences Teachers. A Study at Andalusian Universities (Spain)

Int. J. Environ. Res. Public Health 2021, 18, 2552. https://doi.org/10.3390/ijerph18052552 www.mdpi.com/journal/ijerph Article The Teaching Digital Competence of Health Sciences Teachers. A Study at Andalusian Universities (Spain) Julio Cabero-Almenara, Julio Barroso-Osuna, Juan-Jesús Gutiérrez-Castillo and Antonio Palacios-Rodríguez * Department of Teaching and Educational Organization, University of Seville, 41013 Seville, Spain; [email protected] (J.C.-A.); jbarros[email protected] (J.B.-O.); [email protected] (J.-J.G.-C.) * Correspondence: [email protected] Abstract: The impact and benefit that information and communication technologies (ICT) have in the educational field require new teaching skills. This fact has been increased by the recent crisis caused by COVID-19. This study tries to investigate the level of digital teaching competence (DTC) of Higher Education teachers of Health Sciences, and its relationship with several variables. For this, it has the participation of 300 teachers from the 9 universities of Andalusia (Spain). The research is structured through a descriptive (RQ1) and inferential (RQ2) design. The answers given to the DigCompEdu Check-In questionnaire adapted to the Spanish context are analyzed. The results, which show high levels of reliability of the questionnaire (Cronbach and McDonald) and validity (CFA), indicate that the level of competence is basic-intermediate. In addition, the area in which teachers excel is digital resources. For this reason, it is proposed to structure personalized training plans and continue expanding the characteristics of this study at an international level. Keywords: digital competence; DigCompEdu; higher education; health sciences; teacher training; COVID-19 1. Introduction If digital teaching competence (DTC) has become necessary at the beginning of the 21st century, what happened with the pandemic produced by COVID-19 has made it more necessary than ever to have teachers who possess a strong command of DTC. The Council of the EU defines digital competence as that which “involves the safe and critical use of the technologies of the information society for work, leisure and communication” [1]. This presence in society is also perceived in educational institutions, where never before has the teacher had so many technological resources to carry out their professional teaching activities [2]. DTC implies the acquisition of a set of skills, knowledge and attitudes that the teacher must possess for the technical, pedagogical and didactic incorporation of information and communication technologies (ICT) in educational contexts; in short, what a teacher should know for the design, production, use and evaluation for the educational incorporation of ICT in the teaching-learning processes [3–5]. Different studies point to the particular benefits of using and combining various technologies and methodologies in medical education [6]. These include experiences with augmented reality [7], 3D printed models [8], simulations with virtual reality [9] and mobile applications [10]. The combination of these technologies enables Health Sciences students to be updated in the dynamic and ever-changing nature of medical education [11]. To analyze the specific competences that digital competence implies, different frameworks have been proposed. Table 1 shows the most consolidated and significant reference frameworks at the international level [12–14]. Citation: Cabero-Almenara, J.; Barroso-Osuna, J.; Gutiérrez-Castillo, J.-J.; Palacios-Rodríguez, A. The Teaching Digital Competence of Health Sciences Teachers. A Study at Andalusian Universities (Spain). Int. J. Environ. Res. Public Health 2021, 18, 2552. https://doi.org/ 10.3390/ijerph18052552 Academic Editor: Emilio Abad-Segura Received: 21 January 2021 Accepted: 1 March 2021 Published: 4 March 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Int. J. Environ. Res. Public Health 2021, 18, 2552 2 of 13 Table 1. More consolidated and significant competence frameworks. Framework Institution Competence Domains European Framework for Digital Competence for Educators-DigCompEdu Joint Research Center 6 competence areas:  Professional engagement  Digital resources  Teaching and learning  Assessment  Empowering learners  Facilitating learners’ digital competence ISTE Standards International Society for Technology in Education 7 roles or profiles that a teacher can go through in their professional development and which involve different actions to be undertaken by the teacher, these roles are:  Apprentices  Leaders  Citizens  Collaborators  Designers  Facilitators  Analysts Common Framework for Teaching Digital Competence Ministry of Education of Spain 5 areas of competence:  Information and information literacy  Communication and collaboration  Creation of digital content  Security  Problem resolution UNESCO Competency Framework ICT for Teachers UNESCO Teachers must acquire competencies to:  Understand ICT in educational policy  Curriculum and assessment  Pedagogy  Application of digital skills  Organization and administration  Professional Digital teaching framework Ministry of Education UK Set for 7 key areas:  Planning  Approach  Employability of the Student  Specific Teaching  Evaluation  Accessibility and Inclusion  Self ICT skills and standards for the teaching profession Ministry of Education of Chile 5 dimensions:  Technique  Pedagogical  Management  Development and Responsibility  Social, Ethical and Legal ICT skills for teacher professional development Ministry of Education of Colombia 5 competences that teachers must possess to have an effective use of ICT:  Technological  Communicative  Pedagogical  Management  Research These frameworks seek to identify training needs and propose personalized training itineraries [15]. Along these lines, these frameworks were analyzed in a previous work through the external competence coefficient, where the responses of 179 national and international experts in digital competence were validated [16]. In this study, a comparison Int. J. Environ. Res. Public Health 2021, 18, 2552 3 of 13 of the main competencies and areas of the frameworks is made. The results of the study showed that the DigCompEdu framework was the most valued framework, becoming the most suitable for use in the university context, hence its importance and taking it as the object of study in this research. The study values its pedagogical component very positively. This is one of the main advantages over other frames. On the contrary, the rest of the analyzed frameworks pay special attention to the technological dimension of digital competence; the pedagogical dimension is relegated to the background. DigCompEdu framework focuses, as shown in Figure 1, on three large competence dimensions: professional, pedagogical and student competences. The framework aims to support national, regional and local efforts to promote the digital competence of educators, offering a European reference space with a common language and logic. Figure 1. Conceptual view of the DigCompEdu framework [12]. The Professional Engagement dimension focuses on the teachers’ work environment in order to be able to interact with the different agents of the educational community (A); the Digital Resources dimension is related to the creation and distribution of digital resources in the classroom, respecting copyright norms (B); the Teaching and Learning dimension is related to knowing how to design and plan the use of technology in student learning, with active methodologies focused on this group (C); the Assessment dimension focuses on the use of ICT resources for the evaluation of students (D); the dimension Empowering Learners is related to ensuring digital access to all students, offering learning activities adapted to their level of competence, as well as their interests and educational needs (E); and finally, Facilitating Learners´ Digital Competence (F). At the university level, research shows a lack of teacher training for the incorporation of ICT in teaching [5,17] and the need to establish training plans. Regarding digital competences in Health Sciences, digital competence was analyzed in the Faculty of Health Sciences of the UNACH (Ecuador) [18], finding a deficiency in the lack of digital training related to management and generation of information, as well as the dissemination of knowledge. Other studies [19,20], however, have shown the special interest of health professionals in knowing and acquiring the digital competence that society demands in the 21st century. Stud and analysis to establish training actions is more necessary in the pedagogicaldidactic component than in the technological-instrumental [5]. This indicates that the analysis of this competence is necessary, because its low dominance has repercussions in a lower and unqualified educational use of ICT by the teacher [21]. On the other hand, as Int. J. Environ. Res. Public Health 2021, 18, 2552 4 of 13 its mastery has a transversal impact on other competences that the teacher must have, its study and analysis, it becomes more necessary [22]. For this reason, different authors defend that teaching models should be approached from different perspectives that lead them to focus more on the pedagogical than on the didactic [23]. Pedagogical training is presented as a good predictor for the didactic use of ICT [24] and, at the same time, it focuses on improving their beliefs and attitudes towards the educational use of ICT [25]. These aspects will facilitate the educational incorporation of ICT and the performance of innovative practices with them. This study arises from the educational problems that emerged from the COVID-19 crisis, where the digital skills of teachers have been called into question. Consequently, this study tries to answer the following research questions: 1. What are the digital teaching competences of the Andalusian university teachers of Health Sciences? (RQ1) 2. Are there significant differences based on different established variables? (RQ2) 2. Materials and Methods An ex-post facto investigation is proposed, since the nature of the variables is not modified. In this case, a cross-sectional design is carried out since a single measurement is carried out in a certain context (teaching during the crisis caused by COVID-19). The same methodology has been used in studies related to this research [11,18,19]. The research is structured through a descriptive (RQ1) and inferential (RQ2) design. In the same way, data on the reliability and validity of the instrument are also provided. 2.1. Objectives To answer the research questions, the objectives were threefold: O1) To know the reliability index of the diagnostic instrument, O2) analyze the domain in DTC shown by university teachers of Health Sciences during the pandemic produced by COVID-19, and O3) analyze if there are significant differences in the DTC shown by Health Sciences teachers according to the variables: gender (3.1), age (3.2), teaching experience (3.3), years they have been using ICT (3.4) and the technological domain expressed (3.5). 2.2. Sample The sample has been selected by intentional sampling and convenience. 300 university teachers who teach degrees in Health Sciences at the 9 Andalusian public universities (Spain) participated in this study. Table 2 shows the participation index by Andalusian universities (Spain). Table 2. Participation index by Andalusian universities (Spain). University N % Sevilla 62 20.67 Pablo de Olavide 27 9.00 Granada 52 17.33 Huelva 22 7.33 Cádiz 36 12.00 Córdoba 32 10.67 Málaga 30 10.00 Jaén 16 5.33 Almería 23 7.67 Total 300 100.00 There was a participation of 158 women and 142 men. The majority were over 40 years old (78%), and stated that they had more than 10 years of teaching experience Int. J. Environ. Res. Public Health 2021, 18, 2552 5 of 13 (68.7%). Regarding the time they have been using ICT as a learning tool, more than half stated that they have been integrating them in the classroom for 10 or more years (55.3%). Also, the vast majority showed a positive or very positive attitude when asked about the level of curiosity they have regarding ICT (87.4%). In the same way, almost all respondents claimed to be a user of between 2 and 4 social networks (67.3%). Together, the vast majority claimed to have a high or very high technological proficiency in terms of using computers (96.6%), tablets (90.7%), smartphones (93.3%) and the internet (84.3%). 2.3. Instrument DTC was measured with the DigCompEdu Check-In instrument, adapted to the Spanish context [4]. The instrument is made up of 6 competence dimensions and a total of 22 items (Figure 1): 1. Professional engagement: focused on the work environment of teachers, where their ability to use digital technologies to improve teaching and interact professionally with colleagues, students, family and different agents of the educational community is expressed. This area of competence includes 4 items: organizational communication, professional collaboration, reflective practice and digital training. 2. Digital resources: related to the sources, creation and distribution of digital resources. One of the key competencies that any teacher must develop is to identify good educational resources. Additionally, she must be able to modify, create and share them to suit her goals, learners and teaching style. At the same time, she must know how to use and manage digital content responsibly, respecting copyright rules and protecting personal data. This includes 3 items: selection; creation and modification; and administration, exchange and protection. 3. Teaching and learning: knowing how to design, plan and implement the use of digital technologies in the different stages of the teaching and learning process. In addition, a change in approaches and methodologies that are student-centered is advocated. This encompasses 4 items: teaching, guidance, collaborative learning and selfdirected learning. 4. Assessment: linked to the use of digital tools and strategies in the evaluation and improvement of teaching-learning processes. Digital technologies can improve existing assessment strategies and lead to new and better assessment methods. Space in this area of competence is given to 3 items: evaluation strategies, analysis of evidence and tests, and feedback and planning. 5. Empowering learners: one of the key strengths of digital technologies in education is their potential to promote the active participation of students in the learning process and their autonomy over it. In addition, digital technologies can be used to offer learning activities tailored to each student’s level of competence, interests and learning needs. This area of competence is composed of 3 items: accessibility and inclusion, differentiation and personalization, and active participation of the students. 6. Facilitating learner´s digital competence: how to develop and facilitate the students’ digital competence, through 5 items: information and media literacy, digital communication and collaboration, creation of digital content, responsible use and well-be- ing, and digital problem solving. Each item is measured on a 5-interval Likert scale. In each of them, the participants indicate to what extent they reflect their own teaching practice by selecting one of the five options. These are organized progressively, reflecting the general progression logic of DigCompEdu (competence levels) through an internal scoring system. This progression follows the structure of: no commitment (0 points), partial knowledge (1 point), occasional use (2 points), increasing use (3 points) and systematic and comprehensive use (4 points). The anonymous questionnaire was sent through the online platform EuSurvey and it can be consulted through the following link: https://bit.ly/2ZyfyQR. Int. J. Environ. Res. Public Health 2021, 18, 2552 6 of 13 2.4. Data analysis Procedure Consistent with the research questions, data analysis includes several procedures. First, the internal structure (reliability) of the instrument is verified through the Cronbach’s Alpha and McDonald’s Omega, as well as the construct validity with the confirmatory factor analysis (CFA), convergent validity and discriminant validity. As statistical software, SPSS V.26 and AMOS V.24 are used to check the modeling of structural equations (SEM) on the relationships between the items of the instrument. After that, a descriptive study of the data is made using statistics of central tendency (mean) and dispersion (standard deviation). To calculate the total competence level and competence by dimension, a summation of the related items over 4 points is made [4]. For the contrast of the different hypotheses, non-parametric contrast statistics were applied: U of Mann–Whitney and K of Kruskal–Wallis with a post-hoc test (Dunn’s test). 3. Results 3.1. Reliability and Validity (O1) The first objective of our work was to know the reliability index of the instrument used for diagnosis. To check the validity of the construct, a series of indices, together with the values necessary for the model proposed in the CFA to be satisfactory [26], is proposed. Mardia´s coefficient (CM) shows multivariate normality by finding values between 3 and 70 [27]; residual mean square root (RMR) values lower than 0.10 are considered favorable [28]; a Chi-square ratio with degrees of freedom (χ2/gl; CMIN/DF) values lower than 5 indicate a good fit [29]; the non-normed fit index (NNFI), the Tucker–Lewis index (TLI), the comparative fit index (CFI) and the incremental fit index (IFI) consider values above 0.90 as a good fit. Mean square error of approximation (RMSEA) values between 0.05 and 0.08 [30], composite reliability (CR) coefficients with values greater than 0.7, average variance extracted (AVE) with values greater than 0.5 [31], and maximum shared variance (MSV) with a value less than the AVE coefficient [32] are all considered good. Table 3 shows the coefficients for each of these adjustments. Table 3. Construct, discriminant and convergent validity and reliability of the instrument. C.M. CFI TLI IFI NNFI RMR RMSEA 90% CI AFC 22.495 0.946 0.931 0.942 0.931 0.0344 0.058 0.055–0.065 Dimensions A B C D E F TOTAL CR 0.731 0.721 0.802 0.820 0.790 0.780 - AVE 0.584 0.525 0.603 0.614 0.554 0.540 - MSV 0.872 0.871 0.820 0.821 0.790 0.787 - Alpha 0.747 0.701 0.742 0.821 0.808 0.831 0.940 Omega 0.953 0.955 0.953 0.951 0.953 0.965 0.991 For the reliability analysis, the Cronbach’s Alpha and McDonald’s Omega coefficients reveal very satisfactory levels in the total of the instrument, as well as satisfactory results in the different dimensions. Int. J. Environ. Res. Public Health 2021, 18, 2552 7 of 13 3.2. Teaching Digital Competence Level (O2) Having corroborated the results obtained in terms of validity and reliability of the instrument used, our second objective is to analyze the domain in DTC that these teachers show in their teaching during the pandemic produced by COVID-19. Table 4 represents the mean values obtained for each item of the instrument (out of 4 points), as well as their standard deviation. Table 4. Mean and standard deviation of DigCompEdu items. Item M SD I systematically use different digital channels to improve communication with students, families and my colleagues. For example: emails, WhatsApp type messaging applications, blogs, the school website… 2.29 0.718 I use digital technologies to work with my colleagues inside and outside my educational organization. 2.16 0.897 I actively develop my teaching digital competence. 2.16 1.086 I participate in online training courses. For example: online administration courses, MOOCs, webinars… 2.5 1.178 I use different internet sites (web pages) and search strategies to find and select a wide range of digital resources. 2.19 1.004 I use my own digital resources and modify existing ones to adapt them to my needs as a teacher. 2.32 0.928 I protect sensitive content safely. For example: exams, grades, personal data… 2.05 1.105 I carefully consider how, when and why to use digital technologies in class, to ensure that their added value is used. 2.03 1.106 I monitor my students’ activities and interactions in the online collaborative environments we use. 2.08 1.165 When my students work in groups or teams, they use digital technologies to acquire and document knowledge. 2.33 1.159 I use digital technologies to allow students to plan, document, and assess their learning on their own. For example: self-assessment tests, digital portfolio, blogs, forums… 1.91 1.066 I use digital assessment strategies to monitor students’ progress. 1.75 0.946 I analyze all available data to identify the students who need additional support. 1.58 0.913 I use digital technologies to provide effective feedback. 1.78 0.809 When I propose digital tasks, I consider and address possible problems such as equal access to digital devices and resources; compatibility problems or low level of digital competence of students. 2.02 1.336 I use digital technologies to offer students pe rsonalized learning opportunities. For example: assigning different digital tasks to address individual learning needs, taking into account preferences and interests… 1.42 1.330 I use digital technologies so that students actively participate in class. 2.15 0.886 I teach students how to evaluate the reliability of information searched online and to identify erroneous and/or biased information. 1.87 0.977 I propose tasks that require students to use digital media to communicate and collaborate with each other or with an external audience. 1.81 0.899 I propose tasks that require students to create digital content. For example: videos, audios, photos, presentations, blogs, wikis… 1.93 1.116 I teach students how to behave safely and responsibly online. 1.18 0.940 I encourage students to use digital technologies creatively to solve specific problems. For example, overcoming obstacles or emerging challenges in their learning process. 1.81 0.945 Note: The scale of values is between 0 and 4 points, where the values between 0 and 1 represent a low level of competence, 2 and 3 points an intermediate level and 4 a high level. Values range from 1.18 (basic level) to 2.33 (intermediate level). Specifically, teachers show problems (basic level) in teaching students how to behave safely and responsibly online, using digital technologies to offer students personalized learning opportunities Int. J. Environ. Res. Public Health 2021, 18, 2552 8 of 13 and analyzing all available data to identify students who need additional support. The competences that stand out (intermediate level) are to systematically use different digital channels to improve communication, create your own digital resources and modify existing ones to adapt them to my needs as a teacher, and use digital technologies to acquire and document knowledge when students work in teams. At a general level, the mean scores obtained in each dimension are represented in Figure 2. These values have been calculated from the items (Table 3) that make up each dimension. Figure 2. Average value of the competence level by DigCompEdu areas. The average value reached in the instrument as a whole is 1.95 points with a standard deviation of 0.69. This leads us to point out that, in general, the level of competence is basic-intermediate. In addition, ordering from lowest to highest, the results by dimensions are as follows: Assessment (1.7); Facilitating Learners´ Digital Competence (1.72); Empowering Learners (1.86); Teaching and Learning (2.09); Professional Engagement (2.17); Digital Resources (2.18). Therefore, in general, teachers present basic levels of DTC, although they are considered moderately competent in professional commitment and digital resources. It is significant that the highest score was in digital resources dimension. 3.3. Contrast Analysis (O3) The third objective of this research is to analyze whether there are significant differences in the DTC shown by Health Sciences teachers based on the variables: gender (O3.1), age (O3.2), teaching experience (O3.3), years of using ICT (O3.4) and the expressed technological domain (O3.5). Regarding the gender variable, the results achieved in the Mann–Whitney U test are 10146000 with a significance of 0.153. These values allow accepting the H0 formulated in reference to significant differences based on gender. Consequently, it can be stated that there are no significant differences in the dominance of DTCs based on gender (O3.1). For the analysis of the significance of age, experience and time of use, the Kruscal– Wallis test was applied, obtaining the results that are presented in Table 5. Int. J. Environ. Res. Public Health 2021, 18, 2552 9 of 13 Table 5. Kruskal–Wallis tests: age, experience and use time. Variable Kruskal–Wallis df Sig. Age 22,878 5 0.037 Experience 15,735 5 0.008 Time of use of ICT 26,686 7 0.000 The values reached allow the rejection of all H0 at a significance level of p ≤ 0.05. There are statistically significant differences between the level of DTC and the age, experience and time of ICT use in the classroom. Regarding its technological domain, Table 6 presents the Mann–Whitney U value. Table 6. Relationship of the technological domain with digital teaching competence (DTC). Variable Mann–Whitney U W Z Sig. Technological domain 2974.00 12,019 −5.007 0.00 In this case, H0 is also rejected at a significance level of sig. ≤ 0.01. There are statistically significant differences between the level of DTC and the technological domain. Table 7 shows the values that are statistically significant (sig. ≤ 0.05). They are drawn from the application of Dunn’s multiple comparisons test in the Kruskal–Wallis test for the rest of the variables under study. Table 7. Significant results between the variables under study and the DTC shown by the Health Sciences teachers. Variable Contrast Groups Test Dev. Error Dev. Test Sig. Age 50–59 years–40–49 years 32.93 13.47 2.44 0.02 50–59 years–30–39 years 42.09 15.09 2.79 0.01 Experience 1–3 years–10–14 years −46.40 18.75 −2.48 0.01 1–3 years–05.04 years −54.78 20.94 −2.62 0.01 20 or more–10–14 40.55 14.91 2.72 0.01 20 or more–4–5 years 48.94 17.59 2.78 0.01 Timeuse ICT notuse–Less than 1 year −112.50 53.10 −2.12 0.03 No use–15–19 years −123.48 44.93 −2.75 0.01 No use–1–3 years −130.44 45.06 −2.90 0.00 No use–6–9 years −155.46 44.99 −3.46 0.00 No use–20 or more −155.57 45.20 −3.44 0.00 No use–10–14 years −160.88 44.65 −3.60 0.00 No use–4–5 years −192.60 47.49 −4.06 0.00 15–19 years–10–14 years 37.40 15.91 2.35 0.02 15–19 years–4–5 years 69.12 22.70 3.05 0.00 1–3 years–4–5 years −62.16 22.94 −2.71 0.01 Technological domain Low–Very low −176.06 61.70 −2.85 0.00 Very high–Very low −119.06 36.06 −3.30 0.00 High–Very low −112.06 43.89 −2.55 0.01 In reference to age (O3.2), fundamental differences have been established between teachers of “50–59 years” and those of “30–39 years”, the second being the group with the highest level of DTC. Additionally, between the teachers of “50–59 years” and those of “40–49 years”, in this case also the second ones are the ones with the highest level of DTC. Therefore, it can be stated that the null hypothesis is rejected for the two cases mentioned above.