16. ICT in Education and Training
Paper
Digital Transformation? A Longitudinal Interview Study on Teachers’ Acceptance and Usage of Digital Tools in Times of Covid-19
Olivia Wohlfart, Ingo Wagner
KIT, Germany
Presenting Author: Wohlfart, Olivia
The role of teachers in the digital transformation of education is recognized as a very important and complex holistic phenomenon (Ertmer & Ottenbreit-Leftwich, 2010; Wohlfart & Wagner, 2022). But which factors promote the lasting implementation of digital tools by teachers? Research shows that successful integration of existing or new digital tools depends on knowledge of and access to, as well as time to explore them (Tondeur et al., 2012). Teachers’ willingness and ability to integrate technology is also influenced by their attitudes or personal fears (Njiku, 2022; Wilson et al., 2020), and exposure to a student-centered constructivist pedagogical approach during teacher education can have a positive effect on digital literacy development and integration of digital tools in teaching practice (Chai et al., 2013). Contrary to the study results, we are far from an exhaustive integration of digital tools in formal education. The International Computer and Information Literacy Study 2018 (ICILS) shows that around 49 % of teachers used digital tools on a day-to-day basis and highlights considerable differences in the availability of technological infrastructure and conditions for professional learning across countries (Fraillon et al., 2019). With the outbreak of the Covid-19 pandemic in 2020, teachers no longer had the liberty to choose whether to incorporate digital tools into their teaching, as the circumstances made this inevitable (Wohlfart et al., 2021). Within the past three years, schools were forced to adapt and re-adapt to varying situations to fulfil their educational mission. Teachers are central in this environment and especially affected by this process of digital transformation, which makes their experiences particularly interesting and relevant. Current research, however, has often relied on one point of data collection. These studies therefore struggle in explaining individual dependencies in transformation processes. With our study, we aim to better understand how the past years have affected the experience with digital tools in the context of teaching. Analogously, we examine whether the Covid-19 pandemic has thereby led to a sustainable transformation of teachers’ acceptance and usage of digital tools.
Our study is based on an extended version of Davis’ (1986) widely accepted technology acceptance model (TAM). The core of the model consists of the variables perceived usefulness and perceived ease-of-use. In addition, the model describes the variable attitude towards using as a direct product of the former two variables in explaining user motivation for usage of a certain technology. Notwithstanding, these three core variables fail to fully explain the actual use of technologies. This is due to the influence of an array of external factors that determine user acceptance. Previous research has discussed and highlighted in detail the interaction and relevance of considering further external variables such as subjective standards (perception of how important the use of technology is to other people) or self-efficacy (one’s own ability to deal with technology) (Burton-Jones & Hubona, 2006; Lee et al., 2003). To gather a better understanding of the actual use of digital tools in teaching, we apply a refined TAM (Teo et al., 2008) as well as previous research to conduct and analyze longitudinal interviews with secondary schools’ teachers from Germany. Our study examines the following research questions:
How has teachers’ acceptance and usage of digital tools developed across time since the outbreak of the Covid-19 pandemic?
Which factors influence a lasting integration of digital tools in teaching?
Methodology, Methods, Research Instruments or Sources UsedTo answer our research questions, we conducted a longitudinal interview study over three years in the federal state of Baden-Wuerttemberg, Germany. Here, the federal government suspended on-site school activities for nearly three months after the outbreak of the Covid-19 pandemic in March 2020, re-opening for smaller groups in mid-June of the same year. Teachers, meanwhile, were required to enable distance learning and therefore produce appropriate learning content and transmit this to students. The mutations of the virus over the course of the next years led to iterative restrictions of school life and parallel on-site and distance teaching and learning. With our study design, we wanted to capture specific situations and relevant changes without delay or falsification caused by the dynamics involved with remembered experience over time (Becker et al., 2002). Thus, we conducted three rounds of interviews with the same teachers at secondary schools in 2020, 2021 and 2022.
The first round of interviews in May and June of 2020 focused the experience which 15 teachers had with this unfamiliar situation. With a semi-structured interview guide, we asked the interviewees about their personal experiences with the adoption of digital tools in times of distance teaching. We followed up on these interviews with the same teachers in May and June of 2021 (n=12) and 2022 (n=10) respectively, interested in the personal development of the interviewed teachers and changes in the adoption of digital tools in face-to-face teaching over time. The 37 interviews lasted between 29 and 66 minutes, were audio-recorded, and transcribed – leading to a comprehensive database of about 400 pages of single-spaced transcribed text.
We performed an iterative qualitative content analysis on the 37 transcripts according to Mayring (2015) with deductive categories based on the literature review (e.g. perceived usefulness, tools applied, infrastructure, etc.) as well as inductive categories that emerged from the transcribed interview material (e.g. changes, classroom management, school development etc.). The first two rounds of qualitative data analysis resulted in 42 codes and 2.177 coded segments (status of 18.01.2023).
Conclusions, Expected Outcomes or FindingsThe analysis of interviews from 2020 indicate contrary to previous literature, that Covid-19 as an external factor has a universal impact on all variables along the TAM and thereby positively and directly affects the acceptance and usage of digital tools in teaching. Furthermore, we identified three vital external factors: (1) regulations and specifications, (2) technological infrastructure and (3) the heterogeneity of students and teachers (Wohlfart et al. 2021). With the second collection of interviews, we wanted to better understand how teachers’ usage and acceptance of specific digital tools developed across time and experience. The findings highlight the development of user motivation of most teachers and while some inhibiting external factors remained (e.g. lack of infrastructure), others had been overcome (e.g. universal regulations/specifications). Overall, the acceptance and integration of digital tools increased over the first year. With the third round of interviews, we expect to find valuable information concerning lasting adaption of digital tools in face-to-face teaching and better understand why this may not be the case for all teachers. With this, we hope to derive lessons learned from this unique situation and conclude the pandemic to have been (at least in parts) a catalyst for digital transformation in education.
ReferencesBecker, H., Berger, P., & Luckmann, …, Mills, T. (2002). Observation and Interviewing: Options and Choices in Qualitative Research. In T. May (Ed.), Qualitative Research in Action (pp. 200–224). SAGE Publications Ltd. https://doi.org/10.4135/9781849209656.n9
Burton-Jones, A., & Hubona, G. S. (2006). The mediation of external variables in the technology acceptance model. Information & Management, 43(6), 706–717. https://doi.org/10.1016/j.im.2006.03.007
Chai, C. S., Koh, J. H. L., & Tsai, C.‑C. (2013). A review of technological pedagogical content knowledge. Educational Technology & Society, 16(2), 31–51.
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results [PhD]. Massachusetts Institute of Technology, Cambridge, Mass. https://tinyurl.com/y5xgfetl
Ertmer, P. & Ottenbreit-Leftwich, A. (2010). Teacher technology change. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
Fraillon, J., Ainley, J., Schulz, W. Friedman, T. & Duckworth, D. (2019). Preparing for life in a digital world – IEA International Computer and Information Literacy Study 2018 International Report. Springer. https://doi.org/10.1007/978-3-030-38781-5
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Lee, Y., Kozar, K. A., & Larsen, K. R.T. (2003). The Technology Acceptance Model: Past, Present, and Future. Communications of the Association for Information Systems, 12. https://doi.org/10.17705/1CAIS.01250
Mayring, P. (2015). Qualitative Inhaltsanalyse. Grundlagen und Techniken [Qualitative content analysis. Fundamentals and Techniques] (12th ed.). Weinheim: Beltz Verlag.
Njiku, J. (2022). Attitude and technological pedagogical and content knowledge: The reciprocal predictors? Journal of Research on Technology in Education. https://doi.org/10.1080/15391523.2022.2089409
Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre-service teachers’ computer attitudes: Applying and extending the technology acceptance model. Journal of Computer Assisted Learning, 24(2), 128–143. https://doi.org/10.1111/j.1365-2729.2007.00247.x
Tondeur, J., van Braak, J., Sang, G., Voogt, J., Fisser, P. & Ottenbreit-Leftwich, A. (2012). Preparing pre-service teachers to integrate technology in education: A synthesis of qualitative evidence. Computers & Education, 59(1), 134–144. https://doi.org/10.1016/j.compedu.2011.10.009
Wilson, M. L., Ritzhaupt, A. D., & Cheng, L. (2020). The impact of teacher education courses for technology integration on pre-service teacher knowledge: A meta-analysis study. Computers & Education, 156, 103941. https://doi.org/10.1016/j.compedu.2020.103941
Wohlfart, O., Trumler, T. & Wagner, I. (2021). The unique effects of Covid-19—A qualitative study of the factors that influence teachers’ acceptance and usage of digital tools. Education and Information Technologies, 26(6), 7359–7379. https://doi.org/10.1007/s10639-021-10574-4
Wohlfart, O. & Wagner, I. (2022). Teachers’ role in digitalizing education: an umbrella review. Educational technology research and development. https://doi.org/10.1007/s11423-022-10166-0
16. ICT in Education and Training
Paper
Development of Digital Competences Through the Academic Use of Digital Technologies During the Beginning and Ending of COVID-19 Lockdown
Cristian Cerda1, Miriam León1, José Luis Saiz1, Lorena Villegas2
1Universidad de La Frontera, Chile; 2Universidad Católica de Temuco, Chile
Presenting Author: Cerda, Cristian
Trying to define and label what people do with digital technologies has always been an interesting area to address. A literature review of this topic goes beyond the classic definition of digital natives proposed by Presky (2012), and it includes the work of Blank and Groselj (2014), who indicate that the use of Internet could be organized in three dimensions: amount of use, variety of different uses and types of use. The type or purpose of use is highly relevant nowadays due to the autonomous use of Internet available on laptops and smartphones. This is especially significant for student teachers, who as many other university students, take personal decisions about how to use technologies with several purposes, not only in activities related to learning and teaching (Cerda et al., 2022b).
The Chilean education system has a long tradition of integrating digital technologies in initial teacher education (Brun & Hinostroza, 2014). However, remote learning due to COVID-19 lockdown forced even more the adoption of digital technologies use. As in other countries, the commonly called “emergency remote teaching period” at higher education institutions represented, for professors and students, an immeasurable spent of energy in order to take concrete advantages of the potential that digital technologies offer (Sum & Oancea, 2022). The academic community demanded, from technology specialists, effective solutions to the challenge that remote teaching represented. Concerning student teachers, they had to deal with the enforced use of digital technologies for academic purposes in paralell with other personal purposes of uses of these tools.
Although the relevance of the topic, a few research has been done on understanding the implicit contribution in the development of digital competences during the emergency remote teaching period. Research of digital competences has mainly followed the development of generic digital competences (Carretero et al., 2017; Ferrari, 2013) and digital competences for educators (Redecker, 2017). In the case of student teachers, a few research has considered both frameworks (Reisoglu & Cebi, 2020). In Chile, several studies have replicated this trend, separating both frameworks, mainly due to the fact that not all the universities that deliver degrees in education have strong policies to explicitly promote computer literacy, general digital competences or digital competences for educators (Tapia et al., 2020).
The goal of this study is twofold. First, to compare the level of academic use of digital technologies between student teachers with limited experience in remote learning with those who spent four academic semesters learning in that academic environment. Second, to analyse the effect of interaction among variables related to academic digital competences (periods of measurement, sex, number of years in the student teacher program). The results of this study showed relevant information to better understand how the virtual learning experience supported the development of digital competences in student teachers during the COVID-19 lockdown.
Methodology, Methods, Research Instruments or Sources UsedA total number of 1,338 student teachers participated in this study (43.3% men and 56.6% women) divided in two periods of measurement. The first measurement considered 615 participants (35.9% men and 64.1% women) with limited experience in remote learning. It was taken during the first semester of the year 2020. The second measurement considered 723 students (49.7% men and 50.3% women), with almost two years experiencing remote learning. It was taken during the second semester of the year 2022. The emergency remote teaching period, due to COVID-19 lockdown, took place in Chile since March 2020 until December 2021 (four academic semesters).
The information was gathered using a 17 items scale about academic use of digital technologies. This instrument, which is part of the Scale of Purposes of Use and Digital Competences, measures frequently of use of digital technologies with academic, entertainment, social and economic purposes (Cerda et al., 2022a). The items for each purpose of use were based in the following five digital competences defined by DIGCOMP (Ferrari, 2013) A = Browsing, searching and filtering data, information and digital content; B = Managing data, information and digital content; C = Interacting through digital technologies; D = Sharing through digital technologies; E = Developing digital content.
Two strategies to collect data were used in this study. The information collected from the first measurement (in 2020) was obtained digitally through QuestionPro. The information collected from the second measurement (in 2022) was paper-based. In both cases, participants received information related to the objective of the study and the relevance of their voluntary participation. To participate, the student teachers had to read and sign an informed consent form approved by the university’s Scientific Ethics Committee.
Data analysis of the two measurements considered several steps. First, the collected information was examined in terms of accuracy of data entry and missing values. Second, after reaching adequate level of internal consistency, five variables were created considering the digital competences declared. Third, the variables were assessed in terms of normality, reviewing their level of skewness and kurtosis following the criteria (-1 to +1) suggested by Muthen and Kaplan (1985). Fourth, the independent t Student test was used to compare the two measurements within the five academic use variables. Finally, a MANOVA test was used to explore if there was a relationship between the measuring time periods and years in the program, and the measuring time and sex by the type of digital competences.
Conclusions, Expected Outcomes or FindingsResults from t Student tests showed differences in all the digital competences. Regarding Browsing, searching and filtering data, information and digital content, participants in 2022 got higher scores (M = 3.74, SD = 0.90) than the ones in 2020 (M = 3.50, SD = 0.99), t(1336) = -4.765, p < .001. Cohen’s d (-0.261). The same happened with others variables: Managing data, information and digital content (M = 3.74, SD = 0.96 versus M = 3.63, SD = 1.03), t(1336)= -2.014, p 0.04. Cohen’s d (-0.111), Interacting through digital technologies (M = 4.19, SD = 0.87 versus M = 391, SD = 1.05), t(1336)=-5.234, p < .001. Cohen’s d (-0.287), Sharing through digital technologies (M = 3.31, SD = 1.15 versus M = 2.90, SD = 1.17), t(1336)= -6.409, p < .001. Cohen’s d (-0.352) and Developing digital content (M = 3.30, SD = 1.10 versus M 2.85, SD = 1.13), t(1336)= -7.455, p < .001. Cohen’s d (-0.409). MANOVA test could not find any interaction effect among variables considered.
In conclusion, it can be stated that the emergency remote teaching period experienced for student teachers during four academic semesters allowed them to develop a few digital competences that can be used with academic purposes. Even though it is highly complicated to establish a cause-effect relationship among variables, the experience obtained for them during the remote teaching period might triggered new ways to use digital technologies for academic purposes. During this period, student teachers and professors did not receive any specific training in general digital competences or digital competences for teaching. All the strategies used were the result of personal initiatives implemented to experience an equivalent type of traditional on-site teaching. As Sum and Oancea (2022) establish, the scenario is not different to other contexts under similar circumstances.
ReferencesBlank, G., & Groselj, D. (2014). Dimensions of Internet use: Amount, variety, and types. Information, Communication & Society, 17(4), 417-435. https://doi.org/10.1080/1369118X.2014.889189
Brun, M., & Hinostroza, J. E. (2014). Learning to become a teacher in the 21st century: ICT integration in initial teacher education in Chile. Educational Technology & Society, 17(3), 222-238. https://www.jstor.org/stable/jeductechsoci.17.3.222
Carretero, S., Vuorikari, R., & Punie, Y. (2017). DigComp 2.1: The Digital Competence Framework for Citizens with eight proficiency levels and examples of use (EUR 28558). https://ec.europa.eu/jrc
Cerda, C., León, M., Saiz, J. L., & Villegas, L. (2022a). Chilean student teachers’ purposes of use of digital technologies: Construction of a scale based on digital competences. Píxel-Bit. Revista de Medios y Educación, 64, 7-25. https://doi.org/10.12795/pixelbit.93212
Cerda, C., León, M., Saiz, J. L., & Villegas, L. (2022b). Relación entre propósitos de uso de competencias digitales y variables asociadas a estudiantes de pedagogía chilenos. Edutec. Revista Electrónica de Tecnología Educativa (82), 183-198. https://doi.org/10.21556/edutec.2022.82.2557
Ferrari, A. (2013). DIGCOMP: A framework for developing and understanding digital competence in Europe. Publications Office of the European Union. https://doi.org/10.2788/52966
Muthén, B., & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38(2), 171-189. https://doi.org/10.1111/j.2044-8317.1985.tb00832.x
Prensky, M. (2012). From digital natives to digital wisdom: Hopeful essays for 21st century learning. Corwin.
Redecker, C. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office. https://doi.org/doi/10.2760/159770
Reisoglu, I., & Cebi, A. (2020). How can the digital competences of pre-service teachers be developed? Examining a case study through the lens of DigComp and DigCompEdu. Computers & Education, 156, 16, Article 103940. https://doi.org/10.1016/j.compedu.2020.103940
Sum, M., & Oancea, A. (2022). The use of technology in higher education teaching by academics during the COVID-19 emergency remote teaching period: A systematic review. International Journal of Educational Technology in Higher Education, 19(1), 59. https://doi.org/10.1186/s41239-022-00364-4
Tapia, H., Campaña, K., & Castillo, R. (2020). Análisis comparativo de las asignaturas TIC en la formación inicial de profesores en Chile entre 2012 y 2018. Perspectiva Educacional, 59(1), 4-29. https://doi.org/10.4151/07189729-Vol.59-Iss.1-Art.963
16. ICT in Education and Training
Paper
Technology Commitment Profiles and Emotional State Among Pre-service Teachers During and Beyond the COVID-19 Related Emergency Remote Education
Frederick Johnson, Joanna Koßmann, Christoph Schneider, Lothar Müller
Trier University, Germany
Presenting Author: Johnson, Frederick
The accelerating rise and widespread adaptation of digital technology in private and business sectors has led to a European consensus in regards to the necessity of the regular integration of technology in educational settings in order to enhance learning in general and prepare students for a competent use of digital technology (Peña-López, 2015). Due to the COVID-19 pandemic, the process has been accelerated even more – especially in European regions (Helm, Huber & Loisinger, 2021). One coping strategy that was adapted broadly in most educational institutions in Europe and beyond was emergency remote teaching (ERT), which shifted presence learning to online learning (Bozkurt & Sharma, 2020). The implementation of ERT in European regions proved to be rather diverse, e.g., with Portugal even using their television channels to cope with the pandemic (Seabra et al., 2021). In higher education, this shift towards online learning has proven to be emotionally challenging for learners – especially for pre-service teachers, with technology attitudes as primary influences (Schneider et al., 2021).
Referring to the elaboration of Tellegen et al. (1999) on the Circumplex Model of Affect, in which positive activation comprises positively valued states such as “enthusiastic” and negative activation comprises negative valued states such as “distressed”, emotional challenge arises either due to a decline in positive activation or an incline in negative activation as changes in emotional state. On a behavioral level, positive activation entails approaching behavior and negative activation avoidant behavior (Watson, 1999). Provided that attitudes are dispositions to respond favorably or unfavorably towards something (Ajzen, 2005), technology attitudes are closely related to positive and negative activation in the context of using technology. Therefore, the emotional state after ERT and the perception of their study experience in the transition away from ERT is expected to change in a more positive or negative direction depending on the underlying attitudes.
A more general approach to a person’s relationship with technology is due to the construct technology commitment. Neyer et al. (2012) conceptualize technology commitment as three dimensional: technology acceptance (referring to the technology attitudes from the Technology Acceptance Model), technology competence (operationalized by the anxiety to use technology), and technology control (as in the specifically technology related locus of control construct). Extensive research across the globe shows that technology commitment predicts the use of technology (Scherer et al., 2019) and emotional state whilst frequently using it (Händel et al., 2020; Schneider et al., 2021). Recent research indicates that the relationship between technology commitment and emotional state differs between clusters of technology commitment for in-service teachers (Pozas et al., 2022). Thus, it remains to be examined if this also holds for pre-service teachers. In summary, the following research questions will be addressed in this contribution:
- What technology commitment profiles exist among pre-service teachers?
- How do their emotional states whilst and after ERT differ in comparison?
The main objective of the research to be presented is to understand the interplay between technology commitment, emotional state and the study perspective of pre-service teachers in order to provide proper grounds for European practitioners to properly support pre-service teachers throughout their course of studies in a digital world.
To examine the research questions and to contribute to the main objective, data from a cohort study design is used in which a sample of pre-service teachers is enrolled in a teacher education (TE) program in Rhineland-Palatinate (Germany) and monitored. The monitoring project (TrigiKOM’MON) started in 2019 and is ongoing for the observation of digital competences and attitudes over the course of their Bachelor of Education (approximate monitoring time frame of 2.5 years for each cohort).
Methodology, Methods, Research Instruments or Sources UsedThe data contains cohorts that started in different stages of the pandemic from pre-pandemic to today. It consists of four measurements during the Bachelor’s program and will approximately cover a time period of about four years at the end of 2023.
Technology readiness data for examining the cluster structure (RQ1) included 969 student teachers having completed the respective scales near the end of their first year in TE. In this sample, proportion of females was 69.66%, mean age was 20.5 years (± 3.05). In examining RQ2, the subjects (N = 128) reported their emotional state on two occasions after their first year (summer term 2021 and winter term 2021/22; 71.9% female, 20.9 ± 1.4 years old).
Teachers’ technology commitment was measured using the according Technology Commitment Questionnaire (TCQ) from Neyer et al. (2012). The aforementioned subscales are operationalized as followed: technology acceptance (e. g., “I am very curious when it comes to new technology developments”; α = 0.83), technology competence (e. g., “I have often fear to fail when dealing with modern technology”; α = 0.85), and technology control (e. g., “It depends essentially on me whether I am successful using modern technology”; α = 0.72). All sub-scales are based on a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree.
To assess teachers’ emotional state during and after ERT, the Positive and Negative Activation and Valence (PANAVA) short scales from Schallberger (2005) were administered: positive activation (PA; α = 0.76) and negative activation (NA; α = 0.65). The PA and NA comprise four bipolar items, respectively, rated on a 6-point Likert scale. Thus, the participants were asked to describe the experience of their current study situation within spectrums between different adjective pairs (e. g., “listless vs. motivated”).
To explore the first research question, a series of cluster analyses will be conducted on the TCQ subscales, beginning with applying a single-linkage clustering algorithm to identify and exclude outliers. Results from subsequent Ward’s method clustering will then be cross-validated by k-means clustering. The second and the third research question will be examined with two-way ANOVAs (IV = clusters and study progress; DV = change in experience and emotional state).
Conclusions, Expected Outcomes or FindingsConcerning the first research question, analyses are expected to yield three clusters in line with Pozas et al. (2022): (1) overall low to average technology commitment on all subscales; (2) mediocre technology commitment with technology competence as the highest subscale score; (3) overall high technology commitment on all subscales. If this pattern was to be found in TE, this might indicate an urgent need for interventions to help student teachers pertaining to cluster (1) to become motivated and competent in the use of technology. Furthermore, student teachers in cluster (2) are likely to overestimate themselves in their technology competence and thus are harder to identify for interventions that are also suited for cluster (1). Cluster (3) could serve as a potential resource for mentoring programs to facilitate Technology Commitment in clusters (1) and (2).
With regard to the second research question, the extrapolation of the results from Schneider et al. (2021) and Pozas et al. (2022) suggests that the pre-service teachers with higher Technology Commitment scores would be emotionally more resilient to the ERT circumstances and also recover faster from the negative impacts of ERT. For teacher education, this could imply that technology commitment is a worthy subject to facilitate as a factor for resilience concerning future ERT scenarios and future technological challenges in general. The results and their implications will be discussed with the aim to optimizer teacher education accordingly.
Additionally, at Trier University, there is a voluntary education program for pre-service teachers as an intervention which aims to prepare them for digital challenges. First post-measurements and thus results will be available and prepared as a basis to discuss approaches to support technology commitment.
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