Introduction
Information and communication technology (ICT) is increasingly vital in daily life and educational institutions, impacting teaching and learning processes (Garrison et al., 2010). Recognising its necessity in higher education, institutions aim to attract students, enhance courses, and improve the overall educational experience (Alzahrani et al., 2021; Gurung, 2021). Despite these efforts, there is a need for more support to grasp and fully leverage the opportunities presented by digital environments (Hernández-Sellés et al., 2020). However, the transformative power of ICT in higher education relies on equipping educators with the necessary skills (Nadkarni & Prügl, 2021) where digital transformation involves not only the implementation of technology but also the active adoption by faculty, staff, and students to align with the Education 4.0 paradigm (Bonfield et al., 2020; Ishak & Mansor, 2020).
While eLearning began to gain momentum in the late 1990s, recent technological advances and the shifting landscape of higher education have significantly increased its relevance. Universities have adapted rapidly to these evolving demands, implementing online and hybrid approaches as integral parts of their pedagogical strategies (Cutri et al., 2020; García-Morales et al., 2021; Martín-García et al., 2019). In this context, online learning involves geographical separation, technological access, digital interaction, and supportive infrastructure (Martín-García et al., 2019). These innovations, discovered during pandemic restrictions, are expected to shape future teaching methodologies and enhance learning experiences (Garrison et al., 2010; Sangrà et al., 2023).
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Technology facilitates collaborative learning across cognitive, metacognitive, and emotional dimensions (Asif Qureshi et al., 2021; Garrison et al., 2010), and this article explicitly explores the impact of online learning on teacher-student collaborative learning, addressing cognitive and emotional aspects. The pandemic compelled all university instructors to teach online, regardless of their prior attitudes toward digitisation (Martin et al., 2019). This study offers a nuanced understanding of Spanish faculty readiness for online teaching, identifying critical variables that may influence instructors’ adaptation to technology.
Theoretical framework and literature review
Adaptation of Higher Education Institutions by COVID-19, global and Spain: an accelerated step towards university 4.0
In March 2020, the closure of schools and universities in 191 countries affected over 1 billion learners, prompting a rapid shift to online learning. The COVID-19 outbreak forced educational institutions worldwide to transition to online teaching, lacking prior plans for such a massive shift (Cesco et al., 2021; Cutri et al., 2020; Elisondo et al., 2023; Karalis & Raikou, 2020) and universities and lecturers invested efforts in e-learning through Learning Management Systems (LMS) to adapt to the sudden shift. The Community of Madrid suspended face-to-face education from March 11 to March 26 in Spain, extending nationwide with the State of Alarm declaration (Asif Qureshi et al., 2021; Cutri et al., 2020).
The technological expertise of student-teachers is crucial for them to be prepared to apply ICT in the classroom and education is critical to equip people with Industry 4.0 skills (Alzahrani et al., 2021; Asif Qureshi et al., 2021; Bonfield et al., 2020; Garcia-Morales et al., 2021; Ishak & Mansor, 2020; Jugembayeva et al., 2022; Nadkarni & Prügl, 2021; Sangrà et al., 2023). However, as institutions focus on digital transformation, it is critical to ensure equal access for all students to the necessary technical resources (Asif Qureshi et al., 2021; Garrison et al., 2010; Nadkarni & Prügl, 2021). Therefore, successful university transformation requires developing ICT teacher training and specific policies to improve institutional resilience to new challenges (Asif Qureshi et al., 2021; Hernández-Sellés et al., 2020; Rahmadi, 2021).
Professional and induvial characteristics and online learning
Online learning
Online learning involves both synchronous and asynchronous delivery through Internet technology. Adopting online learning systems during the coronavirus outbreak presented significant complexity and challenges, as well as a high degree of innovation and flexibility to promote engaging learning activities (Hernández-Sellés et al., 2020).
Studies such as Junus et al. (2021) found that while most teachers were trained to teach online and improved their knowledge and skills to be better prepared, tutors must improve their skills to manage e-learning effectively. In this context, teachers must also support learners in e-learning (Asif Qureshi et al., 2021; Hernández-Sellés et al., 2020). However, different qualifications, experiences and concerns in different teaching situations result in different levels of technology integration and adoption of distance learning among teachers (Sangrà et al., 2023). Rahmadi (2021) designed a questionnaire considering professors’ gender, age, educational background, teaching experience, school status, and location to understand how teachers in Indonesia integrated technology and adopted distance learning during the COVID-19 crisis, revealing that most teachers identified as early adopters, immediately preparing for distance learning when the work-from-home policy was implemented.
Similarly, Gurung (2021), in a survey of teachers in India, found that respondents who were teaching online for the first time due to the COVID-19 pandemic were eager to learn new technologies and teaching methodologies. Despite challenges, such as student engagement and teaching numerical subjects during distance classes, most respondents enjoyed online courses, perceiving them as a rich learning environment with greater flexibility than traditional classroom teaching.
In summary, several research studies have found that teachers’ attitudes and beliefs influence university ICT (presented in Table 1) by relating aspects such as attitude, adoption, and use of technology in education to individual teacher characteristics such as gender, age, academic category, experience, or academic discipline taught. The results (Table 1) are not consistent, and a conclusive significant relationship between these socio-demographic variables and teachers' attitudes towards IT use has not yet been identified.
Table 1
Demographic factors and faculty perceptions of attitude, ability, or technology acceptance of ITC.
Source: own elaboration
Variables | Research result | References |
|---|---|---|
Gender | Higher among females | |
Higher among males | Zalat et al., (2021) | |
Positively correlated without specifying whether male or female | Guillén-Gámez et al., (2020) | |
Not statistically significant | ||
Age | Higher among young people | |
Positively correlated without specifying age | Guillén-Gámez et al., (2020) | |
Not statistically significant | Semerci and Aydin (2018) | |
Discipline area of expertise | Significant differences in discipline | |
No significant differences in discipline | ||
Employment status | Positively correlated (with less work experience) | |
Not statistically significant | Semerci and Aydin (2018) | |
Previous experience in e-learning | Positively correlated | |
Not statistically significant | Semerci and Aydin (2018) |
Faculty readiness for online teaching and scales
The study of readiness for change in the university context is crucial to prepare for the 4.0 industrial revolution in higher education, where organisations must respond quickly and successfully to change (Bonfield et al., 2020; Ishak & Mansor, 2020). In this context, faculty readiness for online teaching represents the state of faculty preparedness for teaching online; it can be measured by faculty attitudes, abilities, and perceptions (Martin et al., 2019), influenced by multiple individual characteristics and contextual and cultural factors (Garrison et al., 2010; Scherer et al., 2021).
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For instance, in the case of perceptions, professional teaching identity is not imposed but is instead the result of experience and the meaning given to that experience. It contributes to generating ideas of “how to be,” “how to act,” and “how to understand” their work and their place in society. Applying new technologies to education may imply a disruption in the identity of university teachers (Garrison et al., 2010). Another influencing factor is the teacher’s comfort with their activity, and the associated risk of teaching online is a factor that can affect comfort.
However, few studies have examined faculty readiness and scale development (Martin et al., 2019). Two recent models found in the literature review are Faculty Readiness to Teach Online (FRTO) (Martin et al., 2019) and Faculty Readiness for Online Crisis Teaching (FROCT) (Cutri & Mena, 2020; Cutri et al., 2020). The FRTO model (Martin et al., 2019) establishes four areas of online teaching competencies: course design (9 variables), course communication (10 variables), time management (6 variables), and technical competence (7 variables); to combine the four competency areas with two aspects of readiness: (1) faculty attitude toward the importance of online teaching and (2) faculty perceptions of their ability to confidently teach online.
In contrast, the study by Cutri et al. (2020), aiming to measure and elaborate constructs of faculty online readiness from pre-COVID-19 pandemic literature, found that teachers’ perceptions of their readiness to teach online were positively correlated with their ability to perform better at online teaching. Following their research, they propose a multifactorial scale called FROCT, which comprises four dimensions: comfort with risk (8 items), identity disruption (4 items), teaching norms (6 items), and equity and tenure norms (5 items).
For comparison, the FRTO model (Martin et al., 2019) emphasises the competencies needed to implement online teaching, which was undoubtedly of decisive importance in the early stages of the online teaching emergency triggered by COVID-19, while the FROCT model (Cutri et al., 2020) accentuates the scale that measures teachers’ online teaching readiness based on cultural and affective factors. Despite the need to prioritise technical training to adequately prepare educators for online instruction, focusing solely on technology rather than pedagogy and perceptions could be an inadequate strategy (Jugembayeva et al., 2022).
Thus, the present study was conducted in a post-pandemic situation, where online teaching is no longer the result of an emergency. It seemed more appropriate to assess teachers' online teaching readiness based on cultural and affective aspects, leading us to use the FROCT scale.
Dimensions of the FROCT scale
Through the theoretical lens of professional vulnerability, Cutri et al. (2020) developed a scale based on affective dimensions and identity ruptures associated with teachers’ readiness to teach online; in this same work, the FROCT scale had an adequate reliability score, Cronbach’s alpha 0.71. As introduced in the previous section, the FROCT scale is structured in four dimensions summarised below:
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Cutri et al., (2020, p. 7) stated that “risk was operationalised as addressing an unfamiliar mode of teaching, departing from known teaching practices, and fear of failure” to refer to the willingness of faculty to try new things and their confidence to be flexible and creative when facing the challenges of online teaching. This factor includes fears and concerns that faculty may have when making this transition and the feeling of uncertainty when not receiving constant feedback (Ishak & Mansor, 2020).
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On the other hand, the transition from in-person to online teaching can affect the identity and sense of self of experienced educators and professionals, presented as vulnerability affecting their well-being, emotions, and motivation. Additionally, teachers must help understand students’ feelings and facial signals while teaching because online education has limited physical space (Elizondo et al. 2023).
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Online practices and teaching philosophies are known as teaching norms. It relates to standard classroom practices and expectations. It may vary depending on the teaching method, the degree of student autonomy, and the emotional labour required of the teacher.
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Finally, faculty equity norms identify the possibility of unequal access to online learning for students, while tenure and promotion norms address academic services and learning outcomes. Teachers should work to create a learning environment that is inclusive and accessible to all students, including those with disabilities or from minority groups, and that recognises and values students’ cultural and linguistic diversity (Garrison et al., 2010).
Cutri et al. (2020) summarised results when respondents obtained a mean score of 3.9 on the dimension “comfort with risk”, indicating that they are comfortable taking on less familiar forms of teaching. Similar results occurred with the second one, the “teaching norms” factor, with a mean score of 3.9. This result may imply that they feel in favour of the greater autonomy the student acquires online and/or feel prepared to manage relationships with students non-face-to-face. In third place comes “identity disruption,” with a measured score of 3.4, perhaps showing a certain level of insecurity, lowering their sense of self as experienced professionals. Finally, the dimension “equity and tenure norms,” with a shallow mean score of 2.20 and a high standard deviation, is the most controversial among the respondents. It is convenient to note that the FROCT scale combines positive and negative items on three of the four constructs.
Research questions and hypotheses
Faculty readiness for adapting to evolving pedagogies can be assessed through the FROCT scale, encompassing four key dimensions: comfort with risk, identity disruption, teaching norms, and equity and tenure norms. These factors provide insight into how prepared faculty members are to engage with new pedagogical approaches, including but not limited to online and hybrid teachin. The studies reviewed have also shown that there may be significant differences in teachers' perceptions and appraisals based on their personal and occupational characteristics.
The following two research questions are therefore proposed, with their associated hypotheses.
RQ1 After two years of experience where entirely online, combined, or hybrid modalities have continued to be used in response to the evolution of COVID-19, Are there differences in the average importance of the constructs measuring Spanish faculties’ online readiness?
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Hypothesis 1: University lecturers feel more comfortable with risk after 2 years of online or hybrid education.The forced transition to online education during the pandemic exposed university lecturers to new pedagogical and technological challenges. Over time, this continued exposure to digital teaching has enhanced their risk tolerance and willingness to experiment with innovative teaching approaches. Recent studies indicate that familiarity and experience in online settings foster a greater openness to adopting digital methods in education (Cutri et al., 2020; Martín-García et al., 2019; Sangrà et al., 2023).
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Hypothesis 2: After 2 years of compulsory education, university professors feel less identity disruption with online or hybrid education.Online teaching affects educators’ professional identity, especially when shifting from a face-to-face to a digital context. However, prolonged exposure and adaptation to digital tools have enabled educators to integrate online teaching into their professional identity, reducing perceived “disruption” to their role. Literature shows that repeated exposure and familiarity with digital environments strengthen educators’ professional confidence and competence (Garrison et al., 2010; Nadkarni & Prügl, 2021; Scherer et al., 2021).
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Hypothesis 3: University professors feel that online education or hybrid improves teaching norms.The pandemic promoted student autonomy and collaborative learning, prompting educators to reconsider their pedagogical practices. Prior studies highlight that the flexibility of online environments fosters teaching norms that are more focused on student self-directed learning, perceived as a positive shift in educational dynamics (Gurung, 2021; Junus et al., 2021). Additionally, digital methodologies have encouraged less traditional instruction and greater emphasis on interaction and collaboration (García-Morales et al., 2021).
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Hypothesis 4: University lecturers feel more comfortable with equity and tenure norms after 2 years of online or hybrid education.Adopting online modalities has underscored the importance of equity in access to technology and learning resources. Literature suggests that, over time, educators have adapted to and feel more comfortable with these equity norms, becoming increasingly aware of the need to provide an inclusive and accessible environment for all students (Cesco et al., 2021; Garrison et al., 2010).
RQ2 How can lecturers’ characteristics influence Spanish faculties’ online readiness?
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Hypothesis 5: Female university professors feel more faculty readiness for online teaching than males.Literature suggests that female educators tend to exhibit greater readiness and openness toward online teaching, partly due to their flexibility in adapting to changes and a positive attitude toward self-efficacy in using educational technology (Bozkurt & Arslan, 2018; Cabero‐Almenara et al. 2022; Kaqinari et al., 2021; Liesa-Orús et al., 2020; Martin et al., 2019; Scherer et al., 2021). These would indicate that women in academia often view digital tools as an enriching resource for teaching.
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Hypothesis 6: Health, Science and Technology teachers feel more faculty readiness for online teaching than teachers in other subject areas.Science, technology, and engineering faculty are generally more familiar with digital tools, facilitating their transition to online teaching. Studies indicate that being more accustomed to integrating technology into their practices, these educators feel better prepared for online teaching than colleagues in other disciplines (Martín-García et al., 2019; Olafare et al., 2018).
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Hypothesis 7: Younger teachers feel more faculty readiness for online teaching than mature teachers.Having grown up in digitally enriched environments, younger generations tend to exhibit greater comfort and readiness to adopt online teaching. The literature indicates that younger faculty members adapt more quickly to educational technology, while older educators may encounter more barriers to its adoption (Hosny et al., 2021; Liesa-Orús et al., 2020; Olafare et al., 2018; Zalat et al., 2021). These findings reflect generational differences in the acceptance and adaptation of technology.
Material and methods
Study design
To contrast research Question One, it was necessary to have data collected during and after the pandemic. The authors of this work did not collect information related to the pandemic; instead, the averages of the four FROCT dimensions obtained by the authors in their study (Cutri et al., 2020) conducted during the pandemic were used.
The quantitative survey methodology was employed in this study for data collection after the pandemic. The sampling process was carried out in one step. Convenience sampling was used due to the unavailability of complete lists of Spanish teachers, and the authors’ colleagues were asked to participate and send the questionnaire to other university colleagues.
The measuring instrument used was the original FROCT scale (Cutri et al., 2020). Respondents evaluated each scale item of the survey using a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). The questionnaire was implemented in English on an online platform (Google Forms. An information page was included to educate respondents about the survey’s objectives. Participants were assured anonymity on another page, and their explicit consent was requested. It was distributed in Spain in July of 2021. Previously, in June, the survey was pilot tested with a sample of twenty professors to ensure that the study measures were accurate.
Sample
One hundred seventy-nine academic professionals employed in HEIs in Spanish universities participated in the study. One of the surveys was eliminated, a result of which we worked on a total of 178 valid surveys. Our sample distribution included 105 female (59.0%) and 59 male professors (33.1%); 14 participants (7.1%) indicated: “prefer not to say gender”. Regarding the age of the respondents, 47 were novel teachers—less than 36 years old (26.4%)—, 86 (48.3%) were between 36 and 50 years old, and 45 (25.3%) were senior teachers in their 50 s to their 60 s (or older). Finally, the area of knowledge was asked utilising open-ended items. To analyse, they were recoded into three categories: Arts & Humanities (16.4%); Social Sciences, Business, and Law (72.6%); Health, Engineering, and Science (11%).
Methods
As an initial step, before analysing the research questions, the reliability of the FROCT questionnaire was re-tested for a measurement conducted in a post-pandemic situation. Specifically, the internal consistency reliability of the instrument’s scores was assessed with Cronbach’s alpha (α), the most common way of estimating it in scientific publications (Virla, 2010).
The first research question, whether there are differences in teachers’ ratings of their preparation for online education during and after COVID-19, is a statistical problem of comparing means between two independent samples. The student’s t-test for two independent samples is usually used in these cases, considering the difference in means to be compared and their standard error (Kim, 2015).
Firstly, factor scores were obtained for everyone in the sample as the average for each of the four factors of the FROCT scale of the responses to each component variable. These factor scores are completed with the mean score per individual, calculated as the overall average of the answers to the 23 questions in the questionnaire. To calculate the mean score of a factor, all the variables that compose it must be measured in the same direction; otherwise, scores measured in opposite directions would be compensated, unduly reducing the quantitative value of the factor.
Secondly, to assess the difference between these mean scores obtained after the pandemic and those obtained during the COVID-19 pandemic, a t-test of the difference in means was performed in each of the four cases. It was possible because the results published by the original authors include its standard deviation and the mean value per factor. The second research question is whether, for each of the factors, there is evidence that their mean value differs between the categories of each of the teacher profile variables considered in the study: gender, age and teaching area. Since gender is a dichotomous variable and age and teaching area include more than two categories, the technique differs from the Wilcoxon test for the gender variable and the Kruskal–Wallis test for the other three variables: this test only detected the existence of differences, and the Dunn test was used to interpret them, because it is the appropriate non-parametric multiple pairwise comparison procedure when the null hypothesis of the Kruskal–Wallis test is rejected. This test has gained popularity in recent decades (Dinno, 2015).
Results
Descriptive analysis
Simple univariate statistics describing central tendencies and variation (means and standard deviations) are the most used descriptive statistics and can be very useful in describing the data before testing hypotheses. Table 2 below shows the main descriptive statistics, item by item.
Table 2
Descriptive statistics for the FROCT questionnaire. Post-pandemic results in the Spanish simple.
Source: own elaboration
Factors | Questions | Mean | Standard deviation |
|---|---|---|---|
Comfort with risk | 1) I am willing to implement novel teaching practices (e.g. online, blended, etc.) | 4.47 | 0.69 |
2) I am comfortable when I teach outside of my regular mode of delivery (e.g. in-person, online, blended) | 3.87 | 0.94 | |
3) I can imagine myself trying new teaching technologies in my class before I personally have fully mastered them | 4.06 | 1.00 | |
4) I can imagine creating new methods of teaching that utilise the affordances of online teaching | 4.08 | 0.92 | |
5) I have strategies to help manage any fears and concerns I might have when I teach outside of my regular mode of delivery (e.g. in-person, online, blended) | 3.76 | 0.92 | |
6) I can acknowledge any fears and concerns in a safe professional environment when I teach outside of my regular mode of delivery (e.g. in-person, online, blended) | 3.78 | 0.89 | |
7) I have not yet established a comfortable way of teaching online (e.g. entirely online or blended) | 2.49 | 1.23 | |
8) I would rather return to my regular mode of teaching (e.g. in-person, online, blended) | 3.17 | 1.25 | |
Identity disruption | 9) Online teaching challenges my sense of who I am as a teacher educator | 3.34 | 1.18 |
10) Online teaching will compromise the teaching persona and presence that I usually maintain during in-person instruction | 3.14 | 1.03 | |
11) Online teaching makes me feel like a novice teacher educator again rather than an experienced professional | 2.37 | 1.11 | |
12) I am interested in learning from experts in online teaching to transition my course and content to an online format (e.g. entirely online, blended, etc.) | 4.08 | 0.96 | |
Teaching norms | 13) I am comfortable with students relying LESS on direct instruction from me to learn class learning objectives | 3.37 | 1.01 |
14) I am willing to lessen the amount of traditional teacher-directed instruction (e.g. lecturing with slides, textbook reading, etc.) that are common when teaching in-person | 3.60 | 0.89 | |
15) Instead of relying on synchronous instruction, I imagine creating opportunities to increase student autonomy regarding when and how they learn (e.g. student self-pacing of learning and selection of learning material) | 3.87 | 0.80 | |
16) I imagine creating opportunities to increase student autonomy regarding what they choose to learn from a selection of topics chosen by me (e.g. choice boards) | 3.78 | 0.89 | |
17) It is important to use instructional time to foster and nurture relationships with students in online classes | 4.23 | 0.85 | |
18) I feel prepared to attend to students in an online setting who are having difficult times in their lives | 4.01 | 1.02 | |
Equity and ternure norms | 19) It is important to adjust my course assignments and requirements to accommodate students' potential inequitable access to online learning necessities (e.g. internet access; device access; safe place to learn, etc.) | 4.22 | 0.77 |
20) I feel prepared to identify students' potential inequitable access to online learning necessities (e.g. internet access; device access; safe place to learn, etc.) | 3.25 | 1.08 | |
21) Transitioning my courses to another mode of delivery (e.g. online or blended) will negatively impact my university-based and academic community service duties | 2.86 | 1.21 | |
22) Transitioning my courses to another mode of delivery (e.g. online or blended) will negatively impact my student professor ratings? | 2.59 | 1.13 | |
23) Transitioning my courses to another mode of delivery (e.g. online or blended) will negatively impact my scholarship productivity? | 2.70 | 1.26 |
The arithmetic mean is between a maximum value of 4.47 for item 1, “I am willing to implement novel teaching practices (e.g., online, blended, etc.)”, followed by item 17 “It is important to use instructional time to foster and nurture relationships with students in online classes” (mean = 4.23), and almost the same score (mean = 4.22) for the item 19 “It is important to adjust my course assignments and requirements to accommodate students’ potential inequitable access to online learning necessities (e.g. internet access; device access; safe place to learn, etc.).
Identifying and recording negative items
Before starting the statistical treatment, the responses to the reverse-phrased items were recorded in the same direction as the rest. The correlation between the different items has been calculated. If the correlation is negative, two positively correlated items point in the same direction and opposite directions. Subsequently, all questions negatively correlated with the one taken as a reference for the order of the factor have been recorded. The item’s value was subtracted from 6 to maintain the original scale. Thus, responses initially scored low are moved to the high end and vice versa while maintaining the range of 1–5 for all responses. Consequently, items 7, 8, 10, 11, 21, 22 and 23 were recorded.
Reliability
The reliability of a scale is a crucial aspect to consider in the social or behavioural sciences. In the specific case of the reliability coefficient linked to internal consistency, the most usual coefficient is α (alpha), proposed by Lee J. Cronbach. For the interpretation of the result, the cut-off point set by most authors as an acceptable value is 0.7, although an alpha of 0.8 or higher is preferable (Virla, 2010).
Cutri et al. (2020) report an alpha value of 0.71. In our questionnaire application, Cronbach’s alpha was higher, 0.85, so we can consider the FROCT questionnaire suitable for measuring faculty readiness to transition to online teaching after the COVID-19 pandemic.
Normality
In this study, we used the result of the sample of Cutri’s paper (n1) = 30 individuals and our sample (n2) = 179 subjects. When both sample 1 and sample 2 are greater than or equal to 30, it can be presumed that the approximation to the normal will be good, and it is not necessary to make specific contrasts. (Kim, 2015).
Homoscedasticity
The homoscedasticity assumption has been checked by applying Snedecor’s F-test. To carry out the contrast:
$${\text{H}}_{0} :\sigma_{{1}} /\sigma_{{2}} = { 1}$$
$${\text{H}}_{{1}} :\sigma_{{1}} /\sigma_{{2}} \ne { 1}$$
The two variances are considered equal if the null hypothesis cannot be rejected (p-value > 0.05).
In four cases calculated (three factors plus the total), equality of variances (Table 3) can be accepted to meet the necessary conditions to apply the usual Student’s t-test. In the case of the Equity and Ternure Norms factors, the hypothesis of equality of variances for the two studies is rejected. In this case, the means must be compared using the corrected t-statistic.
Table 3
Coefficients F-Snedecor in sample 2021.
Source: own elaboration
Variable | F-Snedecor | P-Value |
|---|---|---|
Comfort with risk | 0.95 | 0.842 |
Identity disruption | 0.65 | 0.140 |
Teaching norms | 0.82 | 0.488 |
Equity and tenure norms | 0.02 | 0.00 |
Total | 1.27 | 0.484 |
Analysis of differences in mean values during and before the COVID-19
To assess the difference between these mean scores after the pandemic and those obtained during the COVID-19 pandemic, a t-test of the difference in means was performed in each of the four variables of FROCT.
Cutri et al. (2020) summarised results when respondents obtained a mean score of 3.9 on the dimension “comfort with risk”, indicating that they are comfortable taking on less familiar forms of teaching. Similar results occurred with the second one, the “teaching norms” factor, with a mean score of 3.9. This result may imply that they feel in favour of the greater autonomy the student acquires online and/or feel prepared to manage relationships with students non-face-to-face. In third place comes “identity disruption,” with a measured score of 3.4, perhaps showing a certain level of insecurity, lowering their sense of self as experienced professionals. Finally, the dimension “equity and tenure norms,” with a shallow mean score of 2.20 and a high standard deviation, is the most controversial among the respondents. It is convenient to note that the FROCT scale combines positive and negative items on three of the four constructs.
At a significance level of 0.05, we want to know whether there is a difference between the means of the two situations. Our null and alternative hypotheses are then:
$${\text{H}}_{0} : \, \upmu {\text{A}}\, = \,\upmu {\text{B}}$$
$${\text{H}}_{{1}} : \, \upmu {\text{A}}\, \ne \,\upmu {\text{B}}$$
The t-statistic (Table 4) results show no significant differences in the four dimensions of the FROCT questionnaire when applied after the pandemic.
Table 4
Descriptive coefficients for FROCT subscales in COVID-19 and POST COVID-19 situation and t-Student tests.
Source: own elaboration
During COVID-19 | Post COVID-19 | t-Student test | |||||
|---|---|---|---|---|---|---|---|
Variable | No. of Items | Mean | SD | Mean | SD | t-test | p-value |
Comfort with risk | 8 | 3.91 | 0.43 | 3,81 | 0.49 | − 0.93 | 0.357 |
Identity disruption | 4 | 3.39 | 0.83 | 3,47 | 0.53 | 0.51 | 0.613 |
Teaching norms | 6 | 3.89 | 0.64 | 3,82 | 0.56 | − 0.71 | 0.477 |
Equity and tenure norms | 5 | 2.2 | 4.40 | 3,46 | 0.69 | 1.561 | 0.129 |
Differences by socio-demographic categories when applying the FROCT in a post-pandemic situation
Gender
To contrast the similarity in the scores of the four factors by gender (dichotomous variable), we used the Wilcoxon test, a non-parametric version of the t-test, to compare means based on the range of the observations and not on their values. The sub-sample sizes and the use of Likert scales justify the use of this test.
The significance of the statistic (p > 0.05) in the case of two factors, Comfort with Risk and Equity and Tenure Norms (Table 5), does not allow us to defend the existence of differences in the scores given by men and women.
Table 5
Analysis of differences according to gender
Variable | * | Obs. | Mean | Std. dev. | Std. error | Mean Rank | Wilcoxon | |
|---|---|---|---|---|---|---|---|---|
Comfort with risk | 1 | 59 | 3.72 | 0.51 | 0.10 | 33.85 | z | − 0.95 |
2 | 105 | 3.82 | 0.64 | 0.09 | 38.74 | p | 0.347 | |
Identity disruption | 1 | 59 | 3.28 | 0.48 | 0.09 | 29.39 | z | − 2.49 |
2 | 105 | 3.58 | 0.51 | 0.07 | 42.16 | p | 0.012 | |
Teaching norms | 1 | 59 | 3.64 | 0.58 | 0.11 | 29.44 | z | − 2.05 |
2 | 102 | 3.93 | 0.51 | 0.08 | 39.79 | p | 0.040 | |
Equity and tenure norms | 1 | 56 | 3.29 | 0.68 | 0.14 | 31.86 | z | − 1.50 |
2 | 109 | 3.53 | 0.69 | 0.10 | 39.68 | p | 0.135 | |
However, in the cases of Identity Disruption factor (p = 0.012) and Teaching Norms factor (p = 0.04), it is concluded that the scores obtained are not similar. In both factors, the mean scores awarded by women are higher and show statistically significant differences.
Area of knowledge of university faculty
To contrast the similarity in FROCT scores in the respondents’ area of knowledge variable, we first recorded it into three fields: Arts and humanities, social sciences, business and law, health, engineering, and science.
The Kruskal–Walli’s test, a non-parametric version of the One-Way ANOVA model based on the range of observations rather than their values, was used. The small sub-sample sizes and the use of Likert scales justify this choice.
The significance of the statistic (p > 0.05) in the case of the four factors (Table 6) leads to the conclusion that in the areas of teaching, the scores obtained are similar.
Table 6
Analysis of differences according to the area of teaching.
Source: own elaboration
* | Obs. | Mean | Std. dev. | Std. error | Mean rank | Kruskal–Wallis | ||
|---|---|---|---|---|---|---|---|---|
Comfort with risk | 1 | 27 | 4.04 | 0.71 | 0.21 | 42.13 | Chi | 1.45 |
2 | 121 | 3.83 | 0.53 | 0.07 | 34.38 | p | 0.483 | |
3 | 13 | 3.96 | 0.65 | 0.26 | 38.08 | |||
Identity disruption | 1 | 27 | 3.56 | 0.58 | 0.17 | 38.88 | Chi | 0.23 |
2 | 121 | 3.46 | 0.49 | 0.07 | 36.28 | p | 0.893 | |
3 | 18 | 3.50 | 0.72 | 0.25 | 38.94 | |||
Teaching norms | 1 | 27 | 3.93 | 0.55 | 0.16 | 38.38 | Chi | 1.76 |
2 | 115 | 3.84 | 0.57 | 0.08 | 36.10 | p | 0.415 | |
3 | 16 | 3.62 | 0.70 | 0.26 | 26.21 | |||
Equity and tenure norms | 1 | 27 | 3.53 | 0.74 | 0.21 | 37.88 | Chi | 0.19 |
2 | 115 | 3.48 | 0.70 | 0.10 | 35.34 | p | 0.910 | |
3 | 18 | 3.55 | 0.66 | 0.23 | 37.38 | |||
Age of university faculty
Again, as a first step, the responses were grouped into three categories: under 36, 36 to 50, and over 50. The Kruskal–Walli’s test (Table 7) shows statistically significant differences (p = 0.007) in the Comfort with Risk factor case. For the other factors, no statistically significant mean differences were found.
Table 7
Analysis of differences according to age
Variable | * | Obs. | Mean | Std. dev. | Std. error | Mean rank | Kruskal–Wallis | |
|---|---|---|---|---|---|---|---|---|
Comfort with risk | 1 | 47 | 3.76 | 0.41 | 0.09 | 43.88 | Chi | 9.94 |
2 | 86 | 3.81 | 0.41 | 0.07 | 45.16 | p | 0.007 | |
3 | 45 | 3.46 | 0.36 | 0.08 | 26.13 | |||
Identity disruption | 1 | 47 | 3.51 | 0.46 | 0.10 | 40.90 | Chi | 0.11 |
2 | 83 | 3.45 | 0.57 | 0.09 | 38.97 | p | 0.945 | |
3 | 45 | 3.48 | 0.49 | 0.11 | 39.00 | |||
Teaching norms | 1 | 45 | 3.85 | 0.57 | 0.13 | 38.53 | Chi | 0.50 |
2 | 79 | 3.87 | 0.60 | 0.10 | 39.34 | p | 0.780 | |
3 | 45 | 3.72 | 0.49 | 0.11 | 35.13 | |||
Equity and tenure norms | 1 | 47 | 3.40 | 0.72 | 0.16 | 36.64 | Chi | 0.35 |
2 | 86 | 3.52 | 0.70 | 0.11 | 40.21 | p | 0.840 | |
3 | 41 | 3.43 | 0.64 | 0.15 | 39.19 | |||
As indicated in the methodology, Dunn’s test was used for the post hoc analysis of differences between groups in cases where the Kruskal–Wallis test was significant. Once applied, the differences between groups 1 and 3 and between groups 2 and 3 are statistically significant, indicating that the older faculty group differs from the rest by its lower score on the “Comfort with Risk” dimension.
Discussion
This study had two aims: first, to test whether, after two years of COVID-19-mandated online teaching, there has been a difference in teachers' scores on the constructs measuring their readiness for online education; second, to test whether gender, age, or subject area establish differences in readiness for online education. A total of 7 hypotheses were proposed, and the following section discusses their results.
Hypothesis 1: University lecturers feel more comfortable with risk after two years of online or hybrid education; Hypothesis 2: After two years of compulsory education, university professors feel less identity disruption with online or hybrid education; Hypothesis 3: University professors feel that online education or hybrid improves teaching norms; and Hypothesis 4: University lecturers feel more comfortable with equity and tenure norms after two years of online or hybrid education, are rejected. There are no differences in the average means regarding the analysis of the importance of the constructs measuring faculties’ online readiness for Pre-Pandemic and Post-pandemic COVID-19.
It should be noted that there must be an error in Table 3 of Cutri et al., (2020, p.6), specifically in the line about the Equity and Tenure Norms factor, whose minimum value (3.48) is greater than the maximum (0.5907) and more significant than the mean (2.2). It also seems unlikely that the deviation can be 4.4 for a variable that takes values between 1 and 5. However, after recalculating the mean and deviation of the factor using the information on the total, the conclusions from comparing the mean values with those obtained in our study remain the same.
RQ1: After two years of experience where entirely online, combined, or hybrid modalities have continued to be used in response to the evolution of COVID-19, Are there differences in the average importance of the constructs measuring Spanish faculties' online readiness? No, there are no differences.
Hypothesis 5: Female university professors feel more faculty readiness for online teaching than males has been partially accepted. Women's average score is higher than men's in two scale dimensions: Identity Disruption factor (p = 0.012) and Teaching Norms factor (p = 0.040). The higher scores on some factors among women may be a result consistent with that obtained in other reviewed papers (Bozkurt & Arslan, 2018; Cabero-Almenara et al., 2022; Kaqinari et al., 2021; Liesa-Orús et al., 2020; Martin et al., 2019; Scherer et al., 2021). Although, the similarities in scores between men and women on other factors may be consistent with other authors reviewed (Hosny et al., 2021; Kpolovie & Awusaku, 2016; Olafare et al., 2018; Semerci & Aydin, 2018).
Hypothesis 6: Health, Science and Technology teachers feel more faculty readiness for online teaching than teachers in other subject areas is rejected. The significance of the statistic (p > 0.05) in the case of the four factors of FROCT leads to the conclusion that the scores obtained are similar in the areas of knowledge. The lack of statistically significant differences by knowledge area or discipline is consistent with those obtained in other online education studies (Kpolovie & Awusaku, 2016; Scherer et al., 2021).
Hypothesis 7: Younger teachers feel more faculty readiness for online teaching than mature teachers has been partially accepted. Regarding the comfort with risk factor, the average scores of the under-50s segment are higher than those obtained in the over-50s segment. These findings are consistent with other research reviewed in which younger teachers rate online education more highly (Hosny et al., 2021; Liesa-Orús et al., 2020; Olafare et al., 2018; Zalat et al., 2021).
RQ2 How can lecturers’ characteristics influence Spanish faculties’ online readiness? The results indicate differences in socio-demographic groups when applying the FROCT in a post-pandemic situation regarding gender and depending on the time of insertion and experience.
Conclusions
The online mode of higher education courses and programmes continues to expand across all academic disciplines in colleges and universities. This expansion of online education has been spurred in part by the home isolation imposed by the COVID-19 pandemic. Covid 19 has meant that many universities have made significant (and unexpected) progress in online teaching. University professors in many places were forced to teach online from one day to the next. Before the pandemic, the literature reflects that not all professors were enthusiastic about online teaching and were reluctant to use it (Bussmann et al., 2017; Cutri et al., 2020).
This study investigated the validity of the FROCT questionnaire, administered post-pandemic, to assess teachers’ readiness to use online education. The questionnaire was re-validated. No significant differences were found in any of the four component factors. These results indicate that university teachers do not feel statistically significant differences in their perceptions of ‘comfort with risk’ and ‘equity and tenure norms’, ‘identity disruption’ and ‘teaching norms’ after 2 years of online education.
The average scores above three on three of the four factors may indicate that university teachers already had favourable perceptions of online education and that there were no significant barriers to implementation. It is also interesting to note that trying online education for two years did not worsen their perceptions. However, the high score for the question ‘I am interested in learning from experts in online teaching about moving my course and content to an online format (e.g., fully online, blended, etc.)’ may indicate that 2 years was not enough time to adopt the innovations that online formats represent.
The second research question focused on whether there were differences in the means of each factor between the categories of teacher profile variables considered in the 2021 study: gender, age, and teaching area. The results showed that there were no significant differences in the teaching area. However, gender differences were observed for identity disruption and teaching norms. Specifically, females gave significantly higher scores for these factors. Differences were also found in the factor’ comfort with risk’ related to age, time of insertion, and experience. Participants aged 50 years and older (group 3) had significantly lower scores compared to the group aged up to 35 years (group 1) and the group aged 36–50 years (group 2). These results suggest that older teachers are less comfortable taking risks in online education (Guillén-Gámez et al., 2020).
Limitations of the study
Acknowledging and discussing these limitations is essential to properly contextualise the findings and identify areas for future research and methodological improvement. However, some limitations of this study should also be noted.
Firstly, the sample size may have been limited, which may limit the generalisability of the findings to a broader population of teachers. In addition, the selected participants may not fully represent the whole population of teachers, which could introduce selection bias into the results. It is essential to consider that the data collected in this study was based on participants’ self-perceptions and subjective responses, which may be subject to response bias and lack of accuracy in assessing their readiness for online education. In addition, other external factors, such as previous experience with online education, training received and access to technological resources, may have influenced teachers’ assessments and perceptions and were not considered in this study.
It is necessary to remember that this study was conducted at a specific time after the pandemic, and changes in time and circumstances may have influenced teachers’ perceptions and evaluations. Therefore, it is essential to consider the temporal context when interpreting and generalising the results. However, several areas could be the subject of future research. It is recommended that the sample size be increased to improve the generalisability of the results and that more specific subgroups be studied. In addition, longitudinal studies are suggested to understand how teachers’ perceptions evolve.
Future research
The following are some lines of future research that can be derived from this study. One promising direction is to explore additional variables such as teaching experience, access to technological resources and participation in professional development programmes. This would provide a complete understanding of the factors influencing teacher preparation.
It is recommended that the impact of specific interventions and strategies to improve teacher preparation in online education be evaluated. Such interventions could include training and professional development programmes tailored to teachers’ needs. Another interesting question that could be added to the FROCT questionnaire would be the extent to which university teachers are free or not to propose online training modalities in their subjects or, on the contrary, this kind of decision is a matter for higher academic decision-makers and, even if they wanted to, they would not find it feasible to be authorised to do so.
Finally, comparisons between educational and geographical contexts are suggested to identify effective practices adapted to specific contexts. However, as has been pointed out, the results reported have points of coincidence with those of another recent research. In conclusion, this study provides a solid basis for future research into teacher preparation for online education. The future perspectives open new directions for further research that will contribute to a better understanding and improvement of teacher preparation and performance in a post-pandemic environment and the field of online education.
Acknowledgements
Not applicable.
Declarations
Competing interests
The authors declare no competing interests.
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