4.2.2. The Instructors’ Main Questionnaire Study

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4.2.2.1 Reliability of the Scales of the Data Collection Instrument. Preceding the data analysis of the instructors’ main questionnaire, it was necessary to check if the constructs managed to produce reliable results. To achieve this, the constructs’ Cronbach’s alpha values were calculated, and the items of each construct underwent principal component analysis. Table 35 summarizes this process.
 

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Table 35 Reliability Analyses of the Constructs of the Instructors’ Main Questionnaire
Construct’s name
No. of items
Cronbach’s alpha
Number of components extracted by principal component analysis
  1. Acceptance of ICT
5
.745
1
  1. Availability of ICT
4
.607
1
  1. Reasons for using ICT
5
.873
1
  1. Willingness to use ICT
4
.957
1
  1. Devoted time
4
.918
1
  1. Opportunities for ICT development
7
.969
1
  1. Substitution
5
.818
1
  1. Using ICT for instruction
4
.800
1
  1. DigComp1: Creating and sharing content
5
.872
1
  1. DigComp2: Keeping up with development
5
.905
1
  1. DigComp3: Reliability of digital sources
5
.896
1
  1. DigComp4: Using search engines
4
.763
1
  1. Transitioning to online education
4
.872
1
  1. Effectiveness of online education
4
.846
1
  1. Offline versus online self-image
5
.749
1
 

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As far as the item pool of the final instructors’ questionnaire is concerned, there was a problem with one item, Variable 10 (V10), in the construct Availability of ICT devices (Variables 6 to 10) because one of the items loaded to a second latent dimension as a result of principal component analysis; therefore, this one item (V10: Wherever I use an ICT device, typically there is Wi-Fi available) was excluded from the final analysis (Table 36). Perhaps this item was too specific as it included reference to Wi-Fi connection, whereas the rest of the items in the construct refrained from limiting availability to a very specific type of technology. This is the reason why in the final questionnaire, instead of the piloted five items, only four are part of the analysis regarding the construct of Availability.
 

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Table 36 Principal Component Analysis of the Availability Construct of the Instructors’ Main Questionnaire
Original construct with 5 items
Final construct with 4 items
Component
Component
1
2
1
V06
.639
.293
V06
.685
V07
.619
–.637
V07
.686
V08
.732
.439
V08
.703
V09
.712
–.443
V09
.726
V10
.562
.357
Note. Extraction method: Principal component analysis without rotation.
 

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The scales’ skewness and kurtosis values were also calculated (Table 37). With the skewness of some scales is slightly above 2, the maximum kurtosis value is slightly above 8, the observed levels for the scales can be accepted (Ryu, 2011). This also means that parametric statistical tests can be applied on the dataset to answer the research questions.
 

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Table 37 Skewness and Kurtosis of the Scales of the Instructors’ Main Questionnaire
Descriptive Statistics
N
Skewness
Kurtosis
Statistic
Statistic
Std. Error
Statistic
Std. Error
  1. Acceptance of ICT
71
–.910
.285
.342
.563
  1. Availability of ICT
71
–.852
.285
.226
.563
  1. Reasons for using ICT
71
–2.328
.285
5.671
.563
  1. Willingness to use ICT
71
–1.689
.285
3.116
.563
  1. Devoted time
71
–.188
.285
–.554
.563
  1. Opportunities for ICT development
71
–.631
.285
–.362
.563
  1. Substitution
71
–2.374
.285
8.175
.563
  1. Using ICT for instruction
71
–.716
.285
–.146
.563
  1. DigComp1: Creating and sharing content
71
–2.130
.285
6.609
.563
  1. DigComp2: Keeping up with development
71
–.938
.285
.313
.563
  1. DigComp3: Reliability of digital sources
71
–1.644
.285
3.503
.563
  1. DigComp4: Using search engines
71
–1.753
.285
3.165
.563
  1. Transitioning to online education
71
–.420
.285
–.172
.563
  1. Effectiveness of online education
71
–.004
.285
–.952
.563
  1. Offline versus online self-image
71
–.127
.285
–.780
.563
Valid N (listwise)
71
 

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4.2.2.2 Acceptance, Availability, Willingness and Reasons for Using ICT Devices and the Perceived Levels of Digital Competences (Study2RQs 1-5). The instructors in the sample perceive that using ICT devices nowadays is part of the basic skills of the 21st century (M = 4.79; SD = 0.40), and their general ICT acceptance level proved to be rather high (M = 4.30; SD = 0.58), as detailed in Table 38. Instructors were also willing to use devices (M = 4.29; SD = 0.89) and used them because they can easily substitute tasks such as receiving and marking student assignments or keeping in touch with the learners (M = 4.69; SD = 0.46). However, it also seems that in general, there are fewer opportunities for instructors in the sample to develop their ICT skills, and the relatively high standard deviation value suggests that most individual differences regard this questionnaire construct (M = 3.69; SD = 1.04).
 

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Table 38 Descriptive Statistics of Scales One to 12 of the Instructors’ Main Study
Descriptive Statistics
N = 48
Mean
Std. Deviation
  1. Acceptance of ICT
4.30
.58
  1. Availability of ICT
4.41
.52
  1. Reasons for using ICT
4.79
.40
  1. Willingness to use ICT
4.29
.89
  1. Devoted time
3.56
.95
  1. Opportunities for ICT skills development
3.69
1.04
  1. Substitution
4.69
.46
  1. Using ICT for instruction
4.08
.75
  1. Digital competences 1: Creating and sharing content
4.51
.61
  1. Digital competences 2: Keeping up with development
3.86
.90
  1. Digital competences 3: Reliability of digital sources
4.41
.63
  1. Digital competences 4: Using search engines
4.50
.63
 

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Table 38 also details the perceived digital competences of the instructors participating in the questionnaire study. Informants perceive that their levels of Creating and sharing content (M = 4.51; SD = 0.61), Search engine use (M = 4.50; SD = 0.63), and their skills to assess the Reliability of digital sources (M = 4.41; SD = 0.63) are equally rather high, while their Keeping up with the development of technology, applications and programmes is slightly lower (M = 3.86; SD = 0.90). Based on the observed mean averages, similar to the pilot phase, paired sample t-tests were run to establish a rank order between the constructs of the questionnaire (Table 39).
 

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Table 39 Paired Sample t-tests of the Scales of the Instructors’ Main Study
Paired Differences
95% Confidence Interval of the Difference
Mean
St. Deviation
Std. Error mean
Lower
Upper
t
df
Sig. (2-tailed)
Pair 1: Reasons – DigComp1
0.273
0.612
0.073
0.128
0.418
3.759
70
< 0.001
Pair 2: Reasons – DigComp4
0.286
0.525
0.062
0.162
0.410
4.589
70
< 0.001
Pair 3: Reasons – DigComp3
0.372
0.601
0.071
0.230
0.514
5.211
70
< 0.001
Pair 4: Reasons – Willingness
0.497
0.760
0.090
0.317
0.677
5.515
70
< 0.001
Pair 5: Reasons – Acceptance
0.485
0.443
0.053
0.380
0.590
9.217
70
< 0.001
Pair 6: Substitution – DigComp3
0.279
0.667
0.080
0.121
0.437
3.522
70
= 0.001
Pair 7: Substitution – Willingness
0.404
0.732
0.087
0.231
0.577
4.653
70
< 0.001
Pair 8: Substitution – Acceptance
0.392
0.496
0.059
0.274
0.509
6.649
70
< 0.001
Pair 9: DigComp1 – DigComp2
0.648
0.602
0.071
0.505
0.790
9.063
70
< 0.001
Pair 10: DigComp3 – DigComp2
0.549
0.639
0.076
0.398
0.700
7.249
70
< 0.001
Pair 11: DigComp4 – DigComp2
0.635
0.669
0.079
0.477
0.794
7.996
70
< 0.001
 

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The results of the paired sample t-tests (Table 39) confirmed that the highest rated scales of the questionnaire were Reasons for using ICT devices, Substitution and Digital competence 1, while Digital competence 2 was the lowest rated out of the four digital competences. This latter lower perceived value for DigComp2 (M = 3.86; SD = 0.90) suggests that there could be individual differences in the sample, perhaps in terms of availability or access to technology, or even age-related differences, given that there is evidence for more experienced teachers to be more resistant towards technology inclusion (Monacis et al., 2019). While age as an individual difference in itself is not an accurate predictor of inclusion and development willingness (Bayne & Ross, 2011; Drent & Meelissen, 2008; Mossberger et al., 2008), it is argued in the literature that younger teachers have the advantage of having grown up and perhaps having been trained in technology rich(er) environments, thus they simply had more exposure to technology (Monacis et al., 2019), nevertheless any experienced teachers can decide to invest the time and effort necessary to become on a par with younger generations. Whether age as an individual difference gives grounds for systematic differentiation is presented in 4.2.2.5 (Study2RQ8).
 

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4.2.2.3 The Connections Between Digital Competences and the other Surveyed Dimensions of ICT use (Study2RQ6). To establish links between the dimensions of ICT use of the instructors in the sample, bivariate Pearson correlations were run on the scales. This analysis, however, was preceded by an independent samples t-test run on the sample comparing each scale’s values between two participant groups: participants who are involved in teaching methodology and methodology related subjects (n = 51) and those who are not (n = 20). This step was necessary to establish if the sample can be treated as one homogenous entity or not. The analysis confirmed that only in the case of Digital Competence 2: Keeping up with development, teachers in the first subgroup rated this scale significantly higher (M = 4.00; SD = 0.87) than teachers of the second subgroup (M = 3.53; SD = 0.91). Yet, since the significance level (Sig. (2-tailed): p = 0.049) is very close to the 5% cut-off margin, in the case of this questionnaire, the analysis will treat the sample as a homogenous one.

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Thus, the bivariate Pearson correlations’ results of the entire sample are summarized in Table 40. It shows that the dimensions of ICT use, similar to the case of the learners’ study, show several statistically significant correlations, thus ICT use of instructors could also be seen as complex issue with several intertwined reasons. The highest correlations observed were between:

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  • Digital competences 2: Keeping up with development and Willingness to use ICT (r = 0.732; Sig. (2-tailed): p < 0.001);
  • DigComp2 and Acceptance (r = 0.684; Sig. (2-tailed): p < 0.001);
  • DigComp2 and Devoted time (r = 0.614; Sig. (2-tailed): p < 0.001);
  • and DigComp2 and Opportunities for ICT skills development (r = 0.594; Sig. (2-tailed): p < 0.001).
 

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Using Fisher’s r-to-z transformation to test the statistical significance between the differences of two correlational coefficients, the values listed above can be called the strongest correlations in the dataset because their difference proved not to be statistically significant with each other. These strong correlations confirm the literature in saying that ICT use is rather determined by willingness and invested time as opposed to generational differences, and technology use is rather prompted by the individual’s self-concept (Korlat et al., 2021; Morgan et al., 2021).
 

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Table 40 Statistically Significant Correlations between the Digital Competences and Other Scales of the Instructors’ Main Questionnaire Study
1. Acceptance of ICT
2. Availability of ICT
3. Reasons for using ICT
4. Willingness to use ICT
5. Devoted time
6. Opportunities for ICT skills development
7. Substitution
8. Using ICT for instruction
16. Transitioning to online education
17. Effectiveness of online education
18. Offline versus online self-image
9. Digital competences 1: Creating and sharing content
.491
.272
.333
.518
.512
.432
.418
.484
.423
.274
–.283
10. Digital competences 2: Keeping up with development
.684
.434
.372
.732
.614
.594
.454
.538
.544
.281
–.300
11. Digital competences 3: Reliability of digital sources
.449
.394
.611
.422
.346
.285
.385
.275
12. Digital competences 4: Using search engines
.508
.396
.565
.549
.416
.398
.419
.469
.292
 

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Table 40 also illustrates that the Covid-scales (constructs 13 to 15) are also linked to other dimensions of ICT use. DigComp1 and DigComp2 show statistically significant moderate correlations with Transitioning to online education (r = 0.423; r = 0.544, respectively, Sig. (2-tailed): p < 0.001), which suggests that there is a link between the individual’s ICT competences and the ease of transition. However, the TPACK model prompts that technological knowledge (TK), and technological pedagogical knowledge (TPK) are separate knowledge dimensions (Chai et al., 2011; Koehler et al., 2014; Mishra & Koehler, 2006). In this respect, the observed weak or no correlations between Digital competences and the Effectiveness of online education could signal that the instructors in the sample registered this difference because their individual digital competences show weak or no connections with their online teaching effectiveness. The observed weak negative correlations between DigComp1 and DigComp2 with Online versus offline self-image, similar to the pilot, could point towards a general interconnectedness of 21st century university education and technology, irrespective of Covid-19; thus, ICT-inclusive education is regarded as a general part of teaching as opposed to something that participants had to resort to solely because of the pandemic.
 

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4.2.2.4 ICT use Dimensions Influencing the Instructional use of Technology (Study2RQ7). To find out if the development of certain ICT use dimensions would result in university instructors’ instructional more frequent integration, regression analysis was applied. With the final goal of the construct Using ICT for instruction, the path end was decided to be this construct. Then, backward, statistically significant predictors were listed until there were only significant links in the model without any more significant predictors found. Table 41 details the final model with Acceptance of ICT (B = 0.666) and Digital competences 1: Creating and sharing content (B = 0.279) as the two significant predictors pointing towards a frequented Instructional ICT use. These confirm that it is rather beliefs (positive attitudes) and abilities (competence development) that ensure instructors’ technology use, echoing the literature calling for possibilities and supportive environments in which instructors (as well as prospective teachers) can experiment with technology in a critical way by which experiences their beliefs could be shaped (Aşık et al., 2020; Graham et al., 2012; Goktas et al., 2009; Kárpáti, 2012; Korlat et al., 2021; Molnár, 2011; Morgan et al., 2021; Tezci, 2011).
 

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Table 41 Dimensions of ICT Use influencing Instructional Use of Technology Based on Regression Analysis in the Instructors’ Main Questionnaire Study
Variable
B
p
t
95% CI for B
SE B
β
R2
ΔR2
LL
UL
Constant
–.047
= 0.042
3.02
–1.212
1.118
0.584
.438
.422
1. Acceptance of ICT devices
0.666
< 0.001
4.97
0.399
0.934
0.134
.519
9. DigComp1: Creating and sharing content
0.279
= 0.032
2.20
0.025
0.532
0.127
.229
Note. Dependent Variable: 8. Using ICT for instruction. CI = confidence interval; LL = lower limit; UL = upper limit.
Note. Dependent Variable: 8. Using ICT for instruction. CI = confidence interval; LL = lower limit; UL = upper limit.
 

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Teacher education majors (Dringó-Horváth, 2020; Dringó-Horváth & Gonda, 2018) as well as in-service teachers should also be targeted with such experimental possibilities (Öveges & Csizér, 2018), but this prerequisites an already existing supportive environment on behalf of their institutions (from supportive leadership to local technology assistance) because occasional experimenting with technology could less likely result in reshaping beliefs about the necessity of technologies in education. While workshops are viable means of staying updated about recent educational technological development, instructors enrolling in certain educational workshops run the risk of resorting to only the exact technologies addressed in specific workshops or training modules (Tondeur et al., 2016) without developing a critical attitude and a willingness to keep up with the development of technology that would ensure that they are not only operators of technology, but they view ICT as instructional modalities: alternative ways of processing and sharing knowledge, an extension of learning and teaching possibilities (Lim, 2002; McDougall & Jones, 2006; Sutherland et al., 2004).
 

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4.2.2.5 Age and Gender as Individual Differences in the use of ICT (Study2RQ8). Some studies suggest that gender (Korlat et al., 2021) and teaching experience (Becker, 1999; Drent & Meelissen, 2008; Kler, 2014) might be regarded as grouping variables when mapping the dimensions of ICT use. To test these hypotheses, independent samples t-tests were applied with teaching experience and gender as grouping variables. As argued by Monacis and colleagues (2019), the dividing line for teaching experience was established at 20 years of total teaching experience; thus, one subsample consisted of instructors with one to 20, and the other with 20+ years of teaching experience, respectively. The former group consisted of 22, and the latter of 49 participants.

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Neither of the scales proved to have been rated significantly differently by males and females in the sample; however, some scales showed significant differences regarding teaching experience, as shown in Table 42. Wherever Levene’s test for equality of variance rejects the null hypothesis (i.e., it is significant at p < 0.05), the t-test values of the Equal variances not assumed (i.e., Welch’s t-test) are taken into consideration throughout the interpretation of the test results.
 

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Table 42 Statistically Significant Differences between the Instructor’s Main Questionnaire Scales Rated by Teachers with Less than 20 and Teachers with 20+ Years of Teaching Experience
Group Statistics
Independent Samples Test
Levene’s Test for Equality of Variances
t-test for Equality of Means
Teaching experience
N
Mean
Std. Deviation
F
Sig.
t
df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
1. Acceptance of ICT
One to 20 years
22
4.55
.41
5.03
.028
Equal variances assumed
2.453
69
.017
.353
.144
.066
.641
20+ years
49
4.19
.62
Equal variances not assumed
2.849
58.74
.006
.353
.124
.105
.601
4. Willingness to use ICT
One to 20 years
22
4.56
.51
6.01
.017
Equal variances assumed
1.720
69
.090
.388
.225
–.062
.838
20+ years
49
4.17
1.00
Equal variances not assumed
2.163
67.69
.034
.388
.179
.030
.746
5. Devoted time
One to 20 years
22
3.94
1.01
1.85
.178
Equal variances assumed
2.348
69
.022
.555
.236
.083
1.027
20+ years
49
3.39
.88
Equal variances not assumed
2.225
35.87
.032
.555
.249
.049
1.061
6. Opportunities for ICT skills development
One to 20 years
22
4.25
.79
1.74
.192
Equal variances assumed
3.202
69
.002
.806
.251
.304
1.308
20+ years
49
3.44
1.05
Equal variances not assumed
3.562
52.86
.001
.806
.226
.352
1.260
8. Using ICT for instruction
One to 20 years
22
4.33
.54
4.46
.038
Equal variances assumed
1.942
69
.056
.365
.188
–.009
.740
20+ years
49
3.96
.80
Equal variances not assumed
2.253
58.59
.028
.365
.162
.040
.689
9. DigComp1: Creating and sharing content
One to 20 years
22
4.80
.27
9.04
.004
Equal variances assumed
2.765
69
.007
.416
.150
.115
.716
20+ years
49
4.38
.68
Equal variances not assumed
3.688
68.31
.000
.416
.112
.191
.641
10. DigComp2: Keeping up with development
One to 20 years
22
4.3636
.60043
5.036
.028
Equal variances assumed
3.356
69
.001
.722
.215
.293
1.152
20+ years
49
3.6408
.92464
Equal variances not assumed
3.930
59.845
.000
.722
.183
.354
1.090
12. DigComp4: Using search engines
One to 20 years
22
4.6932
.36131
8.348
.005
Equal variances assumed
1.746
69
.085
.279
.160
–.039
.599
20+ years
49
4.4133
.70996
Equal variances not assumed
2.198
67.790
.031
.279
.127
.025
.534
15. Offline versus online self-image
One to 20 years
22
3.2000
.76345
1.020
.316
Equal variances assumed
–2.299
69
.025
–.457
.198
–.853
–.060
20+ years
49
3.6571
.77996
Equal variances not assumed
–2.318
41.323
.026
–.457
.197
–.855
–.058
 

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Consequently, regarding the participants of the questionnaire, their reported perceptions regarding all scales of the questionnaire showed no differences based on gender as an individual difference, but the scales of Acceptance, Devoted time, Willingness, Opportunities for skills development, Instructional use of technology, Digital competence 1: Creating and sharing content, Digital competence 2: Keeping up with development, Digital competence 4: Using search engines and Online versus offline self-image showed statistically significant differences in terms teaching experience.

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The observed mean rank values (see Table 42) and significance levels confirm that Hungarian university instructors in the sample with one to 20 years of teaching experience attached higher importance to ICT devices, showed a higher willingness to use technology, invest more time in development, perceive that they have developmental opportunities, use technology as part of their instructional practices as well as perceived to have better content creating and search engine use skills and they keep up with recent developments more often. The statistically significant lower mean values for Offline versus online self-image also confirm that less experienced teachers were more self-confident in the online sphere, and teachers with 20+ years of experience were more likely to feel less comfortable as instructors online.

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While these findings echo the literature (Becker, 1999; Drent & Meelissen, 2008; Kler, 2014), the exact dimensions in which there are significant differences between less and more experienced instructors contribute towards a more detailed understanding of their ICT literacy. The mean differences of Opportunities of ICT skills development and Digital competences 2: Keeping up with development might signal that for instructors with less teaching experience, technology itself could be the source of development, while more experienced teachers could be expected to seek help from colleagues or through other channels. More experienced teachers’ lower acceptance scores also point towards weaker beliefs concerning the importance of technology, nevertheless the entire sample’s mean acceptance value was rather high (M = 4.30; SD = 0.58). Lower acceptance is linked to weaker beliefs (Drent & Meelissen, 2008; Kler, 2014), and strong positive beliefs are linked to more likely integration (Lowther et al., 2008; Sang et al., 2010); thus, development and experimentation possibilities should be facilitated for more experienced teachers, and the source of such possibilities could be younger colleagues because learning educational technology is best linked to one’s specific area of expertise (Koehler et al., 2014; Mishra & Koehler, 2006).
 

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4.2.2.6 The Effects of the 1st and 2nd Waves of the Covid-19 Pandemic on Teaching Styles and Digital Competence Levels (Study2RQ9). The university instructors’ main questionnaire briefly addressed how participants perceived the 2020 spring and autumn online teaching periods. The descriptive statistical results of the three Covid-scales of the questionnaire (Table 43) signal that instructors perceived the transition to be challenging, but not insoluble. Informants perceived that they more or less could implement Effective online classes (M = 3.71; SD = 0.78), the Transition was manageable (M = 3.62; SD = 0.94), while in general, the descriptive results signal that teachers feel more confident and at-home in traditional face-to-face teaching environments, where their Offline vs. online self-image do not conflict one another (M = 3.52; SD = 0.80).
 

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Table 43 Descriptive Statistics of Constructs 13 to 15 of the Instructors’ Main Study
Descriptive Statistics
N = 48
Mean
Std. Deviation
13. Transitioning to online education
3.62
.94
14. Effectiveness of online education
3.71
.78
15. Offline versus online self-image
3.52
.80
 

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Earlier it was established (4.2.2.5) that teachers with one to 20 years of teaching experience in the sample perceived that their online teacherly selves were significantly closer to their in-class teacherly selves compared to teachers with 20+ years of teaching experience, which signals that teachers with less experience tend to be more confident in the digital sphere, and teachers with more experience tend to be more confident in the physical classroom.

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To confirm if the spring and autumn 2020 Covid-19-triggered distant education periods (the first of which was an emergency remote teaching, and the second a planned hybrid or digital teaching period), respondents of the questionnaire were asked to rate their 1) technological knowledge, 2) how much the perceived that technology-mediated education proved to be a burden for them, and 3) how much of their classes were implemented either synchronously or asynchronously online in the first and second Covid-waves. In the first two pairs of cases, the questionnaire operated with 5-point Likert-scales, while a percentile value had to be entered by the respondents regarding their implemented classes. To find out if the differences were significant, paired sample t-tests were run on the three pairs of scales, the result of which is detailed in Table 44.
 

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Table 44 Bivariate Pearson Correlations and Paired Samples t-tests of the Items Reflecting the 1st and 2nd Waves of Covid-19 of the Instructors’ Main Questionnaire
Pairs
Paired Samples’ Pearson Correlations
Paired Samples’ t-tests
N
Correlation
Sig.
t
df
Sig. (2-tailed)
Pair 1
Technological knowledge 1st wave –
Technological knowledge 2nd wave
71
.714
< .001
–8.683
70
< .001
Pair 2
Teaching as burden 1st wave –
Teaching as burden 2nd wave
71
.515
< .001
7.287
70
< .001
Pair 3
Classes instructed 1st wave –
Classes instructed 2nd wave
71
.371
= .001
–6.372
70
< .001
 

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In all three pairs, statistically significant differences were observed between participants’ first- and second Covid-wave perceptions (Table 44). The differences signal that the second wave imposed a lesser burden on instructors, and their technological knowledge developed as well, perhaps because teachers managed to implement more online classes and did not lose touch with their learners. These findings echo other observations from the Hungarian K12 context, in which teachers who were clustered to be Beginners, Intermediate and Advanced users of technology reported statistically significant technological knowledge gains (Fekete, 2020a). Similar to Hungarian K12 teachers, the positive consequences of a forced technological knowledge gain were reported by Hungarian university instructors (Fekete & Divéki, 2022). In both cases, knowledge development proved to be effective if it was context-specific and experimental in nature, for example in the form of professional development circles (Fekete, 2020a; Fekete & Divéki, 2022).

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Some collected background variables also targeted participants’ experiences with online education. It was found that the most frequently used learning management system (LMS) was Microsoft Teams (54.9% of the participants used it), followed by Moodle (39.4%), Google Classroom (28.2%), Neptun Meet Street (16.9%) and Edmodo (7.0%). Only 13 informants (18.3%) reported that some form of digital training was directly part of their university education, and 90% of the informants claimed that the source of their technological pedagogical knowledge was self-development, while 81.7% of the informants reported that they have participated in collegial workshops and interdepartmental training programmes. Additionally, 33% of the informants participated in official workshops and the same percentile of instructors claimed to have used online networking sites or pages as sources for development. In a Hungarian study conducted in the K12 educational context, approximately 66% of teachers reported that they had resorted to groups of professional communities during online education, and 22.7% of the informants had educational technological training as part of their university studies (Fekete, 2020a). This points towards the necessity of enriching teacher education and training programmes with subject-specific ICT methodological courses (Fekete, 2020a); Dringó-Horváth & Dombi, 2020; M. Pintér, 2019; MDOS, 2016; Molnár et al., 2020; Öveges & Csizér, 2018).

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It can be concluded that, in a sense, the emergency remote teaching and the hybrid educational experiences in many cases contributed successfully towards providing the environmental and experimental opportunities for instructors to develop their technological as well as techno-pedagogical knowledge domains and that is why they reported gains in their ICT knowledge and expressed that online instruction in the second Covid-wave was less of a burden, which resulted in more implemented university classes among the instructors in the sample.
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