Imre Fekete: Technology in English teaching • The Hungarian university context
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4. Results and Discussion
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4.1. Study 1: The Learners’ Questionnaire Study
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4.1.2. The Learners’ Main Questionnaire Study

4.1.2. The Learners’ Main Questionnaire Study

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4.1.2.1 Reliability of the Scales of the Data Collection Instrument. Before using statistical tests to present the results of the main questionnaire study, the analysis began with checking construct validity. In order to maximize internal reliability, all items in a construct needed to load to the same dimension as well as constructs needed to reach a minimum 0.6 Cronbach’s alpha, which was the cut-off point for regarding a scale reliable (Dörnyei & Csizér, 2012). The constructs included in the analysis after reliability checks are summarized in Table 24. The results of the reliability analysis show that out of the 16 constructs, 15 proved to be reliable, but the construct of Digital competences 4: Using search engines (italicised in Table 24) had to be excluded from the data analysis because it proved to be unreliable.
 

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Table 24 Cronbach’s Alpha Values and Principal Component Analysis Results of the Learners’ Main Questionnaire
Construct’s name
FT = free time use; LEARN = learning use
Cronbach’s alpha of construct
N = 268
Number of components extracted by principal component analysis
Number of items in the construct
  1. 1. Acceptance
.762
1
5
  1. Availability
.745
1
5
  1. Reasons_FT
.816
1
5
  1. Reasons_LEARN
.892
1
5
  1. Willingness _FT
.867
1
4
  1. Willingness_LEARN
.896
1
4
  1. Devoted Time
.899
1
4
  1. Will to develop_FT
.933
1
7
  1. Will to develop _LEARN
.946
1
7
  1. Substitution_FT
.739
1
3
  1. Substitution_LEARN
.847
1
3
  1. Language learning
.737
1
4
  1. Digital competences 1: Creating and sharing content
.827
1
5
  1. Digital competences 2: Keeping up with development
.853
1
5
  1. Digital competences 3: Reliability of digital sources
.859
1
5
  1. Digital competences 4: Using search engines
.585
1
4
 

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The reliable scales were further tested for skewness and kurtosis (Table 25) in order to decide if parametric statistical tests could be run on the dataset. The observed variables confirmed that the level of skewness and kurtosis are acceptable regarding each scale of the main learners’ questionnaire study because they do not reach the skewness = 3 and kurtosis = 21 cut-off margins (Ryu, 2011); thus, the scales can be further analysed using parametric statistical procedures.
 

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Table 25 Skewness and Kurtosis of the Scales of the Learners’ Main Questionnaire
Descriptive Statistics
N
Skewness
Kurtosis
Statistic
Statistic
Std. Error
Statistic
Std. Error
  1. Acceptance
320
–.873
.136
.588
.272
  1. Availability
320
–1.311
.136
1.770
.272
  1. Reasons_FT
320
–1.118
.136
1.220
.272
  1. Reasons_LEARN
320
–1.479
.136
2.511
.272
  1. Willingness _FT
320
–1.174
.136
1.785
.272
  1. Willingness_LEARN
320
–.963
.136
.529
.272
  1. Devoted Time
320
–.132
.136
–.636
.272
  1. Will to develop_FT
320
–.617
.136
.053
.272
  1. Will to develop _LEARN
320
–.537
.136
–.433
.272
  1. Substitution_FT
320
–1.233
.136
1.521
.272
  1. Substitution_LEARN
320
–1.451
.136
2.504
.272
  1. Language learning
320
–.454
.136
–.246
.272
  1. Digital competences 1: Creating and sharing content
320
–1.248
.136
1.407
.272
  1. Digital competences 2: Keeping up with development
320
–.888
.136
.854
.272
  1. Digital competences 3: Reliability of digital sources
320
–.594
.136
–.399
.272
Valid N (listwise)
320
 

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4.1.2.2 Acceptance, Availability, Willingness and Reasons for Using ICT Devices and the Perceived Levels of Digital Competences (Study1RQs 1-5). Descriptive statistics of the scales, summarized in Table 26, confirmed that Hungarian English majors are accepting of ICT devices (M = 4.12; SD = 0.67), and the availability of ICT devices is rather high (M = 4.44; SD = 0.59). Hungarian English majors, on average, perceive that they devote neither too little, nor too much time to develop their skills of using ICT devices (M = 3.27), but the high standard deviation (SD = 1.03) suggests that individuals in the sample represent many different viewpoints. Participants generally agree that ICT use is advantageous for language learning (M = 3.84; SD = 0.78) as they feel that ICT devices make language learning stress-free as well as convenient, and devices make it possible to access practice activities quickly and easily.
 

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Table 26 Descriptive Statistics of the Scales of the Learners’ Main Study
Descriptive Statistics
N = 320
Mean
Std. Deviation
  1. Acceptance
4.12
.67
  1. Availability
4.44
.59
  1. Reasons_FT
4.36
.63
  1. Reasons_LEARN
4.40
.68
  1. Willingness _FT
4.19
.78
  1. Willingness_LEARN
4.17
.81
  1. Devoted Time
3.27
1.03
  1. Will to develop_FT
3.81
.87
  1. Will to develop _LEARN
3.92
.88
  1. Substitution_FT
4.35
.69
  1. Substitution_LEARN
4.41
.71
  1. Language learning
3.84
.78
  1. Digital competences 1: Creating and sharing content
4.38
.64
  1. Digital competences 2: Keeping up with development
3.98
.79
  1. Digital competences 3: Reliability of digital sources
4.14
.69
 

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To rank order participants’ digital competences, paired sample t-tests were run (Table 27). In terms of digital competences, Hungarian English majors perceive to be most confident in Creating and sharing content (M = 4.38; SD = 0.64), followed by judging the Reliability of online sources (M = 4.14; SD = 0.69) and Keeping up with the development of ICT devices and programmes and applications that run on them (M = 3.98; SD = 0.79).
 

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Table 27 Paired Sample t-tests of the Digital Competence Scales of the Learners’ 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: DigComp1 – DigComp3
0.228
0.642
0.036
0.157
0.298
6.342
319
< 0.001
Pair 2: DigComp3 – DigComp2
0.160
0.743
0.042
0.078
0.242
3.855
319
< 0.001
Pair 3: DigComp1 – DigComp2
0.388
0.567
0.032
0.325
0.450
12.233
319
< 0.001
 

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As the digital competence scales of this questionnaire were partially adopted from the EU’s (2015) Digital competences self-assessment grid, the EU’s (2019) Hungarian country report on the second survey of primary and secondary schools targeting ICT in education gives some grounds for comparability with this study, as administration of the EU’s Digital competences grid (2015) was part of the 2019 nationwide data collection (EU, 2019). On the compared two secondary educational levels (lower- and upper-secondary education), students’ perceived level of digital safety was M = 2.95 and M = 2.88 on a 4-point scale, respectively, and in terms of content creation the value was M = 2.70 in terms of both sub-groups. In proportion to the five-point Likert-scale used in this questionnaire study, it can be said that tertiary learner’s perceptions in terms of digital safety and content creation appears to be higher, but statistically significant differences could not be tested because the dataset of the EU’s (2019) country report is not disclosed, and the items of the constructs are slightly different.
 

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4.1.2.3 The Relationship Between the General and the Learning use of ICT Devices (Study1RQ6). To describe the relationship between the scales that informants ranked from both general and learning use, bivariate Pearson correlations were run. The analysis, summarized in Table 28, found statistically significant high correlations between the free time, and learning use of ICT devices. These constructs were Reasons for using ICT devices, Willingness to use ICT devices and Substituting personal contact with ICT-based communication (r = 0.765, r = 0.713, r = 0.691, respectively; p < 0.001) and the correlation between the general and learning use reasons for Developing in ICT skills was very strong (r = 0.804; p < 0.001). The high correlations between the scales suggest that free and learning use of the devices are intertwined in today’s digital society.

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Furthermore, paired sample t-tests were run on the dataset to establish if there are any statistically significant differences between informants’ ICT use regarding the scales they were asked to rate from free time and learning use perspectives. Out of the four pairs, shown in Table 28, only one set of answers, Willingness to develop proved to be significantly different (t = 3.506, Sig. (2-tailed): p = .001) regarding learning and free-time use of the devices. As for the three other pairs, Reasons, Willingness and Substitution, no significant difference was found in the dataset. This is of vital importance because this phenomenon further supports the idea that digital device use is a highly intertwined concept, and it is difficult to isolate its dimensions into a clear-cut dichotomy of free time and educational use.
 

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Table 28 Paired Samples’ Pearson Correlations of Free Time and Learning Scales of the Learners’ Main Questionnaire Study
Constructs (Mean and St. Deviation)
N
Correlation
Sig.
Pair 1
3. Reasons_FT (M = 4.36; SD = 0.63) –
4. Reasons_LEARN (M = 4.40; SD = 0.68)
320
.765
< .001
Pair 2
5. Willingness _FT (M = 4.19; SD = 0.78) –
6. Willingness_LEARN M = 4.17; SD = 0.81)
320
.713
< .001
Pair 3
8. Will to develop_FT (M = 3.81; SD = 0.87) –
9. Will to develop _LEARN (M = 3.92; SD = 0.88)
320
.804
< .001
Pair 4
10. Substitution_FT (M = 4.35; SD = 0.69) –
11. Substitution_LEARN (M = 4.41; SD = 0.71)
320
.691
< .001
 

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4.1.2.4 Age and Gender as Individual Differences in the use of ICT (Study1RQ7). To find out about possible systematic differences in the dataset regarding gender and maturity, independent samples t-tests and ANOVA analyses were run. However, independent samples t-tests confirmed no statistically significant differences between females (N = 243) and males (N = 77) concerning any scales of the questionnaire. Similar to this, the year in which participants were enrolled in at the time of data collection did not result in finding any systematic differences in the set based on an ANOVA analysis of the year of studies (from one to six) and the constructs.
 

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4.1.2.5 The Relationship Between Digital Competences and the Other Surveyed Dimensions of ICT use (Study1RQ8). Several statistically significant bivariate Pearson correlations were found between the digital competences and other constructs of the questionnaire, but no correlation is stronger than moderate. The correlations are detailed in Table 29.
 

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Table 29 Statistically Significant Correlations between the digital Competences and Other Scales of the Learners’ Main Questionnaire Study
1. Acceptance
2. Availability
3. Reasons_FT
4. Reasons_LEARN
5. Willingness_FT
6. Willingness_LEARN
7. Devotedtime
8. Will to develop_FT
9. Will to develop_LEARN
10. Substitution_FT
11. Substitution_LEARN
12. Language learning
13. Digital competences 1: Creating and sharing content
.559
.579
.461
.466
.355
.462
.418
.478
.473
.508
.485
.216
14. Digital competences 2: Keeping up with development
.590
.552
.354
.366
.439
.519
.572
.526
.553
.415
.421
.274
15. Digital competences 3: Reliability of digital sources
.412
.459
.295
.303
.270
.322
.374
.307
.406
.279
.281
.177
Note. FT = free time use, LEARN = learning use.
 

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The connections suggest that Hungarian English majors’ perceived ability to use ICT devices (Digital competences 1 to 3) that include Creating and sharing content, Keeping up with development and Judging the reliability of digital sources are indeed essential elements of their ICT literacy. There are statistically significant moderate correlations among the scales between Digital competences 1: Creating and sharing content and Availability of ICT devices (r = 0.579; Sig. (2-tailed): p < 0.001); and Digital competences 2: Keeping up with development and Acceptance of ICT devices (r = 0.590; Sig. (2-tailed): p < 0.001). Thus, there appears to be a link between the availability of ICT devices and learner perception of good content creating and sharing skills. Furthermore, learners who are generally accepting of ICT devices are open and willing to keep up with new programmes or new features of their preferred devices.

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The weak correlations between Digital competences 1, 2, and the construct of Language learning is a further interesting finding, but it does not necessarily mean that learners do not use ICT devices for language learning purposes. However, it might signal that English majors do not use the full potential of digital possibilities to expand their out-of-class language learning horizons self-directedly, also observed by M. Pintér (2019; 2021). These relationships between the scales could be interpreted as a sign of how intertwined learning is with using digital devices in a sense that learning is mediated by digital technologies (e.g., file sharing, notes editing, LMS systems) rather than being used as integral parts of the instructional processes in the forms of online tasks and digital assignments (Quin & Shuo, 2011). This, however, might have been slightly altered because of the Covid-19 pandemic.
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