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Soldatova Galina U., Rasskazova Elena I. (2016). Models of digital competence and online activity of Russian adolescents. National Psychological Journal. 2, 50-60.

Abstract

Having established the conception of digital competence consisting of four components (knowledge, skills, motivation and responsibility) implemented in four areas (content, communication, consumption, and the techno-sphere), we propose the idea of models of digital competence as a specific systems of adolescents’ beliefs about their abilities and desires in the online world. These models (1) may be realistic or illusory, (2) their development is mediated by the motivation and online activity and (3) they regulate further online activities as well as the further development of digital competence. On the basis of nationwide study of digital competence (N=1203 Russian adolescents of 12-17 years) using latent class method we revealed 5 models of digital competence corresponding to its lowest level, the average level at high and low motivation, high specific (in the components of skill and safety) and high general level. It has been shown that higher appraisal of their digital competence is related to the opportunity of a more prolonged and self-service access to the Internet as well as the history of independent development of skills online. The illusion of digital competence is associated with a wide but shallow exploration activities online. Motivational component is related to the participation and recognition of the role of others in the development of digital competence, in comparison with others’ online skills and knowledge, as well as subjectively lower «digital divide» with parents. We suggest that the motivational component of the digital competence is developed if adolescent has a successful interaction via Internet, learn from other people and also if the range of her activities and interests online activity involves and requires the development of new skills.

Based on digital competence model’s analysis, we have figured out 3 main types of Internet-users: (1) beginners, (2) experienced users, (3) advanced users. All these types fall into different risk groups, determined by variable possibilities of facing content-, communication-, technical- and customers- online-threats.

Received: 05/26/2016
Accepted: 06/02/2016
Pages: 50-60
DOI: 10.11621/npj.2016.0205

Sections: Psychology of virtual reality;

PDF: /pdf/npj-no22-2016/npj_no22_2016_050-060.pdf

Keywords: digital competence; internet activity; online activities; adolescents; digital model of competence; the illusion of digital competence;

Available Online 30.08.2016

Table 1. Comparison of absolute and relative conformities figured out among models with diverse number of student groups.

Parameter

2 groups

3 groups

4 groups

5 groups

6 groups

LogLikelihood

-1749,87

-1156,55

-900,15

-755,91

-565,37

Informational criteria

AIC (Akaike information criteria)

3597,74

2445,10

1966,30

1711,83

1364,74

BIC (Bayesian information criteria)

3847,27

2781,21

2388,98

2221,09

1960,58

Sample-size adjusted BIC

3691,63

2571,57

2125,34

1903,45

1588,94

Entropy

0,90

0,91

0,85

0,88

0,84

Vuong-Lo-Mendell-Rubin Likelyhood ratio test (LRT)

Double LogLikelihood Difference

3860,97

1186,64

512,80

288,47

381,08

Medium (stand. dev.)

117,63 (181,36)

82,15 (290,66)

163,65 (298,81)

117,31 (313,63)

89,30 (473,05)

Significance level

0,00

0,01

0,12

0,29

0,27

Relative Lo-Mendell-Rubin LRT test

Test results

3829,21

1176,88

508,59

286,10

377,95

Significance level

0,00

0,01

0,12

0,30

0,27

Parametric LRT test, based on bootstrap (500 resamples)

Double difference of LogLikelihood

3860,97

1186,64

512,80

288,47

381,08

Significance level

0,00

0,00

0,00

0,00

0,00

Figure 1. An average digital competence profile in 5 groups of adolescents.


Table 2. Average amount of online activity and applicable skills of adolescents and their parents (as viewed by adolescents).

Digital competence models

Online activity – adolescents

Online skill – adolescents

Online activity - parents

Online skills - parents

Average DC, high motivation

3,36

10,07 a

2,77 a

7,72 a

Low DC, low motivation

2,90 a,b

5,19 a,b

1,63 a,b,с

3,19 a,b,с

Average DC, low motivation

3,66 a

10,60 b

1,99 a

5,24 a,b

High specific DC – skills and safety, low motivation

3,77 b

15,47 a,b

2,29 b

6,80 с

High common DC

3,99

17,77 a,b

2,25 c

7,43 b

F-test

19,75***

640,09***

15,42***

34,31***

Static eta effect

0,25

0,83

0,22

0,32

Note. *** - p<0,001. a,b,c – Groups with the same letters differ pairwise if taken under post hoc analysis by Scheffe’s method with p<0,05.

Table 3. Peculiarities of online-activity among adolescents with different DC models.

Internet activity

Average DC, motivated

Low DC, not motivated

Average DC, not motivated

High specific DC – skills and safety

High common DC

A chi-squared test

Static effect

Searching for various interesting information

62 (83,8%)

402 (71,8%)

293 (78,3%)

85 (82,5%)

68 (73,9%)

11,46*

0,10

Reading news

11 (14,9%)

135 (24,1%)

117 (31,3%)

27 (26,2%)

32 (34,8%)

14,29**

0,11

Looking for new friends in social networks

30 (40,5%)

238 (42,5%)

157 (42,0%)

31 (30,1%)

30 (32,6%)

8,27

0,08

Various types of online communication

28 (37,8%)

192 (34,3%)

182 (48,7%)

47 (45,6%)

48 (52,2%)

25,38**

0,14

Free-of-charge downloads

25 (33,8%)

101 (18,0%)

103 (27,5%)

36 (35,0%)

29 (31,5%)

26,54**

0,15

Critics, quarrels, trolling

2 (2,7%)

13 (2,3%)

19 (5,1%)

9 (8,7%)

12 (13,0%)

26,55**

0,15

Looking for data needed for studies/job

37 (50,0%)

264 (47,1%)

191 (51,1%)

52 (50,5%)

48 (52,2%)

1,88

0,04

Visiting educational websites, watching online-courses

5 (6,8%)

25 (4,5%)

34 (9,1%)

9 (8,7%)

15 (16,3%)

19,76**

0,13

Programming

2 (2,7%)

13 (2,3%)

25 (6,7%)

4 (3,9%)

13 (14,1%)

29,21**

0,15

Looking for part time job

1 (1,4%)

7 (1,3%)

12 (3,2%)

6 (5,8%)

7 (7,6%)

17,35**

0,12

Online and mobile games

27 (36,5%)

159 (28,4%)

121 (32,4%)

47 (45,6%)

39 (42,4%)

16,98**

0,12

Chatting with other online gamers while playing

11 (14,9%)

57 (10,2%)

61 (16,3%)

22 (21,4%)

15 (16,3%)

13,66**

0,11

Looking for new arrivals in online-shops

3 (4,1%)

10 (1,8%)

19 (5,1%)

6 (5,8%)

10 (10,9%)

20,54**

0,13

Online-purchases

3 (4,1%)

9 (1,6%)

23 (6,1%)

5 (4,9%)

5 (5,4%)

14,22**

0,11

Creating and uploading new content

5 (6,8%)

7 (1,3%)

31 (8,3%)

8 (7,8%)

6 (6,5%)

28,95**

0,15

Notes. * - p<0,05, ** - p<0,01.

Figure 2. Average number of online activities among adolescents with different DC models, making and not making serious grammar mistakes while being tested.


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For citing this article:

Soldatova Galina U., Rasskazova Elena I. (2016). Models of digital competence and online activity of Russian adolescents. National Psychological Journal. 2, 50-60.

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