Models of digital competence and online activity of Russian adolescents

Recieved: 05/26/2016

Accepted: 06/02/2016

Published: 08/30/2016

p.: 50-60

DOI: 10.11621/npj.2016.0205

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

Available online: 30.08.2016

To cite this article:

Soldatova, Galina U. , Rasskazova Elena I. . Models of digital competence and online activity of Russian adolescents. // National Psychological Journal 2016. 2. p.50-60. doi: 10.11621/npj.2016.0205

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Soldatova, Galina U. Lomonosov Moscow State University

Rasskazova Elena I. Lomonosov Moscow State University

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.

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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