Motivation in the structure of the digital competence of Russian adolescents

Soldatova, Galina U. Lomonosov Moscow State University

Rasskazova Elena I. Lomonosov Moscow State University

Fig. 1. The average rate of specific motivation of improving digital competence (per cent of the maximum possible) for different types of general motivation

Table 1. Distribution of general motivation types in adolescents and their parents, and their relation to digital competence in adolescents

DC Sphere and Component	Low motivation and lack of competence	Low motivation and illusory competence	Passive Motivation	Active motivation – self-learning	Active motivation – training programs	F- Test	Statistical Effect η
Adolescents – Total	7.80%	13.60%	14.60%	21.10%	42.90%	-	-
Parents – Total	14.50%	11.10%	17.60%	17.10%	39.80%	-	-
DCI	33.8%	38.7%	30.5%	37.2%	33.0%	8.41**	0.17
Knowledge	45.1%	47.1%	37.5%	47.3%	38.6%	10.04**	0.18
Skills	36.1%	43.4%	32.2%	39.9%	33.7%	10.96**	0.19
Responsibility	35.5%	50.3%	33.6%	42.0%	37.0%	10.81**	0.19
Motivation	18.7%	14.1%	18.5%	19.5%	22.6%	8.83**	0.17
DC in Content Sphere	44.7%	50.2%	42.0%	47.3%	44.9%	5.15**	0.13
DC in Communication Sphere	34.6%	39.5%	33.5%	40.0%	33.3%	6.41**	0.15
DC in Technical Sphere	32.2%	36.8%	29.5%	36.3%	31.6%	4.61**	0.13
DC in Consumption Sphere	19.5%	24.3%	11.3%	20.6%	17.3%	12.37**	0.20

NB ** – p<0.01

Table 2. Features of adolescent online activities and experiences and type of general motivation improving DC: contingency table

Online Activities and Experiences	Groups by types of general motivation					Pearson's chi-squared test χ2	Statistical effect η
	Low motivation and lack of competence	Low motivation and illusory competence	Passive Motivation	Active motivation – self-learning	Active motivation – training programs
Online Risks – Never Faced Any	27.0%	14.2%	36.5%	22.4%	20.0%	27.58**	0.16
Online Risks – Sex Image	28.1%	29.7%	29.3%	32.4%	41.4%	15.76**	0.12
Online Risks – Profile Hacking and personal data theft	28.1%	38.1%	17.4%	34.4%	26.7%	21.98**	0.14
Online Risks – Malware	29.2%	38.1%	27.5%	41.1%	40.0%	12.32*	0.10
Online Emotions – Interest	69.7%	78.1%	81.4%	84.2%	81.6%	9.93*	0.09
Online Emotions– Anger	7.9%	1.9%	3.0%	7.9%	5.1%	9.87*	0.09
Online Emotions – Excitement	14.6%	20.6%	12.6%	23.7%	21.6%	10.22*	0.10
Online Self-Image – More Aggressive	0.0%	2.6%	6.6%	2.1%	1.6%	15.71**	0.12
Online Self-Image – Unpunished	5.6%	5.2%	10.8%	5.8%	3.7%	12.14*	0.10
Online Role – Cyber Bullying	14.6%	23.2%	10.2%	8.3%	8.0%	31.52**	0.17

NB Here above there are variables to obtain differences on the significance level: * – p<0.05, ** – p<0.01

Table 3. Features of adolescent online activities and experiences and motivation to improving DC: Student’s t-test

	DC Motivation in in subjects who failed to answer		DC Motivation in subjects who gave their answer		Student’s t-test	Statistical effect η
	Mean Value	Standard Divergent Value	Mean Value	Standard Divergent Value
Online Risks – Never Faced Any	20.2%	16.6%	17.8%	15.8%	-2.12*	0.06
Online Risks –Information Published about me in Social Networks Was Used against me	19.4%	16.3%	23.5%	19.0%	2.00*	0.06
Online Risks –Spread of Personal Information without my Consent	19.3%	16.2%	24.6%	19.7%	2.83**	0.08
Online Risks – Malware	18.5%	16.1%	21.6%	17.0%	3.14**	0.09
Online Emotions – Happiness	18.4%	16.7%	21.0%	16.1%	2.73**	0.08
Online Emotions – Surprise	18.6%	16.4%	21.8%	16.4%	3.17**	0.09
Online Emotions – Shame	19.5%	16.4%	24.8%	16.5%	1.99*	0.06
Online Emotions – Excitement	19.1%	16.4%	21.8%	16.5%	2.29*	0.07
Online Self-Image – More Respectful	19.3%	16.2%	23.1%	18.7%	2.44*	0.07
Online Self-Image – More Successful	19.1%	16.1%	22.6%	17.9%	2.74**	0.08
Online Self-Image – More Confident	19.1%	16.2%	21.6%	17.1%	2.72*	0.07
Online Self-Image – More Communicative	18.8%	16.2%	21.4%	16.9%	2.60*	0.06
Online Self-Image – I’m Different from Others	20.3%	16.7%	17.9%	15.6%	-2.18*	0.06
Online Role – "Defender"	19.3%	16.4%	23.0%	16.5%	2.42*	0.07
Online Role – "Mediator"	19.3%	16.3%	23.6%	18.4%	2.39*	0.07
Online Role – "Actor"	19.2%	16.4%	22.9%	16.6%	2.66**	0.08

NB Here above there are variables to obtain differences on the significance level: * – p<0.05, ** – p<0.01.

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