Article
Soldatova G.U., Rasskazova E.I. (2017). Motivation in the structure of the digital competence of Russian adolescents. National Psychological Journal. 1, 3-14.
Abstract
In contemporary world, the digital competence of adolescents is not a separate property or capacity any longer, becoming the prerequisite and basis for many types of activities, and the Internet has become a space mediating socialization of children. Russian population study indicated that there is a «gap» in the structure of motivation to improve digital competence: although every four teenagers from five ones declare preparedness for its development, their motivation in relation to specific goals and objectives is extremely low and does not exceed 20 per cent of the maximum possible level. The paper assumes that the «gap» is caused by different contents of general and specific motivation: general motivation describes great awareness of the importance and the declared preparedness, while specific motivation refers to the setting of specific goals. Applying the Digital Competence Index (DCI) in the samples of adolescents 12-17 years old (N=1203) and of parents of adolescents of the same age (N=1208) the relationship between general and specific motivation to improve digital competences and their links to the user’s activity, confidence, emotions, self-image on the Internet and its familiarization are considered. A high level of digital competence and excessive self-confidence in the user’s skills are associated with a less general motivation. A higher level of general and specific motivation is related to the participation of teachers and parents in the development of adolescent skills in the Internet. This extremely low self-confidence and the solution of any online problems by parents are associated with passive motivation, e.g. the desire to explore the Internet spontaneously through other people. Possible methods of developing active motivation to improve digital competence and the prevention of excessive confidence in adolescents are discussed
Accepted: 02/17/2017
Pages: 3-14
DOI: 10.11621/npj.2017.0101
Sections: Psychology of virtual reality;
PDF: Download
Keywords: digital competence; motivation; Russian adolescen; emotions; self-image in the Internet; Online risks;
Available Online 30.03.2017
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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For citing this article:
Soldatova G.U., Rasskazova E.I. (2017). Motivation in the structure of the digital competence of Russian adolescents. National Psychological Journal. 1, 3-14.
