Introduction
Among the activities of modern youth, programming takes the leading position. This particular attention to programming as an activity owes both to the interest of young people in the creation of new products, which requires analytical and creative skills, and to their desire to produce a new unique product, as well as to master this new profession.
At present, there is little clarity about the ontological status of programming as an activity. The conditions that promote effective learning and reveal the psychological essence of this activity are studied insufficiently. Furthermore, the individual and personal prerequisites for the successful mastery of software development and the achievement of high and exclusive results in programming have not been explored (Volkova et al., 2022a).
Empirical research under the Self-Determination Theory demonstrates that the ratio of autonomous and controlled motivation determines a person's performance and psychological well-being. In this, autonomous motivation acts as a stable predictor of perseverance, effort, and emotional well-being in activity, while controlled motivation is associated with emotional exhaustion and professional burnout (Csikzentmihalyi & Csikzentmihalyi, 1988; Deci & Ryan, 2017; Sheldon et al., 2020; Chua & Ayoko, 2021).
While the issues of the well-being of young people are well covered in research (Volk & Savel'eva, 2017; Byzova & Perikova, 2018; Gordeeva & Sychev, 2021), there are very few works on intrinsic motivation in youth (Mandrikova, 2010; Zubova, 2018; Veselova et al., 2021), and works devoted to young people passionate about programming are lacking altogether.
The purpose of the study is to research the features of psychological well-being and intrinsic motivation among young people who are passionate about programming.
Materials and methods
Psychological well-being in youth was researched using the Russian-language version of PERMA-Profiler (Isaeva et al., 2022), based on Martin Seligman’s PERMA well-being model, where well-being (“prosperity”) is defined as “a state of sustainable balance expressed in high levels of emotional, psychological, and social well-being” (Butler & Kern, 2016; Isaeva et al., 2022).
Intrinsic motivation was assessed via the Professional Motivation Questionnaire (PMQ-2) (Osin et al., 2017), developed based on Deci & Ryan (2017), Self-Determination Theory. In this theory, the construct of intrinsic motivation describes a type of determination of behavior, whose initiating and regulating factors arise from the personal Self and reside completely in the behavior itself. “Intrinsically motivated activities have no rewards other than the activity itself. People engage in the activity for its own sake, not for the achievement of any external rewards. Such activity is an end in itself, not a means to achieve some other goal” (Osin et al., 2017; Volkova et al., 2022b).
The resulting questionnaire transferred into Google Forms was offered to young people in the Nizhny Novgorod region via online resources, such as the VK social network. Young people passionate about programming were recruited for the study through the specialized classes of lyceums and gymnasiums, organizations of additional education, clubs, and special courses with classes on programming and preparation for IT lympiads. The study was anonymous and conducted on a voluntary gratuitous basis.
The data obtained were processed by descriptive statistics, frequency analysis, and analysis of variance using IBM SPSS STATISTICS 26 software.
The study sample included 302 participants aged 14-35 years old (M=19.7). Of these, 161 people (53.3%) were interested in programming, and 141 people (46.7%) - in areas outside of programming. 151 young people were male (50.0%) and 151 - female (50.0%).
Detailed information on the study sample is provided in Table 1.
Young people with an interest in programming (N=161), N | Young people with interests in other areas (N=141), N | |
Age | ||
mean (M) | 19.31 | 20.1 |
standard deviation (SD) | 3.69 | 4.1 |
minimum (min) | 14 | 14 |
maximum (max) | 35 | 34 |
Age group | ||
14-17 years old | 38 (23.6%) | 25 (17.8%) |
18-21 years old | 100 (62.1%) | 88 (62.4%) |
22-35 years old | 23 (14.3%) | 28 (19.9%) |
Gender | ||
Male | 108 (67.1%) | 43 (29.8%) |
Female | 53 (32.9%) | 98 (69.5%) |
Education | ||
student (school/technical school/college) | 39 (24.2%) | 32 (22.7%) |
university student | 105 (65.2%) | 89 (63.1%) |
secondary education | 3 (1.9%) | 20 (14.2%) |
higher education | 4 (2.5%) | 23 (16.3%) |
Experience in programming | ||
under 1 year | 41 (25.5%) | 57 (40.4%) |
1-2 years | 55 (34.2%) | 5 (3.5%) |
3-5 years | 46 (28.6%) | 4 (2.8%) |
6-8 years | 9 (5.6%) | 19 (13.7%) |
over 8 years | 2 (1.2%) | 10 (7.1%) |
NA | 8 (5.0%) | 23 (16.3%) |
Results and discusion
The indicators of the well-being of the young people included in the study fall within the average values. The highest scores were found on the " Accomplishment" (M=7.47±1.43) and "Engagement" (M=7.39±1.45) scales in the group of young people passionate about programming and on the "Positive emotion" (M=7.78±1.69), "Relationships" (M=7.70±1.69), and " Accomplishment" (M=7.70±1.40) scales in the group of youth with interests in other activities.
The indicators of general well-being and the "Positive emotion" and "Relationships" scales, as well as the additional Happiness scale, were significantly lower in the group with an interest in programming (p≤ 0.05) (Table 2).
Scale | Group 1 | Group 2 | t | p | ||
---|---|---|---|---|---|---|
M | SD | M | SD | |||
General well-being | 7.28 | 1.50 | 7.68 | 1.47 | 2.33 | 0.020* |
Positive emotion | 7.27 | 1.84 | 7.78 | 1.69 | 2.49 | 0.013* |
Relationships | 7.11 | 2.13 | 7.70 | 1.95 | 2.53 | 0.012* |
Engagement | 7.39 | 1.45 | 7.44 | 1.48 | 0.37 | 0.744 |
Meaning | 7.14 | 2.06 | 7.51 | 1.94 | 1.58 | 0.116 |
Accomplishment | 7.47 | 1.43 | 7.70 | 1.40 | 1.39 | 0.166 |
Negative emotion | 5.54 | 2.15 | 5.71 | 1.96 | 0.70 | 0.484 |
Health | 6.66 | 2.20 | 7.10 | 2.08 | 1.80 | 0.073 |
Loneliness | 4.72 | 2.92 | 4.35 | 2.74 | 1.12 | 0.265 |
Happiness | 7.28 | 2.12 | 7.92 | 1.83 | 2.80 | 0.005** |
Designations: Group 1 - sample of persons with hobbies in programming; Group 2 - sample of persons with hobbies in various areas other than programming; M - arithmetic mean; SD - standard deviation; t - Student's t-test; p - statistical significance of Student's t-test; * - p ≤0.05; ** - p ≤0.01.
In students aged between 18 and 21, significant differences were found in most components of psychological well-being when comparing people interested in programming and other types of activities (Table 3). However, no such differences can be seen in the age groups of 14-17 years old and 22-35 years old.
Scale | 18-21 years old | ||
---|---|---|---|
M (SD) | p | ||
(N=100) |
(N=88) |
||
General well-being | 6.91 (1.50) | 7.53 (1.49) | 0.004** |
Positive emotion | 6.94 (1.87) | 7.65 (1.75) | 0.006** |
Relationships | 6.71 (2.16) | 7.52 (2.02) | 0.006** |
Engagement | 7.09 (1.52) | 7.34 (1.59) | 0.324 |
Meaning | 6.66 (2.12) | 7.38 (1.94) | 0.012* |
Accomplishment | 7.11 (1.53) | 7.54 (1.36) | 0.042* |
Negative emotion | 5.86 (2.05) | 5.74 (1.97) | 0.825 |
Health | 6.22 (2.26) | 7.12 (2.24) | 0.005** |
Loneliness | 5.11 (2.81) | 4.24 (2.77) | 0.030* |
Happiness | 6.97 (2.14) | 7.72 (1.91) | 0.013* |
Designations: G1 - sample of persons with hobbies in programming; G2 - sample of persons with hobbies in various areas other than programming; p - statistical significance of the Mann-Whitney U test; * - p ≤0.05; ** - p ≤0.01.
The results concerning the specifics of motivation in both groups indicate higher values of intrinsic, integrated, and identified motivation, which are attributed to autonomous motivation (Deci & Ryan, 2017) compared to controlled motivation, i.e. introjected and extrinsic motivation, and amotivation (Table 4).
Scale | Group 1 | Group 2 | t | p | ||
---|---|---|---|---|---|---|
M | SD | M | SD | |||
Intrinsic motivation | 4.05 | 1.03 | 4.50 | 0.80 | 4.22 | 0.000** |
Integrated motivation | 3.66 | 1.06 | 4.22 | 0.88 | 4.96 | 0.000** |
Identified motivation | 3.85 | 0.97 | 3.99 | 0.87 | 1.35 | 0.179 |
Introjected motivation | 1.64 | 0.76 | 2.07 | 1.01 | 4.27 | 0.000** |
External motivation | 1.92 | 0.91 | 1.79 | 0.93 | 1.19 | 0.233 |
Amotivation | 2.02 | 1.05 | 1.94 | 1.03 | 0.74 | 0.460 |
Designations: Group 1 - sample of persons with hobbies in programming; Group 2 - sample of persons with hobbies in various areas other than programming; M - arithmetic mean; SD - standard deviation; t - Student's t-test; p - statistical significance of Student's t-test; * - p ≤0.05; ** - p ≤0.01.
Intrinsic motivation had the highest values compared to other types of motivation in both groups (M1=4.05±1.03 and M2=4.50±0.80). However, the types of motivation higher in autonomy were expressed to a lesser extent in the group of young people passionate about programming.
Significant differences in the indicators of young people's motivation for activities of interest to them depending on age are presented in Table 5.
Scale | 14-17 y.o. | 18-21 y.o. | 22-35 y.o. | ||||||
M±SD | p | M±SD | p | M±SD | p | ||||
G1 (N=38) |
G2 (N=25) |
G1 (N=100) |
G2 (N=88) |
G1 (N=23) |
G2 (N=28) |
||||
Intrinsic motivation | 4.43 (0.79) | 4.49 (0.99) | 0.294 | 3.78 (1.10) | 4.45 (0.77) | 0.000** | 4.59 (0.60) | 4.70 (0.70) | 0.196 |
Integrated motivation | 3.75 (1.08) | 3.80 (1.28) | 0.647 | 3.47 (1.06) | 4.24 (0.72) | 0.000** | 4.39 (0.65) | 4.57 (0.72) | 0.293 |
Identified motivation | 4.05 (0.92) | 3.84 (1.21) | 0.674 | 3.68 (1.01) | 4.00 (0.77) | 0.051 | 4.27 (0.68) | 4.00 (0.99) | 0.388 |
Introjected motivation | 1.48 (0.59) | 1.95 (0.82) | 0.037* | 1.69 (0.75) | 2.11 (1.05) | 0.007** | 1.66 (1.00) | 1.92 (0.84) | 0.039* |
Motivation | 1.69 (0.75) | 1.70 (0.71) | 0.952 | 2.00 (0.88) | 1.86 (1.01) | 0.086 | 1.98 (1.14) | 1.67 (0.85) | 0.450 |
Amotivation | 1.62 (0.74) | 1.98 (1.21) | 0.451 | 2.25 (1.09) | 1.98 (1.04) | 0.077 | 1.72 (1.05) | 1.81 (0.95) | 0.662 |
Designations: Group 1 - sample of persons with hobbies in programming; Group 2 - sample of persons with hobbies in various areas other than programming; M - arithmetic mean; SD - standard deviation; t - Student's t-test; p - statistical significance of Student's t-test; * - p ≤0.05; ** - p ≤0.01.
In the age group of 18-21 years old, significant differences between the groups of young men and women passionate aboutprogramming versus other types of activities were found on the scales of "Intrinsic motivation", "Integrated motivation", and "Introjected motivation" (Table 5). "Introjected motivation" also shows significant differences in the 14-17 and 22-35 age groups.
No differences were found in the indicators of psychological well-being and motivation for activity in young women and men of different age groups.
Correlation analysis demonstrates that the indicator of general well-being significantly correlates with all motivation indicators in young people keen on programming. In addition, there are many various statistically significant correlations between the indicators of motivation and psychological well-being (Table 6).
Psychological well-being | Motivation | |||||
---|---|---|---|---|---|---|
Intrinsic motivation | Integrated motivation | Identified motivation | Introjected motivation | Extrinsic motivation | Amotivation | |
General well-being | 0,348** | 0.367** | 0.284** | -0.156* | -0.281** | -0.284** |
Positive emotion | 0.199* | 0.239** | 0.172* | -0.168* | -0.229** | -0.181* |
Relationships | 0.157* | 0.206** | 0.090 | -0.085 | -0.121 | -0.030 |
Engagement | 0.388** | 0.396** | 0.330** | -0.145 | -0.257** | -0.287** |
Meaning | 0.464** | 0.465** | 0.429** | -0.112 | -.349** | -.461** |
Accomplishment | 0.457** | 0.444** | 0.362** | -0.101 | -0.233** | -0.368** |
Negative emotion | -0.250** | -0.185* | -0.077 | 0.344** | 0.419** | 0.361** |
Health | 0.293** | 0.291** | 0.264** | -0.014 | -0.140 | -0.104 |
Loneliness | -0.116 | -0.141 | -0.052 | 0.262** | 0.291** | 0.114 |
Happiness | 0.115 | 0.113 | 0.073 | -0.152 | -.195* | -0.121 |
Note: the table shows the values of Pearson's r
Most of the scores of autonomous motivation - intrinsic, integrated, and identified - positively correlate with general well-being and scores on the PERMA scales of "Positive emotion", "Relationships", "Engagement", "Meaning", and " Accomplishment". One exception is the lack of a significant correlation between identified motivation and the "Relationships" scale and correlations with the additional Loneliness and Happiness scales.
Correlations of the controlled motivation types, i.e. introjected and extrinsic motivation, are much weaker and negative (Table 6).
Table 7 presents results on the relationships between motivation for activity and psychological well-being in young people with interests in activities other than programming.
Psychological well-being | Motivation for activity | |||||
---|---|---|---|---|---|---|
Intrinsic motivation | Integrated motivation | Identified motivation | Introjected motivation | Extrinsic motivation | Amotivation | |
General well-being | 0.241* | 0.342** | 0.351** | -0.019 | -0.186 | -0.292** |
Positive emotion | 0.195 | 0.332** | 0.282* | -0.090 | -0.216 | -0.238* |
Relationships | 0.168 | 0.142 | 0.112 | -0.101 | -0.245* | -0.222* |
Engagement | 0.256* | 0.388** | 0.372** | 0.087 | -0.052 | -0.244* |
Meaning | 0.264* | 0.382** | 0.421** | 0.032 | -0.125 | -0.362** |
Accomplishment | 0.129 | 0.253* | 0.389** | 0.072 | -0.046 | -0.183 |
Negative emotion | -0.131 | -0.233* | -0.050 | 0.217 | 0.332** | 0.305** |
Health | 0.200 | 0.286** | 0.245* | -0.052 | -0.127 | -0.238* |
Loneliness | -0.228* | -0.082 | 0.030 | 0.424** | 0.542** | 0.473** |
Happiness | 0.205 | 0.275* | 0.271* | -0.053 | -0.215 | -0.222* |
Note: the table shows the values of Pearson's r
Table 7 demonstrates that intrinsic, integrated, and identified motivation (included in autonomous motivation) positively correlate with the integral indicator of well-being. In addition, positive correlations are found between these types of motivation and particular PERMA scales (e.g., "Engagement" and "Meaning").
Introjected and extrinsic motivation (belonging to controlled motivation) are not related to the integral indicator of well-being. Moreover, there are very few links between these types of motivation and the PERMA scales.
It is a fact that the specifics of motivation are defined by the features of personal and social factors in activity.
The reasons behind the lower values of autonomous motivation in young people who study are passionate about programming, in our view, can be explained by the characteristics and nature of this activity: the high difficulty of tasks, uncertainty in decisions, mediation of communications by artificial language sign systems, specificity of feedback, the obviousness of success/failure both in solving a particular task and in the activity as a whole.
Passion for programming (compared to other activities) may be more intertwined with reasons outside of the activity itself (prestige, demand, the popularity of programming, etc.). This assumption is supported by lower values of autonomous motivation in youth engaged in programming.
The two studied groups have similar correlations between intrinsic motivation and psychological well-being.
Positive correlations are found between the autonomous types of motivation and general well-being and other well-being parameters. Meanwhile, the group of people with interests in other activities shows less of such statistically significant correlations. This especially applies to intrinsic motivation, as general well-being has no correlations with the scales of "Positive emotion", "Relationships", and " Accomplishment", as well as the additional indicators "Negative emotion" and "Health". In addition, in the group of youth with interests outside of programming, there are fewer correlations between extrinsic motivation (activity performed for the sake of external reward or avoiding negative consequences) and the indicators of psychological well-being.
Despite the general trends of relationships between psychological well-being and motivation, a major part in the nature of these relationships is played by the content of the activity, its specifics, and its structure.
The presence of significant differences in the components of psychological well-being and various types of motivation in the group of 18-22-year-olds interested in programming versus other activities may be due to the greater representation of young men and women in it compared to other groups.
Conclusions
The conducted study gives insight into the characteristics of psychological well-being in young people interested in different activities.
Based on the obtained results, we can conclude that:
Interest in activity is indicative of the development of intrinsic motivation for the activity and the individual's psychological well-being.
Young people who are keen on programming have less pronounced intrinsic, integrated, and introjected motivation, compared to the development of these types of motivation in young people interested in other activities. This owes to the specificity of programming as an activity and the content of motives for it.
The level of psychological well-being of young people enthusiastic about programming is on par with the indicators typical of Russian youth in general.
Interrelations of motivation for activity in young people interested in programming versus other types of activities are similar in structure and orientation. These interrelations have some specific features in young people keen on programming, expressed in a greater number of statistically significant interrelations of psychological well-being indicators with intrinsic (positive correlations) and extrinsic (negative correlations) motivation.
The findings can be used to design and organize activities related to working with young people interested in programming.
The study was financially supported by the Russian Science Foundation (RSF) under the research project № 22-28-20262.