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Understanding Variables Affecting High Schools' Educational Outcomes Based on Self-Determination Theory

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Title: Understanding Variables Affecting High Schools' Educational Outcomes Based on Self-Determination Theory
Language: English
Authors: Dongsim Kim (ORCID 0000-0002-8044-4968)
Source: Journal of Advanced Academics. 2025 36(3):453-469.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 17
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: High Schools
Secondary Education
Descriptors: Outcomes of Education, Self Determination, High School Students, Instructional Design, Teaching Methods, Metacognition, Student Attitudes, Student School Relationship, Foreign Countries, Teacher Student Relationship, Parent Child Relationship, Vocational Maturity, Educational Improvement
Geographic Terms: South Korea
DOI: 10.1177/1932202X251321544
ISSN: 1932-202X
2162-9536
Abstract: Self-determination is an important motive that influences youth development outcomes. Self-determination theory emphasizes the relationship between social context, self-determination, and school educational outcomes. The purpose of this study is to examine the relationship between parental and teacher support, self-determination, career maturity, self-regulated learning, and satisfaction with school. For this study, 1,155 high school students in Korea completed surveys. The results of this study are the following. First, parental support and teacher support affect self-determination. Second, parental support, teacher support, and self-determination influence career maturity, self-regulated learning, and satisfaction with school. On the basis of these findings, this study has implications for the improvement of instructional design methods and management strategies.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1476968
Database: ERIC
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  Value: <anid>AN0186620141;[261p]01aug.25;2025Jul16.01:32;v2.2.500</anid> <title id="AN0186620141-1">Understanding Variables Affecting High Schools' Educational Outcomes Based on Self-Determination Theory </title> <p>Self-determination is an important motive that influences youth development outcomes. Self-determination theory emphasizes the relationship between social context, self-determination, and school educational outcomes. The purpose of this study is to examine the relationship between parental and teacher support, self-determination, career maturity, self-regulated learning, and satisfaction with school. For this study, 1,155 high school students in Korea completed surveys. The results of this study are the following. First, parental support and teacher support affect self-determination. Second, parental support, teacher support, and self-determination influence career maturity, self-regulated learning, and satisfaction with school. On the basis of these findings, this study has implications for the improvement of instructional design methods and management strategies.</p> <p>Keywords: parental support; teacher support; career maturity; self-regulated learning; satisfaction with school; self-determination; structural equation modeling (SME)</p> <hd id="AN0186620141-2">Introduction</hd> <p>High school education profoundly influences an individual's development, offering diverse learning opportunities that shape future success. In Korea, however, high school education largely functions as a preparatory stage for college entrance examinations, emphasizing cognitive and outcome-based measures such as academic achievement ([<reflink idref="bib7" id="ref1">7</reflink>]) and university admission ([<reflink idref="bib38" id="ref2">38</reflink>]; [<reflink idref="bib50" id="ref3">50</reflink>]). This exam-driven focus often limits students' ability to develop broader competencies critical for navigating a rapidly evolving world shaped by technological advancements, such as artificial intelligence (AI).</p> <p>Artificial intelligence is automating routine tasks while amplifying the demand for higher-order thinking, adaptability, and lifelong learning ([<reflink idref="bib49" id="ref4">49</reflink>]). To prepare students for this landscape, education systems must cultivate competencies beyond cognitive skills, including career maturity (CM), self-regulated learning (SRL), and satisfaction with school (SS). Career maturity enables adolescents to make informed career decisions and adapt to shifting professional landscapes shaped by AI-driven innovation ([<reflink idref="bib35" id="ref5">35</reflink>]). Self-regulated learning, a cornerstone of personalized education, equips students with the skills to manage their learning independently, a key requirement for future academic and professional success ([<reflink idref="bib12" id="ref6">12</reflink>]). Satisfaction with school, often overlooked, is essential for students' subjective well-being and serves as a comprehensive measure of positive educational experiences ([<reflink idref="bib1" id="ref7">1</reflink>]).</p> <p>Despite the importance of these competencies, Korea's achievement-focused system often hinders its development. This study seeks to address this gap by examining the relationships between CM, SRL, and school satisfaction, alongside the factors that support them. By shifting the Korean education system toward fostering holistic student development, this research aims to inform strategies for more inclusive and comprehensive educational outcomes.</p> <p>The study applies Self-Determination Theory (SDT) to explore the role of motivation in shaping these outcomes. Self-Determination Theory posits that motivation is not a fixed trait but a dynamic state shaped by the interplay of personal interests, social context, and the nature of tasks. A supportive social context can enhance motivation, which in turn positively impacts educational outcomes ([<reflink idref="bib47" id="ref8">47</reflink>]). By investigating how social and motivational factors influence key competencies, this research aims to contribute actionable insights for creating educational environments that support students' holistic growth and prepare them for a dynamic future.</p> <hd id="AN0186620141-3">Theoretical Background</hd> <p></p> <hd id="AN0186620141-4">Self-Determination Theory</hd> <p>Self-Determination Theory, which treats human autonomous motivational regulations, is a general motivational theory empirically supported by existing research ([<reflink idref="bib11" id="ref9">11</reflink>]). Many studies have been carried out to clarify the variables affecting self-determination (SD). In particular, [<reflink idref="bib48" id="ref10">48</reflink>] propose a SD model about these relationships in education (see Figure 1). In this model, an autonomy-supportive educational climate did not directly affect educational outcomes, but several studies ([<reflink idref="bib6" id="ref11">6</reflink>]; [<reflink idref="bib19" id="ref12">19</reflink>]) confirm that social context affects educational outcomes. Building on this foundational model, I propose a streamlined version (see Figure 2) that focuses on the key components of the relationship: social context, SD, and educational outcomes.</p> <p>Graph: Figure 1. Self-determination model applied to education. [<reflink idref="bib48" id="ref13">48</reflink>] p. 304.</p> <p>Graph: Figure 2. Self-determination model in this research.</p> <p>High-school education is a critical period for the development of autonomy, making SD an essential focus of educational research ([<reflink idref="bib10" id="ref14">10</reflink>]). In this study, I examine the key mechanisms through which social context influences educational outcomes, with SD serving as a mediator. By focusing on these essential components, this model highlights the foundational relationships necessary to understand the role of autonomy in educational settings.</p> <hd id="AN0186620141-5">School Educational Outcomes</hd> <p>This study focuses on CM, SRL, and SS as outcomes of high school education. First, CM, which is emphasized in high school, is an important prerequisite for individuals to make appropriate and accurate career decisions ([<reflink idref="bib33" id="ref15">33</reflink>]). Students with a high level of CM will make wise career choices and have less confusion about career choices. Second, SRL is a process that helps students manage their thoughts, behaviors, and emotions in order to successfully navigate their learning experiences ([<reflink idref="bib36" id="ref16">36</reflink>]). A twenty-first century student skill is SRL. Third, SS has been defined as a cognitive-affective assessment of the overall level of satisfaction with one's experiences at school ([<reflink idref="bib17" id="ref17">17</reflink>]). For adolescents, who have to spend most of their daily life in school, SS relates to life satisfaction, which has to be watched carefully, since it can influence the whole life of adolescents.</p> <hd id="AN0186620141-6">Social Context</hd> <p>This study focuses on Parental Support (PS) and Teacher Support (TS) as social context. Parental Support refers to the emotional, academic, and school-life-related support provided by parents, which students recognize as having a significant influence on their educational outcomes ([<reflink idref="bib46" id="ref18">46</reflink>]). Especially, PS is gaining traction as a representative factor influencing school educational outcomes. Moreover, PS in the socioeconomic dimension has been reported to have a strong influence on educational outcomes ([<reflink idref="bib24" id="ref19">24</reflink>]). Even though the socioeconomic dimension of PS cannot be changed easily, PS of the emotional dimension can be changed by education. Therefore, the focus of this study will be on the emotional dimension of PS. Furthermore, multidimensional TS, which includes emotional, informational, and physical support related to school life, is also considered as one of the representative factors affecting SD ([<reflink idref="bib5" id="ref20">5</reflink>]). Strong TS can increase students' motivation, but students' motivation will not be provoked well if TS is low, so teachers should always be interested in school education.</p> <hd id="AN0186620141-7">Relationship Between Variables</hd> <p>Numerous studies have explored the relationships between PS, TS, SD, and educational outcomes. Parental Support and TS have consistently been associated with SD ([<reflink idref="bib25" id="ref21">25</reflink>]; [<reflink idref="bib40" id="ref22">40</reflink>]; [<reflink idref="bib8" id="ref23">8</reflink>]), CM ([<reflink idref="bib31" id="ref24">31</reflink>]; [<reflink idref="bib41" id="ref25">41</reflink>]; [<reflink idref="bib20" id="ref26">20</reflink>]), SRL ([<reflink idref="bib9" id="ref27">9</reflink>]; [<reflink idref="bib18" id="ref28">18</reflink>]), and SS ([<reflink idref="bib23" id="ref29">23</reflink>]; [<reflink idref="bib42" id="ref30">42</reflink>]). These findings highlight the critical role of social contexts in shaping students' educational outcomes across diverse settings.</p> <p>Self-determination has also been shown to mediate the relationship between social contexts (PS and TS) and educational outcomes, including CM, SRL, and SS ([<reflink idref="bib30" id="ref31">30</reflink>]; [<reflink idref="bib22" id="ref32">22</reflink>]; [<reflink idref="bib43" id="ref33">43</reflink>]). However, prior studies have primarily examined these relationships in isolation, without integrating them into a comprehensive model.</p> <p>To address this gap, this study investigates the structural relationships among PS, TS, SD, and key educational outcomes (CM, SRL, and SS) within the framework of SDT. This approach aims to provide a holistic understanding of how social and motivational factors interact to influence high school students' educational experiences and outcomes.</p> <hd id="AN0186620141-8">Current Study and Hypotheses</hd> <p>This study aims to build on prior research by examining the integrated relationships among PS, TS, SD, CM, SRL, and SS. Based on SDT, I hypothesize that social contexts (PS and TS) are positively associated with SD, which in turn is linked to educational outcomes (CM, SRL, and SS). The following hypotheses were tested using this hypothetical model derived from the literature review (see Figure 3).</p> <p>Graph: Figure 3. Research hypotheses.</p> <p>The following hypothesis is proposed:</p> <p></p> <ulist> <item> Hypothesis 1. Parental Support and TS are associated with high-school students' SD.</item> <p></p> <item> Hypothesis 2. Parental Support, TS, and SD are associated with high-school students' CM, SRL, and SS.</item> </ulist> <hd id="AN0186620141-9">Method</hd> <p></p> <hd id="AN0186620141-10">Participants and Procedure</hd> <p>Paper-based surveys were distributed to 11th-grade students across eight high schools in South Korea to assess PS, TS, SD, CM, SRL, and SS. To facilitate the survey process, the researcher provided homeroom teachers with detailed explanations of the study's purpose and survey items. The homeroom teachers distributed the surveys during class and collected the completed forms, ensuring a high response rate and improving the students' understanding of the content.</p> <p>Participants were recruited using convenience sampling due to the researcher's access to the student cohort and their willingness to participate. The surveys targeted second-year high school students (11th graders), as they are more acclimated to the school environment compared to 10th graders, who are still adjusting to high school life, and 12th graders, who are primarily focused on university entrance exams.</p> <p>The sample comprised students from eight high schools located in the Seoul metropolitan area, including six schools in Seoul and two in Gyeonggi Province. The schools included a mix of public (three) and private (five) institutions, with two all-girls schools and six coeducational schools. By school type, four general high schools were located in Seoul (two public and two private), while the four specialized high schools were evenly distributed between Seoul and Gyeonggi Province (one public and three private).</p> <p>A total of 1,245 students were invited to participate, and 1,155 students completed the survey, yielding a response rate of 92.77%. After excluding 14 participants due to significant missing data (over 50% of responses), the final sample consisted of 1,141 students. This included 851 students (74.56%) from general high schools and 290 students (25.44%) from specialized high schools. Reflecting the overall gender distribution in the participating schools, the sample included more females (<reflink idref="bib915" id="ref34">915</reflink>, 80.19%) than males (<reflink idref="bib226" id="ref35">226</reflink>, 19.80%).</p> <hd id="AN0186620141-11">Measures</hd> <p>To better align the instruments with the goals and context of this study, six instruments were revised and adapted. The modifications primarily addressed redundancy in the interpretation process, ensuring that items accurately captured the constructs without unnecessary overlap. These revisions involved rephrasing certain items for clarity and cultural relevance, as well as removing or combining conceptually repetitive items. The adaptation process was rigorously reviewed by a professor in education and two English–Korean experts to ensure the validity of the modified instruments.</p> <p>All instruments utilized a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), providing consistency across the measures. For SD, a scoring method based on the Relative Autonomy Index was applied to calculate a composite score reflecting the degree of autonomy in students' motivation. This method captures the relative balance of controlled versus autonomous motivation ([<reflink idref="bib15" id="ref36">15</reflink>]).</p> <p>The formula used is as follows: <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi mathvariant="normal">RAI</mi></mrow><mo>=</mo><mo>−</mo><mn>2</mn><mo stretchy="false">(</mo><mrow><mrow><mi mathvariant="normal">external</mi></mrow></mrow><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">(</mo><mrow><mrow><mi mathvariant="normal">introjected</mi></mrow></mrow><mo stretchy="false">)</mo><mo>+</mo><mn>1</mn><mo stretchy="false">(</mo><mrow><mrow><mi mathvariant="normal">identified</mi></mrow></mrow><mo stretchy="false">)</mo><mo>+</mo><mn>2</mn><mo stretchy="false">(</mo><mrow><mrow><mi mathvariant="normal">intrinsic</mi></mrow></mrow><mo stretchy="false">)</mo></math> </ephtml></p> <p>Graph</p> <p>The instruments for measuring the other variables were adapted from established sources. Parental Support was measured using six items from [<reflink idref="bib3" id="ref37">3</reflink>], while TS was assessed using four items from [<reflink idref="bib44" id="ref38">44</reflink>]. SD was measured with 24 items from [<reflink idref="bib21" id="ref39">21</reflink>], reflecting its multidimensional nature. CM was assessed with ten items from the [<reflink idref="bib34" id="ref40">34</reflink>], SRL with eight items from [<reflink idref="bib4" id="ref41">4</reflink>], and SS with eight items from [<reflink idref="bib13" id="ref42">13</reflink>].</p> <p>Details regarding the original sources, the number of items used after revision, and the recalculated Cronbach's alpha values are presented in Table 1. The recalculated Cronbach's alpha values for all instruments exceeded.88, demonstrating strong internal consistency despite the modifications.</p> <p>Table 1. Measurement Scales.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="left" /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" rowspan="2">Variables</th><th align="left" rowspan="2">Source</th><th align="left" rowspan="2">Sample</th><th align="left" rowspan="2">Items</th><th align="left" colspan="2">Reliability</th></tr><tr><th align="left">Original</th><th align="left">Current</th></tr></thead><tbody><tr><td>Parental support (PS)</td><td><xref ref-type="bibr" rid="bibr3">Bandura (2006)</xref></td><td>My parents helps me to get good grades at school.</td><td>6</td><td>.76</td><td>.88</td></tr><tr><td>Teacher support (TS)</td><td><xref ref-type="bibr" rid="bibr44">Torsheim, Wold & Samdal (2000)</xref></td><td>My teachers are interested in me as a person.</td><td>4</td><td>.81</td><td>.90</td></tr><tr><td>Self-determination (SD)</td><td><xref ref-type="bibr" rid="bibr21">Kim (2010)</xref></td><td>I study because my parents give me a prize.</td><td>24</td><td>.77</td><td>.90</td></tr><tr><td>Career maturity (CM)</td><td><xref ref-type="bibr" rid="bibr34">National Youth Policy Institute (2009)</xref></td><td>I made a clear decision about my career.</td><td>10</td><td>.77</td><td>.88</td></tr><tr><td>Self-regulated learning (SRL)</td><td><xref ref-type="bibr" rid="bibr4">Bong et al. (2012)</xref></td><td>I choose the appropriate learning method according to the learning situation.</td><td>8</td><td>.89</td><td>.91</td></tr><tr><td>Satisfaction with school (SS)</td><td><xref ref-type="bibr" rid="bibr13">Gilman, Huebner & Laughlin (2000)</xref></td><td>I like to go to school.</td><td>8</td><td>.91</td><td>.95</td></tr><tr><td /><td /><td /><td align="center" colspan="3">Total 60 items</td></tr></tbody></table> </ephtml> </p> <hd id="AN0186620141-12">Statistical Analysis</hd> <p>First, I conducted exploratory factor analysis with the collected data. The result showed that all latent variables (PS, TS, CM, SRL, and SS) were proved to be a single factor.</p> <p>I employed item parceling to prevent excessive weighting on the measurement model and improve estimation efficiency. Item parceling involves grouping multiple items into composite indicators by using their total or average scores ([<reflink idref="bib26" id="ref43">26</reflink>]). This approach reduces the number of indicator variables for each latent variable, minimizes estimation error, and helps meet the multivariate normality assumption in structural equation modeling ([<reflink idref="bib39" id="ref44">39</reflink>]). For instance, the six items measuring PS were divided into two parcels, "PS A" and "PS B," by averaging three items per parcel.</p> <p>Second, I calculated the mean, standard deviation, kurtosis, and skewedness of each measurement variable through statistical analyses. Multivariate normality can be satisfied when the magnitude of skewedness is less than 3 and kurtosis is less than 10 ([<reflink idref="bib27" id="ref45">27</reflink>]). The results showed that the collected data satisfied the assumption of multivariate normal distribution. In addition, correlation analysis was conducted to find out whether there were any significant correlations among the examined variables.</p> <p>Third, confirmatory factor analysis was conducted to examine how well the measured variables represent the latent variables. Each variable is considered to have a convergent validity when it maintains a high variance ratio. Convergent validity is secured when confirmatory factor analysis shows that a correlation coefficient between the latent variable and the measured variable is higher than.50 ([<reflink idref="bib16" id="ref46">16</reflink>]). In addition, when a correlation between exogenous variables is less than 0.80, each variable is considered to be not contaminated by measured variables and has discriminant validity.</p> <p>Fourth, the validation of the structural model was conducted to examine relationships between the variables set in this study. The fitness values of the model were estimated by the Maximum Likelihood Estimation method. To evaluate the model fit, I used the RMSEA, the TLI, and the CFI. Values smaller than.08 for RMSEA indicate an acceptable fit. For the rest of the indices, values greater than.90 indicate a good fit.</p> <hd id="AN0186620141-13">Results</hd> <p></p> <hd id="AN0186620141-14">Descriptive Statistics and Correlations among the Variables</hd> <p>In the structural equation model, when each measured variable does not have a normal distribution, it is not possible to accurately verify statistical significance because of the distorted estimation. I checked the mean, standard deviation, skewedness, and kurtosis of each item parcel to confirm the multivariate normal distribution. The means ranged from 2.43 to 3.72, with the standard deviations of.70 to 3.13. In addition, the means, standard deviations, skewness, and kurtosis for all the measured variables were analyzed together to check the normality assumption. According to [<reflink idref="bib27" id="ref47">27</reflink>], absolute skewness values less than 3 and absolute kurtosis values less than 10 meet the assumption of the multivariate normal distribution of the data for structural equation modeling. Correlations were also examined to check the strength of the relationships between the variables of interest, and the results revealed significant correlations between all the variables at the alpha level of.05 (see Table 2).</p> <p>Table 2. Descriptive Statistics and Correlation Coefficients.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Variables</th><th align="left">1</th><th align="left">2</th><th align="left">3</th><th align="left">4</th><th align="left">5</th><th align="left">6</th><th align="left">7</th><th align="left">8</th><th align="left">9</th><th align="left">10</th><th align="left">11</th></tr></thead><tbody><tr><td>1. Parental support A</td><td>–</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>2. Parental support B</td><td>.75*</td><td>–</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>3. Teacher support A</td><td>.46*</td><td>.44*</td><td>–</td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>4. Teacher support B</td><td>.45*</td><td>.47*</td><td>.82*</td><td>–</td><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>5. Self-determination</td><td>.30*</td><td>.28*</td><td>.25*</td><td>.30*</td><td>–</td><td /><td /><td /><td /><td /><td /></tr><tr><td>6. Career maturity A</td><td>.34*</td><td>.35*</td><td>.26*</td><td>.28*</td><td>.31*</td><td>–</td><td /><td /><td /><td /><td /></tr><tr><td>7. Career maturity B</td><td>.31*</td><td>.33*</td><td>.25*</td><td>.29*</td><td>.34*</td><td>.87*</td><td>–</td><td /><td /><td /><td /></tr><tr><td>8. Self-regulated A</td><td>.41*</td><td>.41*</td><td>.29*</td><td>.34*</td><td>.40*</td><td>.36*</td><td>.38*</td><td>–</td><td /><td /><td /></tr><tr><td>9. Self-regulated A</td><td>.41*</td><td>.41*</td><td>.33*</td><td>.36*</td><td>.42*</td><td>.38*</td><td>.39*</td><td>.91*</td><td>–</td><td /><td /></tr><tr><td>10. Satisfaction with school A</td><td>.45*</td><td>.41*</td><td>.53*</td><td>.51*</td><td>.39*</td><td>.32*</td><td>.30*</td><td>.37*</td><td>.39*</td><td>–</td><td /></tr><tr><td>11. Satisfaction with school B</td><td>.42*</td><td>.40*</td><td>.50*</td><td>.49*</td><td>.36*</td><td>.32*</td><td>.32*</td><td>.35*</td><td>.36*</td><td>.92*</td><td>–</td></tr><tr><td><italic>M</italic></td><td>3.43</td><td>3.44</td><td>3.54</td><td>3.66</td><td>2.43</td><td>3.61</td><td>3.72</td><td>3.18</td><td>3.19</td><td>3.28</td><td>3.36</td></tr><tr><td><italic>SD</italic></td><td>.88</td><td>.81</td><td>.90</td><td>.83</td><td>3.13</td><td>.73</td><td>.70</td><td>.74</td><td>.74</td><td>.92</td><td>.92</td></tr><tr><td>Skewness</td><td>−.44</td><td>−.20</td><td>−.33</td><td>−.19</td><td>.43</td><td>−.09</td><td>−.21</td><td>.01</td><td>−.02</td><td>−.15</td><td>−.27</td></tr><tr><td>Kurtosis</td><td>.31</td><td>.28</td><td>.00</td><td>.04</td><td>.59</td><td>−.21</td><td>.00</td><td>.33</td><td>.27</td><td>−.03</td><td>.03</td></tr></tbody></table> </ephtml> </p> <hd id="AN0186620141-15">Assessment of Measurement Model</hd> <p>Based on the result of maximum likelihood estimation, Table 3 shows the goodness of fit indices for the a priori measurement model, indicating that the measurement model exhibited a good fit with the data collected.</p> <p>Table 3. Fitness Estimation Results of the Measurement and Structural Model.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="left" /></colgroup><thead><tr><th align="left" /><th align="left">CMIN</th><th align="left">p</th><th align="left">df</th><th align="left">TLI</th><th align="left">CFI</th><th align="left">RMSEA (90%)</th></tr></thead><tbody><tr><td>Measurement model</td><td>70.242</td><td>.000</td><td>25</td><td>.991</td><td>.995</td><td>.040 (.029–.051)</td></tr><tr><td>Structural model</td><td>137.120</td><td>.000</td><td>33</td><td>.982</td><td>.989</td><td>.053 (.044–.062)</td></tr><tr><td>Criteria</td><td /><td /><td /><td>>.900</td><td>>.950</td><td><.080</td></tr></tbody></table> </ephtml> </p> <p>As shown in Figure 4, the factor loadings of the measured variables ranged from.86 to.98 and were significant at.05, indicating sufficient convergent validity. The correlation coefficients between all exogenous variables were below.80. This confirms that there is sufficient discriminant validity among the latent variables ([<reflink idref="bib16" id="ref48">16</reflink>]).</p> <p>Graph: Figure 4. Confirmatory factor analysis.</p> <hd id="AN0186620141-16">Structural Model</hd> <p>In order to test the structural model, the proposed relationships between the variables were analyzed, and the structural model showed a good fit to the study data with TLI of.982, CFI.989, and RMSEA.053 (see Table 3).</p> <p>For the first research question, the direct effects of PS and TS on SD were examined by reviewing the beta weights. As Figure 5 shows, all the direct effects were significant. For the second research question, the direct effects of PS, TS, and SD on the CM, SRL, and SS were examined. All the direct effects were significant. Since PS and TS affect SD, and then SD influences CM, SRL, and SS, the significance of indirect effect was confirmed through the procedure of bootstrapping. It was found that PS and TS had a significant indirect effect on CM, SRL, and SS through SD. Table 4 and Figure 5 show the direct and indirect effect decomposition of the structural model.</p> <p>Graph: Figure 5. Model with standardized path coefficients.</p> <p>Table 4. Coefficients of the Structural Model.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="left" /><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" colspan="3" rowspan="2">Path</th><th align="left" colspan="3">Unstandardized coefficients (B)</th><th align="left" colspan="3">Standardized coefficients (β)</th></tr><tr><th align="left">Total</th><th align="left">direct</th><th align="left">indirect</th><th align="left">Total</th><th align="left">direct</th><th align="left">indirect</th></tr></thead><tbody><tr><td rowspan="2">Self-determination</td><td>←</td><td>Parental support</td><td>1.112*</td><td>1.112*</td><td>.000</td><td>.247*</td><td>.247*</td><td>.000</td></tr><tr><td>←</td><td>Teacher support</td><td>.660*</td><td>.660*</td><td>.000</td><td>.160*</td><td>.160*</td><td>.000</td></tr><tr><td rowspan="3">Career maturity</td><td>←</td><td>Parental support</td><td>.361*</td><td>.307*</td><td>.054*</td><td>.368*</td><td>.313*</td><td>.055*</td></tr><tr><td>←</td><td>Teacher support</td><td>.101*</td><td>.069*</td><td>.032*</td><td>.112*</td><td>.076*</td><td>.036*</td></tr><tr><td>←</td><td>Self-determination</td><td>.049*</td><td>.049*</td><td>.000</td><td>.222*</td><td>.222*</td><td>.000</td></tr><tr><td rowspan="3">Self-regulated learning</td><td>←</td><td>Parental support</td><td>.427*</td><td>.357*</td><td>.070*</td><td>.426*</td><td>.356*</td><td>.070*</td></tr><tr><td>←</td><td>Teacher support</td><td>.134*</td><td>.092*</td><td>.042*</td><td>.146*</td><td>.100*</td><td>.046*</td></tr><tr><td>←</td><td>Self-determination</td><td>.063*</td><td>.063*</td><td>.000</td><td>.284*</td><td>.284*</td><td>.000</td></tr><tr><td rowspan="3">Satisfaction with school</td><td>←</td><td>Parental support</td><td>.352*</td><td>.287*</td><td>.065*</td><td>.269*</td><td>.219*</td><td>.050*</td></tr><tr><td>←</td><td>Teacher support</td><td>.515*</td><td>.476*</td><td>.039*</td><td>.430*</td><td>.398*</td><td>.032*</td></tr><tr><td>←</td><td>Self-determination</td><td>.058*</td><td>.058*</td><td>.000</td><td>.201*</td><td>.201</td><td>.000</td></tr></tbody></table> </ephtml> </p> <p>1 *<emph>p </emph><.05.</p> <hd id="AN0186620141-17">Discussion and Conclusion</hd> <p>This study examined the integrated relationships of PS, TS, and SD on school outcomes such as CM, SRL, and school satisfaction among high school students in South Korea. The following discussion and conclusions are based on the findings.</p> <p>First, PS and TS were significantly associated with SD. Consistent with prior studies, the findings confirm that PS positively influences SD ([<reflink idref="bib25" id="ref49">25</reflink>]; [<reflink idref="bib40" id="ref50">40</reflink>]). Therefore, fostering an educational environment that supports parental engagement may be beneficial for enhancing students' SD. This study focused not on socioeconomic PS, but instead on emotional PS, which can be sufficiently strengthened by parents' will and awareness. Therefore, schools should develop a strategy of promoting PS for improving educational outcomes. Indeed, PS Advisor has been piloted to increase PS in school education ([<reflink idref="bib32" id="ref51">32</reflink>]). Parental Support Advisor consistently delivered the students' attitude and behavior at the school to the parents so that they could be interested in the students. It also encouraged parents to participate in the school events. In Korea, there has been no school education policy for PS. However, the national government announced plans for revitalizing parents' education and is trying to expand parental education using admission briefing sessions, parental counseling week, and school violation prevention education. In order to accomplish this, schools need to establish a culture in which parents can participate in school events and should monitor and provide training programs in which parents can express their opinions on school education. Particularly, to increase interest and participation of the father, who is typically less interested in the education of the children than the mother, the father participation program, such as school volunteer, parental education, or father-son and -daughter camp should be expanded.</p> <p>Next, previous research on TS ([<reflink idref="bib8" id="ref52">8</reflink>]; [<reflink idref="bib37" id="ref53">37</reflink>]) found that TS had a static effect on SD. In this study, I found that TS had a significant effect on SD. Therefore, it can be concluded that a strategy to increase TS must be prepared in order to increase SD. Korean teachers' training participation rate is 91.9%, which is higher than the OECD average of 88.5% ([<reflink idref="bib28" id="ref54">28</reflink>]); however, much of it is supported at the national level and is supplier-centered training. In order to cultivate the professionalism of teachers, the education office recommends 60–80 h per year, but this is voluntary, not mandatory. Therefore, a teachers' training policy should be prepared to strengthen TS that affects the school educational outcomes.</p> <p>Second, in this study, PS, TS, and SD were significantly associated with the key school outcomes: CM, SRL, and SS. These findings align with previous research demonstrating the individual relationships among these variables ([<reflink idref="bib2" id="ref55">2</reflink>]; [<reflink idref="bib41" id="ref56">41</reflink>]; [<reflink idref="bib45" id="ref57">45</reflink>]). However, this study extends prior work by integrating these relationships within a comprehensive structural equation model, providing a holistic framework to confirm their interconnections within the broader construct of school outcomes. Using the abovementioned strategies to increase PS and TS, I can confirm that SD and school educational outcomes can be improved. SD was found to be a factor affecting school educational outcomes as well as PS and TS. Therefore, [<reflink idref="bib29" id="ref58">29</reflink>] 12 strategies are suggested to increase SD. Those are as follows: First, identify and nurture what students need and want. Second, have students' internal states guide their behavior. Third, encourage active participation. And so on. To prepare for the environment in which students can actively participate if school and teachers provide various communications and supports, then the student alone can maintain interest and effort toward the academy. The school and the teacher should try using various strategies for the students' improvement in school educational outcomes as the students' SD increases.</p> <p>This study has clarified the relationships between social context, SD, and school educational outcomes based on SDT. In addition, PS, TS, and SD had significant effects on high-school students' school educational outcomes. These outcomes suggest that high schools should manage students' PS, TS, and SD. Also, there are few studies on different school types, and studies on difference in its effects on school educational outcomes are even more limited. The difference in effects on school educational outcomes between general and vocational high school is identified through this study. The results suggest that it can help establish priorities for high-school management.</p> <p>Based on the current findings and limitations, the following suggestions are proposed for future research. First, while this study incorporated two key predictors (PS and TS), future research should explore additional factors, such as peer support and school climate, to provide a more comprehensive understanding of influences on educational outcomes. Second, the use of convenience sampling in this study limits the generalizability of the findings to all high school students in South Korea. Future studies could employ probability sampling methods to enhance the representativeness of the sample and produce more generalizable results. Third, this research focused on high school students; therefore, subsequent studies should extend the investigation to other educational levels, such as elementary or middle schools, to examine developmental differences. Fourth, while this study employed traditional fit indices to evaluate the structural equation model, I acknowledge that calculating effect sizes (ES) ([<reflink idref="bib14" id="ref59">14</reflink>]) could provide additional value for interpreting the practical significance of the findings. Future research could incorporate ES to enhance the applicability of results for practitioners. Lastly, as the sample was limited to South Korean high schools, further research across diverse cultural and regional contexts is necessary to validate and expand upon these findings, offering broader insights into educational outcomes globally.</p> <hd id="AN0186620141-18">Acknowledgments</hd> <p>This manuscript is based on the doctoral dissertation submitted to Ewha Womans University in partial fulfillment of the requirements for the Ph.D. degree.</p> <ref id="AN0186620141-19"> <title> References </title> <blist> <bibl id="bib1" idref="ref7" type="bt">1</bibl> <bibtext> Addae E. A., Kühner S., Lau M. 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Interdisciplinary Humanities and Communication Studies, 1(9), 1–4. https://doi.org/10.61173/mr9c2t95</bibtext> </blist> </ref> <ref id="AN0186620141-20"> <title> Footnotes </title> <blist> <bibtext> The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The author(s) received no financial support for the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> Dongsim Kim https://orcid.org/0000-0002-8044-4968</bibtext> </blist> </ref> <aug> <p>By Dongsim Kim</p> <p>Reported by Author</p> <p></p> <p>Dongsim Kim is a Professor at Hanshin University, South Korea. Her research focuses on educational technology, AI integrated teaching and learning, and technology-enhanced educational outcomes. She has a particular interest in self-determination theory, AI-driven instructional practices, and promoting student engagement in online learning environments.</p> </aug> <nolink nlid="nl1" bibid="bib38" firstref="ref2"></nolink> <nolink nlid="nl2" bibid="bib50" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib49" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib35" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib12" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib47" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib11" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib48" firstref="ref10"></nolink> <nolink nlid="nl9" bibid="bib19" firstref="ref12"></nolink> <nolink nlid="nl10" bibid="bib10" firstref="ref14"></nolink> <nolink nlid="nl11" bibid="bib33" firstref="ref15"></nolink> <nolink nlid="nl12" bibid="bib36" firstref="ref16"></nolink> <nolink nlid="nl13" bibid="bib17" firstref="ref17"></nolink> <nolink nlid="nl14" bibid="bib46" firstref="ref18"></nolink> <nolink nlid="nl15" bibid="bib24" firstref="ref19"></nolink> <nolink nlid="nl16" bibid="bib25" firstref="ref21"></nolink> <nolink nlid="nl17" bibid="bib40" firstref="ref22"></nolink> <nolink nlid="nl18" bibid="bib31" firstref="ref24"></nolink> <nolink nlid="nl19" bibid="bib41" firstref="ref25"></nolink> <nolink nlid="nl20" bibid="bib20" firstref="ref26"></nolink> <nolink nlid="nl21" bibid="bib18" firstref="ref28"></nolink> <nolink nlid="nl22" bibid="bib23" firstref="ref29"></nolink> <nolink nlid="nl23" bibid="bib42" firstref="ref30"></nolink> <nolink nlid="nl24" bibid="bib30" firstref="ref31"></nolink> <nolink nlid="nl25" bibid="bib22" firstref="ref32"></nolink> <nolink nlid="nl26" bibid="bib43" firstref="ref33"></nolink> <nolink nlid="nl27" bibid="bib915" firstref="ref34"></nolink> <nolink nlid="nl28" bibid="bib226" firstref="ref35"></nolink> <nolink nlid="nl29" bibid="bib15" firstref="ref36"></nolink> <nolink nlid="nl30" bibid="bib44" firstref="ref38"></nolink> <nolink nlid="nl31" bibid="bib21" firstref="ref39"></nolink> <nolink nlid="nl32" bibid="bib34" firstref="ref40"></nolink> <nolink nlid="nl33" bibid="bib13" firstref="ref42"></nolink> <nolink nlid="nl34" bibid="bib26" firstref="ref43"></nolink> <nolink nlid="nl35" bibid="bib39" firstref="ref44"></nolink> <nolink nlid="nl36" bibid="bib27" firstref="ref45"></nolink> <nolink nlid="nl37" bibid="bib16" firstref="ref46"></nolink> <nolink nlid="nl38" bibid="bib32" firstref="ref51"></nolink> <nolink nlid="nl39" bibid="bib37" firstref="ref53"></nolink> <nolink nlid="nl40" bibid="bib28" firstref="ref54"></nolink> <nolink nlid="nl41" bibid="bib45" firstref="ref57"></nolink> <nolink nlid="nl42" bibid="bib29" firstref="ref58"></nolink> <nolink nlid="nl43" bibid="bib14" firstref="ref59"></nolink>
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Understanding Variables Affecting High Schools' Educational Outcomes Based on Self-Determination Theory
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Dongsim+Kim%22">Dongsim Kim</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8044-4968">0000-0002-8044-4968</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Advanced+Academics%22"><i>Journal of Advanced Academics</i></searchLink>. 2025 36(3):453-469.
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  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 17
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22High+Schools%22">High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Outcomes+of+Education%22">Outcomes of Education</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Determination%22">Self Determination</searchLink><br /><searchLink fieldCode="DE" term="%22High+School+Students%22">High School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Design%22">Instructional Design</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Metacognition%22">Metacognition</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Student+School+Relationship%22">Student School Relationship</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Student+Relationship%22">Teacher Student Relationship</searchLink><br /><searchLink fieldCode="DE" term="%22Parent+Child+Relationship%22">Parent Child Relationship</searchLink><br /><searchLink fieldCode="DE" term="%22Vocational+Maturity%22">Vocational Maturity</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Improvement%22">Educational Improvement</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22South+Korea%22">South Korea</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/1932202X251321544
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1932-202X<br />2162-9536
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Self-determination is an important motive that influences youth development outcomes. Self-determination theory emphasizes the relationship between social context, self-determination, and school educational outcomes. The purpose of this study is to examine the relationship between parental and teacher support, self-determination, career maturity, self-regulated learning, and satisfaction with school. For this study, 1,155 high school students in Korea completed surveys. The results of this study are the following. First, parental support and teacher support affect self-determination. Second, parental support, teacher support, and self-determination influence career maturity, self-regulated learning, and satisfaction with school. On the basis of these findings, this study has implications for the improvement of instructional design methods and management strategies.
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  Data: As Provided
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  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1476968
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      – Type: doi
        Value: 10.1177/1932202X251321544
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 453
    Subjects:
      – SubjectFull: Outcomes of Education
        Type: general
      – SubjectFull: Self Determination
        Type: general
      – SubjectFull: High School Students
        Type: general
      – SubjectFull: Instructional Design
        Type: general
      – SubjectFull: Teaching Methods
        Type: general
      – SubjectFull: Metacognition
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: Student School Relationship
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Teacher Student Relationship
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      – SubjectFull: Parent Child Relationship
        Type: general
      – SubjectFull: Vocational Maturity
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      – SubjectFull: Educational Improvement
        Type: general
      – SubjectFull: South Korea
        Type: general
    Titles:
      – TitleFull: Understanding Variables Affecting High Schools' Educational Outcomes Based on Self-Determination Theory
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            NameFull: Dongsim Kim
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            – D: 01
              M: 08
              Type: published
              Y: 2025
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            – TitleFull: Journal of Advanced Academics
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