Background and Research Question

Motivation plays a fundamental role in student learning and achievement. However, research consistently highlights a gradual decline in motivation as students progress through the school system (Gnambs & Hanfstingl, 2016; Raufelder et al., 2022). Understanding how to sustain motivation is essential for effective educational practices. Self-Determination Theory (SDT; Ryan & Deci, 2017) provides a framework for examining how motivation is influenced by the satisfaction of three basic psychological needs: autonomy, competence, and relatedness. Environments that support these needs foster self-determined motivation, leading to higher engagement, well-being, and academic success.

Self-directed learning (SDL) is often promoted as a means to foster autonomy and self-regulation. However, the transition from teacher-directed instruction (TDI) to SDL introduces challenges, particularly for younger students or those with limited self-regulation skills. While SDL encourages greater personal investment in learning, it requires scaffolding to ensure competence support (Reeve & Cheon, 2024). Conversely, TDI provides clear structure and guidance but can promote external regulation, reducing intrinsic motivation over time. An open question remains: How do students' motivational profiles shift when transitioning between SDL and TDI phases?

This study investigates motivational profiles and transitions in an innovative curriculum that integrates SDL and TDI. It examines:

1. Which motivational profiles emerge in this instructional setting,
2. How students transition between motivational profiles across SDL and TDI,
3. The role of psychological need satisfaction and age in shaping these transitions.

Theoretical Framework

SDT suggests that learning environments should balance autonomy-supportive teaching with competence-affirming structure. Prior research has identified distinct motivational profiles, such as high-quality (intrinsic and identified motivation), high-quantity (intrinsic and extrinsic motivation), and low-quality (controlled motivation)(Vansteenkiste et al., 2009; Wormington et al., 2012). However, most studies focus on static, single-context designswithout examining how motivational shifts occur dynamically across SDL and TDI phases.

Additionally, age is a key factor in motivational development, yet its impact on motivational profile transitionsremains underexplored. Research suggests that younger students begin with higher intrinsic motivation, but this declines as academic pressures increase (Liu et al., 2009; Wormington et al., 2012). Older students often transition toward high-quantity motivation, balancing self-determined and externally regulated motivation. By studying how motivation evolves across multiple instructional contexts and age groups, this research provides critical insights into designing innovative learning environments that sustain self-determined motivation over time.

Research Contribution and Relevance

This study contributes to both theory and practice by:

1. Examining motivational profiles within a dynamic SDL-TDI curriculum, addressing gaps in research on instructional context effects.
2. Integrating the fulfillment of psychological needs as predictors of motivational shifts, extending SDT applications in educational research.
3. Considering age-related motivational shifts, recognizing that older students may increase in high-quantity motivation, whereas younger students struggle more with SDL’s autonomy demands.

By bridging motivational research with instructional design, this study informs educators and policymakers on how to structure SDL-TDI transitions to maximize student engagement and self-regulation. Findings have practical implications for designing innovative curricula that optimize student motivation and long-term academic success.

Participants and Procedure This study analyzed questionnaire data from 754 students (M_Age = 13.56, SD = 1.2, 49.7% female) in grades 6–9 across six randomly selected mixed-track schools in rural areas. A random sampling strategy ensured diverse representation, and only students experiencing self-directed learning (SDL) intervals for the first time were included. Schools were selected based on their agreement to implement the SDL framework, ensuring consistency across instructional contexts. The SDL framework, introduced during the study, allowed students to take a one-week academic break each semester to explore self-selected research inquiries within a subject of their choice, anchoring SDL in familiar academic domains while fostering autonomy. The sample’s broad grade range allowed for the examination of age-related motivational shifts. Younger students, typically higher in intrinsic motivation, faced greater self-regulation challenges during SDL, whereas older students, more experienced in self-directed learning, showed a tendency toward high-quantity motivation, possibly reflecting increasing familiarity with performance-based expectations in teacher-directed instruction (TDI), where SDL outcomes were later evaluated. Measurement Instruments Students completed a validated questionnaire including: An adapted Academic Self-Regulation Questionnaire (SRQ-A; Ryan & Connell, adapted by Müller et al., 2007) to measure motivational regulation, and The Support of Basic Needs Scales for Adolescent Students (Müller & Thomas, 2011) to assess autonomy, competence, and relatedness satisfaction. Both instruments used a 4-point Likert scale (1 = "not true at all" to 4 = "completely true"). Latent Profile and Latent Transition Analysis Latent Profile Analysis (LPA) was conducted at four time points to classify students into motivational profiles based on item-response patterns. Model selection followed a stepwise approach, assessing solutions with 1 to 6 profiles using AIC, BIC, entropy, and interpretability criteria. Profiles were determined using composite scores for intrinsic, identified, introjected, and extrinsic motivation, alongside autonomy support, competence, and relatedness. Latent Transition Analysis (LTA) examined motivational profile shifts across TDI and SDL phases. Transitions were analyzed using autoregressive models, estimating profile-specific probabilities and stability rates across time points (T1 → T2, T2 → T3, T3 → T4). Odds Ratio Analysis To examine predictors of profile membership, logistic regression models were applied, estimating odds ratios (ORs) with 95% confidence intervals. Higher ORs (>1) indicated a greater likelihood of profile affiliation based on age and need satisfaction, while lower ORs (<1) suggested a reduced likelihood.

Results and Conclusion (300 words) Results Latent Profile Analysis (LPA) identified five motivational profiles across time points: high-quality, high-quantity, low-quality, low-quantity, and moderately motivated. Profile distributions varied between self-directed learning (SDL) and teacher-directed instruction (TDI) phases, with more students in positive motivational profiles during SDL. Latent Transition Analysis (LTA) revealed substantial profile shifts across instructional contexts. Positive transitions, such as from low-quantity to moderately motivated, were more frequent during SDL, whereas negative shifts, such as from high-quality to high-quantity, were more common in TDI phases. Older students exhibited a greater tendency toward high-quantity motivation over time, particularly after SDL phases, suggesting increased performance orientation. Odds ratio analysis confirmed age and psychological need fulfillment as significant predictors of profile membership. Older students were more likely to belong to the high-quantity profile in later phases, whereas younger students showed higher probabilities of low-quality and low-quantity motivation, especially during initial SDL exposure. Full odds ratios and transition probabilities are presented in Tables 9a to 9d in the Appendix. Conclusion Findings highlight the dynamic nature of motivation across SDL and TDI, demonstrating that self-directed learning supports positive motivational shifts, but teacher-directed instruction may reinforce performance-driven motivation. The age-related transition toward high-quantity motivation suggests that older students increasingly focus on external performance indicators, particularly when SDL outcomes are evaluated in TDI. To sustain high-quality motivation, instructional designs should balance autonomy support with structured competence development, particularly for younger students who struggle with self-regulation. Additionally, minimizing extrinsic performance pressures in TDI could help prevent a shift toward controlled motivation among older students. These results provide important implications for educational policy and curriculum development, emphasizing the need for structured, autonomy-supportive learning environments that integrate SDL effectively to maintain self-determined motivation over time.

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