Development and Standardization of the Strengths and Difficulties Self‑Perception Scale for Adolescent Students (StreDi‑SPeS)

Abstract

Motivation and attributional beliefs are critical for adolescents’ academic participation and performance, yet validated tools capturing these constructs are limited in occupational therapy contexts. This study aimed to develop and standardize the Strengths and Difficulties Self-Perception Scale for Adolescent Students (StreDi-SPeS). A cross-sectional design was used with 539 adolescents (ages 13–18) in Cyprus. Psychometric evaluation included item-total correlations, exploratory factor analysis, and reliability testing. Analyses supported a four-factor structure explaining 40.3% of variance. Internal consistency was acceptable for the overall scale (α = .814) and three factors, with one factor requiring refinement. The StreDi-SPeS demonstrates preliminary validity and reliability as a measure of adolescents’ attributional and motivational self-perceptions. It offers occupational therapy practitioners and researchers a tool for assessing academic participation and guiding interventions.

Plain Language Summary

Development of a new self-assessment scale to help adolescent students understand their own perceptions about their strengths and difficulties in school, focusing on motivation and beliefs about success and failure.

Adolescents often face challenges in school, such as feeling unmotivated, stressed, or unsure about their abilities. These difficulties can affect their grades, confidence, and willingness to participate in learning and academic life. Understanding how young people think about their successes and difficulties is important for supporting them in school and everyday life. This study developed a new questionnaire called the Strengths and Difficulties Self-Perception Scale for Adolescent Students (StreDi-SPeS). The questionnaire asks students about their experiences with schoolwork, motivation, and feelings about success or failure. It is based on Attribution Theory, which explains how people’s beliefs about the causes of success or failure influence their emotions, motivation, and future behavior. We tested the questionnaire with more than 500 students aged from 13 to 18 in Cyprus. The results showed that the questions were grouped into four main areas:

(a) Time management and organization (e.g., procrastination)

(b) Cognitive barriers (e.g., attention or memory difficulties)

(d) Positive engagement and self-regulation (e.g., following schedules, staying motivated)

Overall, the questionnaire was reliable and valid, meaning it measures what it is supposed to measure. One of the four areas needs further improvement, but the tool already provides useful insights. The StreDi-SPeS can help occupational therapists, teachers, and counselors identify students who may be struggling with motivation or confidence. By understanding these patterns, professionals can design better support strategies to improve participation, resilience, and academic success.

Keywords

academic participation motivation occupational therapy attribution theory

Introduction

Advancing academic competencies and cognitive functions is essential for an individual’s overall development (Peng & Kievit, 2020). Academic performance is a critical indicator of adolescents’ educational attainment and reflects their capacity to learn and thrive (Horanicova et al., 2024). Active engagement in school activities, a strong sense of connection and belonging, and intrinsic motivation are fundamental prerequisites for achieving satisfactory academic outcomes (Horanicova et al., 2024). Motivation and self-regulated learning strategies have emerged as pivotal determinants of academic success (Katsantonis & McLellan, 2023).

Academic performance of students is a central concern in the field of education, with profound implications for their future success and societal well-being (Suleiman et al., 2024). A substantial body of research shows that academic performance and participation, as well as motivation, often decline during adolescence, driven by a complex interplay of psychological, behavioral, social, and environmental factors. These factors include mental health and behavioral issues (depression, substance misuse/abuse, and attention problems; Agnafors et al., 2021; McLeod et al., 2012).

Motivation has been widely studied in health, education, and rehabilitation, and is recognized as a fundamental factor of participation and functional outcomes. Within the field of Occupational Therapy (OT), motivation is considered a predictor of outcomes, as it predicts the degree of participation (higher motivation predicts greater participation; Nagashima et al., 2024), and it is connected to the satisfaction of participation (Bergström et al., 2017).

This study introduces a newly developed scale designed to assess the motivations and causal beliefs of adolescent students regarding their participation and performance in school- and learning-related activities. The conceptual foundation of the scale is derived from Attribution Theory (AT), which states that an individual’s causal attributions—the reasons they give for the outcomes of an action—shape both their expectations for future success and the emotions they experience, which, in turn, guide their behavior (Weiner, 1985). Although AT provides a robust framework for understanding motivation and engagement behaviors, translating these constructs into assessment tools that capture everyday occupational challenges remains a methodological challenge and a gap in the literature.

Understanding Attribution Theory

Core Dimensions of Attribution

Attribution Theory provides a framework for understanding how individuals interpret the causes of their successes, and more significantly, their failures. These causal explanations or attributions are categorized into three key dimensions: locus (internal or external), controllability (controllable or uncontrollable), and stability (stable or unstable) (Graham & Weiner, 2011; Maymon et al., 2018; Weiner, 1985).

Impact of Attributional Properties

When failure is attributed to unstable causes such as insufficient preparation, it supports the belief that future success remains attainable; the setback is perceived as temporary or changeable (Graham, 2020). Conversely, attributing failure to uncontrollable and stable factors, such as perceived lack of ability, tends to foster feelings of hopelessness. In contrast, when failure is linked to controllable and unstable factors, such as insufficient studies, individuals are more likely to make greater efforts in the future, motivated by the possibility of improvement (Maymon et al., 2018).

Empirical Evidence on Academic Outcomes

Maymon et al. (2018) emphasized the significance of AT and cited research demonstrating that unstable attributions for academic challenges often have a positive effect on motivation and persistence among post-secondary students. In addition, for university students, controllable attributions were associated with increased positive emotions, reduced negative emotions, improved performance, and lower rates of course withdrawal (Maymon et al., 2018).

Motivation as a Predictor of Academic and Occupational Outcomes

Motivation is a fundamental driver of individual behavior, shaped by perceptions of causality, and governs the initiation, persistence, and cessation of actions across contexts (Graham & Weiner, 2011). As Urhahne and Wijnia (2023) describe, motivation is the process that facilitates goal-directed activity, while motivated behavior might be directed toward approaching a positive event or toward the avoidance of a negative event. Motivations arise from an individual’s personal needs and serve as a force to fulfill the underlying needs and organize all aspects of the psychology of a person (Dweck et al., 2023).

Motivation is a crucial factor that influences the initiation and sustained engagement in daily occupations and, when necessary, in therapy. Attributing a “successful” occupational experience to internal and controllable factors—as described in AT—has a positive impact on adolescents’ motivation to engage in activities (Budman et al., 2025). Engagement in meaningful, age-appropriate occupations, including leisure, self-care, and education, is closely related to health and well-being (Clarkson et al., 2021). This study focuses on academic participation as an occupation of utmost importance for adolescents.

In educational settings, increased student motivation has been associated with superior academic performance, positive classroom conduct, and elevated self-esteem (Sofiyana & Utami, 2022). In those settings, motivation is intricately linked to emotions, effort, persistence, and learning strategies, forming a bidirectional relationship that influences both engagement and outcomes. As Pekrun and Linnenbrink-Garcia (2012) explain, emotions such as frustration, boredom, enjoyment, pride, and shame can, even at an unconscious level, shape students’ motivation to participate in academic activities and affect their performance and concentration. Conversely, academic results and overall performance can generate or intensify these emotions, influencing subsequent motivation and goal setting. These reciprocal dynamics highlight the complex interplay between affective experiences and motivational processes that shape academic achievement (Pekrun & Linnenbrink-Garcia, 2012).

Academic Demotivation and Underperformance

Engagement and active participation are significant predictors of improved academic performance and higher test-grade averages because they are closely linked to students’ intrinsic motivation toward education (Horanicova et al., 2024). Such participation encompasses consistent school attendance, adherence to institutional rules and norms, sustained concentration on learning tasks, and application of effective learning strategies. In contrast, when adolescents experience disengagement or burnout, they face a risk of academic failure, increased rates of school dropout, increased negative emotions (anxiety and depression), and an elevated risk of substance abuse and juvenile crime (Zhao et al., 2021).

Sense of Disengagement or Burnout

A decline in academic performance is often observed during adolescence. This decline has been attributed to several factors, including school burnout, social media misuse, health and psychological problems, failure of the academic environment to fulfill the basic needs of autonomy, competence, and relatedness (Gnambs & Hanfstingl, 2016), and increased academic demands. The importance of emotions has also been highlighted as a factor that influences effort, persistence, motivation, and learning strategies (Pekrun & Linnenbrink-Garcia, 2012).

Life course transitions, such as progression from one academic grade to a higher and more demanding level, are frequently accompanied by significant challenges and adversity (Hamm et al., 2017). As students advance through school, they are required to navigate the uncertainty and unpredictability inherent in each transition, while simultaneously balancing new academic goals with the specific demands of their educational context. These challenges can at times overwhelm highly motivated learners, leading to academic setbacks that prompt an extended search for explanations of negative and unexpected outcomes. In response to these experiences, the causal attributions students construct carry important consequences for their emotions, motivation, goal attainment, and, ultimately, their future participation and engagement in academic activities (Hamm et al., 2017).

Theoretical Integration of Engagement, Attribution, and Occupational Therapy Models

Engagement and active participation in academic contexts are not only behavioral markers of success but also reflect deeper motivational and cognitive processes (Estévez et al., 2021; Lei et al., 2018). AT provides a useful lens for understanding how students’ causal explanations of academic outcomes shape their persistence and willingness to engage. When learners attribute success to internal controllable factors, such as effort or the use of effective strategies, they are more likely to sustain motivation and adopt adaptive learning behaviors. Conversely, attributing failure to uncontrollable or external causes, such as task difficulty or luck, can diminish persistence and engagement over time (Pekrun & Linnenbrink-Garcia, 2012). This attributional cycle directly influences the quality of participation, as students’ beliefs about control and responsibility affect their readiness to attend, concentrate, and apply strategies consistently (Horanicova et al., 2024).

OT models, particularly the Model of Human Occupation (MOHO), complement this perspective by situating engagement within the broader constructs of volition, habituation, and performance capacity (Taylor et al., 2024). Volition, which encompasses personal causation, values, and interests, parallels attributional processes by shaping how individuals perceive their ability to influence their outcomes. Habituation, through the establishment of roles and routines, supports consistent participation, such as regular school attendance and adherence to norms. Performance capacity reflects the cognitive and physical skills required to implement effective learning strategies. Together, these constructs highlight that academic engagement is not merely a behavioral outcome but also an occupation embedded in personal meaning, social context, and environmental affordances.

Integrating AT with OT models underscores the reciprocal nature of engagement: students’ causal beliefs influence their volition and motivation, whereas structured routines and supportive environments reinforce adaptive attribution and sustained participation. Moreover, a sense of connectedness and belonging fostered through positive relationships with teachers and peers can be understood as both an outcome of engagement and a facilitator of occupational participation. This integrated framework positions academic engagement as a dynamic process shaped by cognitive attributes, motivational systems, and occupational structures that enable meaningful participation in school life.

Providing a Context for Successful Participation, Performance and Positive Self-Esteem

The transition to secondary school is widely recognized as a critical and challenging developmental period frequently associated with declines in self-esteem, school engagement, and academic performance (Hamm et al., 2017). Entering a new achievement context can substantially alter students’ attributional patterns as their perceived causes of success and failure shift in response to novel expectations and demands. Within AT, the dimensions of locus of causality, stability, and controllability are particularly relevant. Successful participation in academic and everyday occupations is shaped by a complex interplay between motivational, emotional, and contextual factors. In educational settings, students’ engagement and performance are not only influenced by their cognitive abilities but also by their beliefs about competence, their causal explanations for success and failure, and the quality of their interactions with peers, teachers, and the broader school environment (Graham & Weiner, 2011; Pekrun & Linnenbrink-Garcia, 2012). Positive self-esteem emerges when students experience a sense of agency and belonging, which is reinforced by adaptive attributions that emphasize effort, strategy use, and controllable factors as determinants of achievement. Conversely, maladaptive attributions, such as attributing failure to uncontrollable or external causes, can undermine motivation, diminish self-esteem, and reduce the willingness to participate in learning activities.

From an OT perspective, participation is defined as meaningful engagement in roles, routines, and activities that support development and well-being. Models such as the MOHO highlight the importance of volition, habituation, and performance capacity in sustaining participation across contexts. Volition, in particular, aligns closely with attributional processes as students’ sense of personal causation and belief in their ability to influence outcomes directly shape their motivation to engage (Taylor et al., 2024). Habituation and performance capacity further support consistent school attendance, adherence to routines, and the application of effective learning strategies, all of which contribute to successful performance and reinforcement of a positive self-concept (Li & Lerner, 2013; Zhao et al., 2021).

Therefore, establishing a supportive context for participation and performance is essential to foster resilience and self-esteem during critical educational transitions. By integrating insights from AT and OT models, it is possible to identify the mechanisms through which students’ beliefs, behaviors, and environments interact to shape academic outcomes.

This integrated perspective provides a conceptual foundation for the development of the present scale, which aims to capture the attributional and motivational processes underlying adolescent participation and performance in ways that are theoretically grounded and aligned with concepts of everyday life within the academic context.

Existing Assessment Tools

Despite the availability of tools assessing academic motivation, executive functioning and academic-related emotional difficulties, there seems to be a lack of tools capturing adolescents’ self-perceived strengths and difficulties in the context of everyday academic participation. Existing measures often focus on deficits, rely mostly on teachers’ or parents’ reports, or assess constructs in isolation rather than reflecting the integrated cognitive, emotional, and organizational demands of school occupations.

Measures such as the Strengths and Difficulties Questionnaire (SDQ), the Child Behavior Checklist (CBCL), and Behavior Rating Inventory of Executive Function (BRIEF-2) are primarily deficit-oriented, capturing behavioral or executive function difficulties through adult informants (Achenbach & Ruffle, 2000; Davidson et al., 2016; Goodman, 2001). Other tools, including the Motivated Strategies for Learning Questionnaire (MSLQ), the Academic Self-Regulation Questionnaire (SRQ-A), and the General Academic Self-Efficacy Scale, although self-report measures, tend to focus narrowly on isolated motivational or cognitive dimensions, with limited relevance to OT-specific practice (Pintrich et al., 1993; van Zyl et al., 2022). These instruments do not assess how adolescents perceive their own strengths and difficulties within the authentic demands of everyday academic occupations. This gap underscores the need for a student-reported measure integrating attributional beliefs with lived school participation, addressed through the development of the StreDi-SPeS.

This study developed and standardized a self-report scale assessing adolescents’ perceived strengths and difficulties in academic participation. Grounded in theory and ecological relevance, it provides a psychometrically sound measure aligned with students’ lived experiences and everyday demands. By capturing students’ interpretations of their academic performance, the tool fills a key gap in the OT toolbox and supports practice with adolescents experiencing low motivation and participation. This article reports its development and psychometric evaluation, establishing reliability, validity, and practical utility.

Methods

Study Design

A cross-sectional design was implemented to develop and validate the Strengths and Difficulties Self‑Perception Scale for Adolescent Students (StreDi‑SPeS). A convenience sample of 539 students (56.8% females, n = 306; 43.2% males, n = 233) was recruited between 2021 and 2024.

Ethics approval was obtained from the National Committee of Bioethics (registration number: EEBK.ΕΠ.2024.01.37). As personal identifiers were not asked for by the participants, no signed consent was obtained. Perspective participants were asked to submit their answers only if they agreed to their answers being used for the research.

Participants and Procedures

Participants were typically developing adolescents aged from 13 to 18 years (M = 15.47) enrolled in mainstream middle and high schools in Cyprus. The inclusion criteria were fluent written Greek and attending a typical educational setting. Students with major cognitive difficulties were excluded.

Recruitment occurred through two channels: (a) distribution of questionnaires to clients of a private academic counseling service and (b) advertisement via social media with a link to the electronic survey. The respondents provided demographic information (month/year of birth, grade, and class). No personal identifiers were obtained. To prevent duplicate entries, e-mail addresses were automatically recorded for cross-referencing, although they were not retained for analysis.

Instrument Development

The StreDi‑SPeS was initially developed as a semi‑structured interview within an OT-based academic coaching service. This service supports typically developing students experiencing situational academic challenges, including those with mild attentional or learning difficulties but without formal diagnoses. Early item content emerged inductively from several clinical encounters, where students were asked to describe tasks they thought of as difficult (as barriers to their academic participation/performance), strategies they used and their own beliefs about their academic abilities and skills. Item phrasing and scoring were iteratively refined based on students’ feedback, specifically paying attention to ensuring that items reflected authentic school activities and tasks such as preparing for exams, completing assignments and planning and organizing homework.

The semi-structured interview initially and then the questionnaire intentionally included the four factors that later were supported by the statistical analysis, though the labeling of the factors emerged after the analysis. The questionnaire was developed to examine students’ attributions regarding their difficulties in academic participation across three major categories of factors amenable to OT interventions, as well as their self-perceived strengths. This empirical categorization was later supported by the statistical analysis—see the next section.

Development was led by the first author in collaboration with an MSc-credentialed Health psychologist, with additional input from a Greek literature teacher to ensure linguistic and developmental appropriateness. Remarks and feedback informed revisions to wording, removal of redundancies, and refinement of items to ensure they captured both strengths and difficulties of the students. All phases and items were developed in the Greek language, and the process of development and standardization was conducted with Greek-speaking adolescent students. The English wording presented in this article serves as a reporting translation rather than a validated linguistic adaptation.

In the second phase, the semi-structured interview was converted into a 15‑item, 4-point Likert-type questionnaire rating as 1= Always, 2 = Often, 3 = Rarely and 4 = Never. Students completed the scale on an opt-in basis, and qualitative feedback provided further refinement. The final 15-item version was retained for psychometric evaluation.

Regarding the authors’ contribution, as described above, the first author led the development of the scale (starting from the development of the semi-structured interview) and actively participated in all phases of the project. The second author supervised the standardization process; contributed to eligibility decisions, statistical analysis, and interpretation; and co-authored the manuscript.

Data Analysis

Psychometric properties were examined in terms of validity and reliability. Analyses were performed using SPSS (Version 25.0 [IBM Corp., Chicago, IL, USA]), with significance set at p < .05 (two‑tailed). The dataset comprised 539 adolescents (306 females, 233 males; age range 13–19 years). For psychometric analyses, listwise complete data were available for 537 participants across Q1–Q15. Analyses involving demographic variables (sex and age) were conducted on 534 participants with complete data. Ages ranged from 13 to 19 years (M ≈ 15.47).

Item Scoring

Items were rated on a 4‑point Likert-type scale (1–4). Total scores were calculated by summing responses across the 15 items. Analyses were conducted using item responses as coded in the dataset.

Validity

Construct validity was evaluated using exploratory factor analysis (EFA). Exploratory factor analysis was selected as an appropriate method given the scale’s early stage of development and the aim to examine its underlying dimensional structure (Watkins, 2018). The suitability of the data was confirmed using the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s Test of Sphericity. Factors were extracted using principal axis factoring, based on the aim of identifying latent constructs underlying shared item variance. An oblique promax rotation was applied because of the expectation that the underlying dimensions of academic functioning would be related (Adachi, 2004; Macpherson et al., 2016). The number of factors retained was guided by Kaiser’s criterion (eigenvalues > 1.0) and by scree plot inspection.

Reliability

Internal consistency was assessed using Cronbach’s alpha, both for the total scale and each extracted factor. Item deletion analyses were conducted to examine the contributions of individual items. Item performance was examined using corrected item–total correlations and alpha‑if‑item‑deleted statistics to evaluate the contribution of individual items to overall reliability. For the two‑item factor (see Factor 4), inter‑item correlation and the Spearman–Brown coefficient were examined to provide additional reliability information.

Sensitivity and Demographic Analyses

With the aim of examining the impact of retaining items with weaker psychometric indicators on overall scale reliability, sensitivity analyses were conducted (Kunselman, 2023). Exploratory subgroup analyses were performed to examine score patterns by sex (Welch’s t‑tests) and age (Pearson correlations and age‑band analyses). Given the exploratory nature of these analyses and the number of comparisons, emphasis was placed on effect sizes and overall patterns rather than isolated significance values.

Results

Sample Characteristics and Data Completeness

A total of 539 adolescents (306:233 [females:males]) were included in the database, with complete responses across all 15 items being available for 537 participants. Data from 537 participants were used for psychometric analyses.

Factorability of the Data

The KMO measure of sampling adequacy was .844, indicating meritorious sampling adequacy. Bartlett’s Test of Sphericity was significant, χ²(105) = 1926.56, p < .001, confirming the suitability of the data for factor analysis (Table 1).

Table 1.

KMO and Bartlett’s Test of Sphericity.

Test	Value
KMO Measure of Sampling Adequacy	.844
Bartlett’s Test of Sphericity χ²	1926.56
Df	105
p‑value	<.001

Exploratory Factor Analysis

The EFA yielded a four-factor solution, accounting for 40.3% of total variance, with the first factor explaining 25.8% of the variance (Figure 1, Table 2).

Table 2.

Factor Loadings for StreDi‑SPeS Items (loadings < 0.25 suppressed).

Item	Statement (abbreviated)	F1	F2	F3	F4
Q1	Start studying at the last minute	0.779	–	–	–
Q9	Go to school without assignments	0.690	–	–	–
Q2	Discover late, there is more to read	0.669	–	–	–
Q10	Miscalculate task time	0.424	–	–	–
Q6	Cannot remember despite preparation	–	0.759	–	–
Q8	Effort not analogous to performance	–	0.620	–	–
Q5	Cannot attend to the teachers	–	0.483	–	–
Q15	Lacks necessary exam skills	–	0.439	–	–
Q4	Difficult to concentrate at home	–	0.263	–	–
Q12	Afraid I will not succeed	–	–	0.650	–
Q13	Lack stamina to reach goals	–	–	0.636	–
Q7	Exams make me anxious	–	–	0.417	–
Q11	Tired/frustrated with academics	–	–	0.376	–
Q3	Faithfully follow plans/schedule	–	–	–	0.795
Q14	Satisfied with presence in school	–	–	–	0.389

Note. F1 = academic disorganization and temporal mismanagement; F2 = cognitive-attentional barriers; F3 = attributional distress and academic fatigue; F4 = self-regulated academic engagement.

Inspection of the scree plot (Figure 1) revealed a clear point of inflection after the fourth factor, where the slope of the eigenvalues began to level off. This “elbow” suggests that the first four factors account for the most meaningful variance in the data, while subsequent factors contribute only marginally. This finding is consistent with Kaiser’s criterion (eigenvalues > 1.0) and supports the retention of a four-factor solution. Together, these results provide convergent evidence that the StreDi‑SPeS is best represented by four underlying dimensions of academic functioning.

Factor Intercorrelations

Consistent with the use of an oblique rotation, factor score composites demonstrated moderate intercorrelations among Factors 1–3 (approximately r ≈ .43–.53), with weaker correlations involving Factor 4 (approximately r ≈ .14–.21).

Reliability Analysis

Cronbach’s alpha coefficients indicated good internal consistency for the overall scale (α = .814) and acceptable reliability for three of the four factors (Table 3). Factor 4, comprising only two items, demonstrated lower reliability (α = .492).

Table 3.

Cronbach’s Alpha for Extracted Factors.

Factor	No. of items	Cronbach’s α
Factor 1: Academic Disorganization and Temporal Mismanagement	4	.713
Factor 2: Cognitive‑Attentional Barriers	5	.706
Factor 3: Attributional Distress and Academic Fatigue	4	.670
Factor 4: Self‑Regulated Academic Engagement	2	.492
Total Scale	15	.814

Note. Item‑deletion analyses indicated that removal of any single item did not substantially improve reliability, supporting retention of all items.

Item-Level Diagnostics and Sensitivity Analysis

Item‑level diagnostics indicated variability in item performance across the scale. Most items demonstrated moderate to strong corrected item–total correlations, whereas one item (Q3*) showed a weak association with the total score (≈.09). Sensitivity analyses indicated that removal of Q4 reduced total‑scale internal consistency (α decreased from ≈.81 to ≈.80), whereas removal of Q3* resulted in an increase in reliability (α increased to ≈.83). At the factor level, the four‑factor structure was retained, with one factor (Factor 4) comprising two items.

Conclusion of Results

Taken together, these results provide strong preliminary evidence of the validity and reliability of the StreDi-SPeS. The four-factor structure reflects the distinct yet interrelated dimensions of academic functioning, while the overall internal consistency supports its use as a unified measure. Importantly, these factors not only capture observable academic behaviors and emotional responses but also mirror attributional patterns that shape adolescents’ motivation and engagement. These findings establish a foundation for interpreting the scale within the framework of AT and OT practice. The following discussion elaborates on how these factors can be understood theoretically, applied clinically, and translated into meaningful student profiles that guide everyday interventions.

Discussion

Overview of Findings

EFA provided evidence for a four‑factor structure underlying the StreDi‑SPeS, representing distinct dimensions of academic functioning. These dimensions are conceptually consistent with the causal components of AT and align with occupational therapy’s emphasis on participation in meaningful daily occupations. Although the fourth factor demonstrated lower internal consistency due to its two‑item composition, it emerged as a distinct dimension reflecting self‑regulated academic engagement and is therefore best interpreted as a provisional strengths‑oriented domain requiring further refinement.

The decision to retain the four factors was supported not only by Kaiser’s criterion (eigenvalues > 1.0) but also by the scree plot, which revealed a clear point of inflection after the fourth factor. This statistical evidence strengthens the interpretive coherence of the model, as the four retained factors meaningfully map onto the dimensions of locus, stability, and controllability. Specifically, the factors distinguish between maladaptive attributions (e.g., procrastination, cognitive barriers, and distress) and adaptive attributions (self-regulated engagement), thereby capturing the spectrum of beliefs that shape adolescents’ motivation and persistence. From an OT perspective, the scree plot’s confirmation of a four‑factor solution reflects the real‑world interplay of organizational, cognitive, emotional, and self‑regulatory demands in school participation. Thus, the statistical structure aligned with both theoretical expectations and clinical relevance, providing a robust foundation for interpreting student profiles and designing targeted interventions.

Integration With Attribution Theory

The following section explores how the four-factor structure of the StreDi-SPeS aligns with the core principles of AT, offering a theoretical lens through which adolescents’ academic self-perceptions can be meaningfully interpreted.

Factor 1 (Academic Disorganization and Temporal Mismanagement): reflects unstable but controllable attributions (e.g., procrastination), undermining autonomy.

Factor 2 (Cognitive‑Attentional Barriers): reflects internal, often uncontrollable attributions (e.g., “I lack skills”), eroding competence.

Factor 3 (Attributional Distress and Academic Fatigue): reflects stable, uncontrollable attributions (e.g., “I won’t succeed”), fostering helplessness.

Factor 4 (Self‑Regulated Academic Engagement): reflects adaptive attributions to internal, stable, controllable causes (e.g., “I follow my schedule”), reinforcing resilience.

Occupational Therapy Relevance

Focusing on occupational performance, each factor corresponds to barriers or facilitators of occupational performance:

Factor 1 → executive function and time management.

Factor 2 → attention, memory, and skill acquisition.

Factor 3 → psychosocial barriers (anxiety and fatigue).

Factor 4 → protective strengths supporting engagement.

Occupational Therapy Implications of the StreDi-SPeS Four-Factor Structure

The relevance of the four-factor StreDi-SPeS to OT is evident in its alignment with students’ use of specific and general self-regulation strategies. As demonstrated by Katsantonis and McLellan (2023), students who adopt organizational skills and effective time management, core aspects measured by StreDi-SPeS, are better equipped to achieve academic success. These self-regulation techniques, when combined with motivational beliefs and positive academic emotions such as interest, enjoyment, and satisfaction, contribute to higher grades and greater engagement in schoolwork.

Executive Function and Cognitive Abilities (Factors 1 and 2)

From an OT perspective, Factors 1 and 2 pertain to executive functions and cognitive abilities, specifically, time management, planning and organization, attention, memory, and the capacity to acquire new skills. The critical role of these abilities is supported by Peng and Kievit (2020), who identify cognitive abilities as foundational constructs that directly determine academic outcomes. These include essential elements of executive function such as working memory, flexible thinking, inhibition control (self-regulation and self-control), and reasoning (Peng and Kievit, 2020; Shi & Qu, 2022). Identifying executive dysfunction through screening or assessment is crucial when academic performance concerns arise. Early detection and support in these areas are key to enhancing students’ academic success (Mann et al., 2015).

Psychosocial Barriers and Fatigue (Factor 3)

The amount of time a student dedicates to studying significantly influences academic outcomes (Suleiman et al., 2024). This is closely tied to Factor 3, which encompasses fatigue, lack of stamina, and psychosocial barriers, such as anxiety. Addressing these factors is essential for supporting sustained academic engagement and performance.

Protective Strengths and Role Identity (Factor 4)

The positive aspects captured in Factor 4 underscore the importance of role performance in a complex high school environment. Successful role performance is critical for academic achievement, whereas repeated experiences of failure can foster a negative student identity and diminish volition in the student role (Mann et al., 2015). Therefore, supporting student strengths and engagement is integral to promoting positive academic and occupational outcomes. Educational satisfaction, along with social satisfaction, is related to individuals’ positive outcomes in many domains, affecting educational commitment and success. Moreover, school satisfaction is associated with academic performance, predicting engagement, and supporting academic goals (Magnano et al., 2020).

Emerging Profiles

Scoring and analyzing StreDi-SPeS results in three profiles as clinically meaningful interpretations of the findings, translating psychometric results into actionable insights for individualized interventions. These are described below.

Note that higher scores indicate more adaptive functioning, while lower scores indicate maladaptive functioning.

Perfectionist: This profile describes students who put high effort into their academic participation but experience high distress and low satisfaction. Thus, these students are vulnerable to burnout. Students described as perfectionists will score lower on Factor 1, which briefly describes avoidant behavior toward academic chores or procrastination. They will score higher on Factor 2, which briefly describes the existence of barriers to high performance or performance analogous to effort. This might indicate that these students do not face executive function challenges but feel that hard work (should) pay off. However, their scores on Factor 3 will be low, indicating elevated distress and low self-esteem. The most probable score for factor 4 will be moderate.

Imbalanced: This profile describes students with inconsistent performances and fluctuations in attribution. These students require structures to successfully participate in education. Their scores in all four factors will be medium, highlighting the fluctuations not only in their attributions but also in their academic behavior and participation.

Pessimist: This profile describes students with low self-esteem, an elevated sense of fatigue, disengagement from their role, and participation in school. These are the students who are more often in need of attribution retraining, as they tend to “take everything personally.” Their scores for each factor will be low.

Ecological Validity

Ecological validity refers to how well an assessment reflects everyday life, performance, and context (Crist, 2015). OT measures capture real-world participation, engagement, and environmental demands, and support health, independence, and quality of life (Crist, 2015). It applies both to research, which concerns the generalizability of findings, and to interventions, which reflect the ability to assess behaviors and performance as they occur in typical daily living (Crist, 2015).

StreDi‑SPeS was intentionally designed to align conceptually with adolescents’ authentic academic occupations. Items are embedded in familiar school-related activities (e.g., exam preparation, assignment completion), which enhances the likelihood that the constructs measured correspond to experiences of natural contexts. This potential for ecological validity supports the notion that the results are interpretable in the daily lives of adolescents, thereby enhancing their clinical utility.

A high level of ecological validity is essential for OT assessment scales because it ensures that the assessment truly reflects adolescents’ everyday academic experiences, challenges, and strategies, allowing practitioners to translate assessment findings directly into interventions that support authentic participation, independence, and well-being in community and private practice settings. By focusing on strengths and difficulties that emerge within everyday academic participation, the StreDi-SPeS offers results that are potentially meaningful for guiding interventions aimed at supporting engagement, autonomy, and well-being.

Limitations and Future Directions

Our study has important strengths, but also some limitations. A major limitation, and at the same time a future direction, derives from the fact that the sample was drawn exclusively from mainstream schools in Cyprus, and from students fluent in the Greek language. This raises the concern for the extent to which the findings can be generalized in different linguistic and cultural contexts, beyond the context in which the validation took place. Despite the fact that the questionnaire demonstrates strong psychometric properties in the Greek language, future research would be necessary to examine whether these properties are preserved after translation to other languages using rigorous cross-cultural adaptation procedures.

Closely related, academic cultures differ in substantial ways across countries and educational systems, influencing how students might interpret items related to engagement in academic life, participation, motivation and even stress and burn-out. As mentioned above, replication studies in diverse academic environments will be useful in determining the stability and applicability of the questionnaire across diverse cultural/academic contexts.

A second limitation or weakness is the relatively low reliability of Factor 4: Protective Strengths and Role Identity, which comprises only two items. Although the narrow item pool might explain the low reliability, it is nonetheless a weakness of the tool as it fails to reflect the range of strengths that might support students’ participation and engagement. Future research should consider expanding this domain with additional items, thereby enhancing in this way the utility of the tool for OT assessment and intervention.

Future research should explore longitudinal applications, intervention responsiveness, and adaptation in students with ADHD or learning disorders. In the future, it might be interesting to explore the potential differences between male and female scores and between high and low achievers. In addition, it would be interesting to assess the potential predictive quality of the scale for each student’s future academic participation.

Finally, future research should empirically examine the ecological validity dimension of StreDi-SPeS by exploring how adolescents’ self-reported strengths and difficulties correspond to observed participation in real-life academic environments.

Clinical Implications

The StreDi-SPeS offers occupational therapists a structured tool that is conceptually aligned with the everyday academic activities of adolescent students and therefore holds potential ecological relevance for practice. It can support multiple aspects of clinical reasoning, including:

Assessment: Identifying attributional barriers to academic participation.

Profiling: Classifying students into Perfectionist, Imbalanced, or Pessimist profiles, enhancing a better understanding of motivational tendencies.

Intervention Planning: Targeting executive function, cognitive strategies, coping skills, or strength reinforcement, depending on factor scores.

Progress Monitoring: Tracking changes in attributional beliefs and occupational performance over time.

Collaboration: Sharing results with educators and families to foster supportive environments.

By linking attributional beliefs to occupational performance, the scale bridges theory and practice. It offers clinicians a practical framework for supporting adolescents in school contexts. Further, it provides a tool that supports evidence-based practice in an area in which Occupational Therapy has considerable potential but has thus far been underrepresented: coaching for typically developing adolescents and for adolescents with mild neurodevelopmental difficulties. The questionnaire can be used both as an assessment tool and as a starting point for self-exploration and as a basis for formulating occupational goals.

Conclusion

The StreDi‑SPeS is a novel, psychometrically sound instrument that integrates AT with OT practice. Its four-factor structure reflects the key dimensions of academic functioning, and its ecological validity ensures its relevance to everyday school occupations. The identification of three clinically meaningful profiles enhances their practical utility, enabling therapists to tailor interventions according to individual needs. Although further refinement and cross‑cultural validation are warranted, the StreDi‑SPeS represents a significant advancement in the assessment of adolescent academic participation, offering a bridge between theoretical constructs and real‑world OT practice.

Footnotes

Acknowledgements

We would like to thank Editage () for English language editing.

ORCID iD

Elisavet Pavlou Papagianni

Ethical Considerations

The research was approved by the Cyprus National Bioethics Committee under the registration number: ΕΕΒΚ. ΕΠ. 2024.01.37.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The raw data generated and analyzed during this study cannot be shared due to restrictions under personal data protection laws. To support transparency and reproducibility, coded data are available from the corresponding author upon reasonable request. Access will be granted following an evaluation of the request to ensure compliance with applicable legal and ethical requirements.

References

Achenbach

T. M.

Ruffle

T. M.

(2000). The child behavior checklist and related forms for assessing behavioral/emotional problems and competencies. Pediatrics in Review, 21, 265–271. https://doi.org/10.1542/pir.21-8-265

Adachi

(2004). Oblique promax rotation applied to the solutions in multiple correspondence analysis. Behaviormetrika, 31(1), 1–12.

Agnafors

Barmark

Sydsjö

(2021). Mental health and academic performance: A study on selection and causation effects from childhood to early adulthood. Social Psychiatry and Psychiatric Epidemiology, 56(5), 857–866. https://doi.org/10.1007/s00127-020-01934-5

Bergström

Guidetti

Tham

Eriksson

(2017). Association between satisfaction and participation in everyday occupations after stroke. Scandinavian Journal of Occupational Therapy, 24(5), 339–348. https://doi.org/10.1080/11038128.2016.1245782

Budman

Zaguri-Vittenberg

Maeir

Tal-Saban

(2025). Occupational experiences and attributions of typically developing adolescents. Physical and Occupational Therapy in Pediatrics, 45(4), 468–482. https://doi.org/10.1080/01942638.2025.2463347

Clarkson

Boshoff

Kernot

(2021). Occupational therapy interventions for adolescents: A scoping review. The Allied Health Scholar, 2(1), 51–102. https://doi.org/10.21913/TAHS.V2I1.1575

Crist

P. A.

(2015). Framing ecological validity in occupational therapy practice. Open Journal of Occupational Therapy, 3(3), 1–8. https://doi.org/10.15453/2168-6408.1181

Davidson

Cherry

Corkum

(2016). Validating the behavior rating inventory of executive functioning for children with ADHD and their typically developing peers. Applied Neuropsychology: Child, 5(2), 127–137. https://doi.org/10.1080/21622965.2015.1021957

Dweck

C. S.

Dixon

M. L.

Gross

J. J.

(2023). What is motivation, where does it come from, and how does it work? In Bong

Reeve

Kim

(Eds.), Motivation science: Controversies and insights (pp. 5–9). Oxford University Press. https://doi.org/10.1093/OSO/9780197662359.003.0001

10.

Estévez

Rodríguez-Llorente

Piñeiro

González-Suárez

Valle

(2021). School engagement, academic achievement, and self-regulated learning. Sustainability, 13(6), 3011. https://doi.org/10.3390/SU13063011

11.

Gnambs

Hanfstingl

(2016). The decline of academic motivation during adolescence: An accelerated longitudinal cohort analysis on the effect of psychological need satisfaction. Educational Psychology, 36(9), 1698–1712. https://doi.org/10.1080/01443410.2015.1113236

12.

Goodman

(2001). Psychometric properties of the strengths and difficulties questionnaire. Journal of the American Academy of Child and Adolescent Psychiatry, 40(11), 1337–1345. https://doi.org/10.1097/00004583-200111000-00015

13.

Graham

(2020). An attributional theory of motivation. Contemporary Educational Psychology, 61, 101861. https://doi.org/10.1016/J.CEDPSYCH.2020.101861

14.

Graham

Weiner

(2011). Motivation: Past, present, and future. In Harris

K. R.

Graham

Urdan

McCormick

C. B.

Sinatra

G. M.

Sweller

(Eds.), APA educational psychology handbook, vol 1: Theories, constructs, and critical issues (pp. 367–397). American Psychological Association. https://doi.org/10.1037/13273-013

15.

Hamm

J. M.

Perry

R. P.

Chipperfield

J. G.

Murayama

Weiner

(2017). Attribution-based motivation treatment efficacy in an online learning environment for students who differ in cognitive elaboration. Motivation and Emotion, 41(5), 600–616. https://doi.org/10.1007/S11031-017-9632-8/TABLES/1

16.

Horanicova

Husarova

Madarasova Geckova

Lackova Rebicova

Sokolova

de Winter

A. F.

Reijneveld

S. A.

(2024). Adolescents’ academic performance: What helps them and what hinders them from achievement and success? Frontiers in Psychology, 15, Article 1350105. https://doi.org/10.3389/FPSYG.2024.1350105/BIBTEX

17.

Katsantonis

McLellan

(2023). Students’ voices: A qualitative study on contextual, motivational, and self-regulatory factors underpinning language achievement. Education Sciences, 13(8), 804. https://doi.org/10.3390/EDUCSCI13080804/S1

18.

Kunselman

A. R.

(2023). A brief overview of sensitivity analyses. Fertility and Sterility, 119(6), 904–906. https://doi.org/10.1016/J.FERTNSTERT.2023.04.031

19.

Lei

Cui

Zhou

(2018). Relationships between student engagement and academic achievement: A meta-analysis. Social Behavior and Personality, 46(3), 517–528. https://doi.org/10.2224/SBP.7054

20.

Lerner

R. M.

(2013). Interrelations of behavioral, emotional, and cognitive school engagement in high school students. Journal of Youth and Adolescence, 42(1), 20–32. https://doi.org/10.1007/S10964-012-9857-5/FIGURES/2

21.

Macpherson

Pesola

Leamy

Bird

Le Boutillier

Williams

Slade

(2016). The relationship between clinical and recovery dimensions of outcome in mental health. Schizophrenia Research, 175(1–3), 142–147. https://doi.org/10.1016/j.schres.2015.10.031

22.

Magnano

Boerchi

Lodi

Patrizi

(2020). The effect of non-intellective competencies and academic performance on school satisfaction. Education Sciences, 10(9), 222. https://doi.org/10.3390/EDUCSCI10090222

23.

Mann

D. P.

Snover

Boyd

J. R.

List

A. J.

Kuhn

A. J.

Devereaux

B. N.

Chenoweth

S. M.

Middaugh

G. L.

(2015). Executive functioning: Relationship with high school student role performance. The Open Journal of Occupational Therapy, 3(4), 1–8. https://doi.org/10.15453/2168-6408.1153

24.

Maymon

Hall

N. C.

Goetz

Chiarella

Rahimi

(2018). Technology, attributions, and emotions in post-secondary education: An application of Weiner’s attribution theory to academic computing problems. https://doi.org/10.1371/journal.pone.0193443

25.

McLeod

J. D.

Uemura

Rohrman

(2012). Adolescent mental health, behavior problems, and academic achievement. Journal of Health and Social Behavior, 53(4), 482–497. https://doi.org/10.1177/0022146512462888

26.

Nagashima

Hayasaka

Teruya

Hoshino

Murao

Matumoto

Maruki

Katagiri

Imamura

Kurihara

Tsuboi

Watanabe

Sakurai

(2024). Factors encouraging participation in social activities after hospital discharge in people with severe mental illness who received occupational therapy. Frontiers in Psychiatry, 15, Article 1421390. https://doi.org/10.3389/FPSYT.2024.1421390/BIBTEX

27.

Pekrun

Linnenbrink-Garcia

(2012). Academic emotions and student engagement. In Christenson

Reschly

Wylie

(Eds.), Handbook of research on student engagement (pp. 259–282). Springer. https://doi.org/10.1007/978-1-4614-2018-7_12

28.

Peng

Kievit

R. A.

(2020). The development of academic achievement and cognitive abilities: A bidirectional perspective. Child Development Perspectives, 14(1), 15–20. https://doi.org/10.1111/CDEP.12352

29.

Pintrich

P. R.

Smith

D. A. F.

Garcia

Mckeachie

W. J.

(1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (Mslq). Educational and Psychological Measurement, 53(3), 801–813. https://doi.org/10.1177/0013164493053003024

30.

Shi

(2022). Analysis of the effect of cognitive ability on academic achievement: Moderating role of self-monitoring. Frontiers in Psychology, 13, Article 996504. https://doi.org/10.3389/FPSYG.2022.996504/BIBTEX

31.

Sofiyana

Utami

(2022). Enhancing ‘how to learn’ skills: Its impacts on academic performance and students’ motivation. In The Asian conference on education & international development, 2022, official conference proceedings (pp. 143–155). IAFOR. https://doi.org/10.22492/issn.2189-101X.2022.11

32.

Suleiman

I. B.

Okunade

O. A.

Dada

E. G.

Ezeanya

U. C.

(2024). Key factors influencing students’ academic performance. Journal of Electrical Systems and Information Technology, 11(1), 1–18. https://doi.org/10.1186/S43067-024-00166-W

33.

Taylor

R. R.

Bowyer

Fisher

(2024). Kielhofner’s model of human occupation: Theory and application (6th ed.). Wolters Kluwer.

34.

Urhahne

Wijnia

(2023). Theories of motivation in education: An integrative framework. Educational Psychology Review, 35, 45. https://doi.org/10.1007/s10648-023-09767-9

35.

van Zyl

L. E.

Klibert

Shankland

See-To

E. W. K.

Rothmann

. (2022). The general academic self-efficacy scale: Psychometric properties, longitudinal invariance, and criterion validity. Journal of Psychoeducational Assessment, 40(6), 777–789. https://doi.org/10.1177/07342829221097174

36.

Watkins

M. W.

(2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219–246. https://doi.org/10.1177/0095798418771807

37.

Weiner

(1985). An attributional theory of achievement, motivation and emotion. Psychological Review, 92(4), 548–573. https://doi.org/10.1037/0033-295X.92.4.548

38.

Zhao

Zheng

Pan

Zhou

(2021). Self-esteem and academic engagement among adolescents: A moderated mediation model. Frontiers in Psychology, 12, Article 690828. https://doi.org/10.3389/FPSYG.2021.690828/BIBTEX