Perception of Study Requirements Instrument (MWS)

1. Abstract

The Perception of Study Requirements Instrument (known in German as the Messinstrument für die Wahrnehmung von Studienanforderungen, abbreviated as MWS) is a multidimensional, standardized psychometric inventory developed to assess how undergraduate students perceive and navigate the diverse academic, personal, organizational, and social demands encountered during higher education. Originating within the empirical research project Studierfähigkeit – institutionelle Förderung und studienrelevante Heterogenität (StuFHe), funded by the German Federal Ministry of Education and Research (BMBF), the instrument addresses the crucial theoretical and empirical interface between individual student dispositions and institutional learning environments. The MWS was constructed on the foundation of qualitative exploratory investigations (Bosse & Trautwein, 2014; Trautwein & Bosse, 2017) using the Critical Incident Technique across German universities and universities of applied sciences (Fachhochschulen).

Psychometrically validated across large-scale student cohorts comprising a total sample of $N = 1,907$ undergraduate students (specifically analyzed across three distinct empirical samples: an item selection sample of $N = 1,260$, a confirmatory factor analysis sample of $N = 1,165$, and a longitudinal panel sample of $N = 320$), the final operationalization of the MWS comprises 21 items organized into six latent subscales: (1) Wissenschaftsmodus (Academic/Scientific Mode), (2) Studienerwartungen & Anwendungsbezug (Study Expectations and Practical Application), (3) Lernaktivität (Learning Activity and Self-Regulation), (4) Leistungsdruck & Misserfolg (Performance Pressure and Coping with Failure), (5) Studienorganisation (Study Organization and Structural Navigation), and (6) Kontakt & Kooperation (Contact and Peer Cooperation). Items are evaluated on a 5-point Likert response scale ranging from 1 (sehr schwer / very difficult) to 5 (sehr leicht / very easy), supplemented by an explicit non-applicability option (“trifft auf mich/meinen Studiengang nicht zu“). Exploratory principal component analyses and robust confirmatory factor analyses using robust maximum likelihood estimation (MLR) confirm the six-dimensional structure ($\chi^2 = 634.62, df = 174, p < .001; ext{CFI} = .93; ext{RMSEA} = .05, 90% ext{ CI } [.04, .05]$). The subscales demonstrate moderate to satisfactory internal consistency and composite reliability, with Cronbach’s $lpha$ and Raykov’s $
ho$ coefficients spanning $.66$ to $.82$. Longitudinal multiple regression models verify predictive criterion validity, showing that early perceptions of study requirements account for $19%$ of the variance in subjective academic performance and $17%$ of the variance in overall study satisfaction in later semesters. The instrument offers a robust diagnostic apparatus for higher education research, institutional evaluation, and student retention initiatives.

2. Keywords

perception of study requirements, higher education, academic self-efficacy, student retention, transition to higher education, measurement instrument, psychometric validation, study satisfaction, academic performance, institutional research, MWS

3. Authors

The Perception of Study Requirements Instrument (MWS) was conceptualized, developed, and empirically validated by educational scientists and higher education researchers affiliated with the Centre for Higher and Further Education (Zentrum für Weiterbildung / Institut für Hochschulforschung) at the University of Hamburg (Universität Hamburg) and partner universities in Germany:

• Prof. Dr. Elke Bosse — Professor of Higher Education Research and Higher Education Didactics. Principal investigator within the research initiative “Studierfähigkeit” (Study Readiness / Academic Literacy) at Universität Hamburg and subsequent appointments across German higher education faculties.
• Dr. Caroline Trautwein — Educational scientist, higher education researcher, and academic developer specializing in teaching-learning environments, academic acculturation, and curriculum design.
• Collaborating StuFHe Research Consortium — Research project “Studierfähigkeit – institutionelle Förderung und studienrelevante Heterogenität” (BMBF funding code 01PB14005), conducted in institutional cooperation between the University of Hamburg (Universität Hamburg), the University of Kassel (Universität Kassel), the Hamburg University of Applied Sciences (Hochschule für Angewandte Wissenschaften Hamburg – HAW), and the Technical University of Applied Sciences of Central Hesse (Technische Hochschule Mittelhessen – THM).

4. Purpose

The higher education landscape across Europe and internationally has witnessed profound transformations characterized by the democratization of access, increasing student heterogeneity, and persistently troubling student attrition rates (Heublein, 2014; Heublein et al., 2017). The transition into higher education—frequently conceptualized as the Studieneingangsphase (initial study phase or first-year experience)—presents multi-layered challenges that extend far beyond general cognitive aptitude. While earlier diagnostic traditions in differential psychology and personnel selection predominantly concentrated on discipline-specific entry assessments or standardized intelligence testing (e.g., the Methodik zur Ermittlung und Validierung von Anforderungen an Studierende [MEVAS] for economics and business administration by Hell, Ptok, & Schuler, 2007; or teacher education assessments by Bohndick & Buhl, 2014), generic, cross-disciplinary instruments that capture how students interactively experience university demands remained sparse.

The primary purpose of the MWS is to serve as a cross-disciplinary, standardized diagnostic instrument designed to capture how university students perceive the psychological, cognitive, structural, and social demands of studying. Rather than measuring objective difficulty or pure individual cognitive ability in an institutional vacuum, the MWS deliberately operates at the interactionist interface of the individual and the higher education institution. It systematically addresses:

• Diagnostic Screening of Academic Adjustment: Identifying early cognitive and methodological friction points during the critical first year of bachelor’s degree programs, enabling academic advisors to detect domains where students struggle with scientific abstraction or the independent acquisition of academic discourse.
• Evaluation of Institutional Learning Environments: Providing university leadership, quality management directors, and academic developers with standardized, survey-based data on how curricula, scheduling, examination frameworks, and advisory infrastructures are experienced by diverse student cohorts.
• Targeted Pedagogical Interventions: Informing the design of targeted bridge courses, orientation weeks, peer-assisted study schemes (PASS), mentoring programs, and academic writing workshops based on empirically identified deficits across specific study programs.
• Longitudinal Research on Student Attrition: Serving as an empirical research tool in longitudinal cohort studies to trace how requirement perceptions evolve over semesters, and how these perceptions predict premature study dropouts, program changes, or academic persistence.

The instrument provides two temporal instruction versions: Version A retrospectively captures how easily or heavily students handled requirements during their first year of study (after completion of the initial phase), whereas Version B assesses requirement navigation over the preceding academic year across more advanced study stages. Although explicitly designed for aggregated cohort analysis, institutional quality evaluation, and academic research, the authors explicitly emphasize that the instrument is not intended for individual high-stakes diagnostic decision-making (such as university admissions or academic disqualification) due to subscale-level reliability constraints.

5. Psychological Construct

The construct assessed by the MWS is the subjective perception of study requirements (Wahrnehmung von Studienanforderungen). Anchored in cognitive-transactional theories of psychological stress and academic socialization, requirement perception is conceived not as an invariant attribute of the student or as an objective feature of the curriculum, but as the transactional appraisal of the compatibility between environmental demands and internal/external coping resources (Lazarus & Folkman, 1984). When a student assesses an academic challenge as “very difficult,” this appraisal reflects cognitive, methodological, behavioral, or emotional strain resulting from an imbalance between what the university expects and the student’s mastery skills.

The instrument organizes 21 manifest items into six correlated latent dimensions, mapped structurally across four overarching thematic domains identified through prior qualitative research: content-related (inhaltlich), personal (personal), social (sozial), and organizational (organisatorisch):

1. Academic/Scientific Mode (Wissenschaftsmodus; WM)

This subscale (items 5, 14, 20) measures the student’s capacity to adjust to the formal epistemology and linguistic culture of the scientific enterprise. Rather than simply memorizing textbook knowledge, students must adopt an analytical stance, assimilate disciplined scientific language, comprehend epistemological inquiry, develop precise research questions, and master critical literature appraisal. For example, item 5 focuses on acquiring academic terminology for examinations and seminar papers (“sich die Wissenschaftssprache anzueignen”), while item 14 targets adjusting to scientific ways of thinking compared to secondary schooling (“sich auf die wissenschaftlichen Herangehensweisen einzustellen”).

2. Study Expectations and Practical Application (Studienerwartungen & Anwendungsbezug; StA)

Belonging to the content-related domain, this subscale (items 8, 12, 16) captures the student’s continuous reflection on the personal relevance, vocational applicability, and thematic fit of their degree program. It assesses the difficulty students face when trying to connect abstract theories with tangible professional careers, discover meaningful application examples, and continually re-evaluate whether their degree choice aligns with their emerging vocational identity. Sample operationalizations include connecting academic subject matter to future career paths (item 8: “Studieninhalte mit Berufsvorstellungen zu verbinden”) and identifying personal interests to validate degree choice (item 12: “eigene Interessen zu erkennen und die Studiengangswahl zu überprüfen”).

3. Organization of Learning Activities (Lernaktivität; LA)

Part of the personal domain, this subscale (items 4, 9, 13, 18) assesses metacognitive self-regulation, autonomous study habits, time management, and workload balance. Transitioning into higher education requires students to shift from teacher-directed schooling to self-regulated learning (Pintrich, 2004). This factor captures the ease or difficulty of identifying effective cognitive learning methods (item 4: “zu erkennen, wie man sinnvoll lernt”), structuring learning schedules across weeks and months (item 9: “Lernaktivitäten zeitlich sinnvoll zu strukturieren”), managing massive volumes of reading and coursework (item 13: “die Menge an Lernstoff zu bewältigen”), and accurately estimating one’s personal stamina and recovery needs (item 18: “eigene Belastbarkeit einzuschätzen”).

4. Performance Pressure and Coping with Failure (Leistungsdruck & Misserfolg; LD)

Also within the personal domain, this subscale (items 3, 7, 15) captures the emotional, affective, and cognitive strain associated with university assessment systems. Higher education environments frequently subject students to high-stakes examinations, rigorous grading curves, and unprecedented experiences of academic failure or grade deflation. The items quantify the ability to handle negative exam outcomes and poor grades without entering demoralization cascades (item 3: “mit Prüfungsergebnissen umzugehen”), cope with performance anxiety and high personal standards (item 7: “mit Leistungsdruck umzugehen”), and manage restrictive exam conditions such as high testing density (item 15: “mit Prüfungsbedingungen zurechtzukommen”).

5. Study Organization (Studienorganisation; SO)

This subscale (items 2, 6, 11, 19) assesses the structural and bureaucratic competencies required to navigate the institutional infrastructure of modern universities. Under modularized degree programs, students are confronted with complex course timetabling, restrictive enrollment prerequisites, administrative deadlines, and physical resource shortages. Items evaluate the perceived difficulty of adjusting to suboptimal course schedules and limited seminar topic options (item 2: “mit dem vorhandenen Lehrangebot zurechtzukommen”), generating an autonomous semester schedule (item 6: “einen eigenen Stundenplan zu erstellen”), finding appropriate advisory and support services (item 11: “passende Informations- und Beratungsangebote zu finden”), and dealing with unfavorable institutional conditions such as lecture overcrowding or facilities issues (item 19: “mit ungünstigen Rahmenbedingungen umzugehen”).

6. Contact and Peer Cooperation (Kontakt & Kooperation; KK)

This subscale (items 1, 10, 17, 21) embodies the social dimension of academic adjustment. Academic persistence and cognitive integration are inextricably linked to social integration within peer networks (Tinto, 1975). The factor measures how easily students manage social climate challenges such as peer competition (item 1: “mit dem sozialen Klima im Studiengang zurechtzukommen”), establish peer connections for collaborative study groups and leisure (item 17: “Kontakte zu Mitstudierenden zu knüpfen”), coordinate joint study groups (item 10: “Teamarbeit zu organisieren”), and execute collaborative group assignments or seminar presentations (item 21: “im Team zusammen zu arbeiten”).

6. Theoretical Framework

The conceptual architecture of the MWS is anchored at the confluence of three major theoretical paradigms in educational psychology and higher education research: Vincent Tinto’s Longitudinal Model of Institutional Departure, transactional stress theory, and self-regulated learning models.

Tinto’s Interactionist Model of Student Retention

For decades, empirical inquiries into university student success and dropout behavior were polarized into two distinct camps: psychological approaches prioritizing student-centered internal variables (such as cognitive ability, high school GPA, personality traits, and Big Five taxonomy; e.g., Richardson, Abraham, & Bond, 2012), and sociological approaches prioritizing institutional environments (such as teaching quality, campus facilities, and structural policies; e.g., Schaeper & Weiß, 2016). Vincent Tinto’s landmark model (Tinto, 1975, 1993) bridge this dichotomy by asserting that departure decisions emerge from longitudinal interactions between the individual student and the academic and social systems of the institution.

In the German context, Ulrich Heublein and colleagues (Heublein, 2014; Heublein et al., 2017) adapted this framework to analyze the complexities of the Bologna-reformed higher education framework. The MWS explicitly operationalizes the core postulate of these models: dropout or persistence is determined precisely at the nexus of intersection where individual prerequisites meet organizational structures. Bosse and Trautwein (2014) argued that assessing either institutional conditions alone (e.g., student course evaluations) or student competencies alone (e.g., standardized entrance tests) produces an incomplete picture. The MWS captures how demands are phenomenologically experienced by the student, reflecting both institutional clarity and individual coping capability.

Cognitive-Transactional Stress and Coping Theory

From a psychological standpoint, the MWS incorporates the cognitive-transactional framework of Lazarus and Folkman (1984). When confronted with an academic demand—such as an extensive reading list or a team presentation—students engage in primary appraisal (evaluating the personal stakes, whether benign, positive, or threatening) and secondary appraisal (evaluating available cognitive, emotional, and social coping options). When academic demands overwhelm available coping resources, psychological distress, performance decrements, and avoidance behaviors ensue. The MWS’s 5-point rating scale (from “very difficult” to “very easy”) operationalizes this subjective appraisal process, capturing the degree of friction the individual experiences when interacting with specific demands.

Models of Self-Regulated Learning (SRL)

The shift from secondary school to university demands an abrupt elevation in self-regulated learning (Pintrich, 2004; Zimmerman, 2002). In secondary schools, instructional paces, learning schedules, and social environments are largely structured by teachers. In universities, students encounter high academic autonomy coupled with high structural ambiguity. Students must self-diagnose comprehension gaps, choose appropriate deep-processing learning strategies, regulate time allocation, and sustain motivation amidst prolonged delays in evaluative feedback. The subscales Lernaktivität, Studienorganisation, and Wissenschaftsmodus directly reflect the cognitive, metacognitive, and resource-management components central to self-regulated learning theories.

7. Validity

The psychometric validity of the Perception of Study Requirements Instrument (MWS) has been established through multi-stage qualitative and quantitative empirical investigations, providing robust evidence for content validity, factorial validity, and longitudinal predictive criterion validity.

Content Validity

Content validity was established through an extensive, rigorous two-phase qualitative research design (Bosse & Trautwein, 2014; Trautwein & Bosse, 2017). Initially, 50 semi-structured, guideline-based interviews were conducted using the Critical Incident Technique (Flanagan, 1954) with undergraduate students ($N = 25$) and academic/administrative university staff ($N = 25$). Purposive sampling ensured representation across academic disciplines, faculties, and study stages. Grounded in theoretical saturation (Glaser & Strauss, 1967), 32 discrete requirement categories were extracted and classified across four dimensions (content, personal, social, organizational). To confirm generalizability across institutional types and regions, the categorized requirements were cross-validated through 19 supplementary interviews conducted across two universities and two universities of applied sciences in the federal states of Hamburg and Hesse. This rigorous qualitative grounding ensured that every generated item represented an authentic, frequently encountered friction point in students’ lived university experience.

Factorial and Construct Validity

Initial item generation yielded 40 items. Psychometric filtering based on item difficulties, discriminatory power, and exploratory factor analyses (Sample 1: $N = 1,260$) refined the pool to 21 items. The factorial structure was subsequently confirmed in an independent cohort (Sample 2: $N = 1,165$) using robust confirmatory factor analysis (CFA) with robust maximum likelihood (MLR) estimation. The six-factor model exhibited satisfactory goodness-of-fit indices: $\chi^2 = 634.62, df = 174, p < .001, ext{CFI} = .93, ext{RMSEA} = .05 ext{ (90% CI } [.04, .05])$, substantiating the multidimensional construct definition.

Predictive Criterion Validity

Predictive criterion validity was tested longitudinally across a panel cohort of $N = 320$ students (Sample 3) tracked from their third semester (measurement point 1) to their fifth semester (measurement point 2). Criterion variables at the fifth semester included:

• Subjective Academic Performance: Measured via Becker’s validated single-item scale on a 7-point Likert-type metric ($1 = ext{sehr schlecht}$ to $7 = ext{sehr gut}$).
• Study Satisfaction: Assessed using the validated 3-item inventory by Westermann, Elke, Spies, and Trautwein (1996; $lpha = .82$) on a 4-point rating scale ($1 = ext{trifft überhaupt nicht zu}$ to $4 = ext{trifft völlig zu}$).

Bivariate Bravais-Pearson correlations revealed that all six MWS subscales correlated positively and significantly ($p < .001$, with social contact at $p < .01$) with both future academic performance and study satisfaction. Specifically, perceiving learning activities as easier correlated strongly with academic performance ($r = .44, p < .001$), while managing study expectations and practical relevance correlated most strongly with study satisfaction ($r = .37, p < .001$).

Note. $N = 320$; $^{**}p < .01$, $^{***}p < .001$.

Multiple regression analyses confirmed distinct predictive pathways. For subjective academic performance ($R^2 = .19, p < .001$), Lernaktivität ($eta = .35, p < .001$) and Leistungsdruck & Misserfolg ($eta = .15, p < .05$) emerged as significant independent predictors. For study satisfaction ($R^2 = .17, p < .001$), Studienerwartungen & Anwendungsbezug ($eta = .26, p < .001$), Lernaktivität ($eta = .15, p < .05$), and Studienorganisation ($eta = .14, p < .05$) were significant independent predictors, demonstrating high differential validity.

8. Reliability

Internal consistency and composite reliability of the MWS were evaluated across both cross-sectional samples (Sample 1: $N = 791 ext{–}1,161$; Sample 2: $N = 830 ext{–}1,120$). Because confirmatory factor analyses revealed heterogeneous factor loadings across manifest indicators, the measurement models satisfy the condition of tau-congenerity rather than essential tau-equivalence. Under tau-congeneric structures, standard Cronbach’s alpha ($lpha$) tends to underestimate true internal consistency (Cortina, 1993). Consequently, the authors computed both Cronbach’s $lpha$ and Raykov’s composite reliability coefficient rho ($
ho$; Raykov, 1997).

Note. SP1 = Sample 1 ($N = 791 ext{–}1,161$), SP2 = Sample 2 ($N = 830 ext{–}1,120$). Sample sizes vary due to pairwise/casewise exclusion of non-applicable responses.

Overall, internal consistency coefficients ranged from moderate ($lpha = .66,
ho = .66$ for Studienorganisation) to good ($lpha = .82,
ho = .82$ for Wissenschaftsmodus). The slightly lower coefficient for Studienorganisation is attributed to the inherent multidimensional heterogeneity of structural demands (combining course scheduling, advising access, and institutional bureaucracy). Item-total correlations ($r_{it}$) across all 21 items demonstrated solid discriminatory power, ranging from $r_{it} = .37$ (Item 11) to $r_{it} = .71$ (Item 20). These reliability levels are well-suited for group-level comparative research, institutional monitoring, and structural equation modeling, but reconfirm that individual high-stakes profiling is not recommended.

9. Factor Analysis

The structural dimensionality of the MWS was established through an exploratory factor analysis (EFA) on Sample 1 and replicated via confirmatory factor analysis (CFA) on Sample 2.

Exploratory Factor Analysis (EFA)

A principal component analysis (PCA) with orthogonal Varimax rotation was executed on the 21 selected items using Sample 1 ($N = 1,260$). The empirical eigenvalue scree plot supported a six-component solution with eigenvalues of 6.92, 2.00, 1.42, 1.14, 1.02, and 0.87. The extracted six factors collectively accounted for 64.0% of the total variance. The components emerged in order of explained variance as: (1) Leistungsdruck & Misserfolg, (2) Kontakt & Kooperation, (3) Wissenschaftsmodus, (4) Studienerwartungen & Anwendungsbezug, (5) Lernaktivität, and (6) Studienorganisation. Items loaded cleanly on their hypothesized theoretical dimensions, with factor loadings generally exceeding $.50$.

Confirmatory Factor Analysis (CFA)

To cross-validate the six-dimensional model on independent data, a CFA was performed with Mplus 7.31 (Muthén & Muthén, 2012) using Sample 2 ($N = 1,165$). Because multivariate normality was violated, parameters were estimated using robust maximum likelihood (MLR), providing robust standard errors and Satorra-Bentler scaled test statistics. The six-factor model exhibited a satisfactory fit to the data:

• Chi-Square: $\chi^2 = 634.62, df = 174, p < .001$
• Comparative Fit Index (CFI): $.93$
• Root Mean Square Error of Approximation (RMSEA): $.05$ (90% Confidence Interval: $[.04, .05]$)

All manifest indicators demonstrated highly significant standardized factor loadings on their designated latent factors ($p < .001$). Latent inter-factor correlations ranged from $.26$ (between Wissenschaftsmodus and Kontakt & Kooperation) to $.92$ (between Lernaktivität and Leistungsdruck & Misserfolg), confirming that while the constructs represent distinct facets of the study experience, self-regulatory learning processes are strongly tied to managing performance pressures.

Note. $N = 1,163$. All latent correlations are statistically significant at $^{***}p < .001$.

10. Instrument / Measurement Tool

• Instrument Name: Perception of Study Requirements Instrument (Messinstrument für die Wahrnehmung von Studienanforderungen, MWS)
• Target Population: Undergraduate / Bachelor degree students in higher education institutions (universities and universities of applied sciences / Fachhochschulen) across all academic disciplines.
• Administration Format: Standardized self-report questionnaire, administered via online survey platforms or paper-pencil questionnaires.
• Number of Items: 21 items.
• Subscales (6 Dimensions):
  • Wissenschaftsmodus (Academic/Scientific Mode) – 3 items (Items 5, 14, 20)
  • Studienerwartungen & Anwendungsbezug (Study Expectations & Practical Application) – 3 items (Items 8, 12, 16)
  • Lernaktivität (Learning Activity / Self-Regulation) – 4 items (Items 4, 9, 13, 18)
  • Leistungsdruck & Misserfolg (Performance Pressure & Coping with Failure) – 3 items (Items 3, 7, 15)
  • Studienorganisation (Study Organization) – 4 items (Items 2, 6, 11, 19)
  • Kontakt & Kooperation (Contact & Peer Cooperation) – 4 items (Items 1, 10, 17, 21)
• Temporal Versions:
  • Version A (First-Year Experience): Retrospectively evaluates requirement handling during the first year of study: “Schätzen Sie ein, wie leicht bzw. schwer es Ihnen im ersten Studienjahr gefallen ist, mit den folgenden Anforderungen im Studium umzugehen.”
  • Version B (Advanced Study Stages): Evaluates requirement handling during the past academic year: “Schätzen Sie ein, wie leicht bzw. schwer es Ihnen im vergangenen Studienjahr gefallen ist, mit den folgenden Anforderungen im Studium umzugehen.”
• Authentic Response Scale:
  Fünfstufiges Antwortformat mit den Optionen:
  • $1$ = sehr schwer (very difficult)
  • $2$ = eher schwer (fairly difficult)
  • $3$ = teils-teils (partly easy, partly difficult / neutral)
  • $4$ = eher leicht (fairly easy)
  • $5$ = sehr leicht (very easy)
  • Additional option: “trifft auf mich/meinen Studiengang nicht zu” (does not apply to me / my degree program)
• Scoring and Aggregation Rules:
  • All 21 items are formulated in a positive direction (higher values denote higher perceived ease / lower perceived difficulty). No item recoding or reverse scoring is required.
  • Subscales are calculated separately as unweighted arithmetic means of their constituent items. Calculating an overall composite total score is discouraged because the subscales capture distinct institutional and psychological dimensions.
  • Items marked with “trifft auf mich/meinen Studiengang nicht zu” or skipped items are coded as missing values. When missing completely at random (MCAR) assumptions are satisfied, multiple imputation can be applied (Lüdtke et al., 2007). In online administrations, setting items as mandatory while including the non-applicable option is recommended.
• Completion and Scoring Time: Administration requires approximately 5 to 8 minutes; computation of subscale mean scores takes approximately 1 minute.

11. Permissions & Fee and Test Year

The Perception of Study Requirements Instrument (MWS) was developed and validated between 2014 and 2017 within the StuFHe research project funded by the German Federal Ministry of Education and Research (BMBF; grant reference 01PB14005). The instrument was published as an open-access psychometric measurement tool for academic, non-commercial research, institutional evaluation, and higher education quality development.

• Test Publication Year: 2017 / 2018.
• Copyright & Permissions: The authors retain intellectual property. Researchers, university administrators, and educational practitioners may utilize, reproduce, and administer the instrument free of charge for non-commercial scientific research, instructional evaluation, and higher education development purposes, provided that proper scientific attribution and citation of the primary publications (Bosse & Trautwein, 2014; Trautwein & Bosse, 2017) are maintained.
• Commercial Use: Any commercial deployment, distribution in fee-based proprietary testing software, or consulting use requires explicit prior written authorization from the authors.

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• Westermann, R., Elke, G., Spies, K., & Trautwein, U. (1996). Fragebogen zur Erfassung der Studienzufriedenheit (FES). Psychologisches Institut der Universität Göttingen.
• Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

13. Items of the Scale (Questionnaire)