Topic and background 

International large-scale assessments such as the Programme for International Student Assessment (PISA) report a sustained decline in reading and numeracy performance across many Western European countries (OECD, 2023). Educational policy responses have mainly focused on curriculum reform, instructional improvement, and teacher professional development. Although these approaches are important, meta-analytic evidence indicates that their average effects are often modest and highly dependent on contextual and learner-related factors (Hattie, 2009). Comparatively little attention has been paid to students’ own perspectives on learning, despite growing evidence that these perspectives are systematically related to learning processes and outcomes. Students’ ideas, beliefs, and mental representations about learning, referred to as learning conceptions, are associated with learning strategies, motivation, and achievement in core academic domains (Peterson et al., 2010; Robbers et al., 2015). Understanding of learning conceptions is therefore particularly relevant during educational transitions, which are a common feature across international education systems.

Conceptual and theoretical framework 

Research on learning conceptions originates in phenomenographic studies that identified different ways in which learners understand learning (Säljö, 1979). Early distinctions focused on quantitative, content-related conceptions, emphasizing the acquisition and reproduction of knowledge, and qualitative, process-oriented conceptions, emphasizing understanding and meaning-making (Purdie & Hattie, 2002; Peterson et al., 2010). More recent theoretical developments have extended this framework by incorporating the social and motivational embeddedness of learning. Learning is conceptualized as a cooperative and socially situated process involving interaction and shared regulation with others (Tynjälä, 1997; Vermunt & Donche, 2017). In addition, learning conceptions include motivational aspects, such as beliefs about effort, control, obligation, personal growth, and attributions of success and failure (Pinto et al., 2018). When selecting or developing a questionnaire to measure learning conceptions, it is important to ensure that aspects of how students understand learning as identified in the literature are broadly represented, covering content-related, process-oriented, cooperative, and motivational elements, without assuming predefined dimensions or a fixed structure.

Research gap and rationale 

Over the past two decades, several instruments have been developed to assess students’ learning conceptions in primary and secondary education (Klatter et al., 2001; Peterson et al., 2010). Of these instruments, only the questionnaire by Klatter et al. (2001), which is available in Dutch, is directly applicable in the Dutch context. However, both the practical and scientific utility of the questionnaire are limited by two key issues. Although reduced from 100 to 65 items, the questionnaire remains impractically long for 11- and 12-year-old students, increasing the risk of response fatigue. Methodologically, Klatter et al. (2001) divided the questionnaire into three correlated subfactors rather than conducting single factor analyses on the full item set and relied solely on the Kaiser criterion (eigenvalue > 1) to determine factors, which may have led to overfactoring (Goretzko, 2025). These choices raise concerns about the instrument’s validity. As a result, no concise and psychometrically robust Dutch instrument currently exists to assess learning conceptions in secondary education, creating a gap for both educators and researchers. Filling this gap is crucial for advancing educational practice and research.

Research objective 

This study aims to psychometrically re-examinate the factorial structure of the original Learning Conceptions Questionnaire (LCQ; Klatter et al., 2001) to create a robust instrument assessing students’ learning conceptions in the first year of secondary education. 

Central research question 

To what extent can the original item pool of the Learning Conceptions Questionnaire be restructured into a valid, reliable, and robust multidimensional factor model for students in the first year of secondary education, and to what extent is the resulting factor structure theoretically interpretable in relation to established frameworks of learning conceptions in the educational literature?

The study employed the Learning Conceptions Questionnaire (LCQ) developed by Klatter et al. (2001), a 100-item instrument designed to assess students’ conceptions of learning. In contrast to the original validation study, conducted in the final year of primary education and based on multiple separate factor analyses, the present study re-examined the full item set within a single, integrated psychometric framework using data from 431 students in the first year of secondary education. Item-level distributions were inspected using descriptive statistics, including skewness and kurtosis, to evaluate distributional assumptions relevant for factor extraction. Sampling adequacy was subsequently assessed at the item level using a robust bootstrap-based procedure implemented via the RobustMSA.r script (Lorenzo-Seva & Ferrando, 2021) in R. This procedure identifies noisy, weakly discriminating, or content-redundant items that may bias factor recovery and inflate estimates of dimensionality. Items were removed when the lower bound of the 95% bootstrap confidence interval for the Measure of Sampling Adequacy fell below .50. Overall factorability of the refined item set was then evaluated using the Kaiser–Meyer–Olkin (KMO) statistic. Exploratory factor analyses were conducted using maximum likelihood estimation with oblique (Promax) rotation, consistent with the assumption of correlated latent constructs. Given the size of the initial item pool, EFAs were performed iteratively for solutions ranging from two to eight factors. Item retention was guided by established psychometric criteria, including adequate communalities (> .20; Yong & Pearce, 2016), sufficient primary factor loadings (> .30; Klatter et al., 2001), and the absence of substantial cross-loadings (difference ≥ .20 between primary and secondary loadings; Howard, 2016). The number of factors to retain was confirmed using Parallel Analysis and the Comparison Data Forest method (Goretzko, 2025), implemented in the EFAfactors package (Qin & Guo, 2025). Finally, the robustness of the selected factor structure was examined using factor analysis based on a covariance matrix estimated via the Minimum Covariance Determinant method (Bertrand & Zhang, 2013), followed by maximum likelihood extraction and Promax rotation.

The present study identified a stable five-factor structure comprising 30 items, representing the following learning conceptions (including illustrative items): • Deep learning (“In mathematics, I always want to find out exactly how everything works”). • External regulation (“In Dutch, I prefer the teacher to tell me exactly what I have to do”). • Social learning orientation (“I like to get tips from other students about the best way to learn”). • Learning as duty (“If you work hard for a subject, you can always get good marks”). • Negative learning orientation (“I mainly go to school because I have to”). The internal consistency of the resulting scales ranged from acceptable to good, and robustness analyses confirmed the stability of the factor structure. The findings provide empirical support for the multidimensional nature of learning conceptions, in line with established frameworks (e.g., Säljö, 1979; Tynjälä, 1997; Purdie & Hattie, 2002). The refined instrument captures qualitative, process-oriented, social, and motivational dimensions of learning, indicating its potential utility for both educational research and practice. Future research could further explore cross-national differences in learning conceptions, particularly between higher- and lower-performing PISA countries and investigate associations with academic achievement and additional forms of validity, such as criterion-related and predictive validity. This instrument provides a promising basis for advancing both theoretical understanding and evidence-based educational practices.

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