Self-regulated learning (SRL) is widely regarded as a key competence for successful learning, and its relevance has further increased with the expansion of digital and hybrid learning formats in secondary education. SRL refers to a form of learning in which learners proactively and autonomously regulate their learning process by applying a range of cognitive, metacognitive, and volitional strategies to monitor and guide their progress (Schiefele & Pekrun, 1996).

In contemporary learning settings, students are expected to plan, monitor and adjust their learning in more autonomous ways, including the regulation of their motivation when difficulties arise (Wirth & Leutner, 2008). Despite the inclusion of motivational components in prominent SRL models, research on motivational regulation, particularly in relation to learners' selection of an appropriate motivational strategy in a given situation, remains comparatively underdeveloped (Grunschel et al., 2016; Zhang & Dong, 2022). One possible explanation for this may be the lack of adequate measurement instruments. Many established instruments in this area either record motivational orientations and beliefs or assess the general frequency with which strategies are applied (e.g. MSQL; Pintrich et al., 1991, SRQ-A; Ryan & Connell, 1989). However, these instruments do not provide insight into whether a specific strategy choice is situationally appropriate, that is to say, whether it "fits" the particular learning problem at hand.

From a pedagogical-psychological perspective, this gap points to the importance of conditional strategy knowledge. Conditional knowledge refers to knowing when and why a strategy is useful in a specific context, and it is conceptually distinct from simply knowing what a strategy is (Paris et al., 1983). This distinction is particularly relevant for understanding why students sometimes fail to translate what they have learned about effective learning into actual adaptive regulation during learning (often discussed as a knowledge–action gap;  Foerst et al., 2017). While process-near approaches such as SRL microanalysis can address this issue, they are costly and difficult to scale for larger samples (Perels & Dörrenbächer, 2020).

In order to propose a suitable alternative, Perels and Dörrenbächer (2020) developed the Strategy Knowledge Test for Self-Regulated Learning (SKT-SRL), which operationalises conditional knowledge about scenario-based judgments. The theoretical basis of the test is Zimmerman's (2000) cyclical model of self-regulated learning (planning, performance, reflection). The instrument also draws on expectancy-value theory (Wigfield & Eccles, 2000) to assess motivational regulation strategies. Following the recommendations of Engelschalk et al. (2015) and Schwinger et al. (2007), motivational problems are differentiated according to whether they are primarily expectation-related (e.g. doubts about one's own competence) or value-related (e.g. low perceived relevance).

Based on this measurement tool, which was originally designed for university students, an adaptation was made for the target group of secondary school students. Therefore, the case scenarios were transferred to typical situations of self-directed learning in a school context. To make the situational reference more vivid, reduce the amount of text and increase motivation to complete the test, the scenarios were converted into a visual cartoon format. Furthermore, a parallel version of the test was developed to facilitate longitudinal assessments and mitigate potential test-retest or practice effects. This article details the adaptation process and provides an empirical evaluation of the instrument’s construct and convergent validity.

The motivational scenarios from the SKT-SRL approach were rewritten to fit Grade 8 learning situations. As in the original, a motivational scenario was developed for each of the three SRL phases (planning, implementation, reflection) and parallel versions were created to support longitudinal use. To reduce reading load and to support engagement in a school-based assessment setting, scenarios were presented as short comic strips rather than text-only vignettes. The cartoon format was chosen pragmatically to keep the scenarios concise while still conveying situational cues that are critical for conditional judgments. Content validity was examined with an expert sample from educational psychology. Thirty-nine experts were contacted and asked to rate the usefulness of the proposed motivational regulation strategies for each scenario on a four-point scale and provided rankings of strategies. Expert agreement (Kendall’s W) and mean usefulness patterns were used to establish scoring standards and to identify potential deviations from the original instrument logic in the secondary-school adaptation. The instrument was implemented as part of the scientific evaluation of a pilot project in Berlin schools, in which two instructional models of hybrid learning are currently being piloted. 1315 Students completed the comic-based motivational SKT as well as additional self-report measures capturing SRL-related self-efficacy, motivation-related self-efficacy, learning enjoyment, and procrastination-related behaviour. Evidence for convergent validity is examined via correlations between conditional motivational knowledge scores and these theoretically adjacent constructs.

The presentation will focus on the development and validation of instruments. The report will include expert-based evidence for content validity, including the degree to which experts converge in their usefulness judgments and whether the intended usefulness hierarchy is clearly reflected in ratings. It will also include empirical data from the pilot project implementation regarding the instrument's correlational validity. It is generally assumed that conditional knowledge about dealing with expectation-related motivational challenges has a stronger correlation with self-efficacy-related metrics. Conversely, knowledge about dealing with value-related motivational challenges is expected to show stronger correlations with enjoyment and indicators of the value of a task. In addition, it is assumed that higher conditional motivational knowledge is associated with a lower tendency to procrastinate. As part of an exploratory analysis, initial data is also reported on whether conditional motivational knowledge differs between the various hybrid learning models after the first year of testing the hybrid teaching models.

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