Due to the COVID-19-crisis students everyday school life changed enormously with implementing the homeschooling concept, which has produced a lot of pressure on educational improvement and quality assurance in schools (Schleicher, 2020). Neither the students nor the teachers were prepared for learning entirely online which required a high degree of self-control from the students. The success of self-regulated learning is strongly based on the quantity and quality of student´s learning strategies. Learning strategies concern tactics on which students engage in learning and manipulate information processing. They can be modified or learned and applied in all subject areas in our classrooms (e.g., Weinstein, Husman, & Dirking, 2000).

The importance of these learning strategies for student’s achievement is repeatedly proved by researchers. For example, McInerney, Cheng, Mok, and Lam (2012) have identified effects between general deep and surface learning strategies, self-concept, and academic achievement in secondary schools. Habók and Magyar (2018) have found strong effects of language learning strategies on proficiency, attitudes, and school achievement, however depending on years in school. Neuman and Guterman (2017) have distinguished between structured and unstructured homeschooling in which learning contents, processes as well as strategies are also related strongly to student’s wishes with no external dictates. Schober, Lüftenegger, and Spiel (2020) have recently found that COVID-19-conditions have reduced learning motivation, especially for high-level secondary school students. Such results stress the importance of motivational strategies for learning. For example, Park and Yun (2017) have found significant relationships between motivational strategies and cognitive learning strategies. They also found that students use different motivational and learning strategies depending on their academic level.

Considering these theoretical approaches and research findings we aimed first to identify the learning strategies of students in secondary schools during the COVID-19-crisis-related homeschooling. We have especially focused on levels of and relationships between different types of student’s learning strategies considering not only traditional learning strategies for language and mathematics learning but also motivational and self-control strategies as well as homeschooling- and e-learning-related strategies.

Furthermore, we have set our second goal to identify the relationships between the use of learning strategies and personality characteristics of the students (especially gender and age), the quality of instructional processes and student achievement. Using these relationships, we want to design implications to improve future teaching processes and quality assurance by teacher education trainings, in particular during the COVID-19-crisis.

Student’s learning strategies were measured using a self-developed questionnaire. Students assessed themselves concerning the strategies in language education, mathematics, homeschooling, e-learning, motivation, and self-control by Likert-rating scales. Measuring learning strategies was based on (modified and adapted) scales from Barnard, Lan, Yo, Paton, & Lai (2009), Houghton & Neck (2002), Lin & Tai (2015), Mikula, Uray, & Schwinger (1997), and Oxford (1996). The quality of instructional processes in classrooms were measured with a scale on future personal goals for classroom instruction related to a model of effective teaching from Maulana, Helms-Lorenz, and Van de Grift (2017). Student’s achievement was measured applying self-assessments on overall and specific knowledge and skills levels. Additionally, we included open questions on all relevant subject areas (e.g. student’s needs during homeschooling) to consider a mixed-method-approach and validity purposes. Answers on open questions were classified and ranked. In October 2020 and so during the COVID-19-crisis, we carried out an online survey of students of an international secondary school using this questionnaire. 110 students from 7 different classes assessed themselves as part of a four-year school-development program on educational improvement and quality assurance (the so-called TASS (Team-, Assessment-, and Scaffolding-based School-Development)-Project funded by the Luxembourg Government. Statistical computations were based on correlations and regression analyses together with group comparisons. First data analyses show up some methodological issues concerning further research within the project, which should be discussed in ERC. The project is conceptualized to repeat the measurement yearly. Therefore, we will have a repeated measurement (Montoya, 2019) for our multivariate analyses. We are unsure how to design a second test that is related to and considering the results of the first measurement.

Statistical analyses have shown acceptable measurement quality regarding reliability and validity. We found strong correlations between the different types of learning strategies and also academic achievement as well as significant differences between learning strategies of students from different ages resp. academic levels. Gender was not related to learning in our sample. The analysis of student’s answers on open questions provides supporting information on the validity of our findings. Additionally, student’s answers indicate important, student-related contents for educational improvement training. The results of the online-survey were also integrated in processes on school development and quality assurance. Within the TASS-project, a two-day teacher education training considering results and related implications of the online survey was carried out. Contents of such a training will be outlined. Probably, also data on the evaluation of the training and related consequences for the school development process will be depicted. Further methodological issues regarding the results and concerning future research, which should be discussed in ERC are: a) Until now we analysed bivariate correlations and regressions, but further multivariate analyses should be used to consider mediator and moderator effects (Wu & Zumbo, 2008). For complex analyses such as analyzing moderated mediation models we need more knowledge about possibilities for analyses considering a difficult data situation, as we are unsure about whether we have multivariate normality. b) Additionally, our analyses will concern at least two different groups with limited sample sizes. As we are unsure if we will have a consistent or equivalent control group because of a complex school situation, it is possible that we have an artificial control group or have to use alternatives such as “nonequivalent dependent variables” (Coryn & Hobson, 2011). We need more knowledge about alternative research designs with acceptable validity dealing with missing or problematic control group.

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