The crisis caused by the COVID-19 virus in 2020 had far-reaching effects in nearly all social areas, including education. In the context of this new and challenging situation distance education, information was needed instantly to inform education policy and practice. To satisfy this urgent need for information, many scientific and non-scientific online surveys were conducted. While many descriptive studies exist, explanatory studies that investigate associations between different aspects of Corona-related distance education are still rare. Moreover, to the best of our knowledge we are not aware of studies that investigate students learning during school lockdown from multiple informants. Relying on the reports of only one group (either student reports, parent reports, or teacher reports), while common, runs the risk of biased estimates. Our systematic literature review on distance education during Corona indicates large differences between students, parents, and teacher ratings on various aspects of distance education. For instance, students and parents rating regarding the lack of technical equipment at home range between 3%-25%, while the teacher ratings range from 28% to 75%. Hence, the question arises to which studies, that explore student learning on basis of different informants (students, parents, teachers) yield converging findings. Hence, we perform a multiple informant study to answer two main research questions that have not been explored so far:  

1. What are the relevant predictors of student learning during Corona-related distance education in spring 2020?  

2. To which extent do the results of research question 1 converge if the (same) analyses are based on data from different informants (i.e., students, parents, teachers)?  

Student achievement. Across all informant groups (and across all analysis steps), students’ intrinsic motivation and self-organization skills emerge as the most important predictors of self-rated and externally rated student achievement during school-lockdown, while instructional quality during distance education does only contribute little to explaining rated learning success. These findings extend existing research that has previously identified leisure activities conducive to learning such as reading (Champeaux et al., 2020), teaching activities like feedback, student communication and student engagement (Steinmayr et al., 2021), and self-reported ability to use digital media before school closure (Züchner & Jäkel, 2021), among others.  

Student effort. Regarding student effort, students’ self-rated and externally rated achievement as well as student self-organization skills emerge as the most important predictors of students’ effort across all informant groups. Again, instructional quality during distance education does only contribute little to explaining students’ learning effort. The predictive power of rated student achievement is in line with the results by Grewenig et al. (2020b) who reported that high achieving students did invest more hours in school activities during Corona. Though, prior research did identify several further predictive aspects of students’ effort, such as students’ learning environment at home, social support of classmates, teachers’ support intensity (Dietrich et al., 2020), students’ socioeconomic background, school type (Grätz & Lipps, 2021) and regular family learning support (Züchner & Jäkel, 2021), the present study adds new relevant predictors.  

Student motivation. Students’ intrinsic motivation during Corona-related distance learning is most strongly determined by students’ self-organization skills and self-rated and externally rated student achievement across all informant groups. Again, instructional quality during distance education does only contribute little to explaining students’ intrinsic motivation during distance learning. In line with the findings by Holzer et al. (2021), our results indicate that – as postulated by self-determination theory (Deci & Ryan, 1985) – students that perceive competence are more likely to report higher intrinsic motivation during distance learning. Also, in line with previous research (Pelikan et al., 2021) our findings show that students with higher self-organization and self-regulation skills are more strongly intrinsically motivated during distance education. 

Instruments To assess student outcomes during distance education we focused on three dimensions: self- and externally rated student achievement (ach), student effort (eff; invested learning hours per week), and students’ intrinsic motivation (mot). According to the context, input, process, and output model (CIPO, e.g., Scherens, 1990) student outcomes are result of a school-initiated transformation process of inputs. This process is determined by individual and contextual factors. Against the background of empirical studies on distance education during Corona, we decided to assess the following individual and contextual factors: students’ self-organization skills (sos), technical equipment at home (tec), teachers’ support of individual learning (sup), teachers’ support of structure in online lessons (str), teachers’ maximization of learning time (mlt), teachers’ support of cognitive activation (coa), parental support (par), and students’ workplace at home (hom). Analysis First, based on measurement error-adjusted constructs (structural equation modeling), we analyze latent correlations to uncover bivariate relationships between various aspects of distance learning and central student outcomes. Second, by means of latent regression analysis, we test to what extent the latent bivariate relations observed in analysis step (1) persist when the influence of all predictors, is modeled simultaneously. Finally, based on latent bivariate correlations from analysis step (1), we perform a latent relative weight analysis (RWA, Tonidandel & LeBreton, 2011) to extend the findings from step (1) and (2). Prior to the analyses of the associations between the study variables we checked for measurement invariance of the assessed constructs between the three informant groups. That is, whether the items used in this study do work equally (= assess the same construct) in all three informant groups.

Methodologically, the findings point to the strong influence of the selected samples as a source of information. For researchers, the findings imply that both descriptive and explanatory analyses of distance education may arrive at different conclusions depending on which group of actors is used as the source of information. Only a few predictors, such as learners’ self-organization skills, were found to be highly significant in predicting learning outcomes across all informant groups. Future studies should therefore critically reflect on the validity of single informant studies against this background by discussing possible perspective validity and bias. Regarding the relevance of various predictors of student learning outcomes during the Pandemic, our study shows that children’s and adolescents’ abilities to self-organize and to self-direct learning are predictive of the quality and outcomes of learning processes. Against this background, for scenarios in which learning takes place without the support structures of school (e.g., lifelong learning; distance learning due to natural disasters), it is particularly important to foster these skills in children and adolescents in a timely manner. For school practice, this could mean focusing more than before on forms of instruction that support self-regulated learning. Findings from our current student survey (in progress) show that learners who were taught according to the concept of open and cooperative learning prior to Corona are significantly more likely (than students form traditional class) to report that they made higher progress during distance learning. In addition, they rate the quality of the teacher-student relationship during lockdown significantly higher than students from traditional classes. Thus, open and cooperative types of learning might represent promising approaches to fostering skills for self-regulated learning.

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