Introduction

Recently the participation in formal and non-formal teaching-learning settings has increased around the world (OECD, 2021). Organised teaching and learning also is a central issue in international adult education (AE) research (Rubenson & Elfert, 2014). Hence, it is astonishing that the quality of organised AE-courses has hardly been studied so far (for quality dimensions of Web-based AE see Harroff & Valentine, 2010). The Adult Education Survey, for instance, accounts for the quality of AE-courses via the participants’ subjective satisfaction. However, a more differentiated analysis of various quality aspects, which also includes the teachers’ perspective, has hardly been carried out so far.

This contrasts with classroom-research in schools, which is characterized by a large body of research on teaching quality. Various studies and theoretical frameworks consistently point towards three generic dimensions of teaching quality, namely classroom management, supportive climate, and cognitive activation (e.g., Klieme, Pauli, & Reusser, 2009). The three dimensions have been positively linked to student outcomes (e.g., Fauth et al., 2014) and measures have been administered in multiple assessments (e.g., the Teaching and Learning International Survey (TALIS), OECD, 2019 or the Programme for International Student Assessment (PISA), OECD, 2014).

Thus, this proposal aims to provide first evidence regarding the transferability of the teaching quality dimensions to the context of organised AE-courses with a focus on measurement and influencing factors. This is particularly relevant as initial studies investigate the quality of AE-Courses by drawing upon the three teaching quality dimensions (e.g., The German National Educational Panel Study (NEPS), Blossfeld, Roßbach, & von Maurice, 2011). Considering the lack of validated measures, our first research question (RQ1) is: Can scales that have been developed to measure teaching quality in school classrooms be used to validly measure the quality of AE-courses?

To investigate the quality dimensions of AE-Courses one cannot disregard the teachers’ educational and occupational background. The teachers’ background is expected to influence the competence development, which in turn is expected to impact the teaching quality (Terhart, 2012). In Germany, for instance, AE is mainly characterized by a low regulation with hardly any systematic and institutionalized influence on the development of teachers’ competencies (Autorengruppe Bildungsberichterstattung, 2022). Furthermore, teacher education research shows that a simple increase in experience does not necessarily go hand in hand with improved teaching quality. Rather, it is important to link the experience gained with existing knowledge and, above all, to reflect on it systematically (Hascher, 2005; Schön, 1983). Against this background, the course quality dimensions are expected to vary, which we address by the second research question (RQ2): Which characteristics of teachers’ background predict high quality of AE-courses?

Instruments and Sample These questions are answered with statistical analysis, which for RQ1, are combined with the analysis of cognitive probing interviews. The quantitative and qualitative analysis are based on different samples (quantitative analysis: N=191, qualitative analysis: N=12 german AE teachers). In both samples, the teaching quality dimensions are measured using scales from two of the most frequently cited educational large-scale assessments, namely PISA (2012) and TALIS (2013, 2018). The teachers’ background measures include age, a university degree in pedagogy, teaching experience, preparation and teaching hours, pedagogical training, type (e.g., self-employed) and organizations of employment (e.g., publicly funded), and reflective behavior. Analysis Methods To answer RQ1, we first checked scale reliability to investigate the psychometric quality of the teaching quality items for data of AE-Teachers. Afterwards, we applied confirmatory factor analysis (CFA) to test if the theoretically expected three dimensions and their subdimensions are supported empirically. Finally, we analysed the interview data with Qualitative Content Analysis (Kuckartz, 2018) to gain first insights on how to adapt potential problematic teaching quality items to fit the context of AE more adequately. The data-driven coding system was iteratively developed by three independent coders. To answer RQ2, we estimated three separate multilevel regression models one each with classroom management, supportive climate, and cognitive activation as dependent variable and the teachers’ background characteristics and reflective behaviour as predictors.

Results/Expected outcomes RQ1. Values of Cronbach’s alpha indicate good scale reliability for all teaching quality dimensions (α = .79 for classroom management, α = .89 for supportive climate, and α = .77 for cognitive activation). For cognitive activation, CFA supported the theoretically expected subdimensions. For the other dimensions, however, the number of subdimensions and the pattern of indicators vary. The ratings of the interviewed teachers indicate a medium fit for specific teaching quality items to adequately measure course quality. The medium fit, however, can be mitigated with small adaptations in item wording. For instance, according to the interviewees, disruptions (a subdimension of classroom management) only take a short amount of time in AE-Courses. The original items, however, refer to “quite a lot of time”. Moreover, they highlight that there is hardly any “disruptive noise” in adult education, disruptions are rather caused by inattentive participants. RQ2. Based on results of Marx and colleagues (2018), we expect a) a positive effect of the teachers’ participation in AE-Courses, b) a negative effect of age and an interaction between teaching experience and hours of participation in AE-Courses, and c) no effects of teaching experience, preparation and teaching hours, and a university degree in pedagogy on course quality. Furthermore, we assume a positive effect of teachers´ reflective behaviour on course quality (Szogs et al., 2019). Conclusion This proposal provides first evidence that, after a few adaptions to measurements and theoretical considerations, teaching quality is transferrable to the AE context in a moderate manner. These and the regression-analytical findings indicate that course quality and possible influencing factors need to be discussed context-specifically.

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