Theoretical Framework
Designing digital supported teaching-learning-scenarios which are based on theoretical assumptions and aimed at achieving good teaching practices (Helmke, 2009) is time-consuming (Islam et al., 2015) In addition, it requires teachers’ competencies at multiple dimensions and in particular on the level of technology integration in regard to their technological pedagogical content knowledge (Mishra & Koehler, 2006). Several features of instructional quality have been identified and categorized as distinctive predictors for the impact on student learning (Helmke, 2009; Seidel & Shavelson, 2007). Seidel & Shavelson (2007) found in a meta-analysis that the largest impact on student learning had domain-specific features. Teachers design and construct teaching and learning scenarios based on their epistemological assumptions on good teaching and learning (see Hähnlein, 2018) and based on their assumptions on pedagogical and didactical-methodological and their influence on students’ learning processes (see Fischler et al., 2002). This implies, that when assessing instructional quality and its impact on learning, it is important to address teachers’ didactical assumptions behind the teaching learning scenario as well.
Especially in times when it is necessary to quickly transform teaching settings from on-site classrooms to online teaching environments immediately, it is required that the provision of digital teaching learning settings has a structured approach to make them available for reuse. For the identification of teaching-learning settings, it is furthermore required to consider domain-specific features that meet the need of the particular context. When investigating domain-specific terms, concepts, or entities of teaching learning settings, a taxonomy can provide a way to put this data into an ordered, hierarchical structure with categories and sub-categories (Rich, 1992). Furthermore, a taxonomy provides an adequate conceptual framework to structure features of teaching-learning settings in a way that they can be classified and retrieved (Vercoustre & McLean, 2005). Based on the structure of the taxonomy, a database structure can also be modeled, which in turn can serve as a data store for teaching-learning settings. If these teaching-learning settings are stored in a database, reuse can be easier if good practices are described more generally by a number of teaching-learning settings. To generate these generalized descriptions, the settings are systematically analyzed in the for similarities in successful teaching-learning practices. This leads to the pattern approach, which originally comes from architecture (Alexander et al., 1977), and was later applied in industrial training (Bergin et al., 2012) and in computer science education (Derntl, 2005; Standl, 2014).
In our case, we are particularly interested in best practices and effective teaching-learning scenarios in the digital context. The basic idea is to correlate evaluation data from teaching with the associated teaching-learning scenarios to identify successful patterns. Significant correlations are then tested theory-based for a possible causal relationship and conclusions are drawn based on the theory. Since not all possible correlations can be identified exclusively manually, we choose a semi-automated approach.
Research Questions
In accordance with the theoretical assumptions of assessing instructional quality and modelling it through a pattern mining process, the following research questions are driving this study:
(1) To what extend can pre-service teachers’ perception of an online seminar be
represented through a taxonomy?
(2) How can graph networks be utilized to identify effective teaching patterns?
Another focus was to assure the didactical quality and orientation of the teachers by means of video-based interviews.