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.

Method In a first step, we developed and validated an expert-based consensus-based taxonomy. Therefore, an initial taxonomy was independently created and consensus-validated through two experts. Subsequently, this taxonomy was again validated, first through a systematic literature review, and finally through a Delphi process. N = 14 faculty members who teach at the same university rate each concept and its relatedness to the initial taxonomy with regard to accuracy, completeness and relevance in the field of online-based learning and instruction. They then receive feedback on how the other teachers have assessed the taxonomy and have the opportunity to revise their own assessment. On the basis of a workshop, a final taxonomy is determined on a broad consensus-oriented process which can later be used for the pattern design. Finally, an additional panel of experts are selected and asked to rate the taxonomy. In a second step, we first assessed pre-service-teachers’ perception as well as teachers’ perception of an online-based course in mathematics. We’ve chosen a mixed-methods approach (which combines a quantitative and a qualitative approach), because this enables a more detailed insight which would have not been possible with a purely quantitative or qualitative approach (e.g., Teddlie & Tashakkorie, 2020).  The sample consists of N = 6 pre-service teachers in the domain of mathematics. They participate in an online-based seminar on didactics of mathematics which was conducted by three teachers. In the end, both the learners and the teachers evaluated the seminar in respect to key features of instructional quality (Helmke, 2008). All items are rated on a 5-point Likert scale by pre-service-teachers. In addition, the teachers were interviewed afterwards in a structured group interview (Lamnek, 2010). The interview was transcribed and analyzed afterwards by means of the qualitative content analysis (see, Mayring, 2008). Two assessors evaluated the interview by means of a coding scheme and rank the teachers (1 = low, 2 = medium, 3 = high). This was examined in order to get a deeper understanding of the complexity of online based, learning and instruction with digital tools and to better understand the teacher’s setting and didactical assumptions. The collected data then flowed into a database, which serves as a basis for examining effective teaching patterns of best-practice examples by means of pattern-mining processes. This will include Apriori algorithms, based on finding association rules and FSM algorithms in order to identify frequently occurring regulations in graph database.

Expected Results Initial Taxonomy The initial taxonomy that was created independently by two experts in the field of enhanced teaching and learning, consists of 74 key features distributed across three dimensions (context with 36 key features, digital didactics with 26 key feature and seminar script with 12 key features). This supports the findings of Valentijn et al. (2015) which yielded a taxonomy of 59 key features distributing across six dimensions after their literature review. So far, we have not yet analyzed the impact of the literature review or the impact of the Delphi process on our taxonomy which will be done in the next step. Assessment of instructional quality The assessment items revealed a Cronbach’s alpha of 0.81, which is satisfactory. It covered aspects of structuring, activating, motivating, learning strategies. Assessment of teachers’ didactical orientation A first analysis of the group interview revealed a deeper understanding of the complexity of educational technologies. The teachers pointed to the big challenge teaching with digital tools in mathematics when it comes to handwriting panel pictures. This was an issue which we had not been aware of beforehand. Discussion and Conclusion The presented study included only one seminar with a small group of learners. Therefore, we can make no systematic conclusions. The results indicate that when teachers are forced to change their in-classroom teaching into online-teaching, they have difficulties in implementing subject-specific issues like the handwritten panel pictures in mathematics. Our preliminary results indicate that teachers’ beliefs influence both their way of teaching and finally students’ perspective on teaching and learning. Consequently, teachers need to better be prepared and trained in utilizing digital tools. Acknowledgements This work was supported by BMBF InDiKo, effective integration of subject didactical digital teaching-learning-scenarios at the University of Education Karlsruhe (Grant No: 01JA2027).

References Alexander, C., Ishikawa, S., & Silverstein, M. (1977). A Pattern Language: Towns, Buildings,Construction. Oxford: University Press. Derntl, M. (2006). Patterns for person centered e-learning. University of Vienna. Fischler, H./Zedler, P./Schröder, H.-J./Tonhäuser, C. (2002). Unterrichtsskripts und Lehrerexpertise: Bedingungen ihrer Modifikation. In: Zeitschrift für Pädagogik. Hähnlein, I. (2018). Erfassung epistemologischer Überzeugungen von Lehramtsstudierenden.nEntwicklung und Validierung des StEB Inventars (Dissertation). Helmke, A. (2009). Unterrichtsqualität und Lehrerprofessionalität – Diagnose, Evaluation und Verbesserung des Unterrichts. Seelze, Kallmeyer. [School Quality and Teacher Professionalism – Diagnostics, Evaluation and Improvement of Instruction] Islam, N., Beer, M., & Slack, F. (2015). E-learning challenges faced by academics in higher education. Journal of Education and Training Studies, 3(5), 102-112. Lamnek, S. (2010). Qualitative Sozialforschung (5. Aufl.). Weinheim: Beltz. Mayring, P. (2008). Qualitative Inhaltsanalyse. Grundlagen und Techniken. Weinheim: Beltz Verlag. [Qualitative Content Analysis. Basics and Techniques] Mishra, P. & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108 (6), 1017-1054. Rich, P. (1992). The organizational taxonomy: Definition and design. The Academy of Management Review, 17(4), 758–781. https://doi.org/10.2307/258807 Standl, B. (2014). Conceptual Modeling and Innovative Implementation of Person-centered Computer Science Education at Secondary School Level. University of Vienna. Teddlie, C., & Tashakkori, A. (2010). Overview of contemporary issues in mixed methods research. In A. Tashakkori & C. Teddlie (Eds.), Sage handbook of mixed methods in social & behavioral research (2nd ed., S. 1-41). Thousand Oaks, CA: SAGE.  Valentijn PP, Boesveld IC, van der Klauw DM, Ruwaard D, Struijs JN, Molema JJ, et al. (2015). Towards a taxonomy for integrated care: a mixed-methods study. Int J Integr Care. 15. Vercoustre, A.-M. & McLean, A. (2005). Reusing educational material for teaching and learning: Current approaches and directions. International Journal on E-Learning, 4 (1), 57–68.