We have conducted an implementation science project exploring the systematic effect of equipping primary school teachers to incorporate digital learning materials (DLM) into classroom activities. As part of the implementation science framework used in the study–Active Implementation Framework–we drew upon a mixed-methods approach collecting data from a series of interviews, observations, surveys, log-books, and analysis of the data collected from the DLM.

This study explores 34 teachers’ needs, first, and understanding, then, of the integration of DLM into teaching and learning activities developed over time and during the four stages of the implementation project. Teachers participated in a series of workshops based on their expressed needs and then systematically used the DLM, integrating them, during the workshops, into classroom activities. This research is solely based on the explorative thematic analysis of the interview data collected at three intervals, before, during and towards the end of the implementation. All interviews were conducted with Zoom and recorded. We seek to answer the research question: What expectations about digital learning material in relation to teachers’ teaching emerge from the interview data, and how do they differ from a temporal perspective?

The researchers have used ChatGPT-4o to analyse the interview data for two reasons. First, to understand if ChatGPT-4o does a sufficient and maybe even surprising job at capturing the intricacy of practitioners’ concepts about the scope of the research. Second, to check for the accuracy of the analysis by listening to the interviews and going back to the transcribed raw data at random times to build a human-machine trust for future research purposes. It is crucial to pinpoint that the researchers have transcribed all the interviews by Whisper, and then listened to all the interviews while decoding each speaker.

The preliminary results indicate that:1) Teachers anticipated digital tools would enhance student engagement but recognized the need to adjust their teaching methods to integrate these technologies effectively.2) Teachers encountered difficulties such as technical problems, inadequate training, and challenges in keeping students focused and engaged with digital platforms.3) Variations in students' digital literacy affected their ability to engage with digital materials, with some excelling while others struggled, leading to uneven learning experiences.4) The temporal evolution of teacher perspectives on digital learning shows that while initially hesitant, many teachers' attitudes toward digital tools shifted over time as they adapted to and, in some cases, came to value the flexibility these tools offered. Finally, the analysis conducted through ChatGPT-4o was checked for accuracy by randomly choosing 10 events for which the researchers asked ChatGPT-4o to point to specific codes from the interviews and the specific speaker. This resulted in an accurate analysis, which was also supported by the researcher who had listened to all the interviews.

This study demonstrates the positive effects of systematic professional development interventions, the core of our project. It could indicate the impact of professional development on teachers' competence and confidence in using digital tools. But also that ChatGPT-4o can be used for conducting a thematic analysis, especially when the researcher is faced with large interview raw datasets.

This study is part of a longitudinal, interdisciplinary implementation research project examining how systematic professional development shapes teachers' digital teaching practices. As schools and other educational organisations continue to adopt Digital Learning Materials (DLM), it is essential to understand the ways in which these shifts affect educators' perspectives and practices. This specific study addresses the research question: What discourses on digital learning material emerge in schools involved in a digitalisation project when a traditional (manual) discourse analysis is applied? We are particularly interested in how discourses are used and constructed by teachers and principals within pedagogical practice. 

Data consist of group interviews conducted at three points during the course of the project: at the beginning of the project, after one and a half years, and finally after two and a half years. In each phase, a total of 47 teachers, and school principals from five schools across four municipalities in Sweden were interviewed. 

The results of the analysis reveal both (A) emerging discourses on conceptualization and idealization of DLM and (B) how the discursive field evolves during the project. Within A) Emerging discourses centred on: DLM as a pedagogical tool; DLM as an effectivization tool; DLM as the limit in the Digital/Analog divide. Within B) Discourses of the digital-analog; Discourses of DLM; Discourses of DLM in teaching.

To conclude, the first part of the results provides a broad overview of the discourses that were present throughout the project. The focus here is on DLMs as part of the teaching context and how DLMs gain significance through how the analog teaching is conducted. The second part of the results becomes more detailed through its division of data into three sampling points and shows how different discourses become hegemonic, suppressed, and transformed throughout the project.

This paper presents a methodological case study on teachers' digital professional development, emphasizing the integration of automated and humanistic approaches. Drawing from a four-year pilot project led by the research group, we explore how three distinct analytical methodologies-manual discourse analysis, text mining, and large language model-assisted thematic analysis-were employed to examine teachers' discursive practices regarding digital learning materials. The study investigates how integrating these methodologies enhances our understanding of digital learning material-related discourses and their evolution over time. Key findings reveal two primary conceptualizations: digital learning materials as pedagogical/effectivization tools and as complementary to analogue resources. The integrated approach demonstrated advantages in mitigating methodological biases, improving reliability, and enabling a richer analysis of diverse data sources. This work contributes to the development of robust analytical frameworks for studying the intersection of technology and pedagogy in educational settings.

This paper discusses the explanatory power of discourse analysis, an approach to typically considered one of the main qualitative methods for data analysis. Although discourse analysis is typically not used to develop explanatory models, some have claimed that discourse models can causally explain social phenomena, such as institutional change. I analyze a case of institutional change to discuss and provide arguments for two claims. First, discourse analysis cannot generate causal explanations of social phenomena. Secondly, discourse analysis can generate non-causal explanations, specifically constitutive explanations of social phenomena. I conclude the paper by discussing possible implications for social research.

In this article, we discuss the methodological implications of data and theory integration for Theory-Based Evaluation (TBE). TBE is a family of approaches to program evaluation that use program theories as instruments to answer questions about whether, how, and why a program works. Some of the groundwork about TBE has expressed the idea that a proper program theory should specify the intervening mechanisms underlying the program outcome. In the present article, we discuss in what way data and theory integration can help evaluators in constructing and refining mechanistic program theories. The paper argues that a mechanism is both a network of entities and activities and a network of counterfactual relations. Furthermore, we argue that although data integration typically provides information about different parts of a program, it is the integration of theory that provides the most important mechanistic insights.

In this paper, we discuss a set of procedures for the visualization of conversation data, based on machine learning and text mining algorithms. The methodological function of these visualization methods is to support (rather than substitute) humanistic and interpretive qualitative data analysis. The context of our methodological discussion is the study of teachers’ learning communities. Enabling collegial learning among teachers has been argued to play a pivotal role in both teachers’ professional development and students’ learning. The problem is that the interpretive study of collegial discussions among teachers, which are typically considered a fruitful context for the study of teachers' collegial learning, is both time-consuming and cognitively demanding. Several scholars have discussed the potential of including machine learning and text mining in the toolbox of qualitative data analysis as a way to analyze large qualitative data sets and solve the time demand problem. However, the literature discussing the use of machine learning and text mining for qualitative research typically conceives the algorithms as substitutes for the work of the researcher. These automatized approaches are introduced to categorize the data, substituting the researcher in the analysis process. In contrast, we discuss the potential rewards of using automatized methods to support the analysis of teachers’ collegial discussions. We introduce a set of visualization procedures based on semi-supervised topic modeling and supervised classification algorithms and discuss how these visualizations can provide additional elements useful for the analysis of collegial discussions. Whereas these visualizations do not substitute interpretive analysis, we argue that they can support researchers’ interpretations by reducing data, identifying patterns of interactions, and possibly, visualizing group-level learning.

In this paper, I discuss and critically assess how the relationship between philosophy of science and qualitative methods is presented and discussed in research on qualitative research methods education (QRME) and qualitative methods textbooks. I argue that both typically convey the idea that philosophy of science guides or influences the use and choice of qualitative methods but are often unclear about how this influence works. I propose two conceptualizations of the relationship between philosophy of science and qualitative methods: philosophical assumptions can either explicate or explain qualitative methods. I argue that both approaches have pedagogical rewards, but that whereas the explanation approach is explicitly or implicitly used in many examples of research on QRME and textbooks, the explication approach has not had wide application. I conclude by arguing that the lack of clarity and the absence of discussion about explication are potential problems for qualitative research methods education.

I denna artikel diskuterar jag grunderna för tolkning som metodologisk ansats. Pedagogisk forskning har under lång tid gett begreppet tolkning en central metodologisk och teoretisk roll, och flera populära metoder inom pedagogik syftar till att generera tolkningar av mänskligt agerande. Jag argumenterar för två huvudsakliga teser. Enligt den första tesen är tolkningens struktur abduktiv. Enligt en vanlig konceptualisering, som härstammar från C.S. Peirces arbete, är abduktion ett resonemangsschema som innebär att man utifrån premisser bestående av en mängd observationer och en teori, härleder slutsatsen att teorin är (tentativt) sann. Den andra tesen som jag argumenterar för är att när tolkningen används som metodologiskt redskap, måste den vila på en speciell form av abduktion: selektiv abduktion. Selektiv abduktion innebär att flera hypotetiska teorier jämförs och att den teori som bäst förklarar observationerna accepteras. Jag argumenterar för att de resonemang som är typiska för tolkande ansatser i samhällsvetenskapen inte kan undvika en kontrastiv, jämförande och därmed selektiv dimension. Att tolka är i sitt väsen att jämföra. Samtidigt kritiserar jag att Peirces ursprungliga konceptualisering kan vara ett korrekt metodologiskt tillvägagångssätt för tolkning. Jag avslutar med att diskutera vissa konkreta metodologiska konsekvenser för kvalitativa metoder.