WS D: Systematic searching in educational research – smart strategies for better results

Systematic searching in educational research is mandatory for research projects that build up on enormous amounts of literature, like systematic reviews and meta-analyses. Although evidence syntheses in educational research are regarded as important publications, knowledge on systematic review methods, especially on the literature search process, is not as widespread as it should be (Gusenbauer, 2020). According to the nature of the systematic review process, literature searching should also proceed systematically (Booth et al., 2022; Levay & Craven, 2019). This does include among other things the choice of databases and the elaboration of search terms. Transforming the research question expressed in natural language into a search string that can be understood and processed by the search system is intricate and requires knowledge about operators and syntax. Strategies from information science like the building blocks strategy (Efthimiadis, 1996) help researchers to structure their research question and thus make it easier to define and adapt search terms. As content experts, researchers are experienced in determining thematic concepts and working out the details of denominations and descriptions. However, finding the right database(s) in the plenty of academic search systems is challenging (Keller et al., 2022), as absolute and relative subject coverage of databases must be considered, and some databases still are not very explicit in their scope (Gusenbauer, 2022). Researchers doing complex literature searches in educational research characterized by its transdisciplinarity and cross-national and/or international dimension should keep in mind that there is, on the one side, no one-stop database solution for every research question, and, on the other side, the overlap between different databases is small (Wanyama et al., 2022). Besides, next generation search systems driven by artificial intelligence (AI) like machine learning (ML) or large language models (LLM) come into the market. Yet, it is to examine how those promising options for augmented literature retrieval with instantly generated search results analysis can be integrated in a systematic search process without missing relevant information.

This workshop follows the steps required to fulfil a systematic search process (Hirt & Nordhausen, 2019). To avoid getting lost in searching but instead being able to successfully navigate the “ocean of information” the workshop provides an overview of literature services in educational research and discusses the pros and cons of databases and search engines. Particular attention is paid to type of search system, size, scope, document categories and search options. The workshop informs about the dimensions that do not only frame the research question but affect essential steps in the whole search process. Emphasis is put on the notion of area in research questions, involving geographical, temporal, and linguistic issues. Main search strategies are presented. It is demonstrated that only minor changes in search query formulations can have considerable consequences in what concerns the number of relevant search results. Information on being explicit about search strategies and documenting systematic search processes openly and in a transparent manner is given.

Since there is no universal solution in information seeking in educational research, the careful selection of databases, be it traditional or AI driven, search engines, and other resources like academic social networks, remains an individual decision strongly connected to research goals. Hence, it is important to raise researchers’ awareness of the variety of databases and other information resources in a transdisciplinary and multidisciplinary field like educational research. This is strengthened by gaining insight in the methods of searching literature systematically and the many ways to tailor search queries to the particular needs of complex research questions. Learning from others’ practical experiences broadens the knowledge how to search more systematically and also how to avoid fruitless and unsuccessful search tasks.