WS D: Systematic Searching in Educational Research – Workshop on Essential Strategies for Complex Literature Searches

Evidence syntheses, systematic reviews or meta-analyses in educational research are an important publication format for summing up existing knowledge, synthesizing research and making knowledge visible to research communities. Knowledge on systematic review methods like the literature search process is not as widespread as it should be (Gusenbauer & Haddaway, 2020). Emerging researchers particularly face challenges when they are confronted with a systematic review task for the first time. According to the systematic review process, the exhaustive literature search is one essential step to determine the totality of documents and should be performed very carefully to not overlook relevant publications and exclude them unintentionally (Booth et al., 2022; Levay & Craven, 2019). Factors for a successful literature search include the choice of databases and the elaboration of search terms. Transforming the research question expressed in natural language into a machine-understandable search requires knowledge about operators and syntax. Strategies like the Building blocks strategy, Citation pearl growing (Efthimiadis, 1996) or Berry picking (Bates, 1989) do help researchers develop an appropriate search concept. Researchers as content experts  in education — a field characterized by its transdisciplinary and cross-national/international dimension — are used to working out thematic concepts and descriptions. However, finding the right database(s) in the plenty of traditional online search systems (Gusenbauer, 2022) and the growing AI search landscape is challenging. AI search tools based on machine learning (ML) or large language models (LLM) offer promising options for an augmented and semantically rich literature retrieval. However, it is to examine if and how those new functionalities (see Lahrsow, 2025; Da Silva Cardoso et al., 2024; Parisi & Sutton, 2024) can be integrated into the individual search strategy to accelerate the process of a target-oriented and systematic search. 

This workshop follows ten steps required to fulfil a systematic and successful search process (Hirt & Nordhausen, 2019). Researchers learn how to structure complex research questions and choose suitable search terms for comprehensive queries. To avoid getting lost in midstream, knowledge about important databases helps to navigate safely in the “ocean of information”. This workshop aims at providing an overview of existing literature services in educational research discussing important databases and search engines. Particular attention is paid to type, size, scope, document categories, search options and other functionalities of the search system. The workshop informs about dimensions that affect essential steps in the search process like the notion of area in research questions, geographical, temporal, and linguistic issues and national and international aspects of search strategies. Based on several examples, main search strategies are presented. And it is demonstrated how changes in search query formulation can have considerable consequences regarding the number of relevant results. Information on documenting a systematic search process in an explicit and transparent manner is given. Finally, functionalities as well as limitations of selected AI applications for literature search are presented. Altogether, the workshop will encourage further discussion allowing (emerging) researchers to share their experiences regarding literature searches in educational research within national, cross-national, and international contexts.

Since there is no universal, ever fitting solution in information seeking in educational research, the careful selection of databases and other information sources remains a decision strongly connected to individual research goals. Hence, it is important to raise researchers’ awareness of the variety of information sources in a transdisciplinary and multidisciplinary field like educational research and to show them the methods for tailoring search queries. Learning from others’ practical experiences, especially with regard to AI retrieval applications, broadens the knowledge on how to search more systematically, with as many relevant results as possible, and also how to avoid unsuccessful and time-consuming search tasks.