There is no research without information and resources. It is important to recognise that finding resources and information goes beyond simply having a skill. One of the key literacies in this process is search engine literacy. Search engine literacy involves knowing how to use search engines effectively to locate accurate and relevant information. This includes using advanced search techniques, evaluating the credibility of sources, and avoiding misinformation. Search engine literacy can be seen as transliteracy, as it utilises a combination of information, digital, and communication skills (Le Deuff, 2018).
Your search goals dictate the approach you take.
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For instance, a systematic review requires comprehensive searches, while quick information needs a more targeted approach. Heuristics, or search strategies, vary accordingly. Advanced techniques like Boolean operators are useful for systematic reviews, while simple keyword searches work for quick lookups. The systems you use, such as PubMed for medical research or Google Scholar for general searches, should align with your goals and heuristics. |
Adopted from: Gusenbauer, M., & Haddaway, N. R. (2021). What every researcher should know about searching–clarified concepts, search advice, and an agenda to improve finding in academia. Research Synthesis Methods, 12(2), 136-147. https://doi.org/10.1002/jrsm.1457
Effective searching requires a good match between these three components to improve search efficiency and outcomes. Your goals should dictate the heuristics you use, and the heuristics should be supported by the systems you choose. When these components are well-matched, your search is more likely to be efficient and successful.
Why effective search mattters?
| Enhances research quality | Saves time | Broadens knowledge | Supports evidence-based decisions |
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| Ensures you find accurate and relevant information | Helps you quickly locate the information you need. | Exposes you to a wider range of sources and perspectives. | Provides a solid foundation for making informed choices. |
Traditional search methods
| Boolean search |
Search uses logical operators (AND, OR, NOT) to combine or exclude keywords in a search, refining the results. How does it work? It focuses on the relationships between keywords. |
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| Keyword search |
Search involves using specific words or phrases to search for information anywhere in the text (title, abstract, full text). How does it work? It searches for the exact keywords entered by the user. |
| Subject headings |
Subject headings are controlled vocabulary terms assigned by indexers to describe the content of documents. How does it work? It uses standardised terms to ensure consistency and accuracy in search results. |
| Citation chaining (Snowballing) |
Citation networking enables the discovery of additional relevant sources by examining the papers referenced by the authors you read or by checking who later cited those papers. Every citation can guide you to another piece of your research (Stapleton, 2024). This method of referencing can lead you to scholarly journals and publications that you might not have discovered through a standard database search. How does it work? Begin with a highly relevant article and check its references (backward search) and the “cited by” feature (forward search) to discover additional sources. |
AI-assited search methods
Semantic search goes beyond keywords to understand the intent and contextual meaning behind a query. It uses advanced techniques like natural language processing and knowledge networks to capture relationships between concepts.
| AI-powered search engines |
AI-powered academic search engines, like Google Scholar, leverage advanced algorithms to rapidly identify relevant research papers. These systems analyse vast amounts of scholarly data to understand the context, significance, and relationships between publications. They rank search results using factors such as citation counts, publication dates, and semantic relevance to the user's query, enabling more accurate and efficient discovery of academic literature. |
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| Natural Language Processing (NLP) |
Natural Language Processing (NLP) is a branch of artificial intelligence that enables machines to understand, interpret, and respond to human language. When integrated into search engines, NLP allows users to interact using natural, conversational queries rather than relying on keyword combinations. For instance, instead of typing specific terms, users can ask full questions. The search engine uses NLP to grasp the context and intent behind the query, delivering results that are more relevant and meaningful. |
Hybrid Search: Combining keyword and semantic searches
Hybrid search combines the strengths of both lexical and semantic search methods. By doing so, it offers more accurate and relevant search results, particularly for complex queries where users may not know the exact terms to use.
Common challenges you may face
| Keyword selection | Identifying appropriate keywords to accurately represent your research topic. |
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| Boolean logic complexity | Constructing effective Boolean queries, which can be complex and time-consuming. |
| Search result management | Managing large volumes of search results, which can quickly become overwhelming. |
| Content currency | Keeping up with database updates, as content is frequently added or modified. |
| Balancing your time | Balancing time between searching, analysing results, and exploring related areas. |
| Source evaluation | Evaluating the credibility of sources to ensure reliable and academically sound information. |
| Avoiding information overload | Avoiding information overload, especially when dealing with broad or interdisciplinary topics. |