Advances related to the teaching of microbiology: a bibliometric analysis
DOI:
https://doi.org/10.61347/rcem.v1i2.e10Keywords:
Learning, bibliometric analysis, bibliometrix, biology of microorganisms, teaching, microbiologyAbstract
The teaching of microbiology is essential for scientific training, as it promotes the understanding of fundamental biological processes and their impact on areas such as health, industry, environment, and medicine. This study aimed to conduct a bibliometric analysis to characterize the scientific production related to the teaching of microbiology. A systematic search was carried out in the Scopus database, collecting 702 documents published between 1943 and 2025, which were analyzed using bibliometric indicators and network visualization techniques through the Bibliometrix tool. The results revealed a sustained growth in scientific production, particularly since 2015, reflecting a growing interest in pedagogical innovation within the microbiological field. The Journal of Microbiology and Biology Education and Zhurnal Mikrobiologii Epidemiologii were identified as the most influential sources, while institutions such as the Universidade de São Paulo and Heilongjiang University stood out for their productivity. Geographically, China and the United States lead research efforts and maintain strong international collaboration networks. The thematic analysis revealed three main focus areas: fundamental topics centered on microbiology education, specialized topics in clinical and biomedical aspects, and emerging topics related to the use of artificial intelligence and machine learning. Overall, the findings provide insight into the evolution and future projection of microbiology education within the global scientific context.
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Abbas, A., Jusoh, A., Mas’od, A., Alsharif, A., & Ali, J. (2022). Bibliometrix analysis of information sharing in social media. Cogent Business & Management, 9(1), 2016556. https://doi.org/10.1080/23311975.2021.2016556
Abdipour, S., Noushadi, F., Pourasghar, Y., Farkarian, A., Meftah, E., Homayounifar, F., & Amanati, A. (2025). Prevalence, microbiology, and antimicrobial susceptibility profile of bacterial skin and soft tissue infections in pediatric patients with malignancies at a referral teaching hospital in Shiraz, Iran. BMC Infectious Diseases, 25(1), 707. https://doi.org/10.1186/s12879-025-11059-2
Adams, M., McClure, P., & Moss, M. (2015). Food Microbiology (4.ª ed.). Royal Society of Chemistry. https://acortar.link/oikGPs
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Azizoğlu, F., Terzi, B., & Sönmez, D. (2024). Global trends in technology-dependent children, home care, and parental discharge education: A bibliometric analysis using Biblioshiny. Journal of Pediatric Nursing, 79, e213-e222. https://doi.org/10.1016/j.pedn.2024.10.024
Blewett, E., & Kisamore, J. (2009). Evaluation of an interactive, case-based review session in teaching medical microbiology. BMC Medical Education, 9(1), 56. https://doi.org/10.1186/1472-6920-9-56
Brockman, R., Taylor, J., Segars, L., Selke, V., & Taylor, T. (2020). Student perceptions of online and in-person microbiology laboratory experiences in undergraduate medical education. Medical Education Online, 25(1), 1710324. https://doi.org/10.1080/10872981.2019.1710324
Büyükkıdık, S. (2022). A Bibliometric Analysis: A Tutorial for the Bibliometrix Package in R Using IRT Literature. Journal of Measurement and Evaluation in Education and Psychology, 13(3), 164-193. https://doi.org/10.21031/epod.1069307
Campra, M., Riva, P., Oricchio, G., & Brescia, V. (2022). Bibliometrix analysis of medical tourism. Health Services Management Research, 35(3), 172-188. https://doi.org/10.1177/09514848211011738
Chen, Y., Lin, M., & Zhuang, D. (2022). Wastewater treatment and emerging contaminants: Bibliometric analysis. Chemosphere, 297, 133932. https://doi.org/10.1016/j.chemosphere.2022.133932
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
Erdem, I., Ozgultekin, A., Inan, A. S., Dincer, E., Turan, G., Ceran, N., Ozturk Engin, D., Senbayrak Akcay, S., Akgun, N., & Goktas, P. (2008). Incidence, etiology, and antibiotic resistance patterns of gram-negative microorganisms isolated from patients with ventilator-associated pneumonia in a medical-surgical intensive care unit of a teaching hospital in istanbul, Turkey (2004-2006). Japanese journal of infectious diseases, 61(5), 339-342. https://doi.org/10.7883/yoken.JJID.2008.339
Fidiastuti, H., Lestari, S., & Prabaningtyas, S. (2024). Developing microbiology literacy in biology education college: future teacher candidates. Journal of Microbiology & Biology Education, 25(2), e00035-24. https://doi.org/10.1128/jmbe.00035-24
Forouzani, F., Khasti, T., Manzouri, L., Ravangard, S., Shahriarirad, R., Koleini, M., Ayareh, N., & Nikbakht, G. (2023). Resistance pattern of isolated microorganisms from 783 clinical specimen cultures in patients admitted to Yasuj Educational Hospitals, Iran. BMC Microbiology, 23(1), 205. https://doi.org/10.1186/s12866-023-02952-4
González, W. (2023). The Teaching-Learning Process or the Teaching Process and the Learning Process. Culture & Psychology, 29(1), 96-115. https://doi.org/10.1177/1354067X221097610
Guzmán, D., & Castillo, A. (2022). Cambios en el proceso de enseñanza aprendizaje: desafíos en la práctica docente desde análisis de carrera universitaria chilena. Revista Educación, 46(1). 263-279. https://doi.org/10.15517/revedu.v46i1.45593
Jandu, N. (2019). Teaching resources, microbiology. En T. M. Schmidt (Ed.), Encyclopedia of microbiology (4th ed., pp. 395–403). Academic Press. https://doi.org/10.1016/B978-0-12-809633-8.13116-4
Kulwa, F., Li, C., Zhang, J., Shirahama, K., Kosov, S., Zhao, X., Jiang, T., & Grzegorzek, M. (2022). A new pairwise deep learning feature for environmental microorganism image analysis. Environmental Science and Pollution Research, 29(34), 51909-51926. https://doi.org/10.1007/s11356-022-18849-0
Lazarides, M., Lazaridou, I.-Z., & Papanas, N. (2025). Bibliometric Analysis: Bridging Informatics With Science. The International Journal of Lower Extremity Wounds, 24(3), 515-517. https://doi.org/10.1177/15347346231153538
Le, H., Nguyen, A., Vu, L., Nguyen, V., Pham, H., Le, H., Nguyen, S., Le, H., Dinh, H., Le, N., Le, T., Le, M., Nguyen, V., Hoang, K., & Le, H. (2024). Antimicrobial Resistance Patterns of Common Gram-Negative Microorganisms Isolated from Patients with Lower Respiratory Tract Infections in a Teaching Hospital in Vietnam. Japanese Journal of Infectious Diseases, 77(3), 144-154. https://doi.org/10.7883/yoken.JJID.2023.260
Lei, X., Hamamurad, Q., & Zakaria, N. (2025). Assessing business model innovation in high-tech startups with Biblioshiny. Future Business Journal, 11(1), 172. https://doi.org/10.1186/s43093-025-00588-6
Ma, P., Li, C., Rahaman, M., Yao, Y., Zhang, J., Zou, S., Zhao, X., & Grzegorzek, M. (2023). A state-of-the-art survey of object detection techniques in microorganism image analysis: From classical methods to deep learning approaches. Artificial Intelligence Review, 56(2), 1627-1698. https://doi.org/10.1007/s10462-022-10209-1
Opara, E., Mfon-Ette Theresa, A., & Aduke, T. (2023). ChatGPT for Teaching, Learning and Research: Prospects and Challenges. Global Academic Journal of Humanities and Social Sciences, 5(2), 33-40. https://papers.ssrn.com/abstract=4375470
Pervez, A., Sarker, M., Uddin, M., & Kabir, M. (2025). Soil Salinity and Climate Change: A Bibliometric Analysis Based on the Scopus Database. Journal of Agricultural Sciences–Sri Lanka, 20(3), 429–446. https://doi.org/10.4038/jas.v20i3.10760
Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World. Publications, 9(1), 12. https://doi.org/10.3390/publications9010012
Qumsani, A. (2024). The contribution of microorganisms to sustainable development: Towards a green future through synthetic biology and systems biology. Journal of Umm Al-Qura University for Applied Sciences, 1-17. https://doi.org/10.1007/s43994-024-00180-8
Raja, N. (2007). Microbiology of diabetic foot infections in a teaching hospital in Malaysia: a retrospective study of 194 cases. Journal of Microbiology, Immunology and Infection, 40(1), 39-44. https://pubmed.ncbi.nlm.nih.gov/17332905/
Rani, P., Kotwal, S., Manhas, J., Sharma, V., & Sharma, S. (2022). Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments. Archives of Computational Methods in Engineering, 29(3), 1801-1837. https://doi.org/10.1007/s11831-021-09639-x
Scalas, D., Roana, J., Mandras, N., Cuccu, S., Banche, G., Marra, E. S., Collino, N., Piersigilli, G., Allizond, V., Tullio, V., & Cuffini, A. (2017). The Microbiological@mind project: A public engagement initiative of Turin University bringing microbiology and health education into primary schools. International Journal of Antimicrobial Agents, 50(4), 588-592. https://doi.org/10.1016/j.ijantimicag.2017.05.008
Shaikh, A., & Khan, M. (2025). Bibliometric review of behavioral finance and risk: A scientometric analysis using biblioshiny. Multidisciplinary Reviews, 8(7), 2025233. https://doi.org/10.31893/multirev.2025233
Soares, H., Armellini, B., Naves, G., Bueris, V., Moreno, A., & Café, R. (2024). Using “Adopt a Bacterium” as an e-learning tool for simultaneously teaching microbiology to different health-related university courses. FEMS Microbiology Letters, 371, fnae033. https://doi.org/10.1093/femsle/fnae033
Tristán-Ramos, P., Merroun, M., & Ruiz-Fresneda, M. (2024). Current educational strategies addressing antibiotic resistance: A bibliometric analysis. Discover Education, 3(1), 254. https://doi.org/10.1007/s44217-024-00360-y
Vieira, M. (2025). Bringing microbiology to life: Problem-based learning activities enhance student engagement and clinical reasoning in the early medical curriculum. FEMS Microbiology Letters, 372, fnaf075. https://doi.org/10.1093/femsle/fnaf075
Xu, Y., Ding, X., Wang, W., Li, Y., & Nie, M. (2024). Analysis of ten-year teaching evaluation of oral microbiology lab curriculum. BMC Medical Education, 24(1), 309. https://doi.org/10.1186/s12909-024-05298-1
Zafrullah, Z., Ramadhani, A., Ayuni, R., Fadhilla, N., & Safitri, R. (2024). The Using Confirmatory Factor Analysis as Construct Validity in Education Research: An Analysis with Biblioshiny. DIROSAT: Journal of Education, Social Sciences & Humanities, 2(3), 206-220. https://doi.org/10.58355/dirosat.v2i3.70
Zhang, J., Li, C., Rahaman, M., Yao, Y., Ma, P., Zhang, J., Zhao, X., Jiang, T., & Grzegorzek, M. (2022). A comprehensive review of image analysis methods for microorganism counting: From classical image processing to deep learning approaches. Artificial Intelligence Review, 55(4), 2875-2944. https://doi.org/10.1007/s10462-021-10082-4
Zhang, Y., Jing, Z., Feng, Y., Chen, S., Li, Y., Han, Y., Feng, L., Pan, J., Mazarji, M., Zhou, H., Wang, X., & Xu, C. (2023). Using automated machine learning techniques to explore key factors in anaerobic digestion: At the environmental factor, microorganisms and system levels. Chemical Engineering Journal, 475, 146069. https://doi.org/10.1016/j.cej.2023.146069
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