An Exploratory Factor Analysis of the Online Information Literacy Self-Efficacy (OILS) Instrument
DOI:
https://doi.org/10.19173/irrodl.v27i3.8889Keywords:
information literacy, self-efficacy, online learning, factor analysis, undergraduate educationAbstract
The purpose of this study was to develop and validate an effective instrument to measure undergraduate students’ self-efficacy in engaging with information for online coursework. Recognizing the increasing importance of information literacy in online learning environments, the Online Information Literacy Self-Efficacy (OILS) instrument was designed to assess key competencies associated with students’ abilities to engage with information effectively. The validity and reliability of the OILS instrument were examined using exploratory factor analysis (EFA) and reliability analysis. The instrument initially comprised 30 items, grounded in two authoritative frameworks: the Association of College and Research Libraries (ACRL) Framework for Information Literacy and the Association of American Colleges and Universities (AAC&U) Information Literacy VALUE Rubric. Survey responses from 259 undergraduate students enrolled in online courses at a large Midwestern US R1 institution were analyzed using EFA and reliability analysis. EFA results revealed a four-factor structure explaining substantial variance in the item response patterns: scoping research topics, obtaining information, evaluating information quality and producing research documents, and crediting sources. These four factors demonstrated high reliability, with omega coefficients ranging from 0.891 to 0.960. One item was recommended for removal due to cross-loadings and skewness. Exploratory measurement invariance analyses suggested that the factor structure was comparable for students with and without prior online learning experience. The findings support the OILS instrument’s utility in measuring students’ confidence in applying information literacy skills in online learning contexts, offering valuable insights for educators and librarians to enhance instructional strategies and support student success.
References
Adigue, A. P., Tumacder, J. R. O., & Urbano, J. M. (2023). Self-efficacy and learning strategies in the context of online learning. In Proceedings of the 3rd International Conference on Education and Technology (ICETECH 2022) (pp. 547–564). Atlantis Press. https://doi.org/10.2991/978-2-38476-056-5_55
Association of American Colleges and Universities. (2009). Information literacy VALUE rubric. https://www.aacu.org/value/rubrics/information-literacy
Association of College and Research Libraries. (2015). Framework for information literacy for higher education. American Library Association. https://www.ala.org/acrl/standards/ilframework
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
Bradley, C. (2013). Information literacy in the programmatic university accreditation standards of select professions in Canada, the United States, the United Kingdom, and Australia. Journal of Information Literacy, 7(1), 44–68. https://doi.org/10.11645/7.1.1785
DeVellis, R. F. (2017). Scale development: Theory and applications. SAGE Publications.
Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., Järvelä, S., Mavrikis, M., & Rienties, B. (2024). The promise and challenges of generative AI in education. Behaviour & Information Technology, 44(11), 2518–2544. https://doi.org/10.1080/0144929X.2024.2394886
Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17, 63–84. https://doi.org/10.1016/j.edurev.2015.11.002
Hovious, A. (2024). Information creation as an AI prompt: Implications for the ACRL framework. Kansas Library Association College and University Libraries Section Proceedings, 14(1), Article 5. https://doi.org/10.4148/2160-942X.1094
Johnston, N. (2010). Is an online learning module an effective way to develop information literacy skills? Australian Academic & Research Libraries, 41(3), 207–218. https://doi.org/10.1080/00048623.2010.10721464
Kassorla, M., Georgieva, M., & Papini, A. (2024). AI literacy in teaching and learning: A durable framework for higher education. EDUCAUSE. https://www.educause.edu/content/2024/ai-literacy-in-teaching-and-learning/executive-summary
Little, T. D. (2013). Longitudinal structural equation modeling. Guilford Press.
McClellan, S. (2016). Teaching critical thinking skills through commonly used resources in course-embedded online modules. College & Undergraduate Libraries, 23(3), 313–327. https://doi.org/10.1080/10691316.2014.987416
McDonald, R. P. (1970). The theoretical foundations of principal factor analysis, canonical factor analysis, and alpha factor analysis. British Journal of Mathematical and Statistical Psychology, 23(1), 1–21. https://psycnet.apa.org/doi/10.1111/j.2044-8317.1970.tb00432.x
McGrew, S., Breakstone, J., Ortega, T., Smith, M., & Wineburg, S. (2018). Can students evaluate online sources? Learning from assessments of civic online reasoning. Theory & Research in Social Education, 46(2), 165–193. https://doi.org/10.1080/00933104.2017.1416320
Raykov, T., & Marcoulides, G. A. (2011). Introduction to psychometric theory. Routledge. https://doi.org/10.4324/9780203841624
UNESCO. (2023, September 7). Guidance for generative AI in education and research. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research
Weaver, K. D. (2024). The artificial intelligence disclosure (AID) framework: An introduction. College & Research Libraries News, 85(10), 407–411. https://doi.org/10.5860/crln.85.10.407
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License. The copyright for all content published in IRRODL remains with the authors.
This copyright agreement and usage license ensure that the article is distributed as widely as possible and can be included in any scientific or scholarly archive.
You are free to
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms below:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.




