Skip to main content

Advertisement

Log in

A diagnostic classification model of college instructors’ value beliefs towards open educational resources

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Open educational resources (OER) can be cost-effective alternatives to traditional textbooks for higher education faculty to decrease student spending on textbooks. To further advocate college instructors’ use of OER, understanding their value belief towards integrating OER in teaching is necessary but currently absent. This study thus analyzed 513 college instructors’ value beliefs about using OER in college teaching by applying a psychometric model known as diagnostic classification models (DCMs). The findings of this study validated the three constructs in value beliefs measured by an OER user survey: engaging students, customizing classroom materials and supporting personal professional development. The results showed that a considerable number of college instructors maintained a low level of value beliefs towards using OER. We further provided individualized classification for each college instructor in terms of the three types of value beliefs. In addition, this study investigated how pre-determined latent classes of value beliefs influenced college instructors’ practice and perception of using OER. Particularly, college instructors who value OER to address their profession needs are more likely to adapt OER in their teaching rather than merely reusing existing copies. Practical implications of supporting higher education faculty’s use of OER are discussed in the end.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The datasets generated and/or analysed during the current study are available in the Figshare repository, at https://doi.org/10.6084/m9.figshare.1317313.v1, reference number 1,317,313.

References

  • Affordable Learning Georgia. (n.d.). Textbook transformation grants: Data dashboard. Retrieved from https://www.affordablelearninggeorgia.org/about/powerbi

  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.

    Article  MathSciNet  MATH  Google Scholar 

  • Anderson, S. E., & Maninger, R. M. (2007). Preservice teachers’ abilities, beliefs, and intentions regarding technology integration. Journal of Educational Computing Research, 37(2), 151–172.

    Article  Google Scholar 

  • Bao, Y. (2019). The Polytomous Diagnostic Classification Model (Doctoral Dissertation). https://www.libs.uga.edu/etd.

  • Bao, Y., & Bradshaw, L. (April, 2018). A Diagnostic Classification Model for Polytomous Attribute Paper presented at the National Council on Measurement in Education, New York City, New York, U.S.

  • Bice, H., & Tang, H. (2022). Teachers’ beliefs and practices of technology integration at a school for students with dyslexia: A mixed methods study. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11044-1.

    Article  Google Scholar 

  • Bossu, C., & Willems, J. (2017). OER based capacity building to overcome staff equity and access issues in higher education. In: Proceedings ASCILITE2017: 34th International Conference on Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education (pp. 22–26), Toowoomba, Australia.

  • Bozdogan, H. (1987). Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52(3), 345–370.

    Article  MathSciNet  MATH  Google Scholar 

  • Chiorescu, M. (2017). Exploring Open Educational Resources for College Algebra.The International Review of Research in Open and Distributed Learning, 18(4).

  • Colvard, N. B., Watson, C. E., & Park, H. (2018). The impact of open educational resources on various student success metrics.International Journal of Teaching and Learning in Higher Education, 30(2). https://files.eric.ed.gov/fulltext/EJ1184998.pdf

  • Croteau, E. (2017). Measures of student success with textbook transformations: The Affordable Learning Georgia Initiative. Open Praxis, 9(1), 93–108.

    Article  MathSciNet  Google Scholar 

  • Doi, C., Lucky, S., & Rubin, J. E. (2022). Open educational resources in the time of COVID-19: Two case studies of open video design in the remote learning environment. KULA: Knowledge Creation, Dissemination, and Preservation Studies, 6(1), 1–15.

  • Freeman, A. Tang, H. & Geary, J. (2022). Understanding Mathematics Instructors’ Perceptions of OER. Journal of Open Educational Resources in Higher Education, 1(1) 145-162. https://doi.org/10.13001/joerhe.v1i1.7147

    Article  Google Scholar 

  • Farrow, R., Pitt, R., de los Arcos, B., Perryman, L. A., Weller, M., & McAndrew, P. (2015). Impact of OER use on teaching and learning: Data from OER Research Hub (2013–2014). British Journal of Educational Technology, 46(5), 972–976. https://doi.org/10.1111/bjet.12310.

    Article  Google Scholar 

  • Hendricks, C., Reinsberg, S. A., & Rieger, G. W. (2017). The adoption of an open textbook in a large Physics course: An analysis of cost, outcomes, use, and perceptions. The International Review of Research in Open and Distributed Learning, 18(4), 78–99.

    Article  Google Scholar 

  • Hilton, J. (2019). Open educational resources, student efficacy, and user perceptions: A synthesis of research published between 2015 and 2018. Educational Technology Research and Development, 68(3), 853–876.

    Article  MathSciNet  Google Scholar 

  • Hilton, J. (2016). Open educational resources and college textbook choices: A review of research on efficacy and perceptions. Educational Technology Research and Development, 64(4), 573–590.

    Article  Google Scholar 

  • Hilton, J. L., Gaudet, D., Clark, P., Robinson, J., & Wiley, D. (2013). The adoption of open educational resources by one community college math department. The International Review of Research in Open and Distributed Learning, 14(4), 37–50. https://doi.org/10.19173/irrodl.v14i4.1523.

    Article  Google Scholar 

  • Hsu, C. Y., Tsai, M. J., Chang, Y. H., & Liang, J. C. (2017). Surveying in-service teachers’ beliefs about game-based learning and perceptions of technological pedagogical and content knowledge of games. Journal of Educational Technology & Society, 20(1), 134–143.

    Google Scholar 

  • Jhangiani, R. S., Barker, J., Jeffery, K., & Veletsianos, G. (2018). Eight patterns of open textbook adoption in British Columbia. International Review of Research in Open and Distributed Learning, 19(3), 320–334.

    Google Scholar 

  • Jhangiani, R. S., Pitt, R., Hendricks, C., Key, J., & Lalonde, C. (2016). Exploring faculty use of open educational resources at British Columbia post-secondary institutions.BCcampus Research Report. https://bccampus.ca/2016/01/27/exploring-faculty-use-of-open-educational-resources-in-b-c-post-secondary-institutions/

  • Jung, E., Bauer, C., & Heaps, A. (2017). Higher education faculty perceptions of open textbook adoption.The International Review of Research in Open and Distributed Learning, 18(4).

  • Kopcha, T. J. (2012). Teachers’ perceptions of the barriers to technology integration and practices with technology under situated professional development. Computers & Education, 59(4), 1109–1121.

    Article  Google Scholar 

  • Mtebe, J. S., & Raisamo, R. (2014). Investigating perceived barriers to the use of open educational resources in higher education in Tanzania. International Review of Research in Open and Distributed Learning, 15(2), 43–66.

    Article  Google Scholar 

  • Ngimwa, P., & Wilson, T. (2012). An empirical investigation of the emergent issues around OER adoption in Sub-Saharan Africa. Learning Media and Technology, 37(4), 398–413. https://doi.org/10.1080/17439884.2012.685076.

    Article  Google Scholar 

  • OERRH (2014). OER Research Hub. Available at http://oerresearchhub.org/ (Last accessed 8 May 2015).

  • Ottenbreit-Leftwich, A. T., Glazewski, K. D., Newby, T. J., & Ertmer, P. A. (2010). Teacher value beliefs associated with using technology: Addressing professional and student needs. Computers & Education, 55(3), 1321–1335.

    Article  Google Scholar 

  • Ozdemir, O., & Hendricks, C. (2017). Instructor and student experiences with open textbooks, from the California open online library for education (Cool4Ed). Journal of Computing in Higher Education, 29(1), 98–113.

    Article  Google Scholar 

  • Read, K., Tang, H., Lovett, A., & Bodily, R. (2020). Understanding the impact of OER courses in relation to student socioeconomic status and employment. International Journal of Open Educational Resources, 3(1), https://doi.org/10.18278/ijoer.3.1.5.

  • Ross, H., Hendricks, C., & Mowat, V. (2018). Open textbooks in an introductory sociology course in Canada: Student views and completion rates. Open Praxis, 10(4), 393–403.

    Article  Google Scholar 

  • Rupp, A., Templin, J., & Henson, R. (2010). Diagnostic Measurement: Theory, Methods, and Applications. Guilford Press.

  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.

    Article  MathSciNet  MATH  Google Scholar 

  • Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52(3), 333–343. https://doi.org/10.1007/BF02294360.

    Article  Google Scholar 

  • Slapak-Barski, J., Lewis, D., Flum, L., & Spieler, R. (2014, October). Multiple approaches to faculty development: How to play the game with the cards you’ve been dealt. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 1843–1852). Association for the Advancement of Computing in Education.

  • Tang, H. (2021a). Teaching teachers to use technology through Massive Open Online Course: Perspectives of interaction equivalency, Computers & Education, 174(2021), 104307. https://doi.org/10.1016/j.compedu.2021.104307.

  • Tang, H. (2021b). Implementing open educational resources in digital education. Educational Technology Research and Development, 69(1), 389–392. https://doi.org/10.1007/s11423-020-09879-x.

    Article  MathSciNet  Google Scholar 

  • Tang, H. (2021c). Person-centered analysis of self-regulated learner profiles in MOOCs: A cultural perspective. Educational Technology Research and Development, 69(2), 1247–1269. https://doi.org/10.1007/s11423-021-09939-w.

    Article  MathSciNet  Google Scholar 

  • Tang, H. (2020). A qualitative inquiry of teachers’ experience with open educational practices: Perceived benefits and barriers of adopting open educational resources in K-12 settings. International Review of Research in Open and Distributed Learning, 21(3), 211–229. https://doi.org/10.19173/irrodl.v21i3.4750.

    Article  MathSciNet  Google Scholar 

  • Tang, H., & Bao, Y. (2021). A person-centered approach to understanding K-12 teachers’ barriers in implementing open educational resources. Distance Education, 42(4), 582–598. https://doi.org/10.1080/01587919.2021.1986371.

    Article  Google Scholar 

  • Tang, H., & Bao, Y. (2020). Social justice and K-12 teachers’ effective use of OER: A cross-cultural comparison by nations. Journal of Interactive Media in Education, 2020(1), https://doi.org/10.5334/jime.576.

  • Tang, H. & Bao, Y. (2022). Self-regulated learner profiles in MOOCs: A cluster analysis based on the item response theory. Interactive Learning Environments. https://doi.org/10.1080/10494820.2022.2129394

  • Tang, H., Lin, Y., & Qian, Y. (2021). Implementing renewable assignments to facilitate K-12 educators’ acceptance of Open Educational Resources: A mixed-methods inquiry. Educational Technology Research and Development, 69(6), 3209–3232. https://doi.org/10.1007/s11423-021-10046-z.

    Article  Google Scholar 

  • Tang, H., Lin, Y., & Qian, Y. (2020). Understanding K-12 teachers’ intention to adopt Open Educational Resources: A mixed methods inquiry. British Journal of Educational Technology, 51(6), 2558–2572. https://doi.org/10.1111/bjet.12937.

    Article  Google Scholar 

  • Tang, H., Wang, S., Qian, Y., & Peck, K. (2016). Students’ perceptions of online instructors’ roles in a Massive Open Online Course. In S. D’Agustino (Ed.), Creating Teacher Immediacy in Online Learning Environments (pp. 273–289). Hershey, PA: IGI Global.

    Chapter  Google Scholar 

  • Templin, J., & Bradshaw, L. (2014). Hierarchical diagnostic classification models: A family of models for estimating and testing attribute hierarchies. Psychometrika, 79(2), 317–339.

    Article  MathSciNet  MATH  Google Scholar 

  • Tlili, A., Ofosu, S., & Zhang, J. (2021). Pedagogical beliefs, teaching practices and use of open educational resources in the Republic of Ghana. Interactive Learning Environments. doi:https://doi.org/10.1080/10494820.2021.1894453.

    Article  Google Scholar 

  • Van Allen, J., & Katz, S. (2019). Developing open practices in teacher education: An example of integrating OER and developing renewable assignments. Open Praxis, 11(3), 311–319.

    Article  Google Scholar 

  • von Davier, M., & Lee, Y. S. (2019). Handbook of Diagnostic Classification Models. Cham: Springer International Publishing.

    Book  Google Scholar 

  • Vongkulluksn, V. W., Xie, K., & Bowman, M. A. (2018). The role of value on teachers’ internalization of external barriers and externalization of personal beliefs for classroom technology integration. Computers & Education, 118, 70–81.

    Article  Google Scholar 

  • Wiley, D. A. (2021). Open educational resources: undertheorized research and untapped potential. Educational Technology Research and Development, 69(1), 411–414.

    Article  Google Scholar 

  • Zhang, Y., Wilcox, R. T., & Cheema, A. (2020). The effect of student loan debt on spending: The role of repayment format. Journal of Public Policy & Marketing, 39(3), 305–318.

    Article  Google Scholar 

Download references

Acknowledgements

The authors appreciate the OERHub at the Open University, UK for their tremendous support in releasing this dataset with creative common licenses (CC-BY) on Figshare.

Funding

This work was partially supported by the Department of Education under #S423A200043 partnered with the first author. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the funder.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hengtao Tang.

Ethics declarations

Competing Interests

The authors declare that they have no competing interests.

Conflict of interest

None.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, H., Bao, Y. A diagnostic classification model of college instructors’ value beliefs towards open educational resources. Educ Inf Technol 28, 6825–6844 (2023). https://doi.org/10.1007/s10639-022-11455-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-022-11455-0

Keywords