Main Article Content
Textbook prices have been soaring at an unprecedented pace for the last four decades with no signs that this trend will end anytime soon. Several studies have suggested that a solution to this problem comes in the form of open textbooks. As a result, the growth of open textbooks is rapid and sustained. However, though the advent of open textbooks is encouraging, whether, how, and to what extent students are using their open textbooks remains unclear. Learning analytics for open textbooks can provide answers to these questions plus many others, and thereby offers the potential for improving planning, development, monitoring, evaluation and revision of open textbooks.
Learning analytics applied to open textbooks has received little attention to date. This on the horizon paper presents and describes developmental work of a method to collect data produced as a result of students’ online and offline interactions with their open textbooks, the first part of a three-step process of learning analytics (the remaining two being data processing and reporting functionalities). The paper concludes with a presentation of future work, in line with the nature of this paper, which is work-in-progress towards developing learning analytics system for open textbooks.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Journal of Perspectives in Applied Academic Practice has made best effort to ensure accuracy of the contents of this journal, however makes no claims to the authenticity and completeness of the articles published. Authors are responsible for ensuring copyright clearance for any images, tables etc which are supplied from an outside source.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
Allen, G., Guzman-Alvarez, A., Molinaro, M., & Larsen, D. (2015). Assessing the impact and efficacy of the open-access chemwiki textbook project. Retrieved from https://net.educause.edu/ir/library/pdf/elib1501.pdf
Allen, N. (2011). High prices prevent college students from buying assigned textbooks. Student PIRGs. Retrieved from http://www.studentpirgs.org/news/ap/high-prices-prevent-college-students-buying-assigned-textbooks
Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: using learning analytics to increase student success. In S. B. Shum, D. Gašević, & R. Ferguson (Eds.), 2nd International Conference on Learning Analytics and Knowledge (LAK ’12) (pp. 267–270). New York, NY, USA: ACM.
Brooks, C., Greer, J., & Gutwin, C. (2014). The Data-Assisted Approach to Building Intelligent Technology-Enhanced Learning Environments. In J. A. Larusson & B. White (Eds.), Learning Analytics: From Research to Practice (pp. 123–156). New York: Springer.
Brown, M. (2011). Learning Analytics: The Coming Third Wave. Retrieved from https://net.educause.edu/ir/library/pdf/ELIB1101.pdf
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695.
Davenport, T. H., Harris, J. G., & Morison, R. (2010). Analytics at work: Smarter decisions, better results. Boston, MA: Harvard Business Press.
Driscoll, E., Comm, C. L., & Mathaisel, D. F. X. (2013). A Lesson Plan For Sustainability In Higher Education. American Journal of Business Education, 6(2), 255–266.
Ferguson, R., & Shum, S. B. (2011). Learning analytics to identify exploratory dialogue within synchronous text chat. In 1st International Conference on Learning Analytics and Knowledge (LAK ’11) (pp. 99–103). New York, NY, USA: ACM.
Fritz, J. (2013, April). Using Analytics at UMBC: Encouraging Student Responsibility and Identifying Effective Course Designs. EDUCAUSE Center for Applied Research. Retrieved from https://net.educause.edu/ir/library/pdf/ERB1304.pdf
Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71.
Gómez-Aguilar, D. A., Hernández-García, Á., García-Peñalvo, F. J., & Therón, R. (2015). Tap into visual analysis of customization of grouping of activities in eLearning. Computers in Human Behavior, 47, 60–67.
Graydon, B., Urbach-Buholz, B., & Kohen, C. (2011). A study of four textbook distribution models. Educause Quarterly, 34(4). Retrieved from http://www.educause.edu/ero/article/study-four-textbook-distribution-models
Haythornthwaite, C., Laat, M. de, & Dawson, S. (2013). Introduction to the Special Issue on Learning Analytics. American Behavioral Scientist, 57(10), 1371–1379.
Hilton, J., 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.
Hilton, J., Robinson, T. J., Wiley, D., & Ackerman, J. D. (2014). Cost-Savings Achieved in Two Semesters Through the Adoption of Open Educational Resources. International Review of Research in Open Distributed Learning, 15(2), 67–84.
Jayaprakash, S. M., Moody, E. W., Lauría, E. J. M., Regan, J. R., & Baron, J. D. (2014). Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative. Journal of Learning Analytics, 1(1), 6–47.
Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., & Koper, R. (2011). Recommender Systems in Technology Enhanced Learning. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), Recommender Systems Handbook (pp. 387–415). Springer US.
Morris-Babb, M., & Henderson, S. (2012). An experiment in open-access textbook publishing: Changing the world one textbook at a time. Journal of Scholarly Publishing, 43(2), 148–155.
Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450.
Perry, M. (2012). The college textbook bubble and how the “open educational resources” movement is going up against the textbook cartel. American Enterprise Institute. Retrieved from http://www.aei-ideas.org/2012/12/the-college-textbook-bubble-and-how-the-open-educational-resources-movement-is-going-up-against-the-textbook-cartel/
Prasad, D., & Usagawa, T. (2014). Towards development of OER derived custom-built open textbooks: A baseline survey of university teachers at the University of the South Pacific. The International Review of Research in Open and Distributed Learning, 14(4), 226–247.
Robinson, T. J., Fischer, L., Wiley, D., & Hilton, J. (2014). The Impact of Open Textbooks on Secondary Science Learning Outcomes. Educational Researcher, 43(7), 341–351.
Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014). Quality Indicators for Learning Analytics. Educational Technology & Society, 17(4), 117–132.
Scheffel, M., Niemann, K., Pardo, A., Leony, D., Friedrich, M., Schmidt, K., … Kloos, C. (2011). Usage Pattern Recognition in Student Activities. In C. Kloos, D. Gillet, R. Crespo García, F. Wild, & M. Wolpers (Eds.), Towards Ubiquitous Learning (Vol. 6964, pp. 341–355). Springer Berlin Heidelberg.
Senack, E. (2014). Fixing the Broken Textbook Market: How Students Respond to High Textbook Costs and Demand Alternatives. Washington, DC. Retrieved from http://www.washpirg.org/sites/pirg/files/reports/1.27.14 Fixing Broken Textbooks Report.pdf
Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5), 30–40.
Tanes, Z., Arnold, K. E., King, A. S., & Remnet, M. A. (2011). Using Signals for appropriate feedback: Perceptions and practices. Computers & Education, 57(4), 2414–2422.
Wiley, D., Hilton, J., Ellington, S., & Hall, T. (2012). A Preliminary Examination of the Cost Savings and Learning Impacts of Using Open Textbooks in Middle and High School Science Classes. The International Review of Research in Open and Distributed Learning, 13(3), 262–276.
Willis, J. E. (2014). Learning analytics and ethics: A framework beyond utilitarianism. EDUCAUSE Review Online. Retrieved from http://er.educause.edu/articles/2014/8/learning-analytics-and-ethics-a-framework-beyond-utilitarianism
Wise, A. F., Zhao, Y., & Hausknecht, S. N. (2014). Learning Analytics for Online Discussions: Embedded and Extracted Approaches. Journal of Learning Analytics, 1(2), 48–71.
Yang, L., & McCall, B. (2014). World education finance policies and higher education access: A statistical analysis of World Development Indicators for 86 countries. International Journal of Educational Development, 35, 25–36.