doi: 10.14297/jpaap.v4i3.190

Progressing Towards Open Textbooks Learning Analytics System

Deepak Prasad, Rajneel Totaramb, Tsuyoshi Usagawa

Abstract


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.


References


Acker, S. R. (2011). Digital textbooks: A state-level perspective on affordability and improved learning outcomes. Library Technology Reports, 47(8), 41–52.

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.

doi: http://dx.doi.org/10.1145/2330601.2330666

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.

doi: http://dx.doi.org/10.1007/978-1-4614-3305-7_7

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.

doi: http://dx.doi.org/10.1080/13562517.2013.827653

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.

doi: http://dx.doi.org/10.19030/ajbe.v6i2.7691

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.

doi: http://dx.doi.org/10.1145/2090116.2090130

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.

doi: http://dx.doi.org/10.1007/s11528-014-0822-x

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.

doi: http://dx.doi.org/10.1016/j.chb.2014.11.001

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.

doi: http://dx.doi.org/10.1177/0002764213498850

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.

doi: http://dx.doi.org/10.1007/978-0-387-85820-3_12

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.

doi: http://dx.doi.org/10.3138/jsp.43.2.148

Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450.

doi: http://dx.doi.org/10.1111/bjet.12152

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.

doi: http://dx.doi.org/10.3102/0013189X14550275

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.

doi: http://dx.doi.org/10.1007/978-3-642-23985-4_27

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.

doi: http://dx.doi.org/10.1016/j.compedu.2011.05.016

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.

doi: http://dx.doi.org/10.1016/j.ijedudev.2012.11.002


Full Text: PDF HTML
Article Views: 290

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2051-9788