Skip to main content
SpringerLink
Log in

Learning Analytics: Negotiating the Intersection of Measurement Technology and Information Technology

  • Living reference work entry
  • First Online:
Learning, Design, and Technology

Abstract

In this chapter, we will review the current state of play in the area of overlap between learning analytics (LA), specifically data mining and exploratory analytics, and the field of measurement science. We will review the logic of measurement science, as instantiated through the BEAR Assessment System (BAS), and illustrate it in the context of a LA example. An example is presented showing how complex digital assessments can be designed through BAS with attention to measurement science, while LA approaches can help to score some of the complex digital artifacts embedded in the design. With that background, we suggest ways that the two approaches can be seen to support and complement one another, leading to a larger perspective. This chapter concludes with a discussion of the implications of this emerging intersection and a survey of possible next steps.

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • American Educational Research Association, American Psychological Association, National Council for Measurement in Education (AERA, APA, NCME). (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.

    Google Scholar 

  • Baker, R. S., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences. Cambridge, UK: Cambridge University Press, (pp. 253–274).

    Google Scholar 

  • Binkley, M., Erstad, O., Herman, J., Raizen, S., Ripley, M., Miller-Ricci, M., & Rumble, M. (2012). Defining twenty-first century skills. In P. Griffin, B. McGaw, & E. Care (Eds.), Assessment and teaching of 21st century skills (Vol. 1). Dordrecht, The Netherands/New York, NY: Springer.

    Google Scholar 

  • Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. doi:10.1080/0969595980050102.

    Article  Google Scholar 

  • Brady, A., Conlan, O., Wade, V., & Dagger, D. (2006). Supporting users in creating pedagogically sound personalised learning objects. Paper presented at the Adaptive Hypermedia and Adaptive Web-based Systems, Dublin, Ireland.

    Google Scholar 

  • Chedrawy, Z., & Abidi, S. S. R. (2006). An adaptive personalized recommendation strategy featuring context sensitive content adaptation. Paper presented at the Adaptive Hypermedia and Adaptive Web-Based Systems, 4th International Conference, AH 2006, Dublin, Ireland.

    Google Scholar 

  • Chi, E. H., Pirolli, P., Suh, B., Kittur, A., Pendleton, B., & Mytkowicz, T. (2008). Augmented social cognition. Palo Alto, CA: Palo Alto Research Center.

    Google Scholar 

  • Council, N. R. (2001). Knowing what students know: The science and design of educational assessment. Washington, DC: National Academy Press.

    Google Scholar 

  • Dagger, D., Wade, V., & Conlan, O. (2005). Personalisation for all: Making adaptive course composition easy. Educational Technology & Society, 8(3), 9–25.

    Google Scholar 

  • Dringus, L. P. (2012). Learning analytics considered harmful. Journal of Asynchronous Learning Networks, 16(3), 87–100.

    Google Scholar 

  • Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 301–317. doi:10.1504/IJTEL.2012.051816.

    Article  Google Scholar 

  • Gasevic, G., Dawson, C., Ferguson, S. B., Duval, E., Verbert, K., & Baker, R. S. J. D. (2011). Open learning analytics: An integrated & modularized platform (Concept paper). Society for Learning Analytics Research. Retrieved from http://solaresearch.org/OpenLearningAnalytics.pdf

  • Griffin, P., McGaw, B., & Care, E. (Eds.). (2012). Assessment and teaching of 21st century skills. Dordrecht, The Netherlands/New York, NY: Springer.

    Google Scholar 

  • Kennedy, C. A., & Draney, K. (2006). Interpreting and using multidimensional performance data to improve learning. In X. Liu (Ed.), Applications of Rasch measurement to science education. Chicago, IL: JAM Press.

    Google Scholar 

  • McFarlane, A. (2003). Assessment for the digital age. Assessment in Education: Principles, Policy & Practice, 10, 261–266.

    Article  Google Scholar 

  • Mislevy, R. J. (2016). [Discussion of learning analytics].

    Google Scholar 

  • Mislevy, R. J., Almond, R. G., & Lukas, J. F. (2003). A brief introduction to Evidence-Centered Design. CRESST Technical Paper Series. Los Angeles, CA: CRESST.

    Google Scholar 

  • Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49–64.

    Google Scholar 

  • Partnership for 21st Century Skills & American Association of Colleges of Teacher Education. (2010). 21st Century Knowledge and Skills in Educator Preparation. http://www.p21.org/storage/documents/aacte_p21_whitepaper2010.pdf

  • Pirolli, P. (2007). Cognitive models of human-information interaction. In F. T. Durso (Ed.), Handbook of applied cognition (pp. 443–470). New York, NY: Wiley.

    Chapter  Google Scholar 

  • Pirolli, P. (2009, April 3–9). An elementary social information foraging model. Paper presented at the CHI 2009, ACM Conference on Human Factors in Computing Systems, Boston, MA.

    Google Scholar 

  • Pirolli, P., Preece, J., & Shneiderman, B. (2010). Cyberinfrastructure for social action on national priorities. IEEE Computer, 43(11), 20–21.

    Article  Google Scholar 

  • Pirolli, P., & Wilson, M. (1998). A theory of the measurement of knowledge content, access, and learning. Psychological Review, 105(1), 58–82.

    Article  Google Scholar 

  • Resnick, L. B., & Resnick, D. P. (1992). Assessing the thinking curriculum: New tools for educational reform. In B. R. Gifford & M. C. O'Connor (Eds.), Changing assessments: Alternative views of aptitude, achievement and instruction (pp. 37–76). Boston, MA: Kluwer.

    Chapter  Google Scholar 

  • Russell, S., & Norvig, P. (2009). Artificial intelligence, a modern approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  • Scalise, K. (2012). Using technology to assess hard-to-measure constructs in the CCSS and to expand accessibility. Invitational Research Symposium on Technology Enhanced Assessments. http://www.k12center.org/events/research_meetings/tea.html

  • Scalise, K. (2016). Student collaboration and school educational technology: Technology integration practices in the classroom. Journal on School Educational Technology, 11(4), 39–49.

    Google Scholar 

  • Scalise, K., & Gifford, B. R. (2006). Computer-based assessment in E-Learning: A framework for constructing “Intermediate Constraint” questions and tasks for technology platforms. Journal of Teaching, Learning and Assessment, 4(6), 7.

    Google Scholar 

  • Scalise, K., Bernbaum, D. J., Timms, M. J., Veeragoudar Harrell, S., Burmester, K., Kennedy, C. A., & Wilson, M. (2007). Adaptive technology for e-Learning: Principles and case studies of an emerging field. Journal of the American Society for Information Science and Technology, 58(14), 001–015.

    Article  Google Scholar 

  • Schrum, L., & Levin, B. B. (2014). Evidence-based strategies for leading 21st century schools. Thousand Oaks, CA: Corwin.

    Google Scholar 

  • Sclater, N. (2014). JISC: Code of practice for learning analytics: A literature review of the ethical and legal issues. http://repository.jisc.ac.uk/5661/1/Learning_Analytics_A-_Literature_Review.pdf

  • Stanton, J. M. (2012). An introduction to data science. Retrieved from http://surface.syr.edu/istpub/165/

  • Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103, 221–263.

    Article  Google Scholar 

  • Timms, M. (2016). Towards a model of how learners process feedback: A deeper look at learning. Australian Journal of Education. doi:10.1177/0004944116652912.

    Google Scholar 

  • Timms, M., DeVelle, S., & Schwanter, U. (2015). Towards a model of how learners process feedback. Paper presented at the Artificial Intelligence in Education Conference 2015, Switzerland.

    Google Scholar 

  • van Barneveld, A., Arnold, K. E., & Campbell, J. P. (2012). Analytics in higher education: Establishing a common language. EDUCAUSE Learning Initiative. https://qa.itap.purdue.edu/learning/docs/research/ELI3026.pdf

  • Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, NJ: Lawrence Erlbaum Assoc.

    Google Scholar 

  • Wilson, M., Bejar, I., Scalise, K., Templin, J., Wiliam, D., & Torres-Irribarra, D. (2012). Perspectives on methodological issues. In P. Griffin, B. McGaw B., & E. Care (Eds.), Assessment and teaching of 21st century skills (pp. 67–142). Dordrecht: Springer.

    Google Scholar 

  • Wilson, M., Scalise, K., & Gochyyev, P. (in press). ICT Literacy – Learning in digital networks. In R. W. Lissitz & H. Jiao (Eds.), Technology enhanced innovative assessment: Development, modeling, and scoring from an interdisciplinary perspective. Charlotte, NC: Information Age Publisher.

    Google Scholar 

  • Wilson, M., Scalise, K., & Gochyyev, P. (2016). Assessment of learning in digital interactive social networks: A learning analytics approach. Online Learning Journal, 20(2). ISSN 2472–5730. http://olj.onlinelearningconsortium.org/index.php/olj/article/view/799/205

  • Wilson, M. (2009). Measuring progressions: Assessment structures underlying a learning progression. Journal for Research in Science Teaching, 46(6), 716–730.

    Article  Google Scholar 

  • Wilson, M., & Scalise, K. (2014). Assessment of learning in digital networks. In P. Griffin & E. Care (Eds.), Assessment and teaching of 21st century skills: Vol. 2. Methods & approaches. Dordrecht: Springer.

    Google Scholar 

  • Wilson, M., & Sloane, K. (2000). From principles to practice: An embedded assessment system. Applied Measurement in Education, 13(2), 181–208.

    Article  Google Scholar 

  • Wilson, M., Scalise, K., & Gochyyev, P. (2015). Rethinking ICT literacy: From computer skills to social network settings. Thinking Skills & Creativity, 18, 65–80.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of California, 4415 Tolman Hall, Berkeley, CA, 94720-1670, USA

    Mark Wilson

  2. University of Oregon, Eugene, OR, 97403-5267, USA

    Kathleen Scalise

Authors
  1. Mark Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kathleen Scalise
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Mark Wilson or Kathleen Scalise .

Editor information

Editors and Affiliations

  1. College of Info, Ste G150, Univ of North Texas, Denton, Texas, USA

    Michael J Spector

  2. School of Education, Virginia Tech, Blacksburg, Virginia, USA

    Barbara B Lockee

  3. School of Education, Baker University, Overland Park, Kansas, USA

    Marcus D. Childress

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this entry

Cite this entry

Wilson, M., Scalise, K. (2016). Learning Analytics: Negotiating the Intersection of Measurement Technology and Information Technology. In: Spector, M., Lockee, B., Childress, M. (eds) Learning, Design, and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-17727-4_44-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-17727-4_44-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Online ISBN: 978-3-319-17727-4

  • eBook Packages: Springer Reference EducationReference Module Humanities and Social SciencesReference Module Education

Publish with us

Policies and ethics

Search

Navigation