Revising computer science learning objects from learner interaction data

  • Authors:

  • L. D. Miller;Leen-Kiat Soh;Beth Neilsen;Kevin Kupzyk;Ashok Samal;Erica Lam;Gwen Nugent

  • Affiliations:

  • University of Nebraska, Lincoln, NE, USA;University of Nebraska, Lincoln, NE, USA;University of Nebraska, Lincoln, NE, USA;University of Nebraska, Lincoln, NE, USA;University , Lincoln, NE, USA;University , Lincoln, NE, USA;University of Nebraska, Lincoln, NE, USA

  • Venue:
  • Proceedings of the 42nd ACM technical symposium on Computer science education
  • Year:
  • 2011

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Abstract

Learning objects (LO) have previously been used to help deliver introductory computer science (CS) courses to students. Students in such introductory CS courses have diverse backgrounds and characteristics requiring revision to LO content and assessment to promote learning in all students. However, revising LOs in an ad hoc manner could make student learning harder for subsequent deployments. To address this problem, we present a systematic revision process for LOs (LOSRP) using proven techniques from educational research including Bloom's Taxonomy levels, item-total correlation, and Cronbach's Alpha. LOSRP uses these validation methods to answer seven questions in order to diagnose what needs to be revised in the LO. Then, LOSRP provides guidelines on revising LOs for each of the seven questions. As an example, we discuss how LOSRP was used to revise the content and assessment for 16 LOs deployed to over 400 students in introductory CS courses in 2009. Lastly, although initially designed for LO revision, we briefly discuss how LOSRP could be used for assessment revision in intelligent tutoring systems.