Learning What Works in ITS from Non-traditional Randomized Controlled Trial Data

The traditional, well established approach to finding out what works in education research is to run a randomized controlled trial (RCT) using a standard pretest and posttest design. RCTs have been used in the intelligent tutoring community for decades to determine which questions and tutorial feedback work best. Practically speaking, however, ITS creators need to make decisions on what content to deploy without the benefit of having run an RCT in advance. Additionally, most log data produced by an ITS is not in a form that can easily be evaluated with traditional methods. As a result, there is much data produced by tutoring systems that we would like to learn from but are not. In prior work we introduced a potential solution to this problem: a Bayesian networks method that could analyze the log data of a tutoring system to determine which items were most effective for learning among a set of items of the same skill. The method was validated by way of simulations. In this work we furth...

• Type: Conference
• Year: 2010
• Where: ITS
• Authors: Zachary A. Pardos, Matthew D. Dailey, Neil T. Heffernan
• Keywords: Bayesian Networks Method, ITS 2010, Log Data, Multimedia, Tutoring