Off-campus UMass Amherst users: To download dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.
Non-UMass Amherst users, please click the view more button below to purchase a copy of this dissertation from Proquest.
(Some titles may also be available free of charge in our Open Access Dissertation Collection, so please check there first.)
Moments of determination: Detecting learning within Intelligent Tutoring Systems
A central criticism of the assessment-based evaluation policies now in vogue in American public education is reduction of student learning time. Likewise, many see the current crop of year-end, summative assessments as only serving the data needs of politicians and higher-level school administrators. Stemming from these criticisms and a combination of technological and cognitive psychological curiosity, the computer-science community has offered a unique alternative the traditional assessment form. Intelligent Tutoring Systems (ITS) offer the hope of just-in-time assessment with no time away from instruction. That is, ITS are purported to both test and teach at the same time. However, inherent to ITS are the inference of learning. While the inference of learning, and ITS's themselves, are placed within the context of education the analytic logic employed for justification are grounded in data mining and artificial intelligence traditions. This proposed dissertation seeks to bridge the analytic traditions of educational measurement and data mining. The proposed study, carried out in three steps, will apply measurement strategies to a form of Intelligent Tutoring to compare the determination of learning between the two different analytic traditions. ^
Educational tests & measurements|Educational technology
Schweid, Jason A, "Moments of determination: Detecting learning within Intelligent Tutoring Systems" (2012). Doctoral Dissertations Available from Proquest. AAI3498369.