IRT Modeling of Tutor Performance to Predict End-of-Year Exam Scores  

Oleh:

Ayers, Elizabeth ; Junker, Brian

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Educational and Psychological Measurement vol. 68 no. 06 (Dec. 2008), page 972-987.
Topik: cognitive modeling; Bayesian inference; intelligent tutoring systems; item response theory; reliability
Fulltext: 972.pdf (186.84KB)

Isi artikel

Interest in end-of-year accountability exams has increased dramatically since the passing of the No Child Left Behind Act in 2001. With this increased interest comes a desire to use student data collected throughout the year to estimate student proficiency and predict how well they will perform on end-of-year exams. This article uses student performance on the Assistment System, an online mathematics tutor, to show that replacing percentage correct with an Item Response Theory estimate of student proficiency leads to better fitting prediction models. In addition, it uses other tutor performance metrics to further increase prediction accuracy. Prediction error bounds are also calculated to attain an absolute measure to which the models can be compared.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali