Secondary teachers across the United States are being asked to use formative assessment data to inform their classroom instruction. At the same time, critics of US government’s No Child Left Behind legislation are calling the bill “No Child Left Untested”. Among other things, critics point out that every hour spent assessing students is an hour lost from instruction. But, does it have to be? What if we better integrated assessment into classroom instruction and allowed students to learn during the test? This dissertation emphasizes using the intelligent tutoring system as an assessment system that just so happens to provide instructional assistance during the test. Usually it is believed that assessment get harder if students are allowed to learn during the test, as it’s then like trying to hit a moving target. So, my results are somewhat shocking that by providing tutoring to students while they are assessed I actually improve the assessment of students’ knowledge. Most traditional assessments treat all questions on the test as sampling a single underlying knowledge component. Yet, teachers want detailed, diagnostic reports to inform their instruction. Can we have our cake and eat it, too? In this dissertation, I provide solid evidence that a fine-grained skill model is able to predict state test scores better than coarser-rained models, as well as being used to give teachers more informative feedback that they can reflect on to improve their instruction. The contribution of the dissertation lies in that it established novel assessment methods to better assess students in intelligent tutoring systems. Through analyzing data of more than 1,000 students across two years, it provides strong evidence implying that it is possible to develop a continuous assessment system that can do all three of these things at the same time: 1) accurately and longitudinally assesses students, 2) gives fine grained feedback that is more cognitively diagnostic, and 3) saves classroom instruction time by assessing students while they are getting tutoring.

Creator

• Feng, Mingyu

Contributori

• Heffernan, Neil
• Koedinger, Kenneth
• Beck, Joseph E.
• Ruiz, Carolina

Degree

• PhD

Unit

• Computer Science

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-081909-134141

Parola chiave

• longitudinal modeling
• assess fine grained knowledge
• Intelligent Tutoring System
• Data Mining
• dynamic testing
• assessment

Advisor

• Heffernan, Neil

Committee

• Beck, Joseph E.
• Ruiz, Carolina
• Koedinger, Kenneth

Defense date

• 2009-08-25

Year

• 2009

Date created

• 2009-08-19

Resource type

• Dissertation

Rights statement

• In Copyright

Ultima modifica

• 2021-02-01