Faculty Advisor

Kathi Fisler

Faculty Advisor

Neil Heffernan

Identifier

etd-041913-085542

Abstract

Numerous methods to assess student knowledge are present throughout every step of a students€™ education. Skill-based assessments include homework, quizzes and tests while curriculum exams comprise of the SAT and GRE. The latter assessments provide an indication as to how well a student has retained a learned national curriculum however they are unable to identify how well a student performs at a fine grain skill level. The former assessments hone in on a specific skill or set of skills, however, they require an excessive amount of time to collect curriculum-wide data. We've developed a system that assesses students at a fine grain level in order to identify non- mastered skills within each student€™s zone of proximal development. €œPLACEments€� is a graph-driven computer adaptive test which not only provides thorough student feedback to educators but also delivers a personalized remediation plan to each student based on his or her identified non-mastered skills. As opposed to predicting state test scores, PLACEments objective is to personalize learning for students and encourage teachers to employ formative assessment techniques in the classroom. We have conducted a randomized controlled study to evaluate the learning value PLACEments provides in comparison to traditional methods of targeted skill mastery and retention.

Publisher

Worcester Polytechnic Institute

Degree Name

MS

Department

Computer Science

Project Type

Thesis

Date Accepted

2013-04-19

Accessibility

Unrestricted

Subjects

prerequisite graph, learning progressions, computer adaptive, personalized learning, Intelligent Tutoring System, ITS

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