Web-Based Adaptive Testing for Large-Scale Student Assessment

A paper by Laura S. Hamilton discusses a new approach to large-scale achievement testing using Internet-delivered, adaptive assessments tailored to individual student proficiency. The paper is intended to stimulate discussion and research among policy makers, educators, and test developers. It notes that similar systems are already operational in several U.S. school districts and other countries.

Use Cases

• Researching adaptive testing algorithms based on the described web-based, tailored assessment approach.
• Analyzing policy implications for high-stakes testing based on the discussion of statewide assessment contexts.
• Developing educational technology systems based on the operational examples from U.S. school districts mentioned.
• Studying the potential benefits and limitations of shifting from paper-and-pencil to web-based testing as outlined.

Strengths

• Focuses on a specific, timely topic in educational assessment reform.
• Authored by a named researcher, Laura S. Hamilton, providing a point of reference.
• Discusses real-world implementation, citing operational systems in several U.S. school districts.

Limitations

• The dataset itself (e.g., test results, student interactions) is not described; only the methodological paper is presented.
• Row count, columns, and file formats are unknown, which limits suitability assessment for direct ML use.
• License is listed as closed, restricting potential reuse.

Provenance

Source

paperswithcode

Collection Method

Likely a published academic paper or report.

Geography

Primarily discusses the United States, with mentions of other countries.