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jsCAT: Computer Adaptive Testing in JavaScript |
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5 October 2023 |
paper.bib |
- A list of the authors of the software and their affiliations, using the correct format (see the example below).
- A summary describing the high-level functionality and purpose of the software for a diverse, non-specialist audience.
- A Statement of need section that clearly illustrates the research purpose of the software and places it in the context of related work.
- A list of key references, including to other software addressing related needs. Note that the references should include full names of venues, e.g., journals and conferences, not abbreviations only understood in the context of a specific discipline.
- Mention (if applicable) a representative set of past or ongoing research projects using the software and recent scholarly publications enabled by it.
- Acknowledgement of any financial support.
jsCAT is a JavaScript library to support fully client-side computerized adaptive testing. Computerized Adaptive Testing is an approach to assessment that uses an algorithm to select the most informative item to present to a test-taker based on their previous responses. Although CAT has been widely researched and applied in areas such as education and psychology (e.g., GRE and Duolingo), designing a CAT solution suitable for large-scale school administration remains a challenge. Imagine we are administering 500 children in a school at the same time, where reliable wifi is not guaranteed at all times, which means that we should minimize data communications between the client and servers to prevent disruptions. Most existing adaptive testing solutions, written in R or Python, do not support this requirement. jsCAT is designed to overcome this challenge by implementing the item response theory (reference) and CAT in JavaScript. The simulation results demonstrate that jsCAT is highly comparable to the popular R-based adaptive testing algorithm package, catR (Magis & Raîche, 2011).
This tool can be widely used in education and behavioral sciences research to design browser-based adaptive measurements.
The source code is publicly accessible in a GitHub repository (https://github.com/neuroneural/brainchop) with detailed step-by-step documentation. Built-in models also are provided with brainchop live demo.
We describe contributions to this paper using the CRediT taxonomy [@credit]. Writing – Original Draft: WM; Writing – Review & Editing: WM, ARH, JY; Conceptualization and methodology: WM, ARH; Software and data curation: WM, ARH; Validation: WM, ARH; Resources: JY; Visualization: WM; Supervision: JY; Project Administration: WM; Funding Acquisition: JY.