适应性评估可视化识字能力
IEEE transactions on visualization and computer graphics
|October 25, 2023
概括
新的计算机化适应性测试 (CAT) 简化了可视化素养评估,在更少的问题中提供可靠和有效的评估. 这些适应性测试有效地提高了数据解释技能.
科学领域:
- 认知科学 认知科学
- 教育技术的教育技术
- 数据可视化 数据可视化
背景情况:
- 可视化素养对于数据解释和决策至关重要.
- 现有的评估,如可视化识字评估测试 (VLAT) 是漫长和耗时的.
- 需要简洁和可重复的评估来衡量可视化素养.
研究的目的:
- 为可视化素养开发精简的计算机化适应性测试 (CAT).
- 创建适应版本 (A-VLAT和A-CALVI),测量与原始测试相同的技能,但问题较少.
- 提高可视化识字评估的效率和可访问性.
主要方法:
- 用物品反应理论 (IRT) 和非心理测量约束来构建适应性测试.
- 通过模拟研究,最终确定了适应配置.
- 一项定性研究改进了A-CALVI评估的测试项目.
- 四项在线研究评估了测试重复测试可靠性和趋同有效性.
主要成果:
- 开发了两种计算机化适应性测试,A-VLAT和A-CALVI,需要约一半的传统测试问题.
- 两种CAT都表现出高的测试复试可靠性 (ICC:两者均为0.98).
- 观察到强烈的收有效性 (相关性:A-VLAT的0.81,A-CALVI的0.66).
结论:
- 开发的CATs提供了高效可靠的可视化素养测量方法.
- 这些适应性评估可以在实践中实施,以评估和增强数据解释技能.
- 存在进一步的定制机会,以最大限度地发挥适应性评估技术的潜力.
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