Li Guiyu, Cai Yan, Gao Xuliang, Wang Daxun, Tu Dongbo
School of Psychology, Jiangxi Normal University, Nanchang, China.
Department of Curriculum and Instruction, East China Normal University, Shanghai, China.
Front Psychol. 2021 May 6;12:509844. doi: 10.3389/fpsyg.2021.509844. eCollection 2021.
Computer multistage adaptive test (MST) combines the advantages of paper and pencil-based test (P&P) and computer-adaptive test (CAT). As CAT, MST is adaptive based on modules; as P&P, MST can meet the need of test developers to manage test forms and keep test forms parallel. Cognitive diagnosis (CD) can accurately measure students' knowledge states (KSs) and provide diagnostic information, which is conducive to student's self-learning and teacher's targeted teaching. Although MST and CD have a lot of advantages, many factors prevent MST from applying to CD. In this study, we first attempt to employ automated test assembly (ATA) to achieve the objectives of MST in the application of CD (called CD-MST) via heuristic algorithms. The mean correct response probability of all KSs for each item is used to describe the item difficulty of CD. The attribute reliability in CD is defined as the test quantitative target. A simulation study with the G-DINA model (generalized deterministic input noisy "and" gate model) was carried out to investigate the proposed CD-MST, and the results showed that the assembled panels of CD-MST satisfied the statistical and the non-statistical constraints.
计算机多级自适应测试（MST）结合了纸笔测试（P&P）和计算机自适应测试（CAT）的优点。与CAT一样，MST基于模块进行自适应；与P&P一样，MST可以满足测试开发者管理测试形式并保持测试形式平行的需求。认知诊断（CD）能够准确测量学生的知识状态（KSs）并提供诊断信息，这有利于学生的自主学习和教师的针对性教学。尽管MST和CD有很多优点，但诸多因素阻碍了MST应用于CD。在本研究中，我们首次尝试通过启发式算法采用自动组卷（ATA）来实现MST在CD应用中的目标（称为CD-MST）。用每个项目所有KSs的平均正确反应概率来描述CD的项目难度。将CD中的属性可靠性定义为测试定量目标。利用广义确定性输入噪声“与”门模型（G-DINA模型）进行了模拟研究，以考察所提出的CD-MST，结果表明CD-MST的组卷满足统计和非统计约束。
