ISSN 0439-755X
CN 11-1911/B

心理学报 ›› 2023, Vol. 55 ›› Issue (8): 1383-1396.doi: 10.3724/SP.J.1041.2023.01383

• 研究报告 • 上一篇    


付颜斌, 陈琦鹏, 詹沛达()   

  1. 浙江师范大学心理学院; 浙江省儿童青少年心理健康与心理危机干预智能实验室; 浙江省智能教育技术与应用重点实验室, 金华 321004
  • 收稿日期:2023-01-04 出版日期:2023-08-25 发布日期:2023-05-12
  • 通讯作者: 詹沛达, E-mail:
  • 基金资助:

Binary modeling of action sequences in problem-solving tasks: One- and two-parameter action sequence model

FU Yanbin, CHEN Qipeng, ZHAN Peida()   

  1. School of Psychology, Zhejiang Normal University; Intelligent Laboratory of Child and Adolescent Mental Health and Crisis Intervention of Zhejiang Province; Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Jinhua 321004, China
  • Received:2023-01-04 Online:2023-08-25 Published:2023-05-12


行动序列作为一种典型的过程数据, 可反映被试解决问题的详细步骤。鉴于行动或状态转移可区分正误, 本文基于二分类Logistic建模提出两个复杂度相对较低的行动序列模型——单/两参数行动序列模型(1P-/2P-ASM); 两者差异在于是否允许自由估计问题状态的区分度。通过实证研究和模拟研究对比探究两个新模型与基于多分类Logistic建模的序列作答模型(SRM)的表现。研究结果主要发现:(1)两个ASM能够获得与SRM几乎一致的问题解决能力估计值; (2)两个ASM的计算耗时明显低于SRM的; (3) 2P-ASM比1P-ASM的综合表现更优。总之, 两个模型复杂度相对低的ASM均能够实现对行动序列的有效分析, 有益于行动序列数据分析的落地。

关键词: 过程数据, 行动序列, 问题状态转换, 行动序列模型, 项目反应理论


Process data refers to the human-computer or human-human interaction data recorded in computerized learning and assessment systems that reflect respondents’ problem-solving processes. Among the process data, action sequences are the most typical data because they reflect how respondents solve the problem step by step. However, the non-standardized format of action sequences (i.e., different data lengths for different participants) also poses difficulties for the direct application of traditional psychometric models. Han et al. (2021) proposed the SRM by combining dynamic Bayesian networks with the nominal response model (NRM) to address the shortcomings of existing methods. Similar to the NRM, the SRM uses multinomial logistic modeling, which in turn assigns different parameters to each possible action or state transition in the task, leading to high model complexity. Given that actions or state transitions in problem-solving tasks have correct and incorrect outcomes rather than equivalence relations without quantitative order, this paper proposes two action sequence models based on binary logistic modeling with relatively low model complexity: the one- and two-parameter action sequence models (1P and 2P-ASM). Unlike the SRM, which applies the NRM migration to action sequence analysis, the 1P-ASM and 2P-ASM migrate the simpler one- and two-parameter IRT models to action sequence analysis, respectively.

An illustrated example was provided to compare the performance of SRM and two ASMs with a real-world interactive assessment item, “Tickets,” in the PISA 2012. The results mainly showed that: (1) the latent ability estimates of two ASMs and the SRM had high correlation; (2) ASMs took less computing time than that of SRM; (3) participants who are solving the problem correctly tend to continue to present the correct actions, and vice versa; and (4) compared with the fixed discrimination parameter of the SRM, the free estimated discrimination parameter of the 2P-ASM helped us to better understand the task.

A simulation study was further designed to explore the psychometric performance of the proposed model in different test scenarios. Two factors were manipulated: sample size (including 100, 200, and 500) and average problem state transition sequence length (including short and long). The SRM was used to generate the state transition sequences in the simulation study. The problem-solving task structure from the empirical study was used. The results showed that: (1) two ASMs could provide accurate parameter estimates even if they were not the data-generation model; (2) the computation time of both ASMs was lower than that of SRM, especially under the condition of a small sample size; (3) the problem-solving ability estimates of both ASMs were in high agreement with the problem-solving ability estimate of the SRM, and the agreement between 2P-ASM and SRM is relatively higher; and (4) the longer the problem state transition sequence, the better the recovery of problem-solving ability parameter for both ASMs and SRM.

Overall, the two ASMs proposed in this paper based on binary logistic modeling can achieve effective analysis of action sequences and provide almost identical estimates of participants' problem-solving ability to SRM while significantly reducing the computational time. Meanwhile, combining the results of simulation and empirical studies, we believe that the 2P-ASM has better overall performance than the 1P-ASM; however, the more parsimonious 1P-ASM is recommended when the sample size is small (e.g., 100 participants) or the task is simple (fewer operations are required to solve the problem).

Key words: process data, action sequence, problem state transition, action sequence model, item response theory