ISSN 0439-755X
CN 11-1911/B


    25 March 2024, Volume 56 Issue 3 Previous Issue    Next Issue

    Reports of Empirical Studies
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    Reports of Empirical Studies
    The effect of task relevance on serial dependence in numerosity
    LIU Yujie, LIU Chenmiao, ZHOU Liqin, ZHOU Ke
    2024, 56 (3):  255-267.  doi: 10.3724/SP.J.1041.2024.00255
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    Influence of group information on facial expression recognition
    WANG Weihan, CAO Feizhen, YU Linwei, ZENG Ke, YANG Xinchao, XU Qiang
    2024, 56 (3):  268-280.  doi: 10.3724/SP.J.1041.2024.00268
    Abstract ( 138 )   HTML ( 21 )  
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    Different roles of initial and final character positional probabilities on incidental word learning during Chinese reading
    LIANG Feifei, FENG Linlin, LIU Ying, LI Xin, BAI Xuejun
    2024, 56 (3):  281-294.  doi: 10.3724/SP.J.1041.2024.00281
    Abstract ( 81 )   HTML ( 13 )  
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    In this study, two parallel experiments were conducted to explore the mode of change in the probability information of the initial and final character positions on word segmentation when learning novel words repeatedly. The incidental word learning paradigm was used during reading, and a two-character pseudo word was used as a novel word. In Experiment 1, the probability of the initial character's position was manipulated to ensure that the final character was the same. In Experiment 2, the probability of the final character's position was manipulated to ensure that the initial character was the same. An eye tracker was used to record the eye movements of college students while reading. The results showed that: (1) The word segmentation effect of the probability information of the initial and final character positions gradually decreased with an increase in the number of times novel words were learned through reading, showing a “familiarity effect.” (2) The "familiarity effect" of the probability information of the initial character position was observed in the two relatively late eye movement indicators of go-past time and total number of fixation, while the “familiarity effect” of the probability information of the final character position began with gaze duration, proceeded to go-past time, and then persisted until total reading time. The results indicated that both the position probability information of the initial and final characters played a role in word segmentation during reading, but the initial character had a longer and more consistent time course, supporting the view that the initial character had an advantage in two-character processing.

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    The transition of latent classes of children’s learning engagement in primary school against the background of the “double reduction” policy
    YANG Jingyuan, YU Xiao, ZHANG Jingyi, LU Lifei, YANG Zhihui
    2024, 56 (3):  295-310.  doi: 10.3724/SP.J.1041.2024.00295
    Abstract ( 138 )   HTML ( 26 )  
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    Learning engagement, an important indicator of the learning process, has garnered extensive attention. Developmental contextualism and the integrative model of engagement posit that the interaction between individuals and environmental factors results in heterogeneous learning engagement development among individuals. Previous studies have demonstrated learning engagement heterogeneity among primary school students. However, in the context of the “double reduction” policy, the dynamic development of children’s learning engagement remains unclear. Moreover, positive parenting style, teacher-student relationships, and peer relationships, as important environmental factors, may predict children’s learning engagement transitions. Thus, this study adopts a people-centered research method to address these issues from a longitudinal perspective.

    This study recruited participants from three ordinary public primary schools in Shandong Province, China. Participants at T1 (June 2021, before the implementation of the “double reduction” policy) were 378 children (164 boys; mean age: 9.97 ± 0.91 years old). Participants at T2 (December 2021, six months after the implementation of the policy) were 357 primary school students (155 boys; mean age: 10.50 ± 0.94 years old). Participants at T3 (June 2022, a year after the implementation of the policy) were 347 primary school students (147 boys; mean age: 10.97 ± 0.91 years old). Students completed the Children’s Learning Engagement Scale (at T1, T2, and T3), Short-form Egna Minnen av Barndoms Uppfostran (at T1 and T2), Student Teacher Relationship Scale (at T1 and T2) and Children’s Peer Relationship Scales (at T1 and T2) during the three measurements. Latent profile analysis and latent transition analysis were employed in this study to explore children’s potential learning engagement subtypes and examine transitions between different subtypes across the three waves. Multiple logistic regressions were also used to investigate the impact of various environmental factors (i.e., positive parenting style, student-teacher relationships, and peer relationships) on the latent transitions of different learning engagement subtypes.

    All data were analyzed by SPSS 26.0 and Mplus 8.0 (The results of the relevant analyses are presented in Table 1). The results revealed four distinct subgroups of learning engagement among primary school students: the “Low Engaged”, “Moderately Engaged”, “High Absorption with Vigorous Disengagement”, and “Highly Engaged” groups (see Table 2, Table 3, and Figure 1). In addition, due to the “double reduction” policy, students in the “Moderately Engaged” and “Highly Engaged” groups displayed relative stability, while those in the “Highly Disengaged” group tended to transition toward the “Moderately Engaged” group. Regarding the “High Absorption with Vigorous Disengagement” group, the findings indicated a higher likelihood of transitioning to the “Moderately Engaged” group from T1 to T2; however, from T2 to T3, these students were more likely to remain in their original subgroup (see Table 4 and Figure 2). Moreover, the study identified the varying roles of different environmental factors in children’s learning engagement subgroups. Specifically, under the “double reduction” policy, positive parenting style and teacher-student relationships exhibited robust effects on children’s learning engagement transitions. The predictive effects of teacher-student relationships varied across different learning engagement subtypes among primary school students. Additionally, the study found that peer relationships had a positive influence on the transition of children within the “Moderately Engaged” group following the implementation of the “double reduction” policy (see Table 5).

    This study provides the first evidence of heterogeneity and dynamic changes in learning engagement among Chinese primary school students, which indicates that following the implementation of the “double reduction” policy, family-school-collaborative education has made initial progress. These findings not only enhance our understanding of the dynamic development of learning engagement among primary school students but also provide empirical evidence regarding the effectiveness of the “double reduction” policy implementation.

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    Positive effects of leader perceived overqualification on team creativity
    WANG Yating, CHEN Zhijun, LI Rui, ZHOU Mingjian
    2024, 56 (3):  326-338.  doi: 10.3724/SP.J.1041.2024.00326
    Abstract ( 86 )   HTML ( 6 )  
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    Previous research has primarily focused on the negative aspects of perceived overqualification, with relatively little attention directed towards the phenomenon of leader overqualification. This research combines self-regulation theory and the process-based theory of team creative synthesis to examine when and how overqualified leaders enhance team creativity. Based on multi-wave and multi-source survey data from 106 nursing teams, the results indicated that leader perceptions of team capability moderated the indirect effect of leader overqualification on team creativity through leader encouragement of creativity and team creative process engagement. When team capability was high, the serial mediation effect of leader overqualification on team creativity through leader encouragement of creativity and team creative process engagement was stronger. By focusing on the phenomenon of leader overqualification, this research reveals the boundary conditions and processes through which it positively influences team creativity, providing new perspectives and avenues for overqualification research.

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    Cognitive diagnostic assessment based on signal detection theory: Modeling and application
    GUO Lei, QIN Haijiang
    2024, 56 (3):  339-351.  doi: 10.3724/SP.J.1041.2024.00339
    Abstract ( 67 )   HTML ( 5 )  
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    Cognitive diagnostic assessment (CDA) is aimed at diagnosing which skills or attributes examinees have or do not have as the name expressed. This technique provides more useful feedback to examinees than a simple overall score got from classical test theory or item response theory. In CDA, multiple-choice (MC) is one of popular item types, which have the superiority on high test reliability, being easy to review, and scoring quickly and objectively. Traditionally, several cognitive diagnostic models (CDMs) have been developed to analyze the MC data by including the potential diagnostic information contained in the distractors.
    However, the response to MC items can be viewed as the process of extracting signals (correct options) from noises (distractors). Examinees are supposed to have perceptions of the plausibility of each option, and they make the decision based on the most plausible option. Meanwhile, there are two different states when examinee responses to items: knows or does not know each item. Thus, the signal detection theory can be integrated into CDM to deal with MC data in CDA. The cognitive diagnostic model based on signal detection theory (SDT-CDM) is proposed in this paper and has several advantages over traditional CDMs. Firstly, it does not require the coding of q-vector for each option. Secondly, it provides discrimination and difficulty parameters that traditional CDMs cannot provide. Thirdly, it can directly express the relative differences between each option by plausibility parameters, providing a more comprehensive characterization of item quality.
    The results of simulation study 1 showed that (1) the marginal maximum likelihood estimation approach via Expectation Maximization (MMLE/EM) algorithm could effectively estimate the model parameters of the SDT-CDM. (2) The SDT-CDM had high classification accuracy and parameter estimation precision and could provide option-level information for item quality diagnosis. (3) Independent variables such as the number of attributes, item quality, and sample size affected the performance of the SDT-CDM, but the overall results were promising. Figures 1 and 2 showed the parameter estimation bias and RMSE of SDT-CDM in simulation study 1, respectively.
    In simulation study 2, we compared the performance of the SDT-CDM model with a traditional CDM (nominal response diagnostic model, NRDM) that can handle MC data. We used both the SDT-CDM and the NRDM as the true model to generate response data respectively, for investigating the advantages of the SDT-CDM in terms of classification accuracy compared to the NRDM. Compared with the NRDM, the SDT-CDM was more accurate in classifying examinees under all data conditions (see in Figures 3 and 4 for details).
    Further, an empirical study on the TIMSS 2011 mathematics assessment were conducted using both the SDT-CDM and the NRDM to inspect the ecological validity. The results showed that the SDT-CDM had better model-data fit and a smaller number of model parameters than the NRDM did (see in Table 1). The difficulty parameters of the SDT-CDM were significantly correlated with those of the two- (three-) parameter logistic models: r(-eDK, β2PL) = 0.63, p = 0.015, r(-eDK, β3PL) = 0.71, p = 0.002, r(-eK, β2PL) = 0.89, p < 0.001, r(-eK, β3PL) = 0.79, p < 0.001. And the same was true of the discrimination parameters for the SDT-CDM: r(d, a2PL) = 0.66, p = 0.005, r(d, a3PL) = 0.79, p < 0.001. However, the correlation between the discrimination parameters of the NRDM and those of the two- (three-) parameter logistic models were low and not significant: r(GDI, a2PL) = 0.20, p = 0.247, r(GDI, a3PL) =0.15, p = 0.304. Besides, the classification accuracy and classification consistency values of the SDT-CDM were higher than those of the NRDM (see in Table 2). All the results indicated that the SDT-CDM was worth promoting.

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    Modeling the dependence between response and response time: A bifactor model approach
    GUO Xiaojun, BAI Xiaoyun, LUO Zhaosheng
    2024, 56 (3):  352-362.  doi: 10.3724/SP.J.1041.2024.00352
    Abstract ( 58 )   HTML ( 10 )  
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    New research paradigms and agenda of human factors science in the intelligence era
    XU Wei, GAO Zaifeng, GE Liezhong
    2024, 56 (3):  363-382.  doi: 10.3724/SP.J.1041.2024.00363
    Abstract ( 147 )   HTML ( 12 )  
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    This paper first proposes the innovative concept of “human factors science” to characterize engineering psychology, human factors engineering, ergonomics, human-computer interaction, and other similar fields. Although the perspectives in these fields differ, they share a common goal: optimizing the human-machine relationship by applying a “human-centered design” approach. AI technology has brought in new characteristics, and our recent research reveals that the human-machine relationship presents a trans-era evolution from “human-machine interaction” to “human-AI teaming.” These changes have raised questions and challenges for human factors science, compelling us to re-examine current research paradigms and agendas.
    In this context, this paper reviews and discusses the implications of the following three conceptual frameworks that we recently proposed to enrich the research paradigms for human factors science. (1) human-AI joint cognitive systems: This model differs from the traditional human-computer interaction paradigm and regards an intelligent system as a cognitive agent with a certain level of cognitive capabilities. Thus, a human-AI system can be characterized as a joint cognitive system in which two cognitive agents (human and intelligent agents) work as teammates for collaboration. (2) human-AI joint cognitive ecosystems: An intelligent ecosystem with multiple human-AI systems can be represented as a human-AI joint cognitive ecosystem. The overall system performance of the intelligent ecosystem depends on optimal cooperation and design across the multiple human-AI systems. (3) intelligent sociotechnical systems (iSTS): human-AI systems are designed, developed, and deployed in an iSTS environment. From a macro perspective, iSTS focuses on the interdependency between the technical and social subsystems. The successful design, development, and deployment of a human-AI system within an iSTS environment depends on the synergistic optimization between the two subsystems.
    This paper further enhances these frameworks from the research paradigm perspective. We propose three new research paradigms for human factors science in the intelligence ear: human-AI joint cognitive systems, human-AI joint cognitive ecosystems, and intelligent sociotechnical systems, enabling comprehensive human factors science solutions for AI-based intelligent systems. Further analyses show that the three new research paradigms will benefit future research in human factors science. Furthermore, this paper looks forward to the future research agenda of human factors science from three aspects: “human-AI interaction,” “intelligent human-machine interface,” and “human-AI teaming.” We believe the proposed research paradigms and the future research agenda will mutually promote each other, further advancing human factors science in the intelligence era.

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