Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Problem-solving competence is an individual’s capacity to engage in cognitive processing to understand and resolve problem situations where a method of solution is not immediately obvious. The measurement of problem-solving competence requires the use of relatively more complex and real problem situations to induce the presentation of problem-solving behaviors. This brings challenges to both the measurement methods of problem-solving competence and the corresponding data analysis methods. Using virtual assessments to capture the process data in problem-solving and mining the potential information contained therein is a new trend in measuring problem-solving competence in psychometrics. Firstly, this paper reviews the development of measurement methods from pen-and-paper tests to virtual assessments. Compared with the traditional paper-and-pencil test, modern virtual assessments are not only conducive to simulating real problem situations, improving the ecological validity of the test, but also can record the process data generated by individuals in the process of problem-solving. Process data refers to man-machine or man-human interaction data with timestamps that can reflect the process of individual problem-solving. It records the detailed steps of individual problem solving and reflects the strategy and cognitive process of individual problem-solving. However, it is not easy to adopt effective methods to analyze process data. Secondly, two methods of analyzing process data are summarized and compared: data mining methods and statistical modeling methods. Data mining is the process of using algorithms to uncover new relationships, trends, and patterns from big data. It is a bottom-up, data-driven research method that focuses on describing and summarizing data. Its advantage is that it can use existing algorithms to analyze a variety of process data at the same time, screen out variables related to individual problem-solving competence, and realize the classification of individual problem-solving competence. But sometimes, different algorithms could get different conclusions based on the same data, which leads to part of the results can not be explained. This method can not construct variables that can reflect the individual's latent trait, either. Statistical modeling method mainly refers to the method of analyzing data by using the idea of artificial modeling. It is a top-down, theory-driven approach. In statistical modeling, function models are generally constructed based on theoretical assumptions, and the observed variables are assumed to be randomly generated by the probability law expressed by the model. For the data recorded by virtual assessments, the existing modeling methods can be divided into three categories: psychometric joint modeling, hidden Markov modeling, and multi-level modeling. The main advantage of statistical modeling is that its results are easy to interpret and conform to the general process of psychological and educational research. Its limitation lies in that the modeling logic has not been unified yet because different types of process data need to be modeled separately. However, by giving full play to the advantages of the two data analysis methods, different problems in psychological and educational assessments can be dealt with. The interpretability of the results is very important in psychological and educational measurements, which determines the dominant role of statistical modeling in process data analysis. Finally, the possible future research directions are proposed from five aspects: the influence of non-cognitive factors, the use of multimodal data, the measurements of the development of problem-solving competence, the measurements of other higher-order thinking competence, and the definition of the concept and structure of problem-solving competence.
Subjects: Digital Publishing >> Digital Newspaper submitted time 2023-03-24
Abstract： “蜀中桃子姐”账号依靠朴实烟火的乡村视频风格，勾起无数外地务工者的乡愁。从 2018 年起开始拍摄短视频，2020 年因在美食制作中加入了乡村日常、与丈夫包立春互动的亲情等元素，短期内涨粉千万，平均单条视频播放量超百万，数据上升速度直逼李子柒，一跃成为全国美食短视频赛道翘楚。本文将对“蜀中桃子姐”人格 IP 的构建、定位归属、价值和商业模式展开分析，并最后给出相关建议。
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Psychological Measurement submitted time 2022-10-06
Abstract： Longitudinal cognitive diagnostics can assess students' strengths and weaknesses over time, profile students' developmental trajectories, and can be used to evaluate the effectiveness of teaching methods and optimize the teaching process.Existing researchers have proposed different longitudinal diagnostic classification models, which provide methodological support for the analysis of longitudinal cognitive diagnostic data. Although these parametric longitudinal cognitive diagnostic models can effectively assess students' growth trajectories, their requirements for coding ability and sample size hinder their application among frontline educators, and they are time-consuming and not conducive to providing timely feedback. On the one hand, the nonparametric approach is easy to calculate, efficient to apply, and provides timely feedback; on the other hand, it is free from the dependence on sample size and is particularly suitable for analyzing assessment data at the classroom or school level. Therefore, this paper proposed a longitudinal nonparametric approach to track changes in student attribute mastery. This study extended the longitudinal Hamming distance discriminant (Long-HDD) based on the Hamming distance discriminant (HDD), which uses the Hamming distance to represent the dependence between attribute mastery patterns of the same student at adjacent time points. To explore the performance of Long-HDD in longitudinal cognitive diagnostic data, we conducted a simulation study and an empirical study and compared the classification accuracy of the HDD, Long-HDD, and Long-DINA models. In the simulation study, five independent variables were manipulated, including (1) sample sizes N = 25, 50, 100, and 300; (2) number of items I = 25 and 50; (3) number of time points T = 2 and 3; (4) number of attributes measured at each time point K = 3 and 5, and (5) data analysis methods M = HDD, Long-HDD, and Long-DINA. The student’s real attribute mastery patterns were randomly selected with equal probability from all possible attribute patterns, and the transfer probabilities among attributes between adjacent time points were set to be equal (e.g., p(0→0) = 0.8, p(0→1) = 0.2, p(1→0) = 0.05, p(1→1) = 0.95), while the first K items constituting the unit matrix in the Q-matrix at each time point were set to be anchor items, and the item parameters were set to be moderately negative correlation, generated by a ?bivariate normal distribution. For the empirical study, the results of three parallel tests with 18 questions each, measuring six attributes, were used for 90 7th graders. The Q-matrix for each test was equal. The results of the simulation study showed that (1) Long-HDD had higher classification accuracy in longitudinal diagnostic data analysis; (2) Long-HDD performed almost independently of sample size and performed better with a smaller sample size compared to Long-DINA; and (3) Long-HDD consumed much less computational time than Long-DINA. In addition, the results of the empirical data also showed that there was good consistency between the results of the Long-HDD and the Long-DINA model?in tracking changes in attribute development. The percentage of mastery of each attribute increased with the increase of time points. In summary, the long-HDD proposed in this study extends the application of nonparametric methods to longitudinal cognitive diagnostic data and can provide high classification accuracy. Compared with parameterized longitudinal DCM (e.g., Long-DINA), it can provide timely diagnostic feedback due to the fact that it is not affected by sample size, simple calculation, and less time-consuming. It is more suitable for small-scale longitudinal assessments such as class and school level. " "
Subjects: Psychology >> Psychological Measurement submitted time 2021-10-04
Abstract： Problem-solving competence is an individual’s capacity to engage in cognitive processing to understand and resolve problem situations where a method of solution is not immediately obvious. The measurement of problem-solving competence requires the use of relatively more complex and real problem situations to induce the presentation of problem-solving behaviors. This brings challenges to both the measurement methods of problem-solving competence and the corresponding data analysis methods. Using virtual assessments to capture the process data in problem-solving and mining the potential information contained therein is a new trend in measuring problem-solving competence in psychometrics. To begin with, we reviewed the development of the measurement methods of problem-solving competence: from paper-and-pencil tests to virtual assessments. In addition, we summarized two types of process data analysis methods: data mining and statistical modeling. Finally, we look forward to possible future research directions from five perspectives: the influence of non-cognitive factors on problem-solving competence, the use of multimodal data to measure problem-solving competence, the measurement of the development of problem-solving competence, the measurement of other higher-order thinking competencies, and the definition of concept and structure of problem-solving competence.
Peer Review Status:Awaiting Review
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