Q-matrix is an important component of most cognitive diagnosis models (CDMs); however, it mainly relies on subject matter experts' judgements in empirical studies, which introduces the possibility of misspecified q-entries. To address this, statistical Q-matrix validation methods have been proposed to aid experts' judgement. A few of these methods, including the multiple logistic regression-based (MLR-B) method and the Hull method, can be applied to general CDMs, but they are either time-consuming or lack accuracy under certain conditions. In this study, we combine the L1 regularization and MLR model to validate the Q-matrix. Specifically, an L1 penalty term is imposed on the log-likelihood of the MLR model to select the necessary attributes for each item. A simulation study with various factors was conducted to examine the performance of the new method against the two existing methods. The results show that the regularized MLR-B method (a) produces the highest Q-matrix recovery rate (QRR) and true positive rate (TPR) for most conditions, especially with a small sample size; (b) yields a slightly higher true negative rate (TNR) than either the MLR-B or the Hull method for most conditions; and (c) requires less computation time than the MLR-B method and similar computation time as the Hull method. A real data set is analysed for illustration purposes.

The role of information and communication technology (ICT) in adolescent development is considered a double-edged sword because it can both meet the needs of adolescents and cause potential damage to them. Previous studies primarily relied on variable-centered approaches and failed to reveal the heterogeneity among groups concerning problematic technology use and ICT literacy. This study employed a person-centered approach to identify distinct subgroups and examined their associations with smartphone use content types. Using a longitudinal design, we investigated 1275 Chinese adolescents (Mage = 14.22 years, SD = 1.23) twice in two years. Latent class analysis and regression mixture model were implemented. Six classes were identified. Less use of entertainment and more use of information-seeking and learning types of smartphone content a year earlier were predictors of well-adjusted group membership. The heterogeneity of ICT use among adolescents found in this study emphasizes the importance of personalized policy advice.

The negative association between the growth mindset and mental health problems suggests that prevention and intervention programs to improve mental health by targeting mindset may have potential clinical value. However, research on the longitudinal effect of mindset on adolescent mental health and its underlying mechanisms is lacking. Using a three-wave longitudinal design, we obtained data from a diverse sample of Chinese adolescents (n = 2543). Longitudinal multiple mediation models were constructed to examine the effects of the growth mindset on levels of anxiety and depression two years later. In addition, the mediating effects of smartphone use for entertainment and problematic smartphone use (PSU) were examined. After controlling for various covariates and the autoregressive effects of mental health problems, the growth mindset had significant negative effects on anxiety (β = −0.053, p = 0.004) and depression (β = −0.074, p < 0.001). Smartphone use had a significant mediating role in the effect of mindset on anxiety (β = −0.016, p < 0.001) and depression (β = −0.016, p < 0.001). The growth mindset has long-lasting positive effects on adolescent mental health. Smartphone use for entertainment and PSU mediate the effect of mindset on adolescent mental health.

Abstract Background and aims Adolescence is a period of high incidence of problematic smartphone use. Understanding the developmental trajectory of problematic smartphone use in adolescence and its influencing factors could guide the choice of timing for prevention and intervention. This study fitted the growth trajectory of problematic smartphone use among adolescents and examined its associations with the childhood family environment and concurrent parent–child relationships. Methods Using a cohort sequential design, we investigated 2,548 Chinese adolescents and their parents three times in three years. Multiple group multiple cohort growth models were used to fit the growth trajectory. Results The quadratic growth trajectory of problematic smartphone use in adolescents aged 10–18 years showed a clear increasing trend, with a possible decreasing trend in late adolescence or early adulthood. Early life socioeconomic status, childhood family unpredictability, and the concurrent parent–child relationship had unique impacts on the development of problematic smartphone use during adolescence. Discussion and conclusions Early adolescence is a favorable time for problematic smartphone use prevention and intervention. A supportive family environment should be maintained throughout the different developmental stages of children and adolescents.

BP神经网络是目前应用最广泛的人工神经网络模型之一,在分类和识别上表现出良好的特性,因此被研究者用于认知诊断评估以对被试进行诊断分类.通过模拟研究,考查属性个数、属性层级关系、测验长度、题目质量、测试样本量5个因素对BP神经网络在认知诊断中分类准确性的影响.结果表明:1)基于BP神经网络的认知诊断分类准确率不依赖于测试样本量;2)题目质量和测验长度对BP神经网络的诊断准确率有显著的积极影响;3)属性个数对BP神经网络的分类准确率有消极影响;4)题目质量一定程度上会影响BP诊断方法在不同属性层级结构上的分类准确率.