AimThis paper aims to investigate the relationship between social support and rural teachers’ work engagement while exploring the mediating effect of mindfulness in teaching and the moderating effect of psychological safety.MethodsA sample of 866 rural teachers was recruited, in order to complete the Mindfulness in Teaching Scale, Social Support Rating Scale, Psychological Safety Scale, and Utrecht Work Engagement Scale.ResultsThe study findings indicate that: (1) social support positively influenced the work engagement of rural teachers; (2) further, mindfulness in teaching partially mediated the effect of social support on rural teachers’ work engagement; (3) psychological safety moderated the second half of the pathway of “social support → mindfulness in teaching → work engagement” while the positive correlation between mindfulness in teaching and work engagement was stronger among rural teachers with high psychological safety.ConclusionSocial support documented a strong correlation with work engagement while mindfulness in teaching mediated the pathway between the aforementioned variables. Furthermore, psychological safety moderated the second half of the mediated pathway (the link from mindfulness in teaching to work engagement). Hence, the study outcomes reveal the influential mechanism of social support on the work engagement of rural teachers. This finding suggests that we need to further improve the social support system and its effect mechanism in order to improve the rural teachers’ work engagement. At the same time, it is also very important to create a good psychological working environment to ensure that they maintain a good level of mindfulness in teaching.

As a new generation of assessment theory, cognitive diagnosis can provide students with better personalized and formative learning. As an international large-scale test, the Trends in International Mathematics and Science Study (TIMSS) has already had some research foundation in cognitive diagnostic assessment (CDA), however, Mainland China has not yet participated. Therefore, this study aims to understand the specific performance of Mainland Chinese students within the TIMSS framework, compare their performance to other high-performing countries, and analyze the students’ learning paths and progressions. With CDA techniques, this study first identified eight attributes in mathematics cognition and formed a Q-matrix based on TIMSS-2015 items to construct the diagnosis assessment. A total of 4,733 Grade 8 students from Gansu, Guangdong, Guizhou, and Shanghai in Mainland China were measured using a mixed model from the G-DINA package in software R. The findings revealed that Grade 8 students in Mainland China exhibited an absolute advantage in the mastery of mathematical cognitive processes, particularly in the traditional domains such as Calculation and Measurement (CM), Operation and Solution (OS), and Representation Modeling (RM). Furthermore, analyses of the second and third learning progressions demonstrated diverse knowledge states among students. Last, students with the same overall score showed substantial differences in their mastery of specific cognitive processes. This study thoroughly discusses the construction of methods for TIMSS cognitive diagnostic assessment as well as the construction of learning paths and progressions in the assessment. It also highlights the potential of CDA for assessing cognitive abilities and constructing in-depth data mining, advancing the understanding of students’ cognitive strengths and weaknesses in mathematics education.

Discourse analysis, as a mainstream research method in classroom teaching, has gained widespread attention in education. Educators believe that children's thinking development requires support from interactive discourse. In this study, four primary school mathematics classes were segmented based on the form, frequency, content, and purpose of teacher-student interactions. A total of 73 dialogue segments were selected for coding, resulting in 338 codes. The coding process was based on the turn of talk and assigned corresponding coding numbers to the content of teacher-student discourse in the fragments according to the Bloom-Turney teaching questioning code list and the Hierarchical Framework of Student Thinking Level based on Biggs-Collis Structure of the observed learning outcome. The results show that Knowledge level question (Q1), Understanding level question (Q2), Application level question (Q3), Synthesis level question (Q5), and Evaluation level question (Q6) are related to students' low-level thinking. The questions of Analysis level (Q4), Synthesis level (Q5), and Evaluation level (Q6) are related to students' high-level thinking. We found that there are variety of interactive structures between teachers and students in the question and answer session, among which three interaction structures show significant performance, namely Q2 → M (Multiple-point structural level) → Q4 → C (Correlational structural level), Q3 → M → Q4 → C, Q3 → M → Q6 → A (Abstract-extension level), these structures can show how teachers timely adjust the types of questions according to students' answers to improve students' thinking level.

With a set of the Trends in International Mathematics and Science Study (TIMSS) 2015 test items as the assessment tool, this study constructed a mathematics competency assessment framework composed of eight attributes, and formed a Q matrix using data of 4,733 students in the four provinces (cities) of mainland China. Through model comparisons, this study selected the mixed model and made in-depth analyses of attribute mastery from four aspects: international comparative analysis, learning path analysis, learning progression construction, and personalized report development. The study found obvious advantages in most attributes of Chinese students’ basic mathematics competencies. The learning path was rich with large knowledge status distributed in the second, third, and fourth level of learning progression. It provided a basis for a detailed understanding of the basic mathematics competencies of Chinese students and their current situation in international education. It also provided a methodological basis for the cognitive diagnosis assessment of students’ mathematics competencies.

The level of difficulty of mathematical test items is a critical aspect for evaluating test quality and educational outcomes. Accurately predicting item difficulty during test creation is thus significantly important for producing effective test papers. This study used more than ten years of content and score data from China’s Henan Provincial College Entrance Examination in Mathematics as an evaluation criterion for test difficulty, and all data were obtained from the Henan Provincial Department of Education. Based on the framework established by the National Center for Education Statistics (NCES) for test item assessment methodology, this paper proposes a new framework containing eight features considering the uniqueness of mathematics. Next, this paper proposes an XGBoost-based SHAP model for analyzing the difficulty of mathematics tests. By coupling the XGBoost method with the SHAP method, the model not only evaluates the difficulty of mathematics tests but also analyzes the contribution of specific features to item difficulty, thereby increasing transparency and mitigating the “black box” nature of machine learning models. The model has a high prediction accuracy of 0.99 for the training set and 0.806 for the test set. With the model, we found that parameter-level features and reasoning-level features are significant factors influencing the difficulty of subjective items in the exam. In addition, we divided senior secondary mathematics knowledge into nine units based on Chinese curriculum standards and found significant differences in the distribution of the eight features across these different knowledge units, which can help teachers place different emphasis on different units during the teaching process. In summary, our proposed approach significantly improves the accuracy of item difficulty prediction, which is crucial for intelligent educational applications such as knowledge tracking, automatic test item generation, and intelligent paper generation. These results provide tools that are better aligned with and responsive to students’ learning needs, thus effectively informing educational practice.

With the advancement of science and artificial intelligence, education is experiencing significant innovation. The adaptive learning system is emerging as a promising approach to achieving personalized learning. The cognitive model plays a crucial role as the fundamental rationale behind the adaptive learning system. Currently, there is no uniform and highly operational method for constructing cognitive models. This study adopts Interpretive Structural Modeling (ISM) as the foundational approach for constructing a cognitive model of solid geometry. Based on literature and expert opinions, 17 cognitive attributes of high school solid geometry were identified. Subsequently, a questionnaire survey involving 40 experts was conducted to establish the contextual relationships among these attributes. Applying the ISM method resulted in a seven-level model. This model was then revised based on expert opinions to create the final cognitive model, revealing three primary paths within the domain of high school solid geometry.This paper contends that the use of the ISM method for constructing cognitive models is effective and objective. The resulting cognitive model unveils the content structure of high school solid geometry, and provides an innovative perspective on the construction of cognitive models.

A person-centered approach was adopted to identify distinct classes of learning anxiety (LA) using 158,578 Grade 8 students in 1097 schools in China. Through a nonparametric multilevel latent class analysis, it identified a best-fitting model consisting of three student-level latent classes (low, moderate, and high LA) and three school-level latent classes (rare, moderate, and intense LA schools). To further examine the effects of potential covariates on the student and school latent classes of LA, a multinomial logistic regression was conducted. The main results were, first, girls exhibited a higher odds ratio in moderate and high student LA classes. What's more, students' total academic scores were positively related to their odds of being in the moderate LA class while negatively related to their odds of being in the high LA class. Lastly, school leadership, school belongingness, and teacher-student relationship were all negatively correlated with the odds that schools belong to the intense and moderate LA schools.

To better understand the latest developments in global science, technology, engineering, and mathematics (STEM) education research, this study collected STEM education research materials to sort out the development of STEM education as a whole, so as to get a clearer path and trend of STEM education development. This study conducted a visualization and quantitative analysis of the literature on STEM education research in Science Citation Index Extended (SCI-E) and Social Science Citation Index (SSCI) using the CiteSpace (5.8.R3) tool. First, the basic information of STEM education was analyzed in terms of annual publication volume, authors, countries, and research institutions. Secondly, the main fields, basic contents and research hotspots of this research were analyzed by keyword co-occurrence and keyword time zone mapping. Finally, the research frontiers and development trends are presented through co-citation clustering and high-frequency keyword bursts. The research hotspots are focused on engineering education, teachers’ professional development, and gender differences. The research frontiers are mainly related to teacher professional development, 21st century skills, early childhood creativity, and gender differences. This study systematically analyzes the latest developments in global STEM education research, which is beneficial for readers to understand the full picture of STEM education research so that researchers can conduct more in-depth studies and promote better development of STEM education. The number of analyzed literature is limited. We only analyzed articles from SSCI and SCI-E databases, and the articles were written in English. In addition, we only analyzed the literature and lacked empirical studies on the findings of the literature.

The emergence of artificial intelligence has made adaptive learning possible, but building an adaptive system requires a comprehensive understanding of students' cognition. The cognitive model provides a crucial theoretical framework to explore students' cognitive attributes, making it vital for learning assessment and adaptive learning. This study investigates 52 experts, including primary and secondary school teachers, mathematics education experts, and graduate students, based on the 16 cognitive attributes in the TIMSS 2015 assessment framework. Through an analysis of their attribute questionnaires, the Interpretive structural modeling (ISM) method is used to construct a five-level mathematical cognitive model. The model is then revised through oral reports and expert interviews, resulting in a final cognitive model ranging from "memorize" to "justify". The cognitive model describes the relationship between different attributes in detail, enabling the development of adaptive systems and aiding in the diagnosis of students' cognitive development and learning paths in mathematics.

Considering the importance of mathematics in modern society, it is crucial to understand the cognitive processes involved in the acquisition of complex mathematical competency. As a new generation of evaluation theory, cognitive diagnosis has its unique advantages in personalized evaluation. Based on the mathematical cognitive framework of Trends in International Mathematics and Science Study (TIMSS)-2011 and the Chinese mathematics curriculum, this research has formed a mathematical competency model composed of seven cognitive attributes. Sixty-seven released mathematical items for the fourth grade in TIMSS-2011 were used as assessment tools in this study. The deterministic inputs, noisy, "and" gate model was selected as the cognitive diagnosis model in this research, and the parameters of the model were evaluated according to the response data, based on which the effectiveness of the assessment tool was further verified, forming the evaluation framework of students' mathematical competency. This framework was used to analyze the data of 573 students' mathematical competency from Shanghai, China, specifically from three aspects: attribute mastery probability, learning path, and knowledge structure. Results show that students' performance in mastering attributes of mathematical cognition is excellent on the whole; some students' learning paths are leapfrog; there are certain differences in students' knowledge structure despite that they have the same total score. This research is performed as a systematic case study of the evaluation of students' mathematical competency and also provides a new perspective in assessing other knowledge and skills.

The Learning Cycle Model (LCM) is an inquiry-based teaching strategy and curriculum model that has been researched and practiced for almost half a century and has achieved good educational results in science education, especially in primary and secondary schools. This study collected LCM educational research data to better understand the recent progress of LCM educational research. It used the bibliometric analysis software CiteSpace for the first time to sort out the overall development of LCM. Compared to other literature review methods, we obtained a more comprehensive picture of the current research hotspots and future research trends in LCM research. First, we searched 498 articles from the Web of Science core collection between 2000 and May 16, 2023. The trajectory of LCM research was identified by analyzing publication trends, authors, countries/regions, and research institutions. Secondly, we obtained the keyword co-occurrence map and clustering map through CiteSpace's built-in algorithm to analyze the main areas and research frontiers of this study. Meanwhile, the development trend is shown by co-keyword burst detection. After analysis, this study found that the current research hotspot findings focus on conceptual learning, validity research, and integration with different teaching processes. The research frontiers are mainly related to teacher professional development and research on the impact of LCM on learning outcomes. Finally, this study discusses future research directions, including research on the impact of LCM on 21st century skills, comparison with other modes of inquiry teaching and learning, and its use in engineering education, to promote better development of LCM.

As a new generation of assessment theory, Cognitive Diagnostic Assessment (CDA) has unique advantages in diagnosing students' personalized information. Cognitive diagnostic models (CDMs) are the core of CDA, so the selection of models becomes the key link of CDA. Generally, the selection of the models is based on data driven methods, such as comparing Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and other indicators. Few studies pay attention to the voice of subject experts. This study selected 10% of Tatsuoka fraction subtraction data, which were analyzed by 5 mathematics education experts according to the criteria of master (1), not master (0), and part master (0.5) for 8 attributes. We further analyzed the Pearson correlation coefficient of expert results and common model analysis results, and concluded that the DINA (the Deterministic Input, Noisy ‘‘And’’ Gate) model diagnosis results had the highest correlation with expert results, with the coefficient reaching 0.8624. The results showed that, from the perspective of mathematical experts, DINA model was most suitable for the diagnosis of fractional subtraction, which provided evidence for the rationality of DINA model diagnosis of fractional subtraction.

Using education survey data from 153, 317 Grade 4 students and 150, 040 Grade 8 students in China, this study examined the relationship between time on homework and academic achievement and learning anxiety with hierarchical linear modeling (HLM) and classification and regression tree (CART) approaches. With a classification of time spent on homework into four related variables, this study found that, firstly, time spent on in-school homework during weekdays had positive effects on students' achievement for both grades, and the positive effect was stronger for Grade 8 students than Grade 4 students. Moreover, a maximum of 1 h was recommended for Grade 4 students. Secondly, time spent on out-of-school homework on weekdays was negatively correlated with students' academic achievement and positively with learning anxieties. It had greater detrimental effect on Grade 8 than Grade 4. Thirdly, Grade 8 students were encouraged to have more out-of-school homework on weekend with more than 2.8 h on average recommended. It was expected to complement extant studies and provide the practical findings for teachers, practitioners and school policy makers in making any homework assignment planning or conducting interventions.

Value-added assessments have become a reasonable and accepted assessment method for education and teaching. Mathematics reading ability is an important ability in mathematics learning which provides a prerequisite for solving mathematical problems. With the aim of uncovering the effects of mathematics reading ability on the continuous development of mathematics learning, this study focuses on the value added to students' mathematics reading ability as well as their mathematics performance. From a longitudinal perspective, we collected academic achievement data for 463 s-grade students, including their scores on their mathematics reading ability, which were then used a developed measurement tool. Building on Weiss's "Theory of Change", the students were divided into four categories: high academic achievement and high value-added, low academic achievement and high value-added, low academic achievement and low value-added, and high academic achievement and low value-added. Finally, we discussed the impact of the students' reading abilities in mathematics on their overall achievement. This study reveals a close correlation between mathematics reading skills and value-added performance. Higher scores in mathematics reading indicate higher value-added levels. For students with initially high scores, their mathematics reading skills greatly contributed to their high value-added performance.

Intelligent education research has become a research hotspot in recent years. The Citespace software that operates a graph visualization function was used to clarify the current situation, hot spots, and evolutionary trends of intelligent education research development; the authors, institutions, and countries engaged in intelligent education research, as well as the basic knowledge structure, main keywords, citation clustering, dual-map overlay of journals and citation emergence of intelligent education research. The results show that the annual number of publications in the field has shown an upward trend since 2010, with strong communication among research institutions and countries, but weak communication among researchers. Among them, the United States is the center of the global collaborative network of intelligent education research. The basic knowledge structure of intelligence education research is mainly composed of Classroom Management, Evaluation Index, 5G Network, and Big Data Analytics. The dual-map overlay analysis of journals shows that the core areas of intelligence education are increasing, and the analysis of keywords and cited literature shows that Intelligence Tutoring System, AI system, Students and Education, Model, and System are high-frequency words with high-intensity burstness. In addition, research on intelligent education is characterized by multi-country, multi-field, and multi-disciplinary integration, and the adoption of Big Data, Distance Education Technology and Artificial Intelligence Technology to provide scientific support for teaching and learning will become the key research content in this field in the future.

Initiated by the Program for International Student Assessment (PISA), Mathematical Key Competencies (MKCs), which integrate into the process of solving situational problems, becomes a typical example of Mathematical Key Competencies assessment. Cognitive Diagnostic Assessment, as a new generation of measurement theory, integrates the measurement objectives into the cognitive process model via cognitive analysis, and helps increase understanding of students' mastery over fine-grained knowledge points. This paper analyzed 12 PISA test items and calibrated their attributes, thus forming a cognitive model based on six PISA's MKCs, which were namely Mathematical ion, Logical Reasoning, Intuitive Imagination, Mathematical Modeling, Mathematical Operation and Data Analysis. Through the comparison of models fit for DINA, DINO, RRUM, ACDM, GDM, LCDM, LLM, G-DINA and Mixed Model, the LCDM with a good model fit was selected to analyze the data of 19, 454 students in eight countries, and comparisons of the six MKCs among these countries were obtained. Through analyzing the knowledge states, combing the prerequisite relationships between the attributes, and exploring the learning trajectories of students' MKCs in different countries, we found that the students' performance in China was the best among all countries for each of the six MKCs. In all other countries, the students' performance in Logical Reasoning and Intuitive Imagination were weaker than the other attributes. An analysis of learning trajectories found that Russia, Singapore, Australia and Finland had very similar main learning trajectories (highlighted in red), and the main learning trajectories of Russia and Singapore are the same; however, there exist distinct differences in learning trajectories between China and the United States, where the learning trajectories in China is complicated with varied branches, while the ones in the United States is relatively simple with fewer branches.

Ability of data analysis, as one of the essential core qualities of modern citizens, has received widespread attention from the international education community. How to evaluate students' data analysis ability and obtain the detailed diagnosis information is one of the key issues for schools to improve education quality. With an employment of cognitive diagnostic assessment (CDA) as the basic theoretical framework, this study constructed the cognitive model of data analysis ability for 503 Grade 9 students in China. The follow-up analyses including the learning path, learning progression and corresponding personalized assessment were also provided. The result indicated that first, almost all the students had the data awareness. Furthermore, the probability of mastering the attribute Interpretation and inference of data was relatively low with only 60% or so. Also, the probabilities of mastering the rest of attributes were about 70% on average. It was expected that this study would provide a new cognitive diagnostic perspective on the assessment of students' essential data analysis abilities.

Education evaluation plays a key role in promoting education development. The sustainable concept of evaluation provides the basis for the sustainable development of education. Value-added evaluation makes up for the shortcomings of traditional evaluation that only focuses on the results. It takes the development of students and teachers and the improvement of the education system as the main variables of evaluation, providing a basis for the sustainable development of students. This study summarizes the origin and development of value-added evaluation, including its theoretical basis, value orientation, evaluation content and typical cases, and attempts to gain a deeper understanding of it through multiple evaluation methods. The research shows that the value-added evaluation showed a trend of more diversified evaluation indicators, diagnostic evaluation results, and emphasis on longitudinal analysis; value-added evaluation is based on the relative increase in value and emphasizes the "net increment" of students' learning achievements; the content of value-added evaluation focuses on students' academic achievements and teacher effect; the evaluation methods mainly include direct evaluation method, indirect investigation method and multivariate and hierarchical statistical method. This research has carried out a comprehensive analysis and interpretation of value-added evaluation to ensure the deep understanding and rational application of it.

Learning path and learning progression have received extensive attention from broad disciplines. The existing research In the field of learning path is rarely applied in curriculum learning and teaching. Learning progression is usually constructed through observations, interviews but not quantitative analyses. With 726 Grade 8 students' mathematical knowledge in TIMSS-2015 as the research object, this research adopted a newly generated assessment theory - cognitive diagnosis assessment as the research tool and exploited methods such as K-means clustering analysis to construct learning path by combing the relationships among the attributes. We obtained the students' ability theta s for each classified group through the 3PL model in the Item Response Theory (IRT) and constructed the learning progressions based on the theta s and the attribute relationships. From a data-driven approach, this method has provided a new perspective as well as the data support for the construction of the learning paths and the learning progressions.

Initiated by the Program for International Student Assessment (PISA), Mathematical Key Competencies (MKCs), which integrate into the process of solving situational problems, becomes a typical example of Mathematical Key Competencies assessment. Cognitive Diagnostic Assessment, as a new generation of measurement theory, integrates the measurement objectives into the cognitive process model via cognitive analysis, and helps increase understanding of students’ mastery over fine-grained knowledge points. This paper analyzed 12 PISA test items and calibrated their attributes, thus forming a cognitive model based on six PISA’s MKCs, which were namely Mathematical Abstraction, Logical Reasoning, Intuitive Imagination, Mathematical Modeling, Mathematical Operation and Data Analysis. Through the comparison of models fit for DINA, DINO, RRUM, ACDM, GDM, LCDM, LLM, G-DINA and Mixed Model, the LCDM with a good model fit was selected to analyze the data of 19, 454 students in eight countries, and comparisons of the six MKCs among these countries were obtained. Through analyzing the knowledge states, combing the prerequisite relationships between the attributes, and exploring the learning trajectories of students’ MKCs in different countries, we found that the students’ performance in China was the best among all countries for each of the six MKCs. In all other countries, the students’ performance in Logical Reasoning and Intuitive Imagination were weaker than the other attributes. An analysis of learning trajectories found that Russia, Singapore, Australia and Finland had very similar main learning trajectories (highlighted in red), and the main learning trajectories of Russia and Singapore are the same; however, there exist distinct differences in learning trajectories between China and the United States, where the learning trajectories in China is complicated with varied branches, while the ones in the United States is relatively simple with fewer branches.