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The Motivated Strategies for Learning Questionnaire (MSLQ) is one of the most widely used instruments to measure students' motivation and self-regulated learning. However, the MSLQ was developed and has been predominantly used in the Western context, is a domain-general measure, and is quite lengthy. Hence, adapting the MSLQ to the Chinese educational context, validating its application in specific subjects, and developing a short form would be an optimal way to improve its accessibility. This study aimed to investigate the psychometric properties of the Chinese version of the MSLQ in mathematics learning (i.e., MSLQ-C) and develop a short form (i.e., MSLQ-CS) using set exploratory structural equation modeling. The sample consisted of 563 senior secondary students in China. Results demonstrated that both MSLQ-C and MSLQ-CS showed acceptable construct validity, reliability, and concurrent validity. Furthermore, structural relationships and interrelationships among the subscales and their relationships with mathematics achievement were highly similar for MSLQ-C and MSLQ-CS. Theoretical and practical implications are discussed.

East Asian students have consistently performed well in mathematics compared to their international peers. Though many researchers have attempted to explore the factors that underpin their success, most studies have focused only on a limited set of variables. Mathematics achievement, however, is a complex phenomenon, determined by multiple factors, and best understood when different variables across levels of analysis are taken into account. Guided by the socio-ecological theory, the present study attempts to understand the relative importance of individual, microsystem, and mesosystem factors in predicting mathematics achievement. We used the Trends in International Mathematics and Science Study (TIMSS) 2019 data provided by 21,340 eighth-grade East Asian students (Chinese Taipei, Hong Kong, Japan, Korea, and Singapore), 1242 mathematics teachers, and 802 principals. Machine learning (i.e., random forest regression) was used to analyze the data. Results identified 11 key variables that best predicted East Asian students' mathematics achievement. Among these, students' confidence in mathematics, socioeconomic status, and school emphasis on academic success were the top three. Theoretical and practical implications are discussed.

Research experience is widely used in quality assurance exercises to benchmark postgraduate education at the institutional level. However, individual differences in students' research experience have been largely neglected. Furthermore, little is known about how differences in students' research experience are associated with skill development and overall satisfaction. This study addressed these gaps using an explanatory sequential mixed-methods design. Study 1 was a quantitative study that involved surveying 590 research postgraduate students (i.e. 421 PhD and 168 MPhil students). A person-centered approach, specifically latent profile analysis, was used to analyze the data. Our findings revealed that students could be divided into three groups based on their research experience: rewarding, ordinary, and unsatisfactory. Those with a rewarding research experience experienced greater development in their skills and higher levels of satisfaction, while those in the unsatisfactory group demonstrated the worst outcomes. Study 2 was a qualitative study that involved interviews with 10 PhD students. The qualitative findings largely triangulated the quantitative results but also uncovered emerging themes, including the importance of student-supervisor misfit, publication pressure, and the COVID-19 pandemic context. Theoretical and practical implications of these findings are discussed.

Expectancy-value theory mostly focuses on how expectancy and value beliefs are associated with academic achievement. Both expectancy and value beliefs are multidimensional and can be further decomposed into general and specific factors. However, few studies have explored the associations of these general and specific factors with academic achievement and learning strategy use. This study aimed to investigate the bifactor structure of expectancy and value beliefs and investigate the linkages between general and specific expectancy-value factors and mathematics achievement and learning strategy use. Based on the expectancy and value dimensions measured by the Motivated Strategies for Learning Questionnaire (MSLQ), bifactor models were used to analyse the data collected from 563 senior secondary school students in China. Three key findings emerged: (1) both expectancy and value beliefs could be disentangled into general and specific factors; (2) general factors were significantly associated with mathematics achievement and learning strategy use; and (3) the specific factors were positively associated with mathematics learning strategy use but not mathematics achievement. Methodological and practical implications are discussed. [Spanish translation by Mercè Rius.]

Despite the importance of artificial intelligence (AI) for university students to thrive in the future workplace, few studies have been conducted to assess and foster their intentions to learn AI. Guided by the situated expectancy-value theory, this study adopted both variable- and person-centered approaches to explore the role of supportive environments and expectancy-value beliefs in fostering university students' intentions to learn AI. The data were drawn from 494 university students. In Study 1, the variable-centered approach of structural equation modeling showed the critical role of supportive environments and expectancy-value beliefs in promoting students' intentions to learn AI. In Study 2, the person-centered approach of latent profile analysis identified three subgroups of students based on their levels of supportive environments and expectancy-value beliefs. Consistent with Study 1, students who perceived more supportive environments and higher levels of expectancy-value beliefs had stronger intentions to learn AI. We also documented the influence of study of field, gender, and year level on students' perceptions of supportive environments, expectancy-value beliefs and intentions to learn AI. The implications of these findings in improving students' intentions to learn AI are discussed.

Past studies on doctoral education have mostly focused on the critical role of supervisors in students' research experience. However, the role of peers has been relatively neglected. It is not clear how both supervisors and peers contribute to doctoral students' research experience. One possibility is that they are additive, which means that both supervisors and peers make distinct contributions to skill development and research satisfaction ("additive hypothesis"). The other possibility is a compensatory relationship, where low levels of supervisory support could be compensated for by the presence of peers ("compensatory hypothesis"). This study addressed this question using an explanatory sequential mixed methods design. In Study 1 (N = 421), structural equation modeling was conducted to examine which of the two hypotheses was supported. Quantitative results supported the additive hypothesis, indicating that both supervisors and peers were important in developing students' skills and enhancing satisfaction with the research experience. Study 2 was a qualitative study that involved interviews with 10 PhD students. The qualitative findings provided a nuanced understanding of the additive hypothesis and demonstrated the importance of research support and socio-emotional support provided by supervisors and peers. Overall, this study demonstrated that the greatest benefits would emerge when both quality supervision and peer support are present. The identification of factors affecting supervision and peer support shed light on the future directions for institutions, supervisors, and doctoral students to enhance the quality of doctoral education.

The importance of the opportunity to learn (OTL) for mathematics achievement has been extensively researched. However, there were still unanswered questions regarding OTL's measurement, analytical level, and relationship with motivational beliefs. To fill in the gaps, we aimed to (1) scrutinize the reliability and validity of OTL, (2) investigate the multilevel nature of OTL, and (3) explore the relationship among OTL, self-efficacy, and mathematics achievement. We used Programme for International Student Assessment (PISA) 2012 data provided by 470,745 15-year-old students from 63 economies. Psychometric analyses and doubly latent multilevel structural equating modeling were conducted. Results indicated that (1) OTL measured by familiarity with mathematical concepts displayed high reliability and validity, (2) student-reported OTL exhibited high reliability at the school-level, (3) OTL significantly predicted achievement both at student- and school-level, and (4) OTL mediated the relationship between self-efficacy and mathematics achievement at student-level. Theoretical and practical implications are discussed.