In this study we modelled possible causes and consequences of student burnout and engagement on academic efficacy and dropout intention in university students. Further we asked, can student engagement protect against the effects of burnout? In total 4,061 university students from Portugal, Brazil, Mozambique, the United Kingdom, the United States of America, Finland, Serbia, and Macao SAR, Taiwan participated in this study. With the data collected we analyzed the influence of Social Support, Coping Strategies, and school/course related variables on student engagement and burnout using structural equation modeling. We also analyzed the effect of student engagement, student burnout, and their interaction, on Academic Performance and Dropout Intention. We found that both student engagement and burnout are good predictors of subjective academic performance and dropout intention. However, student burnout suppresses the effect of student engagement on these variables. This result has strong implications for practitioners and administrators. To prevent student dropout, it is not enough to promote student engagement—additionally, and importantly, levels of student burnout must be kept low. Other variables such as social support and coping strategies are also relevant predictors of student engagement and burnout and should be considered when implementing preventive actions, self-help and guided intervention programs for college students.

Academic engagement describes students' involvement in academic learning and achievement. This paper reports the psychometric properties of the University Student Engagement Inventory (USEI) with a sample of 3992 university students from nine different countries and regions from Europe, North and South America, Africa, and Asia. The USEI operationalizes a trifactorial conceptualization of academic engagement (behavioral, emotional, and cognitive). Construct validity was assessed by means of confirmatory factor analysis and reliability was assessed using Cronbach's alpha and McDonald's omega coefficients. Weak measurement invariance was observed for country/region, while strong measurement invariance was observed for gender and area of graduation. The USEI scores showed predictive validity for dropout intention, self-rated academic performance, and course approval rate while divergent validity with student burnout scores was also evident. Overall, the results indicate that the USEI can produce reliable and valid data on academic engagement of university students across the world.

This study investigated how various mathematics learning strategies affect the mathematical literacy of students. The data for this study were obtained from the 2012 Programme for International Student Assessment (PISA) data of Taiwan. The PISA learning strategy survey contains three types of learning strategies: elaboration, control, and memorization. To objectively classify students' learning strategies, we conducted a latent class analysis (LCA) to determine the optimal fitting latent class model of students' performances on a learning strategy assessment and to explore the mathematical literacy of students who used various learning strategies. The result of the LCA showed that, among the models of two to five classes, the model of four classes was the optimally fitted model. The results of this study provide crucial information for mathematics educators regarding the achievement-related and strategy-related outcomes of schooling. By combining information from the assessment of mathematical literacy and survey of learning strategies that predispose students to using their mathematical literacy, a more complete picture of mathematics teaching and learning emerges. The implications of these results are discussed.

Fuzzy relationships exist between students' learning performance with various abilities and a test item. However, the challenges in implementing adaptive assessment agents are obtaining sufficient items, efficient and accurate computerized estimation, and a substantial feedback agent. Additionally, the agent must immediately estimate students' ability item by item, which places a considerable burden on the server, especially for a group test. Hence, the implementation of adaptive assessment agent is more difficult in practice. This paper proposes an agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for students' learning performance evaluation and educational applications, and the proposed agent is according to the response data from a conventional test and an item response theory (IRT)-based three-parameter logistic (3PL) model. First, we apply a Gauss-Seidel (GS)-based parameter estimation mechanism to estimate the items' parameters according to the response data, and then to compare its results with those of an IRTbased Bayesian parameter estimation mechanism. In addition, we propose a static-IRT test assembly mechanism to assemble a form for the conventional test. The presented FML-based dynamic assessment mechanism infers the probability of making a correct response to the item for a student with various abilities. Moreover, this paper also proposes a novel PSO-based FML (PFML) learning mechanism for optimizing the parameters between items and students. Finally, we adopt a K-fold cross validation mechanism to evaluate the performance of the proposed agent. Experimental results show that the novel PFML learning mechanism for the parameter estimation and learning optimization performs favorably. We believe the proposed PFML will be a reference for education research and pedagogy and an important co-learning mechanism for future human-machine educational applications. Index Terms-Dynamic assessment, Fuzzy Markup Language (FML), Genetic FML (GFML), item response theory (IRT), particle swarm optimization (PSO)The authors would like to thank the financial support sponsored by 1) Ministry

Student's performance is classified into four levels, including below basic, basic, proficient, and advanced levels. The descriptions of the performance standard make students understand their learning achievement via percentile rank (PR), a norm-referenced score, and T score (T). This paper develops an adaptive student assessment system and invites elementary-school students to do a test in mathematics. Additionally, one adaptive item selection strategy mechanism is developed to choose next item that meets the student's current estimated ability. After that, the response data are collected to execute the type-2 fuzzy set (T2FS) construction mechanism to build a personalized T2FS for each student's performance and a T2FS for all students with an identical level. Finally, the student evaluation mechanism is executed to show students and teachers some useful information to assist in their future teaching and guidance. The simulation results show the proposed approach is feasible to adaptively select items from the item bank and construct T2FS for students' ability. In the future, we plan to use the technologies of optimization and computational intelligence to infer each student's ability in the test based on the constructed T2FSs.