Measuring mathematics self-efficacy: multitrait-multimethod comparison

Previous studies had shown that there is a certain relationship between mathematics self-efficacy and math performance. For students, parents, and front-line scholars, it is urgent and important to study the measurement relationship between math achievement and self-efficacy. The research aimed to observe how to measure mathematics self-efficacy and find which of the three traits and which of the three methods better reflect individuals’ self-efficacy. The present study used a multitrait-multimethod (MTMM) design to measure mathematics self-efficacy by constructing the confirmatory factor analysis (CFA) model. “Number and Algebra,” “Graphics and Geometry,” and “Synthesis and Practice” were considered three traits, and General-Math-Task-referenced self-efficacy, Unconventional-Math-Problem-referenced self-efficacy, and Motivated Strategies for Learning Questionnaire (MSLQ) self-efficacy were discussed as three methods to study. A questionnaire survey was used to obtain data. A total of 100 students completed all the questionnaires. Excel was used to collect math scores, and SPSS version 26.0 and AMOS version 26.0 were used to manage the data, confirm a hypothesis, and build a model by using MTMM design and CFA. CFA was used to verify convergent validity and discriminant validity. A total of eight models were constructed in the study that includes first-order CFA models and second-order CFA models, and model D was finally selected as the most perfect model in the second-order CFA model. The results showed that the “Synthesis and Practice” fields were the most significant reflection of self-efficacy among the three traits. MSLQ was the most significant reflection of self-efficacy among the three methods. It is beneficial to improve the level of self-efficacy from the aspect of mathematics subject. In addition, the research confirmed that CFA can support MTMM data for data modeling and found that the correlation between the Unconventional-Math-Problem-referenced self-efficacy and MSLQ is higher than that of General-Math-Task-referenced self-efficacy in the second-order model. It makes certain theoretical significance for improving students’ mathematics self-efficacy levels

Intervention on mathematics self-efficacy: solution-focused brief therapy

Purpose: Research has demonstrated a strong correlation between mathematics self-efficacy and math performance. Middle school children are increasingly receiving solution-focused brief therapy (SFBT), which is a type of psychotherapy. The study intends to use SFBT intervention to improve mathematics self-efficacy of students and to determine whether SFBT intervention was effective. To examine whether Rasch model can be used to evaluate students’ mathematics self-efficacy. Methods: This study intends to use Radar chart, Rasch model, Line chart to measure the variations of mathematics self-efficacy of three 8th graders (n=3) during SFBT intervention. Results: Radar chart and Rasch model demonstrated a general increment in the mathematics self-efficacy of two pupils, while another one decreased. Additionally, three students showed a decline in their mathematics self-efficacy on particular mathematical problems using a line chart. Conclusion: Overall, students with varied degrees of self-efficacy in math benefited from SFBT interventions, which partially supports the usefulness of SFBT as a tool for assessing students’ mathematics self-efficacy. It supported that Rasch model can reflected the changes in students’ mathematics self-efficacy. This study provides guidance for measuring the improvement of students’ academic self-efficacy through SFBT intervention using Rasch model

The loss of taste genes in cetaceans

Background: Five basic taste modalities, sour, sweet, bitter, salt and umami, can be distinguished by humans and are fundamental for physical and ecological adaptations in mammals. Molecular genetic studies of the receptor genes for these tastes have been conducted in terrestrial mammals; however, little is known about the evolution and adaptation of these genes in marine mammals. Results: Here, all five basic taste modalities, sour, sweet, bitter, salt and umami, were investigated in cetaceans. The sequence characteristics and evolutionary analyses of taste receptor genes suggested that nearly all cetaceans may have lost all taste modalities except for that of salt. Conclusions: This is the first study to comprehensively examine the five basic taste modalities in cetaceans with extensive taxa sampling. Our results suggest that cetaceans have lost four of the basic taste modalities including sour, sweet, umami, and most of the ability to sense bitter tastes. The integrity of the candidate salt taste receptor genes in all the cetaceans examined may be because of their function in Na+ reabsorption, which is key to osmoregulation and aquatic adaptation. Electronic supplementary material The online version of this article (doi:10.1186/s12862-014-0218-8) contains supplementary material, which is available to authorized users

[18F]Tosyl fluoride as a versatile [18F]fluoride source for the preparation of 18F-labeled radiopharmaceuticals

Positron emission tomography (PET) is an in vivo imaging technology that utilizes positron-emitting radioisotope-labeled compounds as PET radiotracers that are commonly used in clinic and in various research areas, including oncology, cardiology, and neurology. Fluorine-18 is the most widely used PET-radionuclide and commonly produced by proton bombardment o

How Important are Good Method Names in Neural Code Generation? A Model Robustness Perspective

Pre-trained code generation models (PCGMs) have been widely applied in neural code generation which can generate executable code from functional descriptions in natural languages, possibly together with signatures. Despite substantial performance improvement of PCGMs, the role of method names in neural code generation has not been thoroughly investigated. In this paper, we study and demonstrate the potential of benefiting from method names to enhance the performance of PCGMs, from a model robustness perspective. Specifically, we propose a novel approach, named RADAR (neuRAl coDe generAtor Robustifier). RADAR consists of two components: RADAR-Attack and RADAR-Defense. The former attacks a PCGM by generating adversarial method names as part of the input, which are semantic and visual similar to the original input, but may trick the PCGM to generate completely unrelated code snippets. As a countermeasure to such attacks, RADAR-Defense synthesizes a new method name from the functional description and supplies it to the PCGM. Evaluation results show that RADAR-Attack can reduce the CodeBLEU of generated code by 19.72% to 38.74% in three state-of-the-art PCGMs (i.e., CodeGPT, PLBART, and CodeT5) in the fine-tuning code generation task, and reduce the Pass@1 of generated code by 32.28% to 44.42% in three state-of-the-art PCGMs (i.e., Replit, CodeGen, and CodeT5+) in the zero-shot code generation task. Moreover, RADAR-Defense is able to reinstate the performance of PCGMs with synthesized method names. These results highlight the importance of good method names in neural code generation and implicate the benefits of studying model robustness in software engineering.Comment: UNDER REVIE