Search

STEM teacher professional development (STEM-TPD) has become a hot topic of educational research. To evaluate the main characteristics, status and trends of this educational field and to provide new insights for future studies, this review applies a bibliometric analysis of relevant publications. The Web of Science (WoS) core collection database was used as the source of bibliographic data for the period 2011--2021; specifically, 776 articles published in 232 journals were selected for this review. The bibliometric analysis was then carried out using Bibliometrix and VOSviewer to explore trends and patterns in the STEM-TDP literature. It is essential to note that the analysis does not account for the quality of the selected studies. However, it presents the results using various perspectives such as citation, co-citation, spatial and lexical networks based on frequencies, network analysis and descriptive patterns. The findings show that the number of relevant publications has grown significantly in recent years, in particular from 2015 to 2021. The findings, based on citation metrics, highlight the most influential authors, institutions and journals in the area. Authors from different countries have contributed to this research area, but there is a low volume of international collaboration among them. The analysis also reveals the dynamic changes in trends, keywords and themes in this research area. The review concludes with some recommendations for future research, and the suggested implications may also benefit other stakeholders in STEM education.

With the rapid development of artificial intelligence (AI), the demand for K-12 computer science (CS) education continues to grow. However, there has long been a lack of trained CS teachers. To promote the AI teaching competency of CS teachers, a professional development (PD) program based on the technological pedagogical content knowledge (TPACK) framework was intentionally designed in this research. A quasi-experimental design with a 25-day (75-h) intervention was conducted among 40 in-service CS teachers to examine its impact on AI teaching competency, including AI knowledge, AI teaching skills, and AI teaching self-efficacy. The quantitative data were collected via a pretest and posttest, and qualitative data were collected via artifact analysis and semistructured interviews. The results indicated that the TPACK-based PD program a) significantly improved CS teachers' AI knowledge, especially in representation and reasoning, interaction, and social impact; b) developed CS teachers' AI teaching skills, including their AI lesson plan ability and AI programming skills; and c) significantly improved CS teachers' AI teaching self-efficacy, both in AI teaching efficacy beliefs and AI teaching outcome expectancy. These findings revealed the effectiveness of the TPACK-based PD program in improving the AI teaching competency of K-12 CS teachers and could help to expand the design of effective PD for CS teachers.

Computational thinking (CT) is a fundamental skill and an analytical ability that children in the twenty-first century should develop. Students should begin to work with algorithmic problem-solving and computational methods in K-12. Drawing on a conceptual framework (IGGIA) that combines CT and problem-solving, this study designed and implemented an interdisciplinary Scratch course in a primary school, examined the impact of the new problem-solving instructional approach (the adapted IGGIA) on pupils' CT skills and self-efficacy, and explored the gender differences in these two aspects. A pretest-posttest nonequivalent group design was conducted among 63 fifth-grade students in two computer science classes over 14 weeks. Both quantitative and qualitative data were collected through the administration of CT scales, Scratch artifacts analysis and focus group interviews. The results revealed that the adapted IGGIA: (1) significantly improved the CT skills of primary school students; (2) had a significant positive impact on pupils' CT self-efficacy, especially on their critical thinking, algorithmic thinking and problem-solving; and (3) significantly enhanced girls' CT skills and self-efficacy. These findings indicated that problem-solving instructional approaches could promote both cognitive and noncognitive aspects of students' deeper computational learning.

Low solar irradiance reaching the canopy due to fog and heavy haze is a significant yield-limiting factor worldwide. However, how plants adapt to shade stress and the mechanisms underlying the reduction in leaf photosynthetic capacity and grain yield remain unclear. In this study (conducted during 2018–2021), we investigated the impact of light deprivation (60%) at the pre-anthesis and post-anthesis stages on leaf carboxylation efficiency, source-to-sink relationships, sucrose metabolism, and grain yield of wheat cultivars with contrasting shade tolerance. Shade stress decreased stomatal conductance, stomatal limitation value, intrinsic water use efficiency, rubisco activity, and carboxylation efficiency of flag leaves during grain-filling, whereas intercellular CO2 concentration increased. These findings indicate that non-stomatal limitation reduces the net photosynthesis rate in a weak-light environment. Shade-tolerant cultivars (MM-51 and CM-39) adapted to low-light conditions via a higher leaf area of flag leaves, light interception rate, and chlorophyll a and b contents; this increased non-structural carbohydrates and sucrose contents in developing grains, ultimately decreasing yield loss by shade stress. Pre-anthesis shading resulted in a greater yield loss than post-anthesis shading because of decreased plant biomass, grain number per spike and 1,000-kernel weight. This study indicates that Rubisco-mediated non-stomatal limitation reduces P N and sucrose content in plants exposed to low-light stress, contributing to decreased grain yield. Our study provides information on the mechanism underlying shade stress tolerance, which will help design future strategies for reducing yield loss in the context of global dimming. [ABSTRACT FROM AUTHOR]

A double-channel methane (CH4) sensor was developed using a dual-pass multipass cell (DP-MPC) and a novel method that combines averaging dual-channel concentration signals with optimized detector gain configuration. This DP-MPC features two input/output coupling holes, resulting in absorption path lengths of approximately 95.8 m and 35.8 m, respectively. By optimizing the photodetector gain configuration and averaging the dual-channel concentration signals, the detection performance of the sensor was further enhanced. Allan deviation analysis indicated that after optimizing the detector gain, the measurement precision after dual-channel averaging reaches 21 ppb with an integration time of 1 s at a concentration of 2 ppm CH4, which is approximately 1.4 times higher than the measurement precision of the long-path channel (31 ppb) and short-path channel (30 ppb). The time required to achieve a measurement precision of 21 ppb is 2.4 s for the long-path channel and 2.1 s for the short-path channel. The response speed of the dual-channel averaging is approximately 2 times that of any single channel. Meanwhile, the sensor demonstrated its stability and reliability through continuous outdoor atmospheric CH4 measurements. [ABSTRACT FROM AUTHOR]