Suchergebnisse

Mutual learning between teachers and their colleagues plays a vital role in their professional growth. However, previous studies show that collaborative teacher learning tends to be confined to small groups, and that it is difficult to make it explicit, showcase, or transfer teachers' tacit knowledge. This study explores this problem by creating a digital system that allows teachers to create, display, and trade their knowledge products; 508 teachers from one K–12 school created and traded their knowledge products, while 1,148 teachers from other schools participated in the knowledge product transaction on the digital system. Using social network analysis, we discovered that teachers use this method to generate a lot of interaction and that teachers who taught similar subject areas communicated more with one another. There is a significant correlation among each teacher's number of created knowledge products, knowledge products sold, and the number of knowledge products purchased from others. The study has potential to contribute to research on teacher learning, thinking, and actions.

Scholars suggest that not every student completely comprehends the content of a lecture in a foreign language as the medium of instruction, especially in the case of those with low language ability. To facilitate comprehension of lectures in a foreign language, learning content was presented to students in multiple modalities; that is, in addition to verbal (speech of the instructor) and visual (lecture slides) content, texts generated by speech-to-text recognition (STR) or speech-enabled language translation (SELT) were shown to the students. The goal was to compare how these two additional content modalities (i.e., STR-texts vs. SELT-texts) facilitate student comprehension of lecture content. Because processing multimodal content requires additional cognitive resources, another goal was to explore whether STR-texts versus SELT-texts impose any cognitive load on the students. To this end, two groups of students were recruited, where they attended two lectures at the intermediate and advanced levels. STR-texts were shown to a control group, and SELT-texts were shown to an experimental group. The posttest results and the cognitive load of the students in both groups after each lecture were compared. Four main findings were obtained: (a) The experimental group outperformed the control group on both posttests. However, when student language ability was considered, the difference was statistically significant for low ability students only; (b) there was not a significant between-group difference in cognitive load; however, if student language ability was considered, a significant between-group difference existed during the more difficult lecture; (c) between-group differences in self-efficacy were statistically insignificant; and (d) associations among some research variables were found. Based on these results, several implications were drawn for the teaching and research community.

Problem-solving skills are an ability that must be cultivated to equip students with the skills needed to deal with today's increasingly complex and volatile environment. Computational thinking represents a new paradigm in problem-solving skills. After Wing proposed Computational Thinking as problem-solving skills in 2006, other scholars investigated this topic; nevertheless, the link between Computational Thinking and problem-solving has not been clearly discussed in previous studies. To uncover evidence for the connection between Computational Thinking and problem-solving skills, we conduct a systematic literature review of 37 papers collected from Web of Science database. The results indicate that (a) problem-solving is discussed in the 37 articles in the context of Computational Thinking, (b) the most frequently employed Computational Thinking stages in problem-solving skills are decomposition, pattern recognition, abstraction, and algorithm, (c) Computational Thinking is closely linked to problem-solving, and (d) Computational Thinking and problem-solving stages serve the same functions in solving problems. The results of this study will encourage the development of education research, particularly in the application of CT as a problem-solving tool in various real-life scenarios.

Wireless sensor networks have garnered considerable attention recently. Networks typically have many sensor nodes, and are used in commercial, medical, scientific, and military applications for sensing and monitoring the physical world. Many researchers have attempted to improve wireless sensor network management efficiency. A Simple Network Management Protocol (SNMP)-based sensor network management system was developed that is a convenient and effective way for managers to monitor and control sensor network operations. This paper proposes a novel WSNManagement system that can show the connections stated of relationships among sensor nodes and can be used for monitoring, collecting, and analyzing information obtained by wireless sensor networks. The proposed network management system uses collected information for system configuration. The function of performance analysis facilitates convenient management of sensors. Experimental results show that the proposed method enhances the alive rate of an overall sensor node system, reduces the packet lost rate by roughly 5%, and reduces delay time by roughly 0.2 seconds. Performance analysis demonstrates that the proposed system is effective for wireless sensor network management.

AbstractIn the evolving landscape of higher education, challenges such as the COVID-19 pandemic have underscored the necessity for innovative teaching methodologies. These challenges have catalyzed the integration of technology into education, particularly in blended learning environments, to bolster self-regulated learning (SRL) and higher-order thinking skills (HOTS). However, increased autonomy in blended learning can lead to learning disruptions if issues are not promptly addressed. In this context, OpenAI's ChatGPT, known for its extensive knowledge base and immediate feedback capability, emerges as a significant educational resource. Nonetheless, there are concerns that students might become excessively dependent on such tools, potentially hindering their development of HOTS. To address these concerns, this study introduces the Guidance-based ChatGPT-assisted Learning Aid (GCLA). This approach modifies the use of ChatGPT in educational settings by encouraging students to attempt problem-solving independently before seeking ChatGPT assistance. When engaged, the GCLA provides guidance through hints rather than direct answers, fostering an environment conducive to the development of SRL and HOTS. A randomized controlled trial (RCT) was employed to examine the impact of the GCLA compared to traditional ChatGPT use in a foundational chemistry course within a blended learning setting. This study involved 61 undergraduate students from a university in Taiwan. The findings reveal that the GCLA enhances SRL, HOTS, and knowledge construction compared to traditional ChatGPT use. These results directly align with the research objective to improve learning outcomes through providing guidance rather than answers by ChatGPT. In conclusion, the introduction of the GCLA has not only facilitated more effective learning experiences in blended learning environments but also ensured that students engage more actively in their educational journey. The implications of this study highlight the potential of ChatGPT-based tools in enhancing the quality of higher education, particularly in fostering essential skills such as self-regulation and HOTS. Furthermore, this research offers insights regarding the more effective use of ChatGPT in education.

AbstractIn the field of Science, Technology, Engineering, and Mathematics (STEM) education, which aims to cultivate problem-solving skills, accurately assessing learners' engagement remains a significant challenge. We present a solution to this issue with the Real-time Automated STEM Engagement Detection System (RASEDS). This innovative system capitalizes on the power of artificial intelligence, computer vision, and the Interactive, Constructive, Active, and Passive (ICAP) framework. RASEDS uses You Only Learn One Representation (YOLOR) to detect and map learners' interactions onto the four levels of engagement delineated in the ICAP framework. This process informs the system's recommendation of adaptive learning materials, designed to boost both engagement and self-efficacy in STEM activities. Our study affirms that RASEDS accurately gauges engagement, and that the subsequent use of these adaptive materials significantly enhances both engagement and self-efficacy. Importantly, our research suggests a connection between elevated self-efficacy and increased engagement. As learners become more engaged in their learning process, their confidence is bolstered, thereby augmenting self-efficacy. We underscore the transformative potential of AI in facilitating adaptive learning in STEM education, highlighting the symbiotic relationship between engagement and self-efficacy.