Education and Information Technologies最新文献

Scientific inquiry ability is closely related to the process of hands-on inquiry practice. However, its assessment is often separated from this practice due to the limitation of technical basis and labor cost. The development of multimodal data analysis provides a new opportunity to realize automated assessment based on hands-on practice. Therefore, this study aims to explore whether and how we can use automated multimodal data analysis approaches to measure the scientific inquiry ability of students during the hands-on inquiry practice. In a scientific inquiry activity called "Explore the Moon," designed for 472 fourth graders, we collected textual, tabular, and video data. Aiming to analyze and evaluate the data, we first designed a modal conversion method based on the multimodal pre-trained model LLaVA-7B and a text scoring method integrating keyword matching, one-way nearness, and Jaccard similarity. Then, to bridge the computing ability with the scoring criteria from science teachers, we constructed a structured representation language and verified the human–machine consistency of automated scoring. Finally, we used a multidimensional item response theory (IRT) model to validate the assessment's overall quality and analyze the participants' scientific inquiry ability. The proposed data analysis method has high man–machine consistency, and the results of IRT analysis present reasonable item characteristics. In summary, we constructed a low-cost and scalable multimodal assessment approach based on scientific inquiry activities, providing methodological support for science teachers to carry out formative evaluation of students' scientific inquiry activities in the daily inquiry-based learning environment.

Integrating ChatGPT into educational contexts has become prevalent due to its potential to revolutionize teaching, learning, and research. However, to ensure positive use, it is necessary to design learning environments that effectively incorporate this technology. This study employed self-determination theory as an undergirding framework to understand the impact of ChatGPT-integrated instruction on undergraduate students’ research skills. Moreover, the study explored the impact of ChatGPT-integrated instruction on students’ autonomous motivation, engagement, and self-directed learning, as well as the mediating role of these variables in the relationship between ChatGPT usage and research skills. In this quasi-experimental study, 366 undergraduate students majoring in education were divided into control and experimental groups. The control group received instruction without ChatGPT, while the experimental group received ChatGPT-integrated instruction and was allowed to use ChatGPT for research-related activities. The results revealed that the experimental group had significantly higher levels of research skills, motivation, engagement, and self-directed learning behavior. Moreover, the use of ChatGPT has a significant indirect impact on students’ research skills through the mediating variables. This suggests that high autonomous motivation and self-directed learning are crucial for students to fully benefit from ChatGPT in developing research skills. The findings offer valuable insights for educators to design AI-enhanced learning environments to enhance students’ research skills and learning outcomes.

This systematic literature review examines the use of computer games as instructional aids in the teaching and learning of programming. With the ubiquitous nature of technology permeating various aspects of modern life, the integration of gaming devices into educational settings has garnered increased attention. This paper investigates whether computer games, primarily designed for entertainment, can effectively facilitate the teaching and learning of programming concepts. By analysing existing literature, this review aims to provide insights into the potential benefits, challenges, and overall efficacy of using computer games as a pedagogical tool for teaching and learning programming. Key themes explored include student engagement, skill acquisition, and the impact on learning outcomes. The findings of this review contribute to a deeper understanding of the innovative intersection between gaming and programming education and offer practical implications for educators and instructional designers.

With the continuous evolution of information technology shaping advancements in education, virtual reality (VR) technology has been increasingly applied to enhance English learning amongst students, aiming to boost learning efficiency and performance. This study introduced a VR technology acceptance model (TAM) to fulfil these requirements. The proposed model highlighted language learning anxiety (LLA) as a critical factor and delineated secondary school students’ acceptance of VR technology through four classroom climate constructs: respecting viewpoints (RV), promoting mutual respect (PMR), academic support from teachers (AS) and promoting interaction (PI). The current study surveyed 512 secondary school students using a questionnaire. Data gathered were scrutinised utilising structural equation modelling for the assessment of the hypothetical model. This research validated the suitability of TAM in elucidating the acceptance of VR technology for English learning. Perceived ease of use (PEOU) positively influenced perceived usefulness (PU). PU and PEOU positively influenced secondary school students’ attitudes towards using VR. Amongst the four classroom climate dimensions, AS and PMR significantly negatively affected secondary school students’ LLA, whereas PI and RV had no significant effect on LLA. Attitudes towards using VR and PU positively influenced students’ willingness to use VR. Practical recommendations were provided to enhance secondary school students’ inclination towards utilising VR technology to enhance their efficiency and performance in learning English.

In STEM hands-on learning activities, collaboration with group members can be a significant motivator for students’ engagement. This research is based on the 6E Learning by DeSIGN™ model and explores the impact of incorporating reflective strategies on students’ learning performance, motivation, and participation in collaborative STEM learning activities. Additionally, this study uses voice recognition processing technology to assess differences in collaborative discussion topics. The findings reveal that while adhering to the 6E learning framework can effectively enhance various aspects of students’ learning outcomes, the importance of reflective strategies remains paramount. Specific reflection stages can foster stronger connections and interactions among students and their learning partners. Furthermore, during the process of discussion and communication, group members gain a better understanding of the team’s shared goals, leading to heightened motivation and participation intent in contributing to the team’s success. Moreover, this study also identifies that members of groups engaged in the 6E + R learning model dedicate more time and effort to critical thinking and problem-solving during collaborative discussions, validating that collective reflective thinking within the team can assist novice learners in achieving higher-order thinking and maximizing their learning outcomes.

Alongside technological advances, the educational potential of artificial intelligence (AI) chatbots and the metaverse has generated significant interest in the field of computer-assisted language learning (CALL). However, despite this heightened interest, there have been no studies that have delved into the effective integration of these two technologies into educational contexts. In response to this concern, this research examined a teacher training course where pre-service teachers designed and used their customized chatbots within the context of the metaverse space. Fifty-five pre-service English teachers were assigned to the chatbot-only group (COG) (n = 31) and the chatbot-metaverse group (CMG) (n = 24). We first explored the CMG’s chatbot design works and teaching demonstrations in metaverse spaces and compared them to those of the COG, who developed and utilized chatbots in a physical classroom setting. We further compared their perceptions related to experiences with chatbot-based lesson designing and teaching demonstrations, using a survey and reflection papers. The comparison of design works and teaching demonstrations revealed that while both groups recognized the value and effectiveness of AI chatbots in the language classroom, the participants in the CMG tended to develop more authentic, immersive, and interactive learning tasks, with the metaverse space playing a crucial role as a context. Analysis of a survey and reflection papers indicated that the CMG reported more positive perceptions than the COG. We discussed how the metaverse space might have influenced the way teachers developed and integrated chatbots into their educational contexts. Pedagogical and theoretical implications regarding the combined use of AI chatbot and metaverse technologies were also provided.

The aim of this study is to determine the effectiveness of using ChatGPT, an artificial intelligence-supported tool, to overcome the challenges in the implementation of early childhood STEM education based on teachers’ views. In the study, a case study, which is a qualitative research method, was used. The participants of the study consisted of 43 early childhood teachers specialized in the field of STEM education who were determined by purposeful sampling method. Within the scope of the study, a training program was developed for teachers to integrate ChatGPT into early childhood STEM education and teachers voluntarily participated in this program. During the training process, the researchers collected data through observation reports and semi-structured interviews conducted after the training. The collected data were subjected to content analysis using MAXQDA software, and themes, categories and codes were identified. The findings revealed the teachers’ views that the use of ChatGPT in early childhood STEM education would be beneficial. It was determined that ChatGPT would provide advantages such as guidance, effective use of existing materials, the opportunity to design student-specific activities, and the potential to complete teachers’ missing knowledge. However, teachers also think that ChatGPT may cause negative situations such as technological addiction, regression in social skills, damage to the teacher-student relationship and misinformation. In conclusion, ChatGPT can benefit students’ education.

This experimental study investigates the effects of retelling, reading response, and combined retelling and reading response activities that second-grade students participated in after watching digital stories on their reading attitudes and narrative comprehension levels. In this pre-test–post-test control group design, 140 students, composed of 35 students in each group, participated in the study in three different experimental groups (retelling, response, and retelling–response) and a control group. The digital stories created by pre-service teachers were used in the written and oral retelling and response activities, in which second graders participated in small groups of seven and watched the digital stories on large smart screens. The experimental period, including the pre-test and post-test, lasted nine weeks. The students watched nine digital stories. In the pre-and post-tests, the students’ total, recreational, and academic reading attitudes and narrative comprehension levels were assessed. The results showed that the narrative comprehension scores of all experimental groups increased significantly compared with those of the control group. In addition, when the pre-and post-test total reading attitude scores and narrative comprehension scores of the experimental groups and the control group were compared, statistically significant increases in the scores of all experimental groups demonstrated the effectiveness of the interventions. This study contributes to the literature by showing that digital stories created by pre-service teachers improved second-grade students’ reading attitudes and narrative comprehension levels when used in the retelling, response, and retelling–response groups.

With the application of virtual venues in the field of education, numerous educational empirical studies have examined the impact of deep learning in the learning environment of virtual venues, but the conclusions are not always in agreement. The present study adopted the meta-analysis method and RStudio software to test the overall effect of 45 domestic and foreign experimental and quasi-experimental studies, and eight moderating variables (experiment period, knowledge type, virtual venue type, and feedback strategy) were analyzed. The research results indicated that moderating variables had different degrees of influence on the deep learning effect of students in the learning environment of virtual venues. There were no intra-group differences in the type of virtual venue and the experiment period, while there were intra-group differences in other moderating variables. According to the results of meta-analysis, suggestions were put forward from four aspects (course design of virtual venues, selection and application of feedback strategies, knowledge type design of virtual venues, and empirical research suggestions) to serve as references for strengthening the deep learning impact of students and the scientific design of course content of virtual venues.

ChatGPT, as an example of generative artificial intelligence, possesses high-level conversational and problem-solving capabilities supported by powerful computational models and big data. However, the powerful performance of ChatGPT might enhance learner dependency. Although it has not yet been confirmed, many teachers and scholars are also concerned about this issue. Therefore, it is necessary to investigate this topic further. This study’s objective is to explore the association between inert thinking, positive experiences with ChatGPT, avoidance learning motivation, and ChatGPT dependence, based on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model. Employing a cross-sectional design, we conducted an online survey with 870 Taiwanese university students, who had an average age of 22.81 years. The study found that inert thinking was positively associated with both positive experiences with ChatGPT and ChatGPT dependence. Furthermore, a significant association was found between inert thinking and avoidance learning motivation. Positive experience with ChatGPT was also positively related to avoidance learning motivation and ChatGPT dependence. Due to the scarcity of empirical research on generative artificial intelligence, the issues that people worry about when discussing AI were confirmed in this study. Moreover, avoidance learning motivation was positively correlated with ChatGPT dependence. Based on these findings, this study calls for educators to help students overcome inert thinking and avoidance learning motivation to prevent dependency on emerging technologies.