Education and Information Technologies最新文献

Collaborative problem solving (CPS) has emerged as a crucial 21st century competence that benefits students’ studies, future careers, and general well-being, prevailing across disciplines and learning approaches. Given the complex and dynamic nature of CPS, teacher-facing learning analytics dashboards (LADs) have increasingly been adopted to support teachers’ CPS assessments by analysing and visualising various dimensions of students’ CPS. However, there is limited research investigating K-12 teachers’ integration of LADs for CPS assessments in authentic classrooms. In this study, a LAD was implemented to assist K-12 teachers in assessing students’ CPS skills in an educational game. Based on the person-environment fit theory, this study aimed to (1) examine the extent to which teachers’ environmental and personal factors influence LAD usage intention and behaviour and (2) identify personal factors mediating the relationships between environmental factors and LAD usage intention and behaviour. Survey data of 300 in-service teachers from ten Chinese K-12 schools were collected and analysed using partial least squares structural equation modelling (PLS-SEM). Results indicated that our proposed model showed strong in-sample explanatory power and out-of-sample predictive capability. Additionally, subjective norms affected technological pedagogical content knowledge (TPACK) and self-efficacy, while school support affected technostress and self-efficacy. Moreover, subjective norms, technostress, and self-efficacy predicted behavioural intention, while school support, TPACK, and behavioural intention predicted actual behaviour. As for mediation effects, school support indirectly affected behavioural intention through self-efficacy, while subjective norms indirectly affected behavioural intention through self-efficacy and affected actual behaviour through TPACK. This study makes theoretical, methodological, and practical contributions to technology integration in general and LAD implementation in particular.

This study is significant as it seeks to establish the connection between the digital literacy levels of pre-service teachers and their teacher identities while also examining the mediating role of mobile learning in this relationship. Additionally, it explores the impact of pre-service teachers’ digital literacy and learning motivations on their professional success. This study’s findings can potentially transform the way we prepare pre-service teachers, emphasizing the critical role of digital literacy and mobile learning in their professional growth. The research was conducted meticulously using relational scanning, a method that allows for exploring relationships between variables, and Process macro analysis, a statistical technique for examining complex relationships. Data were collected using the Mobile Learning Motivation Scale (MLMS), Early Teacher Identity Scale (ETIM), and Digital Literacy Scale (DLS). Three hundred forty-four pre-service teachers participated in the study, ensuring a robust dataset. The study revealed several key findings. Firstly, a positive correlation exists between pre-service teachers’ digital literacy levels, teacher identities, and mobile learning. Secondly, an increase in digital literacy levels positively impacted teacher identity. Thirdly, mobile learning was identified as a mediator in the relationship between digital literacy and teacher identity. These findings underscore the potential of mobile learning to enhance the development of teacher identity and digital literacy, highlighting the importance of incorporating these skills into the pre-professional training of teachers.

Enhancing students’ civic literacy is considered an essential educational goal in the 21st century. Therefore, in the teaching process, how to provide students with opportunities to solve problems and how to provide immediate feedback to help students make correct decisions when facing real problems are important and challenging issues. Digital game-based learning has been identified as an effective method to provide a context with interactive problems. However, a general digital game often lacks a rigorous inspection mechanism for students’ decisions in the game. Hence, some students may complete the learning tasks by guessing or trial and error, thereby affecting their learning outcomes. To deal with this problem, the present study proposed a supportive feedback-based decision-making gaming (SF-DMG) approach. It instantly checked students’ decisions and provided feedback during the game to help them really understand the factors underlying each decision. In order to verify the effectiveness of this approach, this study adopted a quasi-experimental design and applied it in a Civic and Society course in a senior high school. The experimental group adopted the SF-DMG approach, while the control group adopted the conventional decision-making gaming (C-DMG) approach. The results showed that in comparison with the control group, the experimental group had significantly better learning achievement in civic literacy, learning motivation, problem-solving tendencies, and critical thinking awareness. Besides, through the behavioral pattern analysis, it was found that with the immediate feedback on knowledge, the experimental group had better reflection and ability to modify their behaviors when facing the problems.

Emerging technologies in education, such as wearable devices, tangible user interfaces, virtual reality, augmented reality, and robotics can support learners’ motivations, achievement, engagement, and collaboration skills. However, knowledge of teachers’ intentions to adopt and utilize emerging technologies are limited. In this study, a path analysis was conducted of the factors (attitudes, subjective norms, and perceived behavioral control) that contributed to K-12 teachers’ intentions to adopt and emerging technologies in their classrooms. Teachers in the United States (N = 296) in K-12 education settings completed a survey grounded in the Decomposed Theory of Planned Behavior (DTPB). A path analysis (utilizing partial least squares structural equation modeling) indicated the antecedents to behavior: (a) teachers’ subjective norms (peers and superiors) and (b) attitude (compatibility and perceived usefulness) were most influential to predict behavioral intentions to adopt and use emerging technologies. Implications for educational researchers, teacher educators, instructional designers, and school administrators are provided to contextualize the findings.

This article explores the potential of artificial intelligence (AI) for academic advising. Specifically, it examines how AI-powered machine interpretation and data analysis can be used to deliver advising services anytime, anywhere. This system would eliminate the need for students to physically meet with advisors and could answer their academic-related inquiries throughout their studies. Additionally, the system could alleviate the burden on faculty advisors by automating the process of the follow-up with individual students, especially during times of crisis. The research employed a sample of 17 female students enrolled in the pre-master’s program at the Faculty of Women, Ain Shams University, during the academic year 2022. The researcher developed several measurement tools, namely, a satisfaction scale to gauge student satisfaction with the intelligent system and advising services, a stress-relief scale to assess student stress levels after using the system, and an experimental treatment material - the design of the intelligent system itself. The results demonstrated the effectiveness of the intelligent system in both increasing student satisfaction and reducing stress levels, particularly during times of crisis. Research limitations/implication: The limited sample size of this study may restrict the generalizability of the findings. Future research could explore the impact of intelligent academic advisors on students from diverse backgrounds to gain a more comprehensive understanding of their broader implications.

Intersection of education and deep learning method of artificial intelligence (AI) is gradually becoming a hot research field. Education will be profoundly transformed by AI. The purpose of this review is to help education practitioners understand the research frontiers and directions of AI applications in education. This paper reviews the applications of deep learning in education and provides a visualized bibliometric analysis. The data of this paper come from Education Resources Information Center (ERIC) and Web-of-Science (WOS). These two data bases are searched to identify research of deep learning-based education applications from 2015 to 2023. CiteSpace is used to analyze the number of publications, authors, institutions, and keywords of articles that are related to deep learning and education. This paper reviews and systematically analyzes the educational applications of deep learning in the following six aspects: learning effect prediction, educational game, learning recommendation, automatic scoring, assisted teaching and medical education. Based on the visualized bibliometric analysis, the path and inflection point of research evolution can be inferred, the potential motivation of research can be analyzed, and the frontier of research can be explored. At present, AI can enable teachers to focus more on teaching and personalized interactions with students, enhancing rather than replacing human instructions.

Empirical evidence has consistently shown that interactive questions are an effective strategy for strengthening student learning through instructional videos. However, research has not directly addressed the crucial question of whether the beneficial effects of interactive questions in instructional videos are attributable to the presence of questions, or whether they result from a combination of question presence and computer-paced interactivity. This question is important for understanding the mechanisms by which interactive questions work and holds practical significance for instructors’ selection of suitable video formats for student learning. To fill this research gap, this study conducted a quasi-experiment with pre-test post-test design to determine the effects of question presence and interactivity on university students’ cognitive load, emotional engagement, video view counts, and cognitive learning outcomes. Three discrete classes were randomly assigned to one of three experimental conditions: videos with questions (Experimental Group 1, n = 39), videos with interactive questions (Experimental Group 2, n = 35), and videos without questions (Control Group, n = 35). Age, gender, motivation, and actual and perceived prior knowledge were controlled in the experiment. The findings revealed that (a) question presence in traditional linear videos conferred beneficial effects on germane cognitive load, emotional engagement, knowledge acquisition, and knowledge application; (b) question interactivity did not offer additional advantages in reinforcing knowledge acquisition or application compared with question presence alone; and (c) question presence or interactivity exerted no significant effect on video view counts. This study has practical implications for instructors seeking to develop effective instructional videos.

To optimize learning experience and improve learning performance, current virtual experimental systems usually assist students with stepwise guidance before operations and feedback after them. However, stepwise and excessive guidance can lead to student overreliance, while late feedback cannot avoid potential errors during experimental learning. Meanwhile, some studies indicate that providing experimental guidance and feedback in the early stage of experimental tasks is more effective for knowledge transfer and beneficial for students. Therefore, this study aims to investigate the effects of providing precise experimental guidance and feedback via operation intention prediction in the early stage of experimental tasks. Specifically, an operation intention prediction method based on hand–eye coordination is proposed for the experimental scenario, and first applied to a virtual chemistry experimental learning environment containing complex operation context, assuming that people typically focus their gaze on an object before reaching for it. Guidance and feedback are then adaptively and precisely provided based on the prediction results, thus avoiding potential errors and improving subjective satisfaction. A mixed reality (MR) experimental system is further developed using the proposed method and two evaluation experiments were conducted, with all participants being non-chemistry major university students aged 20 to 25. An accuracy evaluation involving 19 participants demonstrates that the method correctly prompts users’ errors in a timely manner with an accuracy of 91.2%, indicating that based on the hand–eye coordination theory, operation intentions can be accurately predicted to provide guidance and feedback. Additionally, a user experiment involving 30 participants investigated the impact of the MR system on learning performance and user subjective satisfaction in chemistry experiments. Results indicated that, compared to the web-based experimental system NOBOOK, although the MR system did not show a significant difference in overall learning performance, it achieved higher average scores among high prior knowledge students, which may be attributed to different learning strategies. High prior knowledge students can better understand guidance information and reduce extraneous cognitive load. Furthermore, the MR system demonstrated significantly better subjective satisfaction in four aspects including user experience, learning content, learning motivation, and practicality. Students generally believed that the MR system could better assist them in learning and hoped it would be used in other learning scenarios. Since the learning assistance is provided based on the individual’s hand and eye movement, it can be further applied to personalized education, even for real experiments.

Virtual Reality technology (VRT) has rapidly evolved in recent years, revolutionizing various industries by providing immersive and interactive experiences. VRT is making a significant impact in the realm of dance teaching (DT). This study explores the innovative application of VRT in the realm of DT, aiming to revolutionize traditional instructional methods. The study data were gathered from 420 college students. The data collected underwent analysis through SPSS software. The results indicate a notable direct relationship between the integration of VRT and enhanced learning outcomes in dance education. This study offers a unique contribution to the field by demonstrating the potential of VRT to develop engaging and dynamic learning settings that surpass the confines of traditional dance teaching methods. The investigation result indicates participants engaged in dance instruction through virtual reality (VR) will exhibit superior retention of dance techniques and choreography in comparison to those instructed through traditional teaching approaches. This study adds to the intersection of technology and dance instruction, highlighting how VR can profoundly transform the arts. It highlights how VR can revolutionize educational paradigms, particularly within the artistic domain. The study demonstrates that integration of VRT into dance instruction correlates positively with heightened levels of student engagement and motivation.

Although the competencies expected from administrators have increased with the development of technology, it is seen that there are a limited number of studies on innovative approaches in training school administrators. The study aims investigate the impact of simulation applications on the problem-solving and decision-making abilities of school administrators and prospective administrators. To achieve this objective, a pretest and posttest quasi-experimental study was conducted, using experimental and control groups. The study employed a parallel mixed design, which combines quantitative and qualitative research approaches. The research group consisted of 61 school administrators and prospective administrators, with 30 participants in the experimental group and 31 in the control group. The Melbourne Decision Making Questionary I-II (MCDS I-II) and the Problem Solving Inventory (PSI) were utilized as data collection instruments before and after the 6-week experimental process. Furthermore, a semi-structured interview form was administered to the experimental group participants following the application. The results of the data analysis indicated that the simulation application effectively enhanced the decision-making and problem-solving skills of the experimental group participants, while reducing the use of approaches that hindered the positive effects of these skills.