Education and Information Technologies最新文献

This study explored the efficacy of video podcasts (VODcasts) in fostering whole-language learning among college EFL students. Employing a quasi-experimental design with pre-tests, post-tests, and a control group, the research compared the effects of a one-year remedial instruction program. The experimental group received instruction incorporating authentic video materials and dramatized literary works alongside their regular curriculum. Conversely, the control group received the same instruction with traditional audio materials. The results revealed statistically significant advancements in all language skills (listening comprehension, reading comprehension, speaking fluency, and written composition) for the VODcast group compared to the control. These findings suggest the potential of video-based instruction (vodcasting) utilizing authentic video content as a valuable tool for EFL classrooms, promoting holistic language development in college-level learners.

We investigated the effect of short-term teaching (3 weeks) using image analysis on gymnasium/secondary school students’ (ISCED3A) skills (science process skills, SPS) and knowledge using Fiji (ImageJ) software. The theoretical teaching was combined with IT-based education (measurement of the area or signal intensity on photos from scientific papers using algae Ulva or Coccomyxa and the fungus Glomerella) and students formed conclusions from data and answered knowledge questions. Experimental education increased score (post-test/pre-test) in the area of knowledge (by 102%) and SPS (by 44%) and greater impact of IT implementation on the development of girls’ SPS and knowledge was found. Besides, the Pearson’s correlation analysis showed a more intense improvement of students who had greater deficiencies in pre-test. This experimental gymnasium group (n = 53) also retained a higher score in re-test in comparison with control gymnasium students (without the implementation of image analysis, n = 54) and detailed analysis of individual questions revealed that there was a development of SPS, especially those that were emphasized in the experimental teaching. Another control group “university students” (ISCED5A, n = 19) had a higher score in re-test than the experimental gymnasium group, but the area of knowledge was comparable. These results indicate that the involvement of IT improves SPS and knowledge of gymnasium students at the level comparable with university students. The test and worksheets are attached for free use with the aim of improving the teaching of less interesting topics such as botany.

In recent years, laptop and ‘Bring Your Own Device’ (BYOD) programs have become an integral part of teaching and learning in many Australian schools. For history teachers, the presence of BYOD in the classroom potentially provides the opportunity to align two key teaching goals: historical research skills; and information, communication and technology (ICT) skills. However, there is a dearth of literature that focuses on BYOD for history teaching and learning. This paper reports on the findings of a case study examining Year 9 students’ (13-15-year-olds) and teachers’ attitudes towards the uses of BYOD in their history classes. The findings are from a larger study that examined the way that BYOD was being used for the teaching and learning of historical research skills online. This study uses reflexive thematic analysis to analyse student questionnaires where students reflected on their learning and attitudes. Teachers completed questionnaires and participated in semi-structured interviews. This study has found that the presence of BYOD has heightened an underlying constructivist approach to history teaching. Both students and teachers greatly value the access that BYOD provides for student-centred historical inquiry, as well as valuing BYOD as a perceived labour saver. However, participants seemed to underestimate the role that BYOD is playing in narrowing students’ understanding of history to facts about the past. Despite the limits of its small sample size, this case study has important implications for history educators, which are discussed.

Based on the adaptive feedback theory, promoting the development of digital educational resource services towards adaptability is key to solving the problem of poor applicability in current K-12 digital educational resource services. Demand-driven resource service construction plays a significant guiding role in this process, making it essential to clarify the demands of K-12 teachers for resource services. Therefore, this study employed a combination of large-scale interviews and Kano model analysis to summarize the composition, categories, and characteristics of teachers’ demands, based on the analysis of resource service elements. The research findings reveal that teachers expect resource services to provide convenient, personalized, and continuously evolving support for teaching, professional development, and students’ self-regulated learning scenarios. In terms of prioritizing these demands, teachers believe that addressing the demands in teaching scenario should take precedence before expanding to other scenarios. Finally, based on these research findings, a three-stage construction strategy has been designed: first, focusing on services for teaching scenario; second, addressing professional development and services for self-regulated learning; third, continuously improving the services. This strategy aims to enhance service quality and efficiency, providing guidance for the development of digital educational resource services in K-12 education.

Higher education is beginning to focus on how to effectively cultivate IoT engineers who possess both hard skills and soft skills. Therefore, from the perspective of activity theory and combining it with project-based learning, this study constructed a project-based learning framework based on activity theory and applied this framework to an IoT course at a university in central China. The first half of the course adopted a traditional lecture-based teaching method, while the second half of the course was carried out based on the proposed framework, thus constituting a control group and an experimental group. To further explore the effectiveness of the proposed framework on IoT courses, the data of this study consisted of two aspects: on the one hand, questionnaires on students’ learning engagement and satisfaction with the IoT course were collected; on the other hand, with the help of an intelligent teaching analytics platform, the effectiveness of the proposed framework in optimizing the classroom structure was objectively analyzed from three dimensions of teaching mode, classroom atmosphere, and teacher-student intimacy. The experimental results showed that compared with traditional teaching, teaching based on the proposed framework can significantly improve students’ learning engagement and satisfaction. Specifically, the increase in emotional engagement was the highest at 43.07%, followed by a 26.42% increase in behavioral engagement and a 35.15% increase in cognitive engagement. Meanwhile, it also optimizes the classroom structure to a certain extent. The teaching mode has shifted from lecture-based to hybrid, the classroom atmosphere is more harmonious and equal, and the teacher-student intimacy is increasing.

This study explores the factors influencing students' online game-based learning (OGBL) performance. At present, motivating students to engage in learning actively represents a significant challenge faced by teachers and other stakeholders. Additionally, the findings of a variety of OGBL studies have been inconsistent or contradictory, particularly regarding virtual rewards. This study integrates self-determination theory and flow theory as a theoretical basis for analyzing data collected from 105 undergraduate students using a quasi-experimental design and the partial least square structural equation modeling (PLS-SEM) approach. The results reveal that students' intrinsic motivation positively influences their enjoyment and focused attention. Autonomous motivation positively influences their learning performance. Furthermore, intrinsic motivation and focused attention are critical mediators in the proposed research model. The findings of this study contribute to the application of self-determination theory and flow theory in the OGBL context and provide suggestions for teachers and stakeholders with regard to promoting students' learning.

Teacher learning in asynchronous online professional development (PD) is often measured by self-reported instruments, despite uncertainties regarding teachers’ accuracy in their self-assessment of learning. This study explored whether teachers’ self-reported gains in content knowledge for teaching aligned with those measured by direct assessments. Through quantitative analyses of data collected from 57 middle school mathematics teachers who participated in a fully asynchronous online PD program, we found no significant correlation between teachers’ self-reported knowledge gains and those measured by direct assessments. Additionally, we examined the role of teachers’ use of self-regulated learning (SRL) strategies, namely organization, elaboration, and monitoring, in their learning from the PD program by conducting linear regression analyses. Our results indicated that teachers who frequently monitored their understanding of the PD content demonstrated greater gains in their content knowledge as measured by direct assessments. However, none of the SRL strategies predicted the accuracy of teachers’ self-assessments of knowledge gains from the asynchronous online PD program. These findings raise concerns about relying solely on self-reported instruments to capture teachers’ learning in asynchronous online PD programs, and they emphasize the need for more robust and accurate outcome measures in the asynchronous online learning context.

Motivated by the proliferation of artificial intelligence that has the potential to promote self-access learning, this study utilizes a sequential explanatory quasi-experimental mixed methods design to investigate the efficacy of Google Assistant (GA) in facilitating second language (L2) vocabulary learning compared to online dictionaries. A cohort of EFL university students (n = 74) was assigned to two groups: a control group using online dictionaries and an experimental group utilizing GA. Over six learning sessions, both groups learned 10% of Coxhead’s (2000) Academic Vocabulary List. With data drawn from multiple sources, including pre- and post-tests, a survey questionnaire, and individual interviews, the findings reveal significant improvements in vocabulary knowledge for both groups, indicating that GA can be utilized as an effective vocabulary learning tool. Despite concerns regarding the quality of GA’s voice recognition as a potential demotivating factor, learners hold positive views on the efficacy of GA. These perceptions reflect influential factors primarily situated within the cognitive and affective domains of learning. Accordingly, key affordances and limitations of GA are identified. This study proposes pedagogical implications and outlines potential avenues for further research in the domain of intelligent personal assistants-assisted L2 vocabulary learning.

Higher education institutions have consistently strived to provide students with top-notch education. To achieve better outcomes, machine learning (ML) algorithms greatly simplify the prediction process. ML can be utilized by academicians to obtain insight into student data and mine data for forecasting the performance. In this paper, the authors proposed an ML-based student prediction model based on the demographic, social, psychological, and economic factors, collectively. The dataset utilized for this study was compiled from a designed questionnaire administered to second-year undergraduate students. The objective of this study is to uncover factors that could assist in predicting students' performance. Eight ML classifiers, logistic regression, random forest, support vector machine, XGBoost, support vector machine with a linear kernel, naïve Bayes, K-Nearest Neighbor, and decision tree are used to forecast student performance. Additionally, nine feature selection techniques, variance threshold, XGBoost, feature importance, recursive feature elimination, chi-square, ridge, Pearson correlation, lasso, and random forest, are employed to determine optimal factors. The authors experimented with each technique by creating two sets of training and testing data with 80:20 and 70:30 proportions, respectively. Comparatively, the ensemble DXK (DT + XGB + KNN) model with cross-validation and 80:20 proportions outperformed other standard classifiers, achieving a highest accuracy of 97.83%, an r-square of 96.17%, a precision of 97.94%, a recall of 97.83%, and an f1-score of 97.88%. These were the highest among all models tested. Additionally, the authors propose the ACO-DT model, which improves the prediction performance of the top-performing DT classifier by utilizing the Ant Colony Optimization technique. The findings demonstrate that the proposed model with 80:20 proportions achieve an accuracy of 98.15%, an f1-score of 98.16%, a precision of 98.18%, a recall of 98.15%, and an r-square of 84.75%, surpassing all other models for forecasting student performance. Using the specified data size, this model creation time is 8.49 s. The authors also recommended the future research directions to further enhance this study.

This study investigated the impact of teachers' perceptions of the effectiveness of formal and informal technology learning on their intention to use technology, mediated by technology self-efficacy and digital literacy, within a structural relationship framework. The research model included formal technology learning types such as in-person and remote training, and informal types like learning through media, learning through experience and reflection, interaction with peers, and participation in professional learning communities. The results showed that only learning through media had a significant positive influence on the technology usage intention. Among the mediating factors, only digital literacy demonstrated a significant direct effect on technology usage intention. The study further revealed that the perceived effectiveness of formal learning types, in-person and remote training, and informal type, personal learning through media, positively correlated with both technology self-efficacy and digital literacy. However, other informal learning types did not exhibit significant relationships with these mediating variables. In terms of mediating effects, significant mediation was found only when digital literacy mediated the relationship between the effectiveness perception of certain learning types and technology usage intention, while technology self-efficacy did not demonstrate significant mediation. Based on these findings, this study presents implications for effective application strategies of formal and informal technology learning to enhance teachers' intentions to use technology.