Teaching gesture recognition is a technique used to recognize the hand movements of teachers in classroom teaching scenarios. This technology is widely used in education, including for classroom teaching evaluation, enhancing online teaching, and assisting special education. However, current research on gesture recognition in teaching mainly focuses on detecting the static gestures of individual students and analyzing their classroom behavior. To analyze the teacher’s gestures and mitigate the difficulty of single-target dynamic gesture recognition in multi-person teaching scenarios, this paper proposes skeleton-based teaching gesture recognition (ST-TGR), which learns through spatio-temporal representation. This method mainly uses the human pose estimation technique RTMPose to extract the coordinates of the keypoints of the teacher’s skeleton and then inputs the recognized sequence of the teacher’s skeleton into the MoGRU action recognition network for classifying gesture actions. The MoGRU action recognition module mainly learns the spatio-temporal representation of target actions by stacking a multi-scale bidirectional gated recurrent unit (BiGRU) and using improved attention mechanism modules. To validate the generalization of the action recognition network model, we conducted comparative experiments on datasets including NTU RGB+D 60, UT-Kinect Action3D, SBU Kinect Interaction, and Florence 3D. The results indicate that, compared with most existing baseline models, the model proposed in this article exhibits better performance in recognition accuracy and speed.

In 2019, the United Nations released “Education for Sustainable Development for 2030”, emphasizing that sustainable learning is an important component of education for sustainable development, as it can enable learners to master the knowledge and skills required to keep learning in a variety of circumstances. To better understand teachers’ sustainable learning within the context of education, this study used a comprehensive method combining quantitative analysis and qualitative analysis to examine the key factors that influence teachers’ reflective practice skill through educational practice for sustainable learning. A total of 349 teachers responded to the survey. Based on the quantitative results, 10 teachers were chosen for qualitative analysis. Results showed that teaching support service, peer feedback, teacher–student interaction, and personal goal orientation were found to have a significant impact on teachers’ reflective practice skill, which is beneficial for promoting sustainable learning. Interestingly, the direct impact of pedagogical self-efficacy on reflective practice skill was not observed. The following qualitative research yielded five topics on teaching support service, peer feedback, teacher–student interaction, pedagogical self-efficacy, and personal goal orientation. These topics helped to explain the results of the quantitative analysis. The findings of the proposed model were conducive to understanding the mechanism that affects teachers’ reflective practice skill as well as providing practical implications for teachers’ sustainable learning in educational practice.

Emotions exist widely in the entire process of learning and affect students’ motivation as well as academic performance. In multimedia learning, academics usually focus on the impact of teachers’ emotions or the emotional design of multimedia learning materials on students’ emotions and learning results. Few studies have investigated how to enhance learning by regulating students’ pre-learning emotions. This study focused on whether playing funny videos before learning could promote students’ positive emotions to enhance their motivation, satisfaction, and learning outcomes. We randomly divided 81 elementary school students into two groups: experimental group and control group. While the experimental group watched funny video clips, the control group watched neutral video clips before starting the video learning. The experimental group had more positive pre-learning emotions than the control group. After the course, the emotion of the experimental group declined while that of the control group enhanced. However, positive pre-learning emotions still promoted students’ understanding and transfer of learning materials. Moreover, no significant differences were observed between the two groups in learning motivation, satisfaction, and retention tests. Furthermore, this paper analyzed the causes of the experimental results and discussed the insights for teaching.

Reflection is important for pre-service teacher development as it can help deepen their understanding of teaching and benefit their teaching practice. There have been several proposals of intelligent teaching analytics developed with the aim to strengthen the connection between practice and reflection by providing feedback to pre-service teachers. However, the implementation and user experience of intelligent teaching analytics in collaborative reflection remain underexplored. This study aimed to explore pre-service teachers’ experiences and shared regulation during collaborative reflection. Using a design-based research approach, 20 pre-service teachers participated in a seven-week collaborative reflection program. The intelligent teaching analytics implementation was iteratively improved over time, with data collected from reflection processes and interviews. Analysis of shared regulation revealed that as the implementation was refined over time, the interconnections among different regulation behaviors increased. Thematic analysis further indicated that pre-service teachers perceived intelligent teaching analytics as beneficial for enhancing their pedagogical content knowledge. Emotionally, they initially felt nervous and embarrassed but gradually found the collaborative reflection atmosphere to be friendly, harmonious, and mutually supportive. This research contributes to the development of principles for integrating intelligent teaching analytics into collaborative reflection and provide suggestions for future advancement of intelligent teaching analytics.

This study investigated the influence of instructors’ expressive nonverbal behavior and nonexpressive nonverbal behavior in video lectures on students’ learning performance and affective experience. We conducted two rounds of experiments using the same materials and procedures, the only difference being the participants. In each round of experiments, participants were randomly assigned to expressive condition or nonexpressive condition. 227 rural primary school sixth-graders took part in experiment 1, participants in expressive condition had better affective experiences and perceived tasks as less difficult, but had lower learning performance than participants in nonexpressive condition. 175 sixth-graders from urban primary schools participated in experiment 2. The results showed that instructors’ expressive nonverbal behavior also improved students’ affective experience and reduced students’ perception of task difficulty, but there was no significant difference in learning performance between the two groups. Comparing the pretest scores of students in the two experiments, it was found that the pretest scores of participants in experiment 2 were higher than those in experiment 1. Overall, instructors’ expressive nonverbal behavior can improve students’ affective experience and reduce their perception of task difficulty. However, when students’ prior knowledge is relatively low, instructors’ expressive nonverbal behavior hinders students’ learning performance. We suggest that teachers adopt expressive nonverbal behavior when lecturing because it is beneficial to maintain students’ long-term interest in learning. However, it should be noted that the difficulty of learning material should be determined by students’ prior knowledge.