Wen, Yun

Though peer assessment has been widely recognized as an effective teaching approach to develop learners’ critical thinking, pedagogical strategies and analysis of students’ peer assessment at the group level but not individual level are underexplored. Group assessment is emphasized in this study by focusing on assessing peer-group work. In the context of 40 student teachers participating in online group assessment activities, this study revealed critical thinking patterns and their correlation with group performances by content analysis and epistemic network analysis (ENA). The results showed that participants’ critical thinking in group assessment activities mainly stayed at the level of understand. The high-score groups’ critical thinking had more connections between understand and evaluate, while the low-score groups’ critical thinking had more connections between recognize and understand, and understand and analyse. In addition, the high- and low-score groups showed different characteristics of critical thinking at different stages and different tasks of group assessment activity. Diversified and developmental critical thinking patterns could be found in the high-score groups, while the low-score groups tended to maintain single and fixed critical thinking patterns. Finally, the discussion and suggestions of intervention for critical thinking development of high- and low-score groups are provided to help design and implement online collaborative learning activities and group assessment activities in the future.

Engagement is positively correlated with many educational outcomes. However, engaging learners in online learning is often challenging. In this study, a conceptual framework comprising five interrelated factors (instructors, learners, content, technology, and environments) was proposed. The purpose of the study was to explore how learners could be engaged by following the conceptual framework in synchronous online learning. Fifty-five adult learners took part in the study. Specific strategies were applied in four classes. A survey with 38 five-point Likert scale items and an open-ended question was administered. Quantitative and qualitative data were collected and analysed. Results showed that instructors, learners, and content were the core factors affecting learners’ engagement. Comparatively, the learners’ engagement was less affected by the factors of technology and environments. Results further showed that useful strategies to engage learners included providing opportunities for instructors and peers to interact frequently; having relevant content that could apply to practice; involving interactive activities like group discussions and peer feedback; and having informal conversations with individual learners. This study suggests that future studies can investigate facilitating synchronous online discussions, establishing social connectedness, and using technology to monitor learners’ engagement automatically.

In this exploratory mixed-methods study, we introduce and test our AI-powered vocabulary learning system—ARCHe, which embeds four AI functions: (1) automatic feedback towards for pronunciation, (2) automatic feedback for towards handwriting, (3) automatic scoring for student-generated sentences and (4) automatic recommendations. Specifically, our study of 140 students taught by six teachers in three primary schools in Singapore explores the links between these AI functions and students' learning engagement and outcomes via the analysis of their pre- and post-tests, post-surveys, focus group discussions and artefacts created via ARCHe. Results show improved Chinese character and vocabulary test scores after using ARCHe. Students' perceptions of ARCHe automatic recommendations and feedback towards pronunciation positively influence their emotional engagement. Also, students who perceived ARCHe automatic recommendations and feedback on handwriting more favourably than others reported greater cognitive engagement. Meanwhile, students whose groups created more sentences in classroom-based collaborative learning than others were more likely to show learning gains. This study provides insights for learning designers and educators on AI's potential in language learning, with recommendations for future research directions.

In recent years, using AI to create an engaging vocabulary learning experience has been a prominent topic. Studies have shown AI can provide educational affordances for enhancing vocabulary learning. However, the appropriation of these affordances varies depending on teachers’ use. This paper presents a case study on six teachers appropriated an AI-powered vocabulary learning system, particularly focusing on the affordances of monitoring and regulation, and engaging co-construction enhanced by AI-enabled automatic feedback and recommendations. By examining teachers’ beliefs and knowledge of self-regulation and collaborative learning, the study details how they appropriated the affordances in their classes. The study provides suggestions for the design of AI in education and teachers’ professional development during the implementation of AI-supported learning system.

Augmented Reality (AR) has been applied to education in a variety of subjects, but in comparison to AR in STEM education, research on integrating pedagogical designs with AR in language learning is less mature. This study presents an AR-supported Chinese character learning game designed for young learners and investigates its effects on learners’ cognitive engagement in classroom learning. A total of 53 grade 2 students and two teachers from a Singapore government primary school participated in the study. The findings indicate an obvious improvement of students’ levels of cognitive engagement in the AR-supported activities. Furthermore, compared with acquiring expert-created content knowledge, students are more continuously engaged in the learning activities designed for enabling self-generated contexts. Suggestions for future system design and pedagogical strategies of leveraging AR to engage young learners in language learning are proposed from this study. The study also provides some insight into how to investigate cognitive outcomes of AR-enabled learning design through analysing learning process.

Online discourse is widely used in diverse contexts of learning and professional training, but superficial interactions and digression often occur. In the face of these problems and the large-scale unstructured text data, the traditional way of learning analytics has been challenged in terms of providing timely intervention and feedback. In this paper, a workflow for automatically detecting in-service teachers’ cognitive engagement in an online professional learning community is described. Discourse data of 1834 in-service teachers involved in a teacher professional development program was collected and processed using the Word2vec toolkit to generate lexical vectors. The method of vector space projection was used to calculate the new information contained in each post, cosine similarity was used to calculate topic relevance, and cluster analysis was used to explore in-service teachers’ discourse characteristics. Results showed that in-service teachers’ average contribution was 4.59 posts and the average length of each post was 39.47 characters in Chinese. In the mathematics online professional learning community, the average amount of new information contained in each post was 0.221 and in-service teachers’ posts contained much new information in the early stages of online discourse. Most in-service teachers’ posts were relevant to the discussion topic. Cluster analysis showed three different groups of posts with unique characteristics: high topic relevance with much new information, high topic relevance with little new information, and low topic relevance with little new information. Finally, limitations are discussed and future research directions are proposed.

The impact of Augmented Reality (AR)-based science learning on cognitive development has been established, but the effect of AR on the improvement of students’ academic performance remains inconclusive. Additionally, while epistemic beliefs as a significant determinant on student learning outcomes is well-documented, there is little research in AR-supported science learning exploring the effectiveness of AR activities on students’ epistemic beliefs. This mixed-methods study investigates the relationship between students’ engagement in an AR-based inquiry learning environment and their epistemic beliefs. It aims to examine if students’ engagement in AR activities has an impact on their epistemic beliefs and academic performance, as well as which aspects of students’ epistemic beliefs may be affected by the designed activities. 159 fifth-grade students participated in the AR activities for two months. The findings revealed that students’ academic performance improved significantly, evidenced in the pre- and post-tests. While students’ perceived engagement did not significantly affect their academic performance, students’ perceived engagement in the activities did influence their epistemic beliefs, particularly in the dimension of justification of knowledge in science. The study identified features of learners’ interactions in the AR learning environment and these findings provide insights into potential areas for improvement in AR-based science learning.