Huaqing Hong

It has been argued that the resultative verb compounds first appear in Chinese during the Han Dynasty (c. 200bce) through grammaticalization or lexicalization. This view, though generally accepted in the Chinese linguistics circle, is flawed. There is a unique partial reduplicative compounding rule that is productive throughout the recorded history of the language. This rule creates a multi-tiered construction that sanctions doublets based on sound and meaning. The meaning-based doublets exhibit multiple interpretations between the component verbs, one of which is the resultative interpretation. Resultative verb compounds are sanctioned by the multi-tiered construction. From the perspective of Diachronic Construction Grammar, grammatical innovations such as RVs do not occur ex nihilo, but rely on aspects of the original construction.

This study examined the associations among emotional intelligence (EI), grit, motivation, and second language (L2) learning performance, with particular attention to the mediating roles of grit and motivation. A sample of 801 Chinese university students completed validated measures of EI, grit, and motivation, and their English test scores were collected as an indicator of performance. Using structural equation modeling (SEM), EI was positively associated with grit (β = 0.574, p < .001) and motivation (β = 0.426, p < .001), while grit was also positively related to motivation (β = 0.515, p < .001). In relation to performance, significant positive associations were observed with motivation (β = 0.635, p < .001), EI (β = 0.185, p < .001), and grit (β = 0.165, p < .001). Bootstrap analyses confirmed robust indirect associations of EI with performance through grit (β = 0.095), through motivation (β = 0.271), and via the sequential chain EI → grit → motivation → performance (β = 0.188). The model accounted for 33% of the variance in grit, 55% in motivation, and 79% in performance. These findings indicate that EI, grit, and motivation are closely interrelated constructs that jointly contribute to L2 performance, highlighting the importance of socio-emotional and motivational resources in second language learning.

Wearable sensor technology is increasingly being integrated into educational settings, offering innovative approaches to enhance teaching and learning experiences. These devices track various physiological and environmental variables, providing valuable insights into student engagement, comprehension, and educational environments. However, the extensive and continuous data streams generated by these sensors create significant challenges for learning analytics. This paper presents a comprehensive review of research on learning analytics incorporating wearable technology, systematically identifying methods and approaches that address wearable sensor data challenges. We begin with a systematic review of wearable sensor technologies’ historical development and the current state of sensor data in learning analytics. We then examine multimodal sensor applications in learning analytics and propose research and application trends aligned with educational development needs. Our analysis identifies three key challenges: ethical considerations, explainable learning analytics, and technological and data management issues. The paper concludes by outlining seven future development directions for wearable sensors in educational contexts.

Presents corrections to the article “A Tutorial-Generating Method for Autonomous Online Learning”.

Generative artificial intelligence has become the focus of the intelligent education field, especially in the generation of personalized learning resources. Current learning resource generation methods recommend customized courses based on learning styles and interests, improving learning efficiency. However, these methods cannot generate personalized tutorials according to learners’ preferences, nor can they adjust tutorial content as moods or levels of knowledge change. Therefore, this study develops an intelligent tutorial-generating system (Self-GT) for self-aid learning, integrating cognitive computing and generative learning to capture learners’ dynamic preferences. The critical components of Self-GT are the tutorial-generating model based on cyclic deep reinforcement learning (RL) and the multimodal knowledge graph containing complex relationships. Specifically, the proposed RL model dynamically explores learners’ preferences from the temporal dimension, enabling RL agents to express learning behavior characteristics accurately and generate personalized tutorials. Then, relying on the internal self-developed education base and external Internet sources, a multimodal knowledge graph with multiple self-defined relationships is designed to enhance the precision of tutorial generation. Finally, the experimental results indicate that the Self-GT performs well in generating tutorials and has been successfully applied in the generating tutorial for “Hospital Network Architecture Planning and Design.”

Abstract: Colloquial Singapore English is an outer‐circle variety that exhibits contact‐induced linguistic change. It has been characterized as the L variant in diglossic opposition to standard English. In this paper, we address two related issues: (1) the extent to which the Singapore English diglossia is supported by corpus data, and (2) the extent to which the diglossia is reducible to register variation. We investigate the usage pattern of two linguistic variables which have acquired novel grammatical meanings, and show that our data support the Singapore English diglossia, but the variation is greater than what is normal in register variation. The diglossia of which one variant is an outer‐circle variety does not reduce easily to register variation.