Correlation between Japanese-language Education and Japanese Studies

Japanese-language education in China after 1949 has been carried out in some of higher learning institutions in order to cultivate persons who can use Japanese-language, with a temporary interruption of the Cultural Revolution. Since the restoration of official relationship between China and Japan, there occurred several booms of learning Japanese-language, and the policy of reforms and openness to international community encouraged the number of Japanese-language students. China has the second most population of learners in the world, 820 thousands, and most of them are students of higher learning institutions. These Japanese-language students in China are deemed potential researchers of Japanese Studies, and the aim of Japanese-language education should be cultivating persons who understand Japanese culture better than any others

A Class-Aware Representation Refinement Framework for Graph Classification

Graph Neural Networks (GNNs) are widely used for graph representation learning. Despite its prevalence, GNN suffers from two drawbacks in the graph classification task, the neglect of graph-level relationships, and the generalization issue. Each graph is treated separately in GNN message passing/graph pooling, and existing methods to address overfitting operate on each individual graph. This makes the graph representations learnt less effective in the downstream classification. In this paper, we propose a Class-Aware Representation rEfinement (CARE) framework for the task of graph classification. CARE computes simple yet powerful class representations and injects them to steer the learning of graph representations towards better class separability. CARE is a plug-and-play framework that is highly flexible and able to incorporate arbitrary GNN backbones without significantly increasing the computational cost. We also theoretically prove that CARE has a better generalization upper bound than its GNN backbone through Vapnik-Chervonenkis (VC) dimension analysis. Our extensive experiments with 11 well-known GNN backbones on 9 benchmark datasets validate the superiority and effectiveness of CARE over its GNN counterparts

Interest on cash, fundamental value process and bubble formation on experimental asset markets

We study the formation of price bubbles on experimental asset markets where cash earns interest. There are two main conclusions. The first is that paying positive interest on cash is ineffective in diminishing bubbles through the reducing-active-participation channel. The second is that the fundamental value generating process plays a critical role in the formation of asset bubbles in the laboratory. In particular, bubbles tend to occur whenever there is a conflict between the sign of the time trend of the fundamental value and the sign of the expected dividend payment. This explanation is consistent with all existing studies that analyze the role of fundamental value processes in inducing bubbles on experimental asset markets.Nous étudions la formation des bulles de prix sur des marchés d’actifs expérimentaux au sein desquels les liquidités permettent de toucher un intérêt. Nous parvenons à deux conclusions principales. Premièrement, le fait d’appliquer un taux d’intérêt positif aux liquidités n’aide pas à diminuer les bulles par la réduction de la participation active aux marchés. Deuxièmement, dans le cadre expérimental, le processus de création de la valeur fondamentale joue un rôle essentiel dans la formation des bulles d’actifs. En particulier, les bulles ont tendance à surgir lorsque la tendance temporelle de la valeur fondamentale et le paiement de dividendes attendu sont de sens contraire. Cette explication est corroborée par toutes les études existantes qui analysent l’incidence du processus de création de la valeur fondamentale sur l’apparition de bulles dans les marchés d’actifs expérimentaux

AN ANALYSIS OF IMPLEMENTATION CHALLENGES FOR ENGLISH FOR SPECIFIC PURPOSES (ESP) FORMATIVE ASSESSMENT VIA BLENDED LEARNING MODE AT CHINESE VOCATIONAL POLYTECHNICS

In the midst of the current digital learning era, traditional assessment has gradually shifted over prioritizing summative assessment towards equally emphasizing formative assessment within blended learning mode. However, there are still limited studies that investigate the implementation of formative assessment via blended learning mode in China particularly looking at the situation at Chinese vocational polytechnics. To explore actual formative assessment practices, a qualitative case study was conducted at two vocational polytechnics and experienced ESP teachers were selected as participants. Rich data were collected through rounds of semi-structured interviews and document analysis and emerging themes from the elicited and analysed data show five categories of challenges. The findings revealed that though most ESP teachers perceived formative assessment as an effective and fair way to monitor students’ learning process, there are still challenges that must be mitigated especially when implemented via online platforms. The challenges come in the form of difficulty in following ESP assessment principles, limited training and absence of guidelines, large class size, and students’ lack of motivation influence their assessment practices. This study justifies the need to develop explicit guidelines for best practices in ESP formative assessment for teachers’ reference

The Impact of E-Portfolio Assessment Implementation on Polytechnic Students’ Speaking Proficiency and Self-Reflection on Learning Business English

As an effective learning and assessment tool, E-portfolio has enjoyed great popularity with its great benefits in improving academic performances. However, few empirical studies have focused on integrating e-portfolio assessment into ESP courses by adopting blended learning mode. This study aims to investigate the effect of e-portfolio on students’ speaking proficiency in an ESP course within the context of blended learning and the learners’ use of self-reflection strategies. Data on students’ performance on the final speaking test, teacher observation and semi-structured interview were collected from second-year Business English students in Ningbo Polytechnics in China. The data were both qualitatively and quantitatively analysed. The findings revealed that the use of e-portfolio had a significant effect on improving students’ speaking proficiency in discourse and interactive communication. Evidence from the study also indicate that guided reflection has enabled students’ active engagement in e-portfolio development and thus their new understanding on the basis of reflection could be integrated into personal practices to help achieve learning outcomes. &nbsp

CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning

The motivations of users to make interactions can be divided into static preference and dynamic interest. To accurately model user representations over time, recent studies in sequential recommendation utilize information propagation and evolution to mine from batches of arriving interactions. However, they ignore the fact that people are easily influenced by the recent actions of other users in the contextual scenario, and applying evolution across all historical interactions dilutes the importance of recent ones, thus failing to model the evolution of dynamic interest accurately. To address this issue, we propose a Context-Aware Pseudo-Multi-Task Recommender System (CPMR) to model the evolution in both historical and contextual scenarios by creating three representations for each user and item under different dynamics: static embedding, historical temporal states, and contextual temporal states. To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization. Within the PMTL paradigm, CPMR employs a shared-bottom network to conduct the evolution of temporal states across historical and contextual scenarios, as well as the fusion of them at the user-item level. In addition, CPMR incorporates one real tower for incremental predictions, and two pseudo towers dedicated to updating the respective temporal states based on new batches of interactions. Experimental results on four benchmark recommendation datasets show that CPMR consistently outperforms state-of-the-art baselines and achieves significant gains on three of them. The code is available at: https://github.com/DiMarzioBian/CPMR.Comment: Accepted by CIKM 2023. Alias: "Modeling Context-Aware Temporal Dynamics via Pseudo-Multi-Task Learning