Interactions in Online Versus Face-to-face Classes: Students’ and Teachers’ Perceptions

The Interaction Hypothesis emphasizes the significance of face-to-face interactions in language proficiency development. However, the global transition to online education prompted by the COVID-19 pandemic has posed significant challenges to education, including the teaching of Chinese as a second/foreign language (CSL/CFL). Anecdotal evidence indicates a decline in both the quality and opportunities for interactions in online classes compared to traditional face-to-face (F2F) classrooms. However, research on the differences in the perspectives of students and teachers regarding this issue is relatively limited. To fill this gap, this study compared the perspectives of students and teachers regarding teacher-student and peer-to-peer interactions in online versus F2F CFL classrooms. Participants were CFL learners and their teachers at a university in Australia. Thematic analysis of the data collected from online surveys and interviews revealed a consensus among students and teachers on the importance of promoting interactions regardless of the delivery mode. However, students expressed a preference for F2F interactions, citing reduced motivation and fewer opportunities for interaction in online classes. Notably, students indicated a preference for interacting with teachers rather than peers during synchronous online sessions. The differences were attributed to multiple factors including a sense of community, interaction opportunities, engagement strategies, individual differences, and technological constraints. The results underscore the pivotal role of building social connections in language learning. The findings provide valuable insights into technology-enhanced language education from the perspectives of both students and teachers. This study contributes to the field of interaction studies in second language education and offers practical implications for addressing the challenges posed by the transition to online learning

How EFL teachers perceive and self-evaluate the knowledge components in forming Technological Pedagogical Content Knowledge (TPACK)

Technology is widely involved across the learning environment including its integration into teaching English as a foreign language (EFL); however, few studies have explored EFL teachers’ perceptions of technological pedagogical content knowledge (TPACK). This study investigates how EFL teachers perceive and self-evaluate knowledge of content (CK), pedagogy (PK), and technology (TK), the interplay of these with each other (TPACK), and the underlying influential factors for TPACK construction. The data were gathered in China from an online survey (n = 64) comprising 35 items on the TPACK components, and self-evaluation by nine survey participants of their TPACK in follow-up interviews. WeChat, the most popular social media App in China, was utilised as the data collection tool. The survey reveals teachers’ strong beliefs in the value of PK, CK and PCK and their positive beliefs about technological applications in EFL instruction. Consistent with these results, interviewees’ self-evaluation of TPACK demonstrates that they felt a high level of confidence in CK, PK and PCK but relatively less confidence when technology was integrated despite commonly applying technology to instruction. Influential factors include: 1) contextual factors; 2) knowledge of students; 3) demographic background; and 4) availability of quality training. Decision-makers’ financial support and policy-making, technological training in the integration of CK and/or PK, and a collaborative learning strategy are recommended

Considering correlation retarded growth for personalized recommendation in social tagging

Due to the massive amounts of data, finding social media suited to their need is a challenging issue. To help such users retrieve useful social media content, we propose a new model of personalized recommendation system by using annotating information from relationship among users, tags, and items. However, the frequency of users’ tagging has strong or weak correlation, which affects the dynamic interest mining of users. In this paper, CRGI is proposed to describe the correlation between users and tags or tags and items. Our approach has two phases, in the first phase, we describe the correlation between users, items and tags by CRGI and in the second phase, we build a tag-item weight model and a user-tag preference model on the basis of the first phase. Then we utilize the two models to find the suitable items with the highest scores. The experimental results demonstrate that the item recommendation performance is improved in both the accuracy and the diversity, and validate that the proposed personalized approach is effective for improving the social media recommendation

Polarity and Intensity: the Two Aspects of Sentiment Analysis

Current multimodal sentiment analysis frames sentiment score prediction as a general Machine Learning task. However, what the sentiment score actually represents has often been overlooked. As a measurement of opinions and affective states, a sentiment score generally consists of two aspects: polarity and intensity. We decompose sentiment scores into these two aspects and study how they are conveyed through individual modalities and combined multimodal models in a naturalistic monologue setting. In particular, we build unimodal and multimodal multi-task learning models with sentiment score prediction as the main task and polarity and/or intensity classification as the auxiliary tasks. Our experiments show that sentiment analysis benefits from multi-task learning, and individual modalities differ when conveying the polarity and intensity aspects of sentiment.Comment: Published at the First Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML) of ACL 201

Recognizing emotions in spoken dialogue with acoustic and lexical cues

Automatic emotion recognition has long been a focus of Affective Computing. It has become increasingly apparent that awareness of human emotions in Human-Computer Interaction (HCI) is crucial for advancing related technologies, such as dialogue systems. However, performance of current automatic emotion recognition is disappointing compared to human performance. Current research on emotion recognition in spoken dialogue focuses on identifying better feature representations and recognition models from a data-driven point of view. The goal of this thesis is to explore how incorporating prior knowledge of human emotion recognition in the automatic model can improve state-of-the-art performance of automatic emotion recognition in spoken dialogue. Specifically, we study this by proposing knowledge-inspired features representing occurrences of disfluency and non-verbal vocalisation in speech, and by building a multimodal recognition model that combines acoustic and lexical features in a knowledge-inspired hierarchical structure. In our study, emotions are represented with the Arousal, Expectancy, Power, and Valence emotion dimensions. We build unimodal and multimodal emotion recognition models to study the proposed features and modelling approach, and perform emotion recognition on both spontaneous and acted dialogue. Psycholinguistic studies have suggested that DISfluency and Non-verbal Vocalisation (DIS-NV) in dialogue is related to emotions. However, these affective cues in spoken dialogue are overlooked by current automatic emotion recognition research. Thus, we propose features for recognizing emotions in spoken dialogue which describe five types of DIS-NV in utterances, namely filled pause, filler, stutter, laughter, and audible breath. Our experiments show that this small set of features is predictive of emotions. Our DIS-NV features achieve better performance than benchmark acoustic and lexical features for recognizing all emotion dimensions in spontaneous dialogue. Consistent with Psycholinguistic studies, the DIS-NV features are especially predictive of the Expectancy dimension of emotion, which relates to speaker uncertainty. Our study illustrates the relationship between DIS-NVs and emotions in dialogue, which contributes to Psycholinguistic understanding of them as well. Note that our DIS-NV features are based on manual annotations, yet our long-term goal is to apply our emotion recognition model to HCI systems. Thus, we conduct preliminary experiments on automatic detection of DIS-NVs, and on using automatically detected DIS-NV features for emotion recognition. Our results show that DIS-NVs can be automatically detected from speech with stable accuracy, and auto-detected DIS-NV features remain predictive of emotions in spontaneous dialogue. This suggests that our emotion recognition model can be applied to a fully automatic system in the future, and holds the potential to improve the quality of emotional interaction in current HCI systems. To study the robustness of the DIS-NV features, we conduct cross-corpora experiments on both spontaneous and acted dialogue. We identify how dialogue type influences the performance of DIS-NV features and emotion recognition models. DIS-NVs contain additional information beyond acoustic characteristics or lexical contents. Thus, we study the gain of modality fusion for emotion recognition with the DIS-NV features. Previous work combines different feature sets by fusing modalities at the same level using two types of fusion strategies: Feature-Level (FL) fusion, which concatenates feature sets before recognition; and Decision-Level (DL) fusion, which makes the final decision based on outputs of all unimodal models. However, features from different modalities may describe data at different time scales or levels of abstraction. Moreover, Cognitive Science research indicates that when perceiving emotions, humans make use of information from different modalities at different cognitive levels and time steps. Therefore, we propose a HierarchicaL (HL) fusion strategy for multimodal emotion recognition, which incorporates features that describe data at a longer time interval or which are more abstract at higher levels of its knowledge-inspired hierarchy. Compared to FL and DL fusion, HL fusion incorporates both inter- and intra-modality differences. Our experiments show that HL fusion consistently outperforms FL and DL fusion on multimodal emotion recognition in both spontaneous and acted dialogue. The HL model combining our DIS-NV features with benchmark acoustic and lexical features improves current performance of multimodal emotion recognition in spoken dialogue. To study how other emotion-related tasks of spoken dialogue can benefit from the proposed approaches, we apply the DIS-NV features and the HL fusion strategy to recognize movie-induced emotions. Our experiments show that although designed for recognizing emotions in spoken dialogue, DIS-NV features and HL fusion remain effective for recognizing movie-induced emotions. This suggests that other emotion-related tasks can also benefit from the proposed features and model structure

ESP : An experiment with mixed and specific groups in reading comprehension

Orientador: Cecília Ines ErthalDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Humanas, Letras e Artes, Curso de Pós-Graduação em LetrasInclui bibliografiaÁrea de concentração: Língua InglesaResumo: Esta dissertação descreve um experimento prático do qual participaram três grupos de alunos universitários matriculados na disciplina de Língua Inglesa Instrumental da Universidade Federal do Paraná em 1984. O experimento foi elaborado a fim de investigar até que ponto o uso de textos específicos relacionados às áreas de estudo dos alunos poderia resultar num melhor desempenho dos mesmos na compreensão de leitura. Assim, o principal objetivo do estudo foi o de descobrir um procedimento satisfatório e eficaz para desenvolver a habilidade de leitura que possibilitaria aos alunos um aperfeiçoamento na compreensão de leitura. 0 método experimental constituiu-se de três tipos de procedimentos ao ensino da compreensão de leitura aplicados a três grupos de alunos. O Grupo A (grupo misto) trabalhou com textos considerados de 'interesse geral', o Grupo B (grupo misto) trabalhou com textos relacionados â área de estudo de cada aluno, enquanto o Grupo C (grupo específico) trabalhou com textos relacionados â área de Nutrição. Os modelos psicolingüísticos do processo de leitura de Goodman e Smith foram usados como base teórica para o planejamento do curso de leitura especialmente elaborado para esta pesquisa. Um pré-teste e um pós-teste foram aplicados para medir o desempenho inicial e final dos alunos na compreensão de leitura. Os resultados da análise estatística demonstraram que grupos homogêneos levando-se em consideração o ano acadêmico, a área de estudo e interesse dos alunos, desempenham melhor do que grupos heterogêneos, especialmente quando trabalham com textos específicos relacionados a suas áreas de estudo. Embora os resultados obtidos não nos ofereçam conclusões mais definidas sobre os grupos heterogêneos, eles nos fornecem diversas implicações pedagógicas úteis ao ensino e aprendizagem da compreensão de leitura.Abstract: This dissertation describes a practical experiment involving three groups of undergraduate students taking English for Specific Purposes at the Federal University of Parana, in 1984. The experiment was conducted to investigate to what extent the use of specific texts related to students' specialist fields of study might result in a better reading comprehension performance from the students. Thus the main purpose of the study was to discover an acceptable, as well as, an effective procedure to develop reading skills which would enable ESP students to improve their reading of texts in English. The experimental method consisted of three procedures to the teaching of reading comprehension applied to three different ESP groups. Group A, a mixed group, worked with texts considered of 'general interest', Group B, a mixed group, worked with texts related to each student's subject area of study, and Group C, a specific group, worked with texts solely related to Food Science. Goodman and Smith's psycholinguistic models of the reading process provided the rationale for the design of the reading course especially devised for this research. A pre-test and a post-test were applied to measure the initial and final reading comprehension performance of the students. The statistical analysis of the results led to the conclusion that homogeneous groups as far as academic year, subject area of study and interest are concerned, perform at a more satisfactory level than heterogeneous groups, especially if specific texts related to the students' field of study are used. Although the results do not provide clear-cut conclusions about the heterogeneous groups they lead to several useful pedagogic implications for the teaching/learning of reading comprehension

Crafting with a Robot Assistant: Use Social Cues to Inform Adaptive Handovers in Human-Robot Collaboration

We study human-robot handovers in a naturalistic collaboration scenario, where a mobile manipulator robot assists a person during a crafting session by providing and retrieving objects used for wooden piece assembly (functional activities) and painting (creative activities). We collect quantitative and qualitative data from 20 participants in a Wizard-of-Oz study, generating the Functional And Creative Tasks Human-Robot Collaboration dataset (the FACT HRC dataset), available to the research community. This work illustrates how social cues and task context inform the temporal-spatial coordination in human-robot handovers, and how human-robot collaboration is shaped by and in turn influences people's functional and creative activities.Comment: accepted at HRI 202