14,408 research outputs found

    INVESTIGATING EFL STUDENTS’ PERCEPTION ON ONLINE LEARNING AMIDST COVID-19 PANDEMIC

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    COVID-19 pandemic has resulted in the shifting of teaching-learning process from the combination of F2F and online learning to full-online one. This study aims to investigate students’ perspective on the implementation of full-online learning mode in English classroom with a low-tech environment. Employing convenience sampling, 104 university students participated in this study. Survey method was utilized. The findings showed that (1) the respondents have had various level of familiarity using search engine, social media, e-resources and learning apps that enable them to comprehend the learning content; (2) the use of social media, e-resources and learning apps results a different impact on respondents’ perception on learning effectiveness; (3) the respondents are more digitally-literate in using learning application or other online-based platforms and enable to autonomously learn the course materials as well as improve their language skills; (4) support system needs to be increased to engage students in teaching and learning activities; (5) there is a requirement of feedback and consistency in determining course schedule and timeline for task and exam submission. It can be concluded that university students have positive and negative perspective on full-online learning mode. Suggestions are presented in the closure

    Towards a multidisciplinary user-centric design framework for context-aware applications

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    The primary aim of this article is to review and merge theories of context within linguistics, computer science, and psychology, to propose a multidisciplinary model of context that would facilitate application developers in developing richer descriptions or scenarios of how a context-aware device may be used in various dynamic mobile settings. More specifically, the aim is to:1. Investigate different viewpoints of context within linguistics, computer science, and psychology, to develop summary condensed models for each discipline. 2. Investigate the impact of contrasting viewpoints on the usability of context-aware applications. 3. Investigate the extent to which single-discipline models can be merged and the benefits and insightfulness of a merged model for designing mobile computers. 4. Investigate the extent to which a proposed multidisciplinary modelcan be applied to specific applications of context-aware computing

    Toward a multidisciplinary model of context to support context-aware computing

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    Capturing, defining, and modeling the essence of context are challenging, compelling, and prominent issues for interdisciplinary research and discussion. The roots of its emergence lie in the inconsistencies and ambivalent definitions across and within different research specializations (e.g., philosophy, psychology, pragmatics, linguistics, computer science, and artificial intelligence). Within the area of computer science, the advent of mobile context-aware computing has stimulated broad and contrasting interpretations due to the shift from traditional static desktop computing to heterogeneous mobile environments. This transition poses many challenging, complex, and largely unanswered research issues relating to contextual interactions and usability. To address those issues, many researchers strongly encourage a multidisciplinary approach. The primary aim of this article is to review and unify theories of context within linguistics, computer science, and psychology. Summary models within each discipline are used to propose an outline and detailed multidisciplinary model of context involving (a) the differentiation of focal and contextual aspects of the user and application's world, (b) the separation of meaningful and incidental dimensions, and (c) important user and application processes. The models provide an important foundation in which complex mobile scenarios can be conceptualized and key human and social issues can be identified. The models were then applied to different applications of context-aware computing involving user communities and mobile tourist guides. The authors' future work involves developing a user-centered multidisciplinary design framework (based on their proposed models). This will be used to design a large-scale user study investigating the usability issues of a context-aware mobile computing navigation aid for visually impaired people

    Mediating chance encounters through opportunistic social matching

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    Chance encounters, the unintended meeting between people unfamiliar with each other, serve as an important social lubricant helping people to create new social ties, such as making new friends or finding an activity, study or collaboration partner. Unfortunately, social barriers often prevent chance encounters in environments where people do not know each other and people have to rely on serendipity to meet or be introduced to interesting people around them. Little is known about the underlying dynamics of chance encounters and how systems could utilize contextual data to mediate chance encounters. This dissertation addresses this gap in research literature by exploring the design space of opportunistic social matching systems that aim to introduce relevant people to each other in the opportune moment and the opportune place in order to encourage face-to-face interaction. A theoretical framework of relational, social and personal context as predictors of encounter opportunities is proposed and validated through a mixed method approach using interviews, experience sampling and a field study of a design prototype. Key contributions of the field interview study (n=58) include novel context-aware social matching concepts such as: sociability of others as an indicator of opportune social context; activity involvement as an indicator of opportune personal context; and contextual rarity as an indicator of opportune relational context. The following study combining Experience Sampling Method (ESM) and participant interviews extends prior research on social matching by providing an empirical foundation for the design of opportunistic social matching systems. A generalized linear mixed model analysis (n=1781) shows that personal context (mood and busyness) together with the sociability of others nearby are the strongest predictors of people’s interest in a social match. Interview findings provide novel approaches on how to operationalize relational context based on social network rarity and discoverable rarity. Moreover, insights from this study highlight that additional meta-information about user interests is needed to operationalize relational context, such as users’ passion level for an interest and their skill levels for an activity. Based on these findings, the novel design concept of passive context-awareness for social matching is put forward. In the last study, Encount’r, an instantiation of an opportunistic social matching system, is designed and evaluated through a field study and participant interviews. A large-scale user profiling survey provides baseline rarity measures to operationalize relational context using rarity, passion levels, skills, needs, and offers. Findings show that attribute type, computed attribute rarity, self-reported passion levels for interest, and response time are associated with people’s interest in a match opportunity. Moreover, this study extends prior work by showing how the concept of passive context-awareness for opportunistic social matching is promising. Collectively, contributions of this work include a theoretical framework encompassing relational, social, and personal context; new innovative concepts to operationalize each of these aspects for opportunistic social matching; and field-tested design affordances for opportunistic social matching systems. This is important because opportunistic social matching systems can lead to new social ties and improved social capital
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