Exploring Self-regulation of More or Less Expert College-Age Video Game Players: A Sequential Explanatory Design

This study examined differences in self-regulation among college-age expert, moderately expert, and non-expert video game players in playing video games for fun. Winnie\u27s model of self-regulation (Winne, 2001) guided the study. The main assumption of this study was that expert video game players used more processes of self-regulation than the less-expert players. We surveyed 143 college students about their game playing frequency, habits, and use of self-regulation. Data analysis indicated that while playing recreational video games, expert gamers self-regulated more than moderately expert and non-expert players and moderately expert players used more processes of self-regulation than non-experts. Semi-structured interviews also were conducted with selected participants at each of the expertise levels. Qualitative follow-up analyses revealed five themes: (1) characteristics of expert video gamers, (2) conditions for playing a video game, (3) figuring out a game, (4) how gamers act and, (5) game context. Overall, findings indicated that playing a video game is a highly self-regulated activity and that becoming an expert video game player mobilizes multiple sets of self-regulation related skills and processes. These findings are seen as promising for educators desiring to encourage student self-regulation, because they indicate the possibility of supporting students via recreational video games by recognizing that their play includes processes of self-regulation

ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER

Prompt-based language models have produced encouraging results in numerous applications, including Named Entity Recognition (NER) tasks. NER aims to identify entities in a sentence and provide their types. However, the strong performance of most available NER approaches is heavily dependent on the design of discrete prompts and a verbalizer to map the model-predicted outputs to entity categories, which are complicated undertakings. To address these challenges, we present ContrastNER, a prompt-based NER framework that employs both discrete and continuous tokens in prompts and uses a contrastive learning approach to learn the continuous prompts and forecast entity types. The experimental results demonstrate that ContrastNER obtains competitive performance to the state-of-the-art NER methods in high-resource settings and outperforms the state-of-the-art models in low-resource circumstances without requiring extensive manual prompt engineering and verbalizer design.Comment: 9 pages, 5 figures, COMPSAC202

Big data workflows: Locality-aware orchestration using software containers

The emergence of the Edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing Big Data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the Edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric Big Data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo Workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.publishedVersio

Big data workflows: Locality-aware orchestration using software containers

The emergence of the Edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing Big Data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the Edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric Big Data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo Workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.publishedVersio

Is the middle cerebral artery bifurcation aneurysm affected by morphological parameters of bifurcation?

Background: Aneurysm formation is a multifactorial process involving genetic, anatomical and environmental risk factors. A research focusing on the relationship between the presence of aneurysm and the morphology of the arteries will help in the pathogenesis and prediction of intracranial aneurysms. In this study, the relationship between the presence of aneurysm and various morphological parameters of aneurysm-related arteries was evaluated in patients with saccular middle cerebral artery (MCA) bifurcation aneurysm.Materials and methods: The archival images of 74 patients (62.2% women) were evaluated retrospectively. In this study, the angle between the ipsilateral MCA M1 segment and the dominant truncus (Φ1), the angle between the M1 segment and the recessive truncus (Φ2), and the bifurcation angle (Φ1 + Φ2) were compared. Bilateral internal carotid artery (ICA), MCA M1 segment, dominant and recessive truncus diameters and these diameters ratios were compared with the aneurysmal side and the contralateral side without aneurysm.Results: When the dominant truncus, recessive truncus angles and bifurcation angle were compared, a significant difference was found on the aneurysmal side (p < 0.0001). In the receiver operating characteristic analysis, when the bifurcation angle of 147.5° was accepted as the limit value, 78.4% sensitivity, 79.7% specificity, 79.5% positive predictive value and 78.7% negative predictive value were determined (area under the curve: 0.85).Conclusions: Our study of the morphological features of arteries associated with MCA bifurcation aneurysms showed that the presence of MCA aneurysms was significantly associated with large bifurcation angles

The impact of the COVID-19 pandemic on the learning and wellbeing of secondary school students: a survey in Southern Europe

The transition from the traditional model of learning and teaching to full online mode had to be implemented in many countries, in an extremely short time, as the 2020-2021 school year was in mid-stream. Secondary education, which includes students in the age range of 12-18, faced many challenges in this rapid change, as many research studies have shown. Researchers raise questions regarding the readiness of the secondary education community to transition to fully online learning. The pilot study reported in this paper deals with the impact of the transition to online learning on secondary schools in southern European countries. More specifically, this paper presents the results of a literature survey and an empirical survey using an online questionnaire which captured non-traceable responses from secondary schools that, voluntarily and anonymously, completed the questionnaire. The questions were mainly closed, with some open-ended questions for students to fill in. The study also aims to capture data on the socio-economic dimension, accessibility/ availability of the necessary technologies that enable online learning, as well as the families’ employment status and their ability to support students. A total of 90 students participated (62% female, 28% male) from three Mediterranean countries. The students’ perspectives as seen by the students themselves along with the difficulties and the issues they faced are compared and contrasted. This investigation offers a pedagogical and socio-technical analysis and highlights the needs for wellbeing as well as quality learning and teaching in the new social distance reality

Asymmetric intergroup bullying: the enactment and maintenance of societal inequality at work

What does inequality mean for dysfunctional organizational behaviours, such as workplace bullying? This article argues that workplace bullying can be understood as a manifestation of intergroup dynamics originating beyond the organization. We introduce the construct of asymmetric intergroup bullying: the disproportionate mistreatment of members of low status groups, with the intended effect of enhancing the subordination of that group in society at large. Analysis of data from 38 interviews with public and private sector workers in Turkey depicts a pattern of asymmetric intergroup bullying, undertaken to achieve organizational and broader sociopolitical goals. Respondents reported bullying acts used to get rid of unwanted personnel, with the goal of avoiding severance pay, or of removing supporters of the former government from positions of political and economic influence. Bullying was also described as working towards the dominance of the sociocultural worldview of one political group over another. We discuss asymmetric intergroup bullying as one mechanism through which acute intergroup hierarchy in the broader society corrupts management practice and employee interactions, in turn exacerbating economic inequality along group lines