London journal of social sciences最新文献_第2页

Artificial Intelligence and Underfunded Education 人工智能和资金不足的教育

London journal of social sciences Pub Date : 2023-09-17 DOI: 10.31039/ljss.2023.6.105

Kiarie Muranga, Ian S. Muse, Emin N. Köroğlu, Yetkin Yildirim

{"title":"Artificial Intelligence and Underfunded Education","authors":"Kiarie Muranga, Ian S. Muse, Emin N. Köroğlu, Yetkin Yildirim","doi":"10.31039/ljss.2023.6.105","DOIUrl":"https://doi.org/10.31039/ljss.2023.6.105","url":null,"abstract":"There has been a lot of general research on education, but the unique topic of underfunded education has been largely untouched. Millions of students around the world get a low-quality education due to problems in underfunded education such as the lack of experienced teachers, a high student-teacher ratio and outdated curricula. This negatively impacts them and their respective countries in the future due to subpar performance both in school and in the workplace; further exacerbating the educational and economic disparities between countries. The lack of research in this area is due to the various unique challenges facing underfunded education and the difficulty in solving them with conventional methods. However, many tools that utilise Artificial Intelligence (AI) such as adaptive learning systems, chatbots, virtual assistants and Intelligent Assessment Systems can be used to solve or at least manage these problems, making the process of education more streamlined, efficient and effective. The purpose of this study is to explore the possibility of implementing AI into underfunded education and the numerous benefits that doing so would bring. We also outline the various challenges that would be faced in implementation and possible solutions to them.","PeriodicalId":482347,"journal":{"name":"London journal of social sciences","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135258847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design Thinking: Best way to supply chain’s success? 设计思维:供应链成功的最佳途径?

London journal of social sciences Pub Date : 2023-09-17 DOI: 10.31039/ljss.2023.6.110

Christina Schabasser

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Sustainable agriculture is a stimulator to stretch out sustainable development goals 可持续农业是实现可持续发展目标的助推器

London journal of social sciences Pub Date : 2023-09-17 DOI: 10.31039/ljss.2023.6.109

R. Malini, None J. Ebanisha, M. Athista Lakshmi

引用次数: 0

Predicting student disengagement: Harnessing visual cues for intelligent tutoring systems 预测学生的脱离:利用视觉线索的智能辅导系统

London journal of social sciences Pub Date : 2023-09-17 DOI: 10.31039/ljss.2023.6.107

None Mehmet Firat

{"title":"Predicting student disengagement: Harnessing visual cues for intelligent tutoring systems","authors":"None Mehmet Firat","doi":"10.31039/ljss.2023.6.107","DOIUrl":"https://doi.org/10.31039/ljss.2023.6.107","url":null,"abstract":"Intelligent tutoring systems have the potential to enhance the learning experience for children, but it is crucial to detect and address early signs of disengagement to ensure effective learning. In this paper, we propose a method that utilizes visual features from a tablet tutor's user-facing camera to predict whether a student will complete the current activity or disengage from it. Unlike previous approaches that relied on tutor-specific features, our method leverages visual cues, making it applicable to various tutoring systems. We employ a deep learning approach based on a Long Short Term Memory (LSTM) model with a target replication loss function for prediction. Our model is trained and tested on screen capture videos of children using a tablet tutor for learning basic Swahili literacy and numeracy in Tanzania. With 40% of the activity remaining, our model achieves a balanced-class size prediction accuracy of 73.3%. Furthermore, we analyze the variation in prediction accuracy across different tutor activities, revealing two distinct causes of disengagement. The findings indicate that our model can not only predict disengagement but also identify visual indicators of negative affective states that may not lead to non-completion of the task. This work contributes to the automated detection of early signs of disengagement, which can aid in improving tutoring systems and guiding pedagogical decisions in real-time.","PeriodicalId":482347,"journal":{"name":"London journal of social sciences","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135259573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

To what extent were schools and stem education affected and rebuilt after the covid-19 pandemic? 2019冠状病毒病大流行后，学校和stem教育在多大程度上受到影响和重建?

London journal of social sciences Pub Date : 2023-09-16 DOI: 10.31039/ljss.2023.6.100

Mustafa Nalcaci, Emre Tunca, Nuhayd Nadi

{"title":"To what extent were schools and stem education affected and rebuilt after the covid-19 pandemic?","authors":"Mustafa Nalcaci, Emre Tunca, Nuhayd Nadi","doi":"10.31039/ljss.2023.6.100","DOIUrl":"https://doi.org/10.31039/ljss.2023.6.100","url":null,"abstract":"The COVID-19 pandemic had a major impact on STEM education, affecting students, teachers, and the entire education system. The switch to online learning brought about many challenges, which have not been fully analyzed in current research. Reading and summarizing articles on this particular topic formed the primary methodology of this research paper. Upon examining sources on the topic, we found some of these challenges facing the transition to online learning include students' lack of motivation, the demoralization of teachers, and the change of priorities in STEM education. Moreover, STEM teachers' attitudes and views toward online teaching were negatively affected, leading to an increase in mental stress and a higher number of teachers quitting their jobs. On the other side, STEM professors who prioritized open communication and leniency in policies were more successful in creating a positive and inviting learning environment for their students. Based on this information, we can presume that the education system needs to be more adaptable and flexible to ensure that students and teachers receive the assistance they need in times of crisis. It is through their resilience and determination that STEM education will be able to continue to thrive in the post-Covid era.","PeriodicalId":482347,"journal":{"name":"London journal of social sciences","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135352497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of intrinsic motivation influence on employee affective commitment during digital change 数字化变革中内在动机对员工情感承诺的影响分析

London journal of social sciences Pub Date : 2023-09-15 DOI: 10.31039/ljss.2023.6.97

Yashar Ibragimov, Nino Berishvili Berishvili

{"title":"Analysis of intrinsic motivation influence on employee affective commitment during digital change","authors":"Yashar Ibragimov, Nino Berishvili Berishvili","doi":"10.31039/ljss.2023.6.97","DOIUrl":"https://doi.org/10.31039/ljss.2023.6.97","url":null,"abstract":"Technological, economic and other innovation-related advances of the 21st century have influenced the old, traditional business models. Presently, organizational change has become an integral part of corporate strategy for the majority of businesses. Such shifts have resulted in both new challenges and opportunities. The expansion of the use of information and communication technologies has driven fundamental shifts towards digital change. Organizations are being forced to revise processes, goals and overall mission in order to stay competitive in the marketplace. However, the implementation of digital transformation brings uncertainty, causes stress and raises concerns about future jobs. The study employs a literature review to fill the gap in understanding the relationship between employee intrinsic motivation and affective commitment during the transformation. A conceptual model proposes the antecedents of employee motivation (Organizational Citizenship Behavior and Leader Member Exchange) and investigates its impact on employee commitment to change. The utilized model elucidates how to maintain employee intrinsic motivation and affective commitment in the context of organizational transformation and sets the ground for future research.","PeriodicalId":482347,"journal":{"name":"London journal of social sciences","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135394219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0