London journal of social sciences最新文献

A global glance on growth of GDP (Gross Domestic Product) After implementing GST (Goods and Service Tax) 实施商品及服务税后全球国内生产总值的增长

London journal of social sciences Pub Date : 2023-09-18 DOI: 10.31039/ljss.2023.6.113

R. Malini, N. Sivagami, M. Parvathi Devi

引用次数: 0

AI Proctor: AI-Based Platform for Remote Learning AI Proctor:基于AI的远程学习平台

London journal of social sciences Pub Date : 2023-09-18 DOI: 10.31039/ljss.2023.6.112

None Jack Mueller

引用次数: 0

Empowering education with AI: Addressing ethical concerns 用人工智能赋能教育:解决伦理问题

London journal of social sciences Pub Date : 2023-09-17 DOI: 10.31039/ljss.2023.6.103

Sara Saylam, Nilgun Duman, Yetkin Yildirim, Kseniya Satsevich

引用次数: 0

A Novel Algorithm for Professor Recommendation in Higher Education 一种新的高等教育教授推荐算法

London journal of social sciences Pub Date : 2023-09-17 DOI: 10.31039/ljss.2023.6.98

Umar Mohammad, Yusuf Hamdan, Aarah Sardesai, Merve Gokgol

{"title":"A Novel Algorithm for Professor Recommendation in Higher Education","authors":"Umar Mohammad, Yusuf Hamdan, Aarah Sardesai, Merve Gokgol","doi":"10.31039/ljss.2023.6.98","DOIUrl":"https://doi.org/10.31039/ljss.2023.6.98","url":null,"abstract":"This paper introduces a novel professor-recommendation system designed specifically for community college and university courses. Building upon an existing algorithm for one-on-one teacher recommendations, we leveraged insights from the literature on Massive Open Online Course (MOOC) recommender algorithms. By analysing various approaches, we combined and refined ideas to develop an optimised system. Our approach utilises a tri-module framework that incorporates supervised and unsupervised learning techniques. The first module employs a Gradient-boosted Decision Tree algorithm, augmented with multiple factors and student dropout rates as ground truth, to generate a ranking score. The second module applies Apriori Association and Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to analyse these factors and identify professors with similar characteristics. In the third module, item-based collaborative filtering is employed, incorporating user ratings and the cosine similarity algorithm. The outputs from these three modules are subsequently integrated through a weighted average. This addition enables the system to prioritise opportunities for new professors, thereby ensuring a balanced recommendation approach. The resulting combined ranking score provides accurate recommendations for course instructors. This approach can be integrated into university course selection software for the benefit of both students and educational institutions.","PeriodicalId":482347,"journal":{"name":"London journal of social sciences","volume":"17 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":"135258850","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

Effectiveness of motivational agents on reducing foreign language anxiety 动机动因对减少外语焦虑的有效性

London journal of social sciences Pub Date : 2023-09-17 DOI: 10.31039/ljss.2023.6.102

Dhir Parekh, Lama Elsayed, Nathan Fernandes

{"title":"Effectiveness of motivational agents on reducing foreign language anxiety","authors":"Dhir Parekh, Lama Elsayed, Nathan Fernandes","doi":"10.31039/ljss.2023.6.102","DOIUrl":"https://doi.org/10.31039/ljss.2023.6.102","url":null,"abstract":"Utilizing resources focused on artificial intelligence (A.I.) and its influence on education, this paper analyzes the impact of “animated agents” to aid students in learning and understanding a new language. We used these articles to understand the effect that A.I. has on the anxiety levels of students and how these learners respond to differing motivational agents. Foreign Language Anxiety (FLA) is a term used to describe the stress exhibited by learners of a new language, specifically when being negatively evaluated by others or when perceiving an inability in themselves. FLA is a major barrier when it comes to the overall growth of a student and his/her proficiency in the new language, as it increases stress and decreases motivation to learn by making students think that they are not good enough. Because this has led to a notable decline in learners’ performance in that subject, pedagogical methods have been developed and examined to provide emotional support. With a group of 56 students and an e-learning system, researchers utilized either motivational or explanatory feedback through the use of text, voice, or animated agents (characters that can engage students and present information), and measured anxiety levels across the different combinations. While the motivational support of an agent was most sufficient, gender played a big role in the efficacy of different pedagogical methods; therefore, incorporating the gender of the learner into artificial intelligence systems and animated agents in the future would personalize feedback in forms that males and females can better take in and implement. To provide more equity in education, these results can be applied to other anxiety barriers that actively prevent students from developing the skills essential for their learning. With the proper assistance from A.I., FLA can become a thing of the past, and language learners can no longer suffer from the stress of learning something entirely new.","PeriodicalId":482347,"journal":{"name":"London journal of social sciences","volume":"131 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":"135258849","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

The impact of A.I on teaching and learning 人工智能对教学的影响

London journal of social sciences Pub Date : 2023-09-17 DOI: 10.31039/ljss.2023.6.111

None Caleb Kimondo, None Lenny Wandeto, None Dazzy Indimuli, None Azra Ercertin

引用次数: 0

Machine learning approaches to analyzing public speaking and vocal delivery 用机器学习方法分析公开演讲和声音传递

London journal of social sciences Pub Date : 2023-09-17 DOI: 10.31039/ljss.2023.6.106

Ali Mohammed, Mehdi Mir, Ryan Gill

{"title":"Machine learning approaches to analyzing public speaking and vocal delivery","authors":"Ali Mohammed, Mehdi Mir, Ryan Gill","doi":"10.31039/ljss.2023.6.106","DOIUrl":"https://doi.org/10.31039/ljss.2023.6.106","url":null,"abstract":"The 21st century has ushered in a wave of technological advancements, notably in machine learning, with profound implications for the analysis of public speaking and vocal delivery. This literature review scrutinizes the deployment of machine learning techniques in the evaluation and enhancement of public speaking skills, a critical facet of effective communication across various professions and everyday contexts. The exploration begins with an examination of machine learning models such as Support Vector Machines, Convolutional Neural Networks, and Long Short-Term Memory models. These models' application in the analysis of non-verbal speech features, emotion detection, and performance evaluation offers a promising avenue for objective, scalable, and efficient analysis, surpassing the limitations of traditional, often subjective, methods. The discussion extends to the real-world application of these techniques, encompassing public speaking skill analysis, teacher vocal delivery evaluation, and the assessment of public speaking anxiety. Various machine learning frameworks are presented, emphasizing their effectiveness in generating large-scale, objective evaluation results. However, the discourse acknowledges the challenges and limitations inherent to these technologies, including data privacy concerns, potential over-reliance on technology, and the necessity for diverse and extensive datasets. The potential drawbacks of these approaches are highlighted, underscoring the need for further research to address these issues. Despite these challenges, the successes of numerous machine learning applications in this field are underscored, along with their potential for future advancements. By dissecting past successes and failures, the review aims to provide guidance for the more effective deployment of these technologies in the future, contributing to the ongoing efforts to revolutionize the analysis of public speaking and vocal delivery.","PeriodicalId":482347,"journal":{"name":"London journal of social sciences","volume":"185 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":"135258716","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

Exploring the role of AI in education 探索人工智能在教育中的作用

London journal of social sciences Pub Date : 2023-09-17 DOI: 10.31039/ljss.2023.6.108

Nathan D. Nguyen

{"title":"Exploring the role of AI in education","authors":"Nathan D. Nguyen","doi":"10.31039/ljss.2023.6.108","DOIUrl":"https://doi.org/10.31039/ljss.2023.6.108","url":null,"abstract":"New advancements in machine learning and AI can be used to augment student learning and teacher capabilities. Examples of AI approaches in education include generating personalized student recommendations, autograding essays, and improving educational resources. AI programs intended to improve education can be categorized informally into three groups: Guidance, Learning, and Teacher. These categories are general and not necessarily mutually exclusive, but provide a framework for organization and further development. This paper intends to look at the past approaches of AI to improve education and categorize them to help guide new development of AI applications in education. The potential benefits of AI-powered education is noteworthy as the current economy is based on higher education. AI can be used to speed up labor-intensive tasks and help close the knowledge gap. Additionally, this paper also looks at potential drawbacks, such as ethics concerns of using student data to power AI. By analyzing the past use of AI in education, this paper seeks to provide a grouping framework to improve understanding of the field and facilitate future development. Framework for organization and further development. This paper intends to look at the past approaches of AI to improve education and categorize them to help guide new development of AI applications in education. The potential benefits of AI-powered education is noteworthy as the current economy is based on higher education. AI can be used to speed up labor-intensive tasks and help close the knowledge gap. Additionally, this paper also looks at potential drawbacks, such as ethics concerns of using student data to power AI. By analyzing the past use of AI in education, this paper seeks to provide a grouping framework to improve understanding of the field and facilitate future development.","PeriodicalId":482347,"journal":{"name":"London journal of social sciences","volume":"229 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":"135259571","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: 1

Copy-Past Culture: Examining the Causes and Solutions to Source Code Plagiarism 复制过去的文化:审查源代码抄袭的原因和解决办法

London journal of social sciences Pub Date : 2023-09-17 DOI: 10.31039/ljss.2023.6.104

Jayden Manyrath, Kaleb Kirubel, Thomas Cruz

{"title":"Copy-Past Culture: Examining the Causes and Solutions to Source Code Plagiarism","authors":"Jayden Manyrath, Kaleb Kirubel, Thomas Cruz","doi":"10.31039/ljss.2023.6.104","DOIUrl":"https://doi.org/10.31039/ljss.2023.6.104","url":null,"abstract":"In an era marked by the increasing digitization of society, the issue of source code plagiarism has emerged as a persistent concern. This research paper delves into the problem of source code plagiarism within educational settings, exploring its implications, potential remedies, and the associated hurdles in implementing these solutions. Source code plagiarism involves the unauthorized copying of code without proper attribution, and it has been on the rise in educational institutions due to various contributing factors. This paper sheds light on the educational system's pressures, time constraints, lofty expectations, and the allure of quick completion that make source code plagiarism appealing to students. Furthermore, it highlights the lack of understanding among students regarding academic integrity and citation methods, exacerbating the problem. Source code plagiarism not only hampers students' intellectual development and problem-solving skills but also undermines the fairness of assessments, posing grading challenges for educators. Nevertheless, there are several potential solutions. While proactive methods focus on prevention through education and policy, reactive methods employ AI-driven plagiarism detectors for detection. However, these solutions are not without their challenges, such as the issue of false positives in plagiarism detection and the potential adversarial response from students. In conclusion, source code plagiarism is a growing problem in modern society that can not be avoided any longer. Potential solutions to source code plagiarism should be taken into account while considering their withdrawals. Computer science and programming courses should foster a sense of integrity to avoid source code plagiarism and develop new generations of coders for the future.","PeriodicalId":482347,"journal":{"name":"London journal of social sciences","volume":"29 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":"135258550","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

Using Explainable Machine Learning to Automatically Provide Feedback to Students Based on Data Analysis 基于数据分析，使用可解释的机器学习自动为学生提供反馈

London journal of social sciences Pub Date : 2023-09-17 DOI: 10.31039/ljss.2023.6.101

Kipkirui Keter, Aden Joseph, Jabir Mohamed

引用次数: 0