- Book学术

发布求助

文献互助智能选刊最新文献

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Big Data Pub Date : 2025-02-28 DOI:10.1089/big.2024.0128

Ikpe Justice Akpan, Rouzbeh Razavi, Asuama A Akpan

{"title":"Evolutionary Trends in Decision Sciences Education Research from Simulation and Games to Big Data Analytics and Generative Artificial Intelligence.","authors":"Ikpe Justice Akpan, Rouzbeh Razavi, Asuama A Akpan","doi":"10.1089/big.2024.0128","DOIUrl":null,"url":null,"abstract":"<p><p>Decision sciences (DSC) involves studying complex dynamic systems and processes to aid informed choices subject to constraints in uncertain conditions. It integrates multidisciplinary methods and strategies to evaluate decision engineering processes, identifying alternatives and providing insights toward enhancing prudent decision-making. This study analyzes the evolutionary trends and innovation in DSC education and research trends over the past 25 years. Using metadata from bibliographic records and employing the science mapping method and text analytics, we map and evaluate the thematic, intellectual, and social structures of DSC research. The results identify \"knowledge management,\" \"decision support systems,\" \"data envelopment analysis,\" \"simulation,\" and \"artificial intelligence\" (AI) as some of the prominent critical skills and knowledge requirements for problem-solving in DSC before and during the period (2000-2024). However, these technologies are evolving significantly in the recent wave of digital transformation, with data analytics frameworks (including techniques such as big data analytics, machine learning, business intelligence, data mining, and information visualization) becoming crucial. DSC education and research continue to mirror the development in practice, with sustainable education through virtual/online learning becoming prominent. Innovative pedagogical approaches/strategies also include computer simulation and games (\"play and learn\" or \"role-playing\"). The current era witnesses AI adoption in different forms as conversational Chatbot agent and generative AI (GenAI), such as chat generative pretrained transformer in teaching, learning, and scholarly activities amidst challenges (academic integrity, plagiarism, intellectual property violations, and other ethical and legal issues). Future DSC education must innovatively integrate GenAI into DSC education and address the resulting challenges.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1089/big.2024.0128","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

摘要

决策科学（DSC）涉及研究复杂的动态系统和过程，以帮助人们在不确定的条件下根据制约因素做出明智的选择。它整合了多学科方法和策略，以评估决策工程流程、确定替代方案并提供见解，从而加强审慎决策。本研究分析了过去 25 年中 DSC 教育和研究趋势的演变趋势和创新。利用书目记录中的元数据，并采用科学绘图法和文本分析法，我们对 DSC 研究的主题、知识和社会结构进行了绘图和评估。研究结果表明，"知识管理"、"决策支持系统"、"数据包络分析"、"模拟 "和 "人工智能"（AI）是 2000-2024 年之前和期间（2000-2024 年）DSC 解决问题所需的一些重要技能和知识。然而，在最近的数字化转型浪潮中，这些技术正在发生重大演变，数据分析框架（包括大数据分析、机器学习、商业智能、数据挖掘和信息可视化等技术）变得至关重要。DSC 教育和研究继续反映实践中的发展，通过虚拟/在线学习开展可持续教育的情况日益突出。创新的教学方法/策略还包括计算机模拟和游戏（"边玩边学 "或 "角色扮演"）。当今时代，人工智能以对话式聊天机器人（Chatbot agent）和生成式人工智能（GenAI）等不同形式被广泛采用，如在教学、学习和学术活动中使用的聊天生成式预训练转换器，它面临着各种挑战（学术诚信、剽窃、侵犯知识产权以及其他伦理和法律问题）。未来的 DSC 教育必须创新性地将 GenAI 融入 DSC 教育，并应对由此带来的挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Evolutionary Trends in Decision Sciences Education Research from Simulation and Games to Big Data Analytics and Generative Artificial Intelligence.

Decision sciences (DSC) involves studying complex dynamic systems and processes to aid informed choices subject to constraints in uncertain conditions. It integrates multidisciplinary methods and strategies to evaluate decision engineering processes, identifying alternatives and providing insights toward enhancing prudent decision-making. This study analyzes the evolutionary trends and innovation in DSC education and research trends over the past 25 years. Using metadata from bibliographic records and employing the science mapping method and text analytics, we map and evaluate the thematic, intellectual, and social structures of DSC research. The results identify "knowledge management," "decision support systems," "data envelopment analysis," "simulation," and "artificial intelligence" (AI) as some of the prominent critical skills and knowledge requirements for problem-solving in DSC before and during the period (2000-2024). However, these technologies are evolving significantly in the recent wave of digital transformation, with data analytics frameworks (including techniques such as big data analytics, machine learning, business intelligence, data mining, and information visualization) becoming crucial. DSC education and research continue to mirror the development in practice, with sustainable education through virtual/online learning becoming prominent. Innovative pedagogical approaches/strategies also include computer simulation and games ("play and learn" or "role-playing"). The current era witnesses AI adoption in different forms as conversational Chatbot agent and generative AI (GenAI), such as chat generative pretrained transformer in teaching, learning, and scholarly activities amidst challenges (academic integrity, plagiarism, intellectual property violations, and other ethical and legal issues). Future DSC education must innovatively integrate GenAI into DSC education and address the resulting challenges.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

9.10

自引率

2.20%

发文量

期刊介绍： Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.