Advanced Learning Analytics Methods: Ai, Precision and Complexity
暫譯: 進階學習分析方法：人工智慧、精確性與複雜性
Saqr, Mohammed, López-Pernas, Sonsoles
- 出版商: Springer
- 出版日期: 2025-10-26
- 售價: $2,550
- 貴賓價: 9.5 折 $2,423
- 語言: 英文
- 頁數: 593
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031953649
- ISBN-13: 9783031953644
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相關分類:
Machine Learning、Large language model、Data-mining
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相關主題
商品描述
This is an open access book.
This comprehensive and timely methodological book introduces several novel topics under the overarching sections of advanced learning analytics (LA), artificial intelligence (AI), precision education, and complex systems. These topics are presented using accessible language, beginning with introductory chapters that cover the fundamentals of each section, followed by step-by-step tutorials featuring code and datasets for various methods within each area. Although the title refers to "advanced LA," the book is written for the broader educational research community and is of interest to quantitative researchers from diverse backgrounds. The first section focuses on Explainable AI and machine learning (ML), with an introduction to the methods, their applications, and tutorials. The second section outlines the foundational concepts of LLMs, their potential applications, and related methodologies, with a tutorial on using LLMs in various analytical tasks. The third section focuses on complex systems, which have become integral to many disciplines and have enabled breakthroughs in modeling intractable problems. Here, three chapters cover Transition Network Analysis (TNA), which fills a critical gap in modeling the temporal unfolding of learning processes over time from a complex systems perspective. The final section addresses precision education, with a particular emphasis on person-centered and person-specific (idiographic) methodologies.商品描述(中文翻譯)
這是一本開放存取的書籍。本書是一部全面且及時的方法論著作，介紹了幾個新穎的主題，涵蓋了進階學習分析（LA）、人工智慧（AI）、精準教育和複雜系統等主要領域。這些主題以易於理解的語言呈現，首先是介紹各個領域基本概念的入門章節，接著是逐步的教學，包含各領域內不同方法的程式碼和數據集。雖然書名提到「進階 LA」，但本書是為更廣泛的教育研究社群所撰寫，對來自不同背景的定量研究者也具有吸引力。第一部分專注於可解釋的人工智慧和機器學習（ML），介紹了這些方法、其應用及教學。第二部分概述了大型語言模型（LLMs）的基礎概念、潛在應用及相關方法論，並提供了在各種分析任務中使用 LLMs 的教學。第三部分聚焦於複雜系統，這些系統已成為許多學科的核心，並促成了對難以處理問題的建模突破。在這裡，三個章節涵蓋了轉換網絡分析（TNA），這填補了從複雜系統的角度建模學習過程隨時間展開的關鍵空白。最後一部分探討精準教育，特別強調以人為中心和個別化（特質性）的方法論。
作者簡介
Mohammed Saqr is an Associate Professor of Computer Science at the University of Eastern Finland (UEF). He holds a PhD in learning analytics from Stockholm University, Sweden. Before joining UEF, he completed a postdoctoral fellowship at Université Paris Cité, France, and obtained the title of Docent in learning analytics from the University of Oulu, Finland. Mohammed established and currently leads UEF's Learning Analytics (LA) Unit, recognized as Europe's most productive LA laboratory in the past five years, with a well-established global standing in methodological diversity, innovation, and scientific impact. Mohammed has authored more than 200 peer-reviewed methodological and empirical studies spanning LA, AI, big data and network science. His research pushes the boundaries of precision AI, complexity, and AI. Mohammed Saqr's recent work emphasizes precision AI, where he develops precise, explainable AI to model individuals using idiographic methods, create personalized, explainable AI, and derive unique insights for each person. Methodologically, Mohammed has edited and authored three methodological books and co-founded Transition Network Analysis (TNA), a novel framework providing robust analysis for complex systems and social dynamics. Mohammed is listed among Stanford's Top 2% of World Scientists and named Europe's Emerging Scholar in Learning Analytics. His research has received several awards, including Best Thesis from Stockholm University and Best Paper Awards at LAK 2024, ICCE 2020, SITE 2022 and TEEM 2023 and 2024. He maintains a vast network of collaborators from eight Finnish universities and over 250 scholars across 36 countries. His editorial roles include serving on the boards of the British Journal of Educational Technology, Associate Editor for IEEE Transactions on Learning Technologies, Frontiers in Computer Science, and Frontiers in Education and Academic Editor for PLOS One.
Sonsoles López-Pernas is currently a Senior Researcher and a fellow of the Finnish Research Council (Academy of Finland) at the University of Eastern Finland (UEF, Finland). Sonsoles received her PhD in Engineering from Universidad Politécnica de Madrid (UPM, Spain) on game-based learning in engineering education and the title of Docent from UEF in educational data mining. Sonsoles's work is diverse and interdisciplinary and spans computer science, learning analytics, game-based learning, and computing education. She has over 150 peer-reviewed publications published in well-regarded conferences and journals. She extensively published on longitudinal learning analytics methods, complex systems, network analysis, sequence and process mining as well as machine learning and artificial intelligence. Sonsoles's work has received multiple awards recognizing her contributions to learning analytics, educational technology, and open-source software development. Notably, she received the best PhD thesis award at UPM, the best doctoral thesis award from the Royal Academy of Doctors of Spain (2022), the SoLAR Emerging Scholar Award (Europe) in 2025, and the IEEE TCLT Early Career Researcher Award in Learning Technologies in 2024. Her research has earned best paper awards at major international conferences, including LAK 2024 (Kyoto, Japan), TEEM 2024 (Alicante, Spain), and TEEM 2023 (Bragança, Portugal), best reviewer at ICALT, and other award nominations at EC-TEL and ICALT in 2024. Additionally, she serves as an Associate Editor for IEEE Transactions on Education, Frontiers in Education, and PLOS One. She has recently received a four-year Academy Research Fellowship grant from the Research Council of Finland for the project Optimizing Clinical Reasoning in Time-Critical Scenarios: A Data-Driven Multimodal Approach (CRETIC) and is the principal investigator of several other research projects in learning analytics and game-based learning funded by the European Commission.
作者簡介(中文翻譯)
Mohammed Saqr 是芬蘭東部大學 (UEF) 的計算機科學副教授。他擁有瑞典斯德哥爾摩大學的學習分析博士學位。在加入 UEF 之前，他在法國巴黎大學完成了博士後研究，並獲得芬蘭奧盧大學的學習分析 Docent 職稱。Mohammed 創立並目前領導 UEF 的學習分析 (LA) 單位，該單位在過去五年被認可為歐洲最具生產力的 LA 實驗室，並在方法多樣性、創新和科學影響力方面享有良好的全球聲譽。Mohammed 已發表超過 200 篇經過同行評審的方法論和實證研究，涵蓋 LA、AI、大數據和網絡科學。他的研究推動了精確 AI、複雜性和 AI 的邊界。Mohammed Saqr 最近的工作強調精確 AI，他開發精確且可解釋的 AI，以使用個案方法建模個體，創建個性化的可解釋 AI，並為每個人提供獨特的見解。在方法論上，Mohammed 編輯和撰寫了三本方法論書籍，並共同創立了轉換網絡分析 (TNA)，這是一個為複雜系統和社會動態提供穩健分析的新框架。Mohammed 被列入斯坦福大學全球科學家前 2% 名單，並被評選為歐洲學習分析新興學者。他的研究獲得了多項獎項，包括斯德哥爾摩大學的最佳論文獎，以及在 LAK 2024、ICCE 2020、SITE 2022 和 TEEM 2023 和 2024 的最佳論文獎。他與八所芬蘭大學的合作者保持著廣泛的網絡，並與來自 36 個國家的 250 多位學者合作。他的編輯角色包括擔任《英國教育技術期刊》的編委、IEEE Transactions on Learning Technologies 的副編輯、《計算機科學前沿》和《教育前沿》的編輯，以及 PLOS One 的學術編輯。
Sonsoles López-Pernas 目前是芬蘭東部大學 (UEF, 芬蘭) 的高級研究員及芬蘭研究委員會 (芬蘭學院) 的研究員。Sonsoles 在西班牙馬德里理工大學 (UPM) 獲得工程學博士學位，研究主題為工程教育中的遊戲式學習，並在 UEF 獲得教育數據挖掘的 Docent 職稱。Sonsoles 的工作範圍廣泛且跨學科，涵蓋計算機科學、學習分析、遊戲式學習和計算教育。她在知名會議和期刊上發表了超過 150 篇經過同行評審的出版物。她在長期學習分析方法、複雜系統、網絡分析、序列和過程挖掘以及機器學習和人工智慧方面有廣泛的發表。Sonsoles 的工作獲得了多項獎項，以表彰她對學習分析、教育技術和開源軟體開發的貢獻。值得注意的是，她在 UPM 獲得最佳博士論文獎，並於 2022 年獲得西班牙皇家醫生學院的最佳博士論文獎，2025 年獲得 SoLAR 新興學者獎 (歐洲)，以及 2024 年獲得 IEEE TCLT 學習技術早期職業研究者獎。她的研究在主要國際會議上獲得最佳論文獎，包括 LAK 2024（日本京都）、TEEM 2024（西班牙阿利坎特）和 TEEM 2023（葡萄牙布拉干薩），並在 ICALT 獲得最佳審稿人獎，以及在 2024 年的 EC-TEL 和 ICALT 獲得其他獎項提名。此外，她擔任 IEEE Transactions on Education、Frontiers in Education 和 PLOS One 的副編輯。她最近獲得芬蘭研究委員會的四年學院研究獎學金，用於項目「在時間關鍵場景中優化臨床推理：數據驅動的多模態方法 (CRETIC)」，並是幾個由歐洲委員會資助的學習分析和遊戲式學習研究項目的主要研究者。
