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商品描述
This book introduces Meaningful Purposive Interaction Analysis (MPIA) theory, which combines social network analysis (SNA) with latent semantic analysis (LSA) to help create and analyse a meaningful learning landscape from the digital traces left by a learning community in the co-construction of knowledge.
The hybrid algorithm is implemented in the statistical programming language and environment R, introducing packages which capture – through matrix algebra – elements of learners’ work with more knowledgeable others and resourceful content artefacts. The book provides comprehensive package-by-package application examples, and code samples that guide the reader through the MPIA model to show how the MPIA landscape can be constructed and the learner’s journey mapped and analysed. This building block application will allow the reader to progress to using and building analytics to guide students and support decision-making in learning.
商品描述(中文翻譯)
本書介紹了有意義的目的互動分析(Meaningful Purposive Interaction Analysis, MPIA)理論,該理論結合了社會網絡分析(Social Network Analysis, SNA)和潛在語義分析(Latent Semantic Analysis, LSA),旨在幫助從學習社群在共同建構知識過程中留下的數位痕跡中創建和分析有意義的學習環境。
這種混合算法在統計編程語言和環境 R 中實現,並引入了通過矩陣代數捕捉學習者與更有知識的他人及資源豐富的內容產物互動的元素的套件。本書提供了全面的逐套件應用範例和代碼範本,指導讀者了解 MPIA 模型,展示如何構建 MPIA 環境並映射和分析學習者的旅程。這一基礎應用將使讀者能夠進一步使用和構建分析工具,以指導學生並支持學習中的決策制定。