查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

114-2 356430 組織學習與知識管理 1 2.0 2 ★ 程聖倫技職所 16 0 　

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
114-2	356430	組織學習與知識管理	1	2.0	2	★	程聖倫	技職所	16	0

教學大綱與進度：

教師姓名程聖倫　教師諮商時間(Office Hours)

Email scheng@ntut.edu.tw

最後更新時間 2026-03-05 22:43:39

課程大綱 The objective of this course is to prepare students to understand the current theories, practices, tools and techniques in knowledge management (KM) to deal with the challenges with the organization and management of knowledge. This course addresses contemporary issues in managing knowledge, intellectual capital and other intangible assets by discussing the fundamental concepts of knowledge and its creation, acquisition, representation, dissemination, use and re-use, the role and use of knowledge in organizations and institutions, KM systems and its application in knowledge generation and transfer, and in the representation, organization, and exchange of knowledge, knowledge codification and system development, its testing, KM tools and portals, and finally ethical, managerial and legal issues in knowledge management.

課程進度 Topics: FOUNDATION (Weeks 1-2) Week 1: Course Introduction & Introduction to Knowledge Management *Overview of KM as a discipline *Introduction to the Dalkir Meta-KM Process framework Week 2: Knowledge Management Models & The KM Cycle *Theoretical frameworks *Understanding the create-share-use cycle *R Lab intro: Setting up R environment, basic text data handling PHASE 1: CREATE OR CAPTURE (Weeks 3-5) Week 3: Independent Research: Knowledge Capture and Codification (Self-Study) *Tacit vs. explicit knowledge *Documentation strategies *Knowledge elicitation techniques Week 4: Knowledge Capture (R Lab I) *R Lab: Text mining basics - extracting knowledge from documents *Word frequencies, tf-idf, document similarity (tm, tidytext) Week 5: Knowledge Discovery & Topic Modeling *R Lab: Topic modeling with LDA - discovering knowledge domains *Identifying hidden themes in organizational documents (topicmodels, stm) PHASE 2: SHARE AND DISSEMINATE (Weeks 6-8) Week 6: Organizational Culture, Motivation, and Knowledge Sharing *Organizational culture as enabler/barrier to knowledge sharing *Motivational factors (goal orientations) in knowledge contribution and knowledge hoarding *Achievement goal theory applied to KM behaviors *Reward systems and cultural dimensions Week 7: Measuring Culture, Motivation & Sharing Behaviors *R Lab: Survey analysis & psychometrics *Measuring organizational culture for KM *Knowledge sharing attitudes and motivation scales *Reliability analysis, CFA validation (lavaan, psych) Week 8: Knowledge Sharing Networks & Communities of Practice *Social capital and network perspectives *Communities of practice frameworks *R Lab: Social Network Analysis I & II *Mapping knowledge sharing behaviors *Network density, centrality, community detection (igraph, tidygraph, ggraph) MID-TERM (Week 9) Week 9: Mid-term Presentation *ASSESS: Knowledge Mining & Discovery (Weeks 10-12) Week 10: Knowledge Mining I - Pattern Discovery *Association rules in organizational knowledge *Finding expertise patterns* *R Lab: Association rules mining (arules, arulesViz) Week 11: Knowledge Mining II - Expertise Mapping *Identifying subject matter experts *Knowledge flow analysis *R Lab: Combining text mining + SNA for expertise identification Week 12: Sentiment Analysis & Knowledge Communication *Analyzing organizational communication patterns *Understanding knowledge sharing tone and effectiveness *R Lab: Sentiment analysis of emails, documents, collaboration tools (tidytext, syuzhet) PHASE 3: USE, APPLY AND RE-USE (Weeks 13-14) Week 13: Knowledge Application & Organizational Learning *Knowledge reuse strategies *Learning from experience *Lessons learned systems *R Lab: Document clustering - organizing knowledge for reuse (K-means, hierarchical) Week 14: Organizational Memory & Knowledge Retention *Memory systems and repositories *Knowledge loss prevention *R Lab: Time-based analysis - tracking knowledge evolution and retention patterns Week 15: Knowledge Management Strategy & Measurement *KM strategy alignment with business goals *Measuring KM value and ROI *KM metrics and dashboards *R Lab: Integration project - combining multiple analytical methods CONTEXTUALIZE: Technology & Implementation (Week 16) Week 16: KM Tools, Technology Infrastructure & The KM Team *ICT infrastructure for KM *KM governance and team roles *Implementation challenges and change management *R Lab: Building KM dashboards and final project support LOOKING FORWARD & FINAL (Weeks 17-18) Week 17: Future Challenges for KM & Emerging Trends *AI and machine learning in KM *Remote work and distributed knowledge Week 18: Final

評量方式與標準 Attendance (in-class participation) 20% Mid-term presentation and report 40% Final presentation and report 40%

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
Course materials will be prepared by the instructor. Optional: Dalkir, K. (2011). Knowledge management in theory and practice (2nd ed.). Sage.

課程諮詢管道 The best way to reach out to me is through my email: scheng@ntut.edu.tw. I am happy to speak with you during my office hours.

延伸教學與資源 ● 無 (None)

課程對應SDGs指標 SDG4：優質教育（Quality Education）
SDG10：減少不平等（Reduced Inequalities）

課程是否導入AI ● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）

備註
Google Meet will be used in case in-person classes are suspended due to the pandemic. The instructor will send an invitation link to the whole class in advance.

教師姓名	程聖倫　教師諮商時間(Office Hours)
Email	scheng@ntut.edu.tw
最後更新時間	2026-03-05 22:43:39
課程大綱	The objective of this course is to prepare students to understand the current theories, practices, tools and techniques in knowledge management (KM) to deal with the challenges with the organization and management of knowledge. This course addresses contemporary issues in managing knowledge, intellectual capital and other intangible assets by discussing the fundamental concepts of knowledge and its creation, acquisition, representation, dissemination, use and re-use, the role and use of knowledge in organizations and institutions, KM systems and its application in knowledge generation and transfer, and in the representation, organization, and exchange of knowledge, knowledge codification and system development, its testing, KM tools and portals, and finally ethical, managerial and legal issues in knowledge management.
課程進度	Topics: FOUNDATION (Weeks 1-2) Week 1: Course Introduction & Introduction to Knowledge Management Overview of KM as a discipline Introduction to the Dalkir Meta-KM Process framework Week 2: Knowledge Management Models & The KM Cycle Theoretical frameworks Understanding the create-share-use cycle R Lab intro: Setting up R environment, basic text data handling PHASE 1: CREATE OR CAPTURE (Weeks 3-5) Week 3: Independent Research: Knowledge Capture and Codification (Self-Study) Tacit vs. explicit knowledge Documentation strategies Knowledge elicitation techniques Week 4: Knowledge Capture (R Lab I) R Lab: Text mining basics - extracting knowledge from documents Word frequencies, tf-idf, document similarity (tm, tidytext) Week 5: Knowledge Discovery & Topic Modeling R Lab: Topic modeling with LDA - discovering knowledge domains Identifying hidden themes in organizational documents (topicmodels, stm) PHASE 2: SHARE AND DISSEMINATE (Weeks 6-8) Week 6: Organizational Culture, Motivation, and Knowledge Sharing Organizational culture as enabler/barrier to knowledge sharing Motivational factors (goal orientations) in knowledge contribution and knowledge hoarding Achievement goal theory applied to KM behaviors Reward systems and cultural dimensions Week 7: Measuring Culture, Motivation & Sharing Behaviors R Lab: Survey analysis & psychometrics Measuring organizational culture for KM Knowledge sharing attitudes and motivation scales Reliability analysis, CFA validation (lavaan, psych) Week 8: Knowledge Sharing Networks & Communities of Practice Social capital and network perspectives Communities of practice frameworks R Lab: Social Network Analysis I & II Mapping knowledge sharing behaviors Network density, centrality, community detection (igraph, tidygraph, ggraph) MID-TERM (Week 9) Week 9: Mid-term Presentation ASSESS: Knowledge Mining & Discovery (Weeks 10-12) Week 10: Knowledge Mining I - Pattern Discovery Association rules in organizational knowledge Finding expertise patterns* R Lab: Association rules mining (arules, arulesViz) Week 11: Knowledge Mining II - Expertise Mapping Identifying subject matter experts Knowledge flow analysis R Lab: Combining text mining + SNA for expertise identification Week 12: Sentiment Analysis & Knowledge Communication Analyzing organizational communication patterns Understanding knowledge sharing tone and effectiveness R Lab: Sentiment analysis of emails, documents, collaboration tools (tidytext, syuzhet) PHASE 3: USE, APPLY AND RE-USE (Weeks 13-14) Week 13: Knowledge Application & Organizational Learning Knowledge reuse strategies Learning from experience Lessons learned systems R Lab: Document clustering - organizing knowledge for reuse (K-means, hierarchical) Week 14: Organizational Memory & Knowledge Retention Memory systems and repositories Knowledge loss prevention R Lab: Time-based analysis - tracking knowledge evolution and retention patterns Week 15: Knowledge Management Strategy & Measurement KM strategy alignment with business goals Measuring KM value and ROI KM metrics and dashboards R Lab: Integration project - combining multiple analytical methods CONTEXTUALIZE: Technology & Implementation (Week 16) Week 16: KM Tools, Technology Infrastructure & The KM Team ICT infrastructure for KM KM governance and team roles Implementation challenges and change management R Lab: Building KM dashboards and final project support LOOKING FORWARD & FINAL (Weeks 17-18) Week 17: Future Challenges for KM & Emerging Trends AI and machine learning in KM Remote work and distributed knowledge Week 18: Final
評量方式與標準	Attendance (in-class participation) 20% Mid-term presentation and report 40% Final presentation and report 40%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 Course materials will be prepared by the instructor. Optional: Dalkir, K. (2011). Knowledge management in theory and practice (2nd ed.). Sage.
課程諮詢管道	The best way to reach out to me is through my email: scheng@ntut.edu.tw. I am happy to speak with you during my office hours.
延伸教學與資源	● 無 (None)
課程對應SDGs指標	SDG4：優質教育（Quality Education） SDG10：減少不平等（Reduced Inequalities）
課程是否導入AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
備註	Google Meet will be used in case in-person classes are suspended due to the pandemic. The instructor will send an invitation link to the whole class in advance.