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Visualization and Data Presentation of Pedagogy, Learning Process & Outcome
The document discusses the application of big data analytics in both sports and education, emphasizing differences in their settings and methodologies. It highlights various pedagogical approaches and frameworks for evaluating student participation and engagement in online discussions. Furthermore, it outlines the importance of tailored analysis to improve learning outcomes based on individual contributions and discourse quality over time.
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Visualization and Data Presentation of Pedagogy, Learning Process & Outcome
- 1. Visualization and DataPresentation of Pedagogy, Learning Process & Outcome Johnny Yuen
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- 5. Big Data Analyticsin sports • Predictive • Behavioral, Quantifiable data • Identical, “closed” settings with game rules • Simple goal, simple behavior • Finding patterns from lots of noises • Very different from educational settings!
- 6. Usage of ITin education • Different nature! – Year long activities, e.g. IES – Independent Enquiry Study • Individual students • The same process • Comparable participation – Topic module base activities, e.g. online discussion • Collaborative • Unequal participation – quantity and quality
- 7. Furthermore… • Differences insetting: • Pedagogy: transmission model, PBL, collaborative knowledge building… … • Pedagogical design frameworks: Contrasting cases, Predict-Observe-Explain, • Subject domain: Sciences, Languages • Cut-in point: Critical Thinking, Personal Epistemology, Motivation,
- 8. Supporting practice &reflections in educational settings, e.g. CSCL • Are students participating in the online discourse? • Are there students who made zero contribution? • Do ideas progress in the online discourse? • Can students raise questions that stimulate idea improvement? • What are the key content themes of the discussion threads? • Which notes have potential for further development? • Which aspect of the discussion can be further improved? • Is there any advancement in my students over 1 or 2 years of KB activities, in terms of question framing, evaluating and summarizing ideas, as reflected from their contributions to the online discourse?
- 9. COLODA 18 Feb 2012 SuccessfulPedagogies for Inquiry & Knowledge Building Collaborative Analysis Project Discussion data from any discussion platform in XML format Individual Participatory Statistics Keywords Analysis Researcher codings & further analyses Threads Analysis Social Networks Downloadable as MS Excel (.xls) format for charts or works with other student data Collaborators
- 10. Outputs can beorganized into diff. levels… 18 Feb 2012 Successful Pedagogies for Inquiry & Knowledge Building : Individual students : notes : discussion threads : discussion views : date or week
- 11. Individual participatory statistics 18Feb 2012 Successful Pedagogies for Inquiry & Knowledge Building Number of Scaffolds UsedNumber of Notes Created Percentage of notes read Percentage of Notes LinkedNumber of Revisions
- 12. Social Network Analysis 18Feb 2012 Successful Pedagogies for Inquiry & Knowledge Building Year 1 Year 2 Year 3
- 13. Threads analysis module: 7/9/2013 0 0.5 1 1.5 2 2.5 3 Year1 Year 2 Depthofdiscussionthread 6.5 7 7.5 Year 1 Year 2Numberofnotes Average Number of Notes per Thread Average Depth of Discussion Thread
- 14. Examples of questiontypes • Attribute – What is the size of the smallest whale? – Where is the notice? • Definition – What is energy crisis? – What does global warming refer to? • Verification – Is it safe to drink the water only through looking at it? – Is carbon dioxide toxic and harmful?
- 15. Examples of questiontypes • Procedure – How to make a water filter? • Reason – Why do water become brown color? – Why do we need to save water? • Mechanism – How can we protect these animals? – How do planktons give birth to the next generation?
- 16. Researcher Coding Types ofquestions raised in the discourse 18 Feb 2012 Successful Pedagogies for Inquiry & Knowledge Building
- 17. Keywords analysis module HKKids discourse HK Kids Word Count 特徵(characteristic) 5 成因(cause) 4 個人(individual) 3 家長(parent) 62 傭人(maid) 42 社會(society) 15 政府(government) 51 教育制度(Education system) 15 精英制度(Elite system) 6 遺傳(inheritance ) 11 影響(influence) 6 經濟(Economics) 0 自理能力(Self- management) 20 責任(responsibility) 19 改善(improvement) 31 解決(solution) 14 訓練(training) 12 7/9/2013
- 18. Summary: Choosing thesuitable type and focus of analysis for diff. types of learning • Behavior is affected by design and facilitation • Requires in-depth understanding about the design and intended learning goal of the discourse in order to interpret the visuals – Time series vs. snapshot – Whole class vs. individual – Quantity vs. quality