X api chinese cop monthly meeting april 2016
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The document outlines the xAPI Chinese community meeting held on April 22, 2016, highlighting resources, courses, and community support for learning about xAPI applications. It discusses complex learning models, instructional methods, and various learning task designs, emphasizing schema construction and support for both non-recurrent and recurrent skills. Additionally, it touches on technology-enabled assessment, the need for semantically legible data points, and the importance of collaborative visualization in knowledge integration.
X api chinese cop monthly meeting april 2016
- 1. XAPI 中文实践社群月会 XAPI Chinese CoP Meeting Apr.22, 2016 Jessie Chuang [email protected]
- 2. 中文实践社群网站(含学习与参考资源) http://xapi-cop.net/zh/ 中文实践社群脸书群组 https://www.facebook.com/groups/648340368618407/ xAPI Visualization and Analytics services (w/i developer doc. & recipes) http://www.visualcatch.org/ 线上课程(含实作与反馈): XAPI 设计入门 http://xapi-cop.net/zh/xapi-design-beginners-guide/ 每周一次(周四上午9:30-11:00AM)，共五堂课，含课後案例实作+指导反馈 建议、询问或报名: [email protected] Community/Resource Links
- 3. Topics ● Visca 嵌入儀表板的討論 ● XAPI 國際實施案例分享 ● 中文社群業者案例分享 ● 新配方的設計與討論(課中、作業、學習設計/計畫)
- 4. Case Study
- 5. 4C/ID Model for Complex Learning Complex learning is always involved with achieving integrated sets of learning goals— multiple performance objectives. NOT acquiring each of these constituent skills separately, but to use all of the skills in a coordinated and integrated fashion while doing real-life jobs. (i.e., integrated objectives) ● Schema construction for nonrecurrent aspects (schema construction) ● Rule automation for recurrent aspects (drill-and-practice) Citation: http://www.cogtech.usc.edu/publications/clark_4cid.pdf http://www.nwlink.com/~donclark/hrd/learning/id/4c_id.html
- 7. 4C
- 8. Learning Tasks Learning Tasks: concrete, authentic, whole task experiences that are provided to learners in order to promote schema construction for nonrecurrent aspects and, to a certain degree, rule automation by compilation for recurrent aspects. Instructional methods primarily aim at induction, that is, constructing schemata through mindful abstraction from the concrete experiences that are provided by the learning tasks. Design steps: ● Design learning tasks ● Sequence task practice ● Set performance objectives Possible mediums: LMS, Learning Design Tools, Diversified Applications for different purposes (integrated through APIs and SSO), badging, real-world job or project ...
- 9. Supportive Information Supportive Information — information that is supportive to the learning and performance of non-recurrent aspects of learning tasks. It provides the bridge between learners' prior knowledge and the learning tasks. Instructional methods primarily aim at elaboration, that is, embellishing schemata by establishing non-arbitrary relationships between new elements and what learners already know. Design steps: ● Design supportive information ● Analyze cognitive strategies ● Analyze mental models Possible mediums: CMS, eBook, curriculum, presentation tools (video, animation, VR, AR…) , tutorial, knowledge base, adaptive content recommender
- 10. JIT Information JIT Information — information that is prerequisite to the learning and performance of recurrent aspects of learning tasks. It gives learners step-by-step knowledge they need to know in order to perform the recurrent skills. Design steps: ● Design procedural information ● Analyze cognitive rules ● Analyze prerequisite knowledge Possible mediums: Mobile Apps, AR, Digital Performance Support kits, Instruction or reminder embedded in job, mentors, peers, AI assistance ...
- 11. Part-Task Practice Part-task Practice — practice items that are provided to learners in order to promote rule automation for selected recurrent aspects of the whole complex skill. Instructional methods primarily aim at rule automation, including compilation and subsequent strengthening to reach a very high level of automatically. Design step: ● Design part-task practice Examples: drilling practice of multiplication tables, playing scales on musical instruments Possible mediums: Quiz, assessment, game, simulation, apprenticeship, IoT sensors in tool / machine / environment ...
- 12. Case Study (應用 xAPI 進行使用資料收集與反饋設計)
- 14. Technology-enabled Assessment Support learning and teaching by communicating evidence of learning progress and providing insights to teachers; administrators; families; and the learners. These assessments can be embedded within digital learning activities to reduce interruptions to learning time. 2016 NATIONAL EDUCATION TECHNOLOGY PLAN U.S. DEPARTMENT OF EDUCATION http: //tech.ed.gov
- 16. Semantically Legible DataPoints For instruction and assessment to become one, however, these need to be ‘semantically legible data points’. Our definition of a semantically legible datapoint is ‘learner- actionable feedback’. Every such datapoint can offer an opportunity that presents to the learner as a ‘teachable moment’. These datapoints can involve either or both a machine response to learner action or machine-mediated human response, thereby harnessing both collective human intelligence and artificial intelligence. Semantically legible data are self-describing, structured data. Bill Cope & Mary Kalantzis (2015) Interpreting Evidence-of-Learning: Educational research in the era of big data, Open Review of Educational Research, 2:1, 218-239, DOI: 10.1080/23265507.2015.1074870
- 17. The Moderating Role of Collaborative Visualization in Team Knowledge Integration Seeing versus Arguing - The Moderating Role of Collaborative Visualization in Team Knowledge Integration (Jeanne Mengis, University of Lugano, Switzerland) Key: self-explanatory dataviz available in real time