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Flat Classroom Project Help

Student Summit Video - whiteboard view and audio only NetGen2010 Timeline 3/5 - Greeting from Don posted to the Ning via video Weekly- discussions posted to the forum 2/20-3/5 - "Handshake process" - Students join Ning - post introductions 3/1 - Teams announce 3/15-4/10 Research phase of project 4/10 - Wikis complete 4/1 - Student Keynote Some time in March, there will be a live session with Don Tapscott 4/10-5/8 - Movie Artifact phase of project (note that there will be some overlap between Research and Movie Artifact) *Storyboarding *Outsourced video requests posted to the Ning by 3/10 5/10 - Final Deadline for All Movies to be posted 5/10-5/20 - Post project reflections, student summits, awards Project Files Don Tapscott's Grown Up Digital Chapter 1 Don Tapscott's Grown Up Digital Chapter 3 Don Tapscott's Grown Up Digital Chapter 5 2010 Horizon Report Daily Assignment points - 25 points Create useful Diigo Bookmarks for the NetGen2010 Project and/or communicate with your Team members on the wiki Discussion tab - up to 10 points Post credible research information (preferably in your own words) to your Team's wiki  - must use proper citations - up to 10 points Blog about your latest NetGen2010 activity/progress - up to 5 points If you are absent a day, you are expected to complete the tasks either from home or the next day at school. NetGen2010 Project Links: Video Project Overview - http://netgened2010.flatclassroomproject.org/OverviewPPHS Video Project Assignment http://netgened2010.flatclassroomproject.org/More+information - overview of the ProjectDiigo Standard Tags:Visual Data Analysis:  visualdataGesture Based Computing:  gesturebasedOpen Content:  opencontentSimple Augmented Reality:  augmentedrealityElectronic Books: ebooksMobile Computing:  mobileNetGen2010 Diigo Tag Cloud - these are the Diigo Bookmark tags that you should use.

How do big data and analytics generate value for higher education? They can improve administrative decision-making and organizational resource allocation. They can identify at-risk learners and provide intervention to assist learners in achieving success. By analyzing discussion messages posted, assignments completed, and messages read in LMSs such as Moodle and Desire2Learn, educators can identify students who are at risk of dropping out.13 They can create, through transparent data and analysis, a shared understanding of the institution’s successes and challenges. They can innovate and transform the college/university system, as well as academic models and pedagogical approaches. They can assist in making sense of complex topics through the combination of social networks and technical and information networks: that is, algorithms can recognize and provide insight into data and at-risk challenges. They can help leaders transition to holistic decision-making through analyses of what-if scenarios and experimentation to explore how various elements within a complex discipline (e.g., retaining students, reducing costs) connect and to explore the impact of changing core elements. They can increase organizational productivity and effectiveness by providing up-to-date information and allowing rapid response to challenges. They can help institutional leaders determine the hard (e.g., patents, research) and soft (e.g., reputation, profile, quality of teaching) value generated by faculty activity.14 They can provide learners with insight into their own learning habits and can give recommendations for improvement. Learning-facing analytics, such as the University of Maryland, Baltimore County (UMBC) Check My Activity tool, allows learners to “compare their own activity . . . against an anonymous summary of their course peers.”15

Analytics in education must be transformative, altering existing teaching, learning, and assessment processes, academic work, and administration.

Undoubtedly, analytics and big data have a significant role to play in the future of higher education. The growing role of analysis techniques and technologies in government and business sectors affirms this trend. In education the value of analytics and big data can be found in (1) their role in guiding reform activities in higher education, and (2) how they can assist educators in improving teaching and learning.

Learning analytics is essential for penetrating the fog that has settled over much of higher education. Educators, students, and administrators need a foundation on which to enact change. For educators, the availability of real-time insight into the performance of learners—including students who are at-risk—can be a significant help in the planning of teaching activities. For students, receiving information about their performance in relation to their peers or about their progress in relation to their personal goals can be motivating and encouraging. Finally, administrators and decision-makers are today confronted with tremendous uncertainty in the face of budget cuts and global competition in higher education. Learning analytics can penetrate the fog of uncertainty around how to allocate resources, develop competitive advantages, and most important, improve the quality and value of the learning experience.