How to Track Progress

This chapter introduces a second kind of tracking game: progressive tracking games. In it, the authors show how students can use progressive tracking games to develop tracking skills that will become more sophisticated over time, rather than a simple matter of mastering facts. They introduce four levels of tracking that can be used to enhance learning. The levels take ideas and start with (1) definitions, move to (2) learning methods, build to (3) listing examples, and finish with (4) applying ideas in new ways. They introduce a series of games that teachers can use to help students learn how to track more progressively. They draw their examples from literature (The Great Gatsby), history (“The Gettysburg Address”), philosophy (miracles), and poetry (“I Could Not Tell”).

2021 ◽  
Vol 59 (4) ◽  
pp. 1191-1239
Author(s):  
Junsen Zhang

After China’s recent great success in eliminating absolute poverty, addressing relative income inequality becomes a more important issue. This survey finds that income inequality rapidly increased in the first three decades since 1978 but stabilized and slightly declined in the past decade, consistent with the well-known Kuznets hypothesis. In addition to documenting the trend and patterns over time and across groups and regions, seven sources of income inequality are systematically discussed with an effort to reconcile and extend the existing literature. Furthermore, a negative correlation is documented between income inequality and intergenerational mobility, consistent with the Great Gatsby curve observed in developed countries. (JEL D31, D63, O15, P36)


2021 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Ninghan Chen ◽  
Zhiqiang Zhong ◽  
Jun Pang

The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.


1996 ◽  
Vol 54 (4) ◽  
pp. 233-236 ◽  
Author(s):  
Nigel Brooks

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