Educational Resources & Tech Tools 05/30/2021

• NAIS - An International School Shares Six Touchstones to Cultivate Global Citizens

  • To be sure, we benefit from a multilingual, multinational school community and from bilingual, French, and International Baccalaureate programs. But we believe any motivated school can offer an internationally minded education. At French American, we call it cross-cultural cognition — the ability to think, feel, and act across cultures.
  • We aim for students to understand and appreciate identity as a rich mix of national, regional, cultural, ethnic, religious, gender, orientation, socioeconomic, and other factors. This keeps the focus on what we have in common so we don’t become ensnared by differences, stereotypes, and simplifications that incite conflict at home and abroad.
    
  • Languages are loaded with culture, attitudes, tradition, history, etiquette, and more. To learn another language is to gain an alternate perspective, increase empathy, and become open to cultural cues.
  • We also honor our students’ home languages. If we are educating global citizens, we are affirming the value of each child’s identity, including his or her language.
  • Rather than pushing international faculty members and students to assimilate into our school culture, we encourage them to share their cultures with us. We believe having an educator or fellow student share his or her personal cultural background significantly enriches all students’ experiences.
  • Middle schoolers in our Arabic and Mandarin language classes do not just learn the language; they are exposed to our educators’ cultures, thereby developing an understanding of different countries’ lifestyles, customs, and attributes.
  • At French American, we define cross-cultural cognition as the ability to grapple with challenging and abstract concepts from science and math to humanities, social science, and arts in more than one culture.
  • In pedagogy, this can mean highlighting the different ways that cultures approach the academic disciplines — and even learning itself.
  • We have seen how an international school approach to identity, language, community, curriculum, travel, and sustainability can shape the character of our students for the benefit of our communities — in this country and across the world.

Posted from Diigo. The rest of my favorite links are here.

Educational Resources & Tech Tools 05/10/2021

• 8 Workflow: projects | R for Data Science

  • There is a great pair of keyboard shortcuts that will work together to make sure you’ve captured the important parts of your code in the editor:

     
     
    1. Press Cmd/Ctrl + Shift + F10 to restart RStudio.
     
    2. Press Cmd/Ctrl + Shift + S to rerun the current script.
     
     

    I use this pattern hundreds of times a week.

  • You should never use absolute paths in your scripts, because they hinder sharing: no one else will have exactly the same directory configuration as you.

• 4 Workflow: basics | R for Data Science

  • All R statements where you create objects, assignment statements, have the same form:

     

    object_name <- value
    

     

    When reading that code say “object name gets value” in your head.

     

  • We recommend snake_case where you separate lowercase words with _.

• 1 Introduction | R for Data Science

  • Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored. In brief, when your data is tidy, each column is a variable, and each row is an observation.
  • A good visualisation will show you things that you did not expect, or raise new questions about the data.
  • Visualisations can surprise you, but don’t scale particularly well because they require a human to interpret them.
  • Models are complementary tools to visualisation. Once you have made your questions sufficiently precise, you can use a model to answer them.
  • by its very nature a model cannot question its own assumptions. That means a model cannot fundamentally surprise you.
  • There’s a rough 80-20 rule at play; you can tackle about 80% of every project using the tools that you’ll learn in this book, but you’ll need other tools to tackle the remaining 20%.

  • The complement of hypothesis generation is hypothesis confirmation. Hypothesis confirmation is hard for two reasons:

     
     

    1. You need a precise mathematical model in order to generate falsifiable predictions. This often requires considerable statistical sophistication.

     

    2. You can only use an observation once to confirm a hypothesis.

    • Hypothesis generation and confirmation
  • models are often used for exploration, and with a little care you can use visualisation for confirmation. The key difference is how often do you look at each observation: if you look only once, it’s confirmation; if you look more than once, it’s exploration.
    • Models for exploration, visualizations for confirmation

• 2 Introduction | R for Data Science

  • The goal of data exploration is to generate many promising leads that you can later explore in more depth.

Posted from Diigo. The rest of my favorite links are here.