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jrosen48
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Jun 1, 2024
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| **Abstract** | |||
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| This chapter describes different ways the reader can use these book to learn about using R and data science tools in their education job. Job descriptions, lifestyles, and programming experience differ for everyone. Learning how to program in R on the job or at home will also look different to each reader. Applying R and data science tools in an education job requires learning these skills in a practical and meaningful context. The chapter describes three suggested ways to learn from the book, taking the reader’s experience into account. It also introduces the reader to ways they can support and contribute to the book’s content. This reinforces the theme of building content based on stories from the data science in education community. | |||
| This chapter describes different ways the reader can use these book to learn about using R and data science tools in their education job. Job descriptions, lifestyles, and programming experience differ for everyone. Learning how to program in R on the job or at home will also look different to each reader. Applying R and data science tools in an education job requires learning these skills in a practical and meaningful context. The chapter describes three suggested ways to learn from the book, so that the reading experience can be customized to different learning journeys. It also introduces the reader to ways they can support and contribute to the book’s content. | |||
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Minor, change "the reader" to "you" to be more personal?
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And change "data science tools" to "new data sources and data analysis techniques"?
| improving the lives of students is our daily practice. Learning to use R and data | ||
| science helped us do that. Join us in enjoying all that comes with R and data science---both the challenge of | ||
| learning and the joy of solving problems in creative and efficient ways. | ||
| Our message is this: learning R for your education job is doable, challenging, and rewarding all at once. We wrote this book for you because we do this work every day. We're not writing as people who have mastered all there is to know about education data science. We're writing as people who learned R and data science *after* we chose education. And like you, improving the lives of students is our daily practice. Learning to use R and data science helped us do what matters most to us. Join us in enjoying all that comes with R and data science---both the challenges of learning and the joys of solving problems in creative and efficient ways. |
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maybe change to "education-related job" or "role in education"? Just thinking some in higher education will not chiefly see themselves as having an "education job".
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| We want all readers to have a rewarding experience, and so we believe there | ||
| should be different ways to use this book. Here are some of those ways: | ||
| As we learned in the introduction, it's tough to define data science in education because education spans many contexts and age groups. Education organizations require different roles to make them work, which creates different kinds of uses for data science skills and tools. The approach to adopting data science will differ depending on job role, while some principles will generalize across contexts. A teacher's approach to data analysis is different from an administrator's. |
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I wonder if we should use a different word from define here (in part because I suggested defining educational data science broadly - see #591)
I am not sure it is hard to define in general so much as it is true that data science looks very different in different education roles --- thoughts?
| to 5, with 1 being "no experience" and 5 being "very experienced". You can try this | ||
| now---take a moment to rate your level of experience in: | ||
| We interviewed R users in education as research for this book. We chose people with different levels of experience in R, in the education field, and in statistics. | ||
| We asked each interviewee to rate their level of experience on a scale from 1 to 5, with 1 being "no experience" and 5 being "very experienced". You can try this now---take a moment to rate your level of experience in: |
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I think it would be fun (and interesting) to very briefly describe what we found - were there consistencies in what the people we interviewed said?
Related, this paper speaks to this a bit: https://link.springer.com/article/10.1007/s41686-023-00083-7
As does another that is on... interviews with educational data scientists! Maybe we can discuss whether we can find any data from the interviews we mention in this text, and if not, I can cite one or both of these papers.
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We could also cite this, which is an earlier version of the interview piece: https://repository.isls.org/handle/1/7394
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| One last note on this approach to the book: we believe that doing data science in education | ||
| using R is, at its heart, an endeavor aimed at improving the student experience. The skills taught in the | ||
| If you're experienced in data analysis using R, you may be interested in starting with the walkthroughs. Each walkthrough is designed to demonstrate basic analytic routines using datasets that look familiar to people working in the education field. |
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change "data analysis" to "data science" here?
ivelasq
reviewed
Jun 2, 2024
| @@ -2,179 +2,95 @@ | |||
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| **Abstract** | |||
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| This chapter describes different ways the reader can use these book to learn about using R and data science tools in their education job. Job descriptions, lifestyles, and programming experience differ for everyone. Learning how to program in R on the job or at home will also look different to each reader. Applying R and data science tools in an education job requires learning these skills in a practical and meaningful context. The chapter describes three suggested ways to learn from the book, taking the reader’s experience into account. It also introduces the reader to ways they can support and contribute to the book’s content. This reinforces the theme of building content based on stories from the data science in education community. | |||
| This chapter describes different ways the reader can use these book to learn about using R and data science tools in their education job. Job descriptions, lifestyles, and programming experience differ for everyone. Learning how to program in R on the job or at home will also look different to each reader. Applying R and data science tools in an education job requires learning these skills in a practical and meaningful context. The chapter describes three suggested ways to learn from the book, so that the reading experience can be customized to different learning journeys. It also introduces the reader to ways they can support and contribute to the book’s content. | |||
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Suggested change
| This chapter describes different ways the reader can use these book to learn about using R and data science tools in their education job. Job descriptions, lifestyles, and programming experience differ for everyone. Learning how to program in R on the job or at home will also look different to each reader. Applying R and data science tools in an education job requires learning these skills in a practical and meaningful context. The chapter describes three suggested ways to learn from the book, so that the reading experience can be customized to different learning journeys. It also introduces the reader to ways they can support and contribute to the book’s content. | |
| This chapter describes different ways the reader can use these book to learn about using R and data science tools in their education job. Job descriptions, lifestyles, and programming experience differ for everyone. Learning how to program in R on the job or at home will also look different to each reader. Applying R and data science tools in an education job requires learning these skills in a practical and meaningful context. The chapter describes three suggested ways to learn from the book so that the reading experience can be customized to different learning journeys. It also introduces the reader to ways they can support and contribute to the book’s content. |
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Edits to Chapter 2 for the second edition of DSIEUR