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This project, titled Collective Argumentation Learning and Coding (CALC), is based on our belief that if teachers had an instructional approach that allowed them to teach coding alongside mathematics and science in integrated ways, then coding would become a mainstream subject taught in the elementary school curriculum. However, few practicing elementary school teachers have the academic backgrounds that allow them to teach coding in a manner that goes beyond allowing students to learn how to code through trial-and-error experimentation and as an additive learning activity such as an after-school program. Current content and practice standards call for the use of argumentation in the teaching of mathematics and science. This project is focused on extending the collective argumentation framework for the teaching of mathematics to the teaching of coding. Teachers at our partnering school district have completed the first design of a prototype CALC course where they used collective argumentation to learn how to code educational robotics. At the end of this course, the teachers developed lesson plans that were implemented in grades 3, 4 and 5.This paper and conference presentation focused on the research question, how do elementary school teachers use the CALC approach to support their students’ learning of coding, mathematics, and science content and practices? Overall, the implementation of the CALC approach demonstrated the growth of the teachers in their ability to teach coding as a reasoning process and as a means to integrate it into everyday classroom activities.
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Fostering better coding practices for data scientists
Many data science students and practitioners are reluctant to adopt good coding practices as long as the code ‘works.’ However, code standards are an important part of modern data science practice, and they play an essential role in the development of data acumen. Good coding practices lead to more reliable code and save more time than they cost, making them important even for beginners. We believe that principled coding is vital for quality data science practice. To effectively instill these practices within academic programs, instructors and programs need to begin establishing these practices early, to reinforce them often, and to hold themselves to a higher standard while guiding students. We describe key aspects of good coding practices for data science, illustrating with examples in R and in Python, though similar standards are applicable to other software environments. Practical coding guidelines are organized into a top ten list.
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- Award ID(s):
- 1923388
- PAR ID:
- 10466634
- Editor(s):
- Meng, X-L
- Publisher / Repository:
- Harvard Data Science Review
- Date Published:
- Journal Name:
- Harvard Data Science Review
- Volume:
- 5
- Issue:
- 3
- ISSN:
- 2644-2353
- Subject(s) / Keyword(s):
- data acumen, data science, data science practice, data science education, code quality, code style
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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