Incorporate Virnaliz's edits on learning theories

Co-authored-by: Virnaliz Cruz  <[email protected]>

Author: ethanwhite

paper.md

@@ -67,7 +67,7 @@ The ‘Data Carpentry for Biologists course’ is designed to help overcome a va
 
 ## General instructional design
 
-The course is focused on teaching practical computational skills in a domain specific context that is directly applicable to ecologists and biologists more broadly. Many general computing courses start with the foundations of computer programming, but this can be demotivating for domain specialists who are learning computing to accomplish specific data management and analysis tasks and can also make learning more difficult since students learn by incorporating new ideas into their existing knowledge [@bada2015]. Therefore this course follows the broader Data Carpentry philosophy of focusing on teaching the skills students need using familiar data and common computational challenges encountered within their scientific domains [@teal2015]. To accomplish this the course uses a number of real ecological datasets and the coding demonstrations and exercises focus on common tasks in the analysis of biological data.
+The course is focused on teaching practical computational skills in a domain specific context that is directly applicable to ecologists and biologists more broadly. Many general computing courses start with the foundations of computer programming, but this can be demotivating for domain specialists who are learning computing to accomplish specific data management and analysis tasks. In addition, some learning theories, such as Constructivism, suggest that students learn by incorporating new ideas into their existing knowledge [@bada2015], thus general computing courses could also make learning inherently more difficult for students. Therefore this course follows the broader Data Carpentry philosophy of focusing on teaching the skills students need using familiar data and common computational challenges encountered within their scientific domains [@teal2015]. To accomplish this the course uses a number of real ecological datasets and the coding demonstrations and exercises focus on common tasks in the analysis of biological data.
 
 The course is built around the “I do, we do, you do” approach to teaching where first the instructor demonstrates how to do something, then the students work on an example with the instructor present to help and answer questions, and finally the students work on additional examples independently. This approach is based on explicit instruction principles, which leverage the benefits of active-learning without leaving students who are less comfortable with the material feeling lost [@rosenshine1987; @archer2010]. This approach, described as “a systematic method of teaching with emphasis on proceeding in small steps, checking for student understanding, and achieving active and successful participation by all students” [@rosenshine1987 p.34], is useful for teaching introductory computing to scientists because it gradually builds comfort and competence for all students in this essential foundation of research.