In his 1980 book Mindstorms, Seymour Papert writes, “Children begin their lives as eager and competent learners. They have to learn to have trouble with learning in general and mathematics in particular” (p. 40). Even though he wrote these words before the advent of Universal Design for Learning, he articulated a big idea that has become central to UDL. It is not the students who are flawed; it is the way in which they are taught that needs to be changed. Inherent in this idea, and central to UDL, are the notions that the inclusion of every student is critical, and that this can be achieved through careful design and a student-centred approach that eliminates or minimizes any barriers that prevent a student from learning.
Papert’s book is, more than anything else, a treatise on computational thinking in the context of programming computers. However, his ideas on CT and programming are inextricably linked to UDL concepts. Early in the book, he dispels the notion that programming is for the gifted. He notes that all students, even those who have not often experienced success with traditional learning contexts and activities, learn to program. He specifically mentions students who are “emotionally or cognitively disabled” and children “so severely afflicted with cerebral palsy that they had never purposefully manipulated physical objects” as ones who find success with programming (p. 13). He writes, “all children will, under the right conditions, acquire a proficiency with programming that will make it one of their more advanced intellectual achievements” (p. 14). The inclusivity presented in these statements shows that Papert sees a relationship between computational thinking and the ideas that have coalesced into Universal Design for Learning.
Papert was not the only scholar to allude to this connection. One primary teacher, commenting on a project in which grade 7 and 8 students coached students in grades 1, 2, and 3 on using coding to learn mathematics, noted, “From my little children [who] haven’t been in the country for the whole year, [who] have only been in school for a few months, they were successful, viewing themselves as capable, able, and mathematicians” (Gadanidis, 2015, p. 170). The most vulnerable students in that class could succeed in mathematics taught through computational thinking and coding. Brennan and Resnick (2012) write that computational thinking, with or without the use of technology, naturally involves “learning through design activities, a constructionist approach to learning that highlights the importance of young people engaging in the development of external artifacts” (pp. 2-3). The move toward CT invariably involves a move away from the traditional textbook- and teacher-driven style of education. In its place is a format that places the student at the centre. This is a hallmark of UDL.
Using computational thinking via coding to teach and learn mathematics is often thought of as having a low floor (little previous knowledge required to enter), high ceiling (there is the potential to explore beyond one’s grade level), and wide walls (a broad range of projects are possible to ensure that many different preferences and learning styles are met) (Gadanidis, 2015). These terms create an image of a large space that will accommodate all kinds of learners. The descriptions of these terms highlight the inclusivity and choice, both key components of UDL, that naturally accompany computational thinking.
The extent to which computational thinking is connected to Universal Design for Learning is worth exploring. In the three sub-pages that are linked below, each of the principles of UDL is examined for its connections to computational thinking.
References
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Retrieved from http://scholar.harvard.edu/kbrennan/publications/new-Frameworks-Studying-And-Assessing-Development-Computational-Thinking
Gadanidis, G. (2015). Coding as a Trojan Horse for mathematics education reform. Journal of Computers in Mathematics and Science Teaching, 34(2), 155-173.
Papert, S. (1980). Mindstorms: children, computers, and powerful ideas. New York: Basicbooks.
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Retrieved from http://scholar.harvard.edu/kbrennan/publications/new-Frameworks-Studying-And-Assessing-Development-Computational-Thinking
Gadanidis, G. (2015). Coding as a Trojan Horse for mathematics education reform. Journal of Computers in Mathematics and Science Teaching, 34(2), 155-173.
Papert, S. (1980). Mindstorms: children, computers, and powerful ideas. New York: Basicbooks.