Once students have developed an understanding of content, they are required to act upon it in some fashion to express their understanding. The third principle of Universal Design for Learning focuses on providing students with multiple means of action and expression. This principle acknowledges that students’ abilities to respond to prompts and communicate understanding are varied, and that the provision of options will allow students to choose modes of expression that work for them. Furthermore, this principle recognizes that students’ executive functioning is also variable, and that they will therefore require options in this regard. The image to the right shows the guidelines and checkpoints that educators use to eliminate or minimize barriers that may prevent their students from acting upon content and communicating understanding. As was the case with the previous two principles, the guidelines are structured in ascending order of complexity from bottom to top.
Provide options for physical action
In the UDL classroom, students are provided with many ways to respond to prompts and work on tasks. Computational thinking within a programming environment can be a highly effective option in this regard. It is significantly different from other options they might choose, and it might be the mode that enables certain students to communicate their thoughts. However, Israel et al. (2015) identify a richer reason for adding computational thinking to students’ repertoires of expression formats. They write, “computing activities inherently have flexibility built into them. There is not typically one way of coding or demonstrating understanding of that code” (p. 47). This flexibility provides students with a multitude of options for action and expression.
Resnick et al. (2009) reveal that their programming platform, Scratch, was designed with student variability in mind. When they designed Scratch, they were careful to ensure that it would be able to support many different types of projects and that users would be able to personalize their work by importing a wide range of media. Inherent in these design decisions is the notion that the educator must relinquish a certain degree of decision-making power in order to grant students the freedom to choose their preferred mode of expression. This choice is a hallmark of UDL.
Provide options for physical action
In the UDL classroom, students are provided with many ways to respond to prompts and work on tasks. Computational thinking within a programming environment can be a highly effective option in this regard. It is significantly different from other options they might choose, and it might be the mode that enables certain students to communicate their thoughts. However, Israel et al. (2015) identify a richer reason for adding computational thinking to students’ repertoires of expression formats. They write, “computing activities inherently have flexibility built into them. There is not typically one way of coding or demonstrating understanding of that code” (p. 47). This flexibility provides students with a multitude of options for action and expression.
Resnick et al. (2009) reveal that their programming platform, Scratch, was designed with student variability in mind. When they designed Scratch, they were careful to ensure that it would be able to support many different types of projects and that users would be able to personalize their work by importing a wide range of media. Inherent in these design decisions is the notion that the educator must relinquish a certain degree of decision-making power in order to grant students the freedom to choose their preferred mode of expression. This choice is a hallmark of UDL.
Provide options for expression and communication
The checkpoints under this guideline call for multiple ways for students to communicate, construct, and compose. Certainly, the addition of computational environments expands the repertoire from which students may choose. Brennan and Resnick (2012) explain that a “computational thinker sees computation as more than something to consume” (p. 10). Rather, computation is viewed as a medium for self-expression and something that he/she can create. Allowing students the option of using computational thinking in a programming environment provides them with another option for action and expression.
Provide options for executive functions
Weintrop et al. (2015) report that the “pedagogical power of computational models comes not just from students using existing models, but also from enabling students to design, build, and assess models of their own” (p. 137). The word “assess” is important to the consideration of executive functions. It implies that the creation of computational models compels the student to monitor his/her progress. This happens naturally as students test and debug their work. This computational skill requires students to periodically assess the quality of their work and plan for its improvement.
Papert (1980) reveals another link between computational thinking and executive functioning. He tells the story of a student whose first attempt to draw a stick figure in LOGO failed. Instead of giving up, the student analyzed his code for the bug. From previous experience in this computational setting, he had developed the executive functioning required to monitor his work and plan for required changes. However, the student had written a very long, “connect-the-dots” sort of program, and this made the bug very difficult to locate. The student eventually realized that he should have modularized his code. By breaking his planned finished product into smaller, more manageable pieces, he could have reduced the cognitive load of the entire project. He would have been less likely to make an error, and if he had, the bug would have been easier to find and solve. This realization was not imposed upon the student by the teacher. Rather, he came to this conclusion on his own through his interaction with the computer. Another student describes his conversion to this type of thinking as creating “mind-sized bites” to create a larger whole (p. 103). The use of computational skills, namely modularization and testing and debugging, within a programming environment allowed these students to monitor their own work and develop strategies to mitigate problems.
The checkpoints under this guideline call for multiple ways for students to communicate, construct, and compose. Certainly, the addition of computational environments expands the repertoire from which students may choose. Brennan and Resnick (2012) explain that a “computational thinker sees computation as more than something to consume” (p. 10). Rather, computation is viewed as a medium for self-expression and something that he/she can create. Allowing students the option of using computational thinking in a programming environment provides them with another option for action and expression.
Provide options for executive functions
Weintrop et al. (2015) report that the “pedagogical power of computational models comes not just from students using existing models, but also from enabling students to design, build, and assess models of their own” (p. 137). The word “assess” is important to the consideration of executive functions. It implies that the creation of computational models compels the student to monitor his/her progress. This happens naturally as students test and debug their work. This computational skill requires students to periodically assess the quality of their work and plan for its improvement.
Papert (1980) reveals another link between computational thinking and executive functioning. He tells the story of a student whose first attempt to draw a stick figure in LOGO failed. Instead of giving up, the student analyzed his code for the bug. From previous experience in this computational setting, he had developed the executive functioning required to monitor his work and plan for required changes. However, the student had written a very long, “connect-the-dots” sort of program, and this made the bug very difficult to locate. The student eventually realized that he should have modularized his code. By breaking his planned finished product into smaller, more manageable pieces, he could have reduced the cognitive load of the entire project. He would have been less likely to make an error, and if he had, the bug would have been easier to find and solve. This realization was not imposed upon the student by the teacher. Rather, he came to this conclusion on his own through his interaction with the computer. Another student describes his conversion to this type of thinking as creating “mind-sized bites” to create a larger whole (p. 103). The use of computational skills, namely modularization and testing and debugging, within a programming environment allowed these students to monitor their own work and develop strategies to mitigate problems.
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
Israel, M., Wherfel, Q. M., Pearson, J., Shehab, S., & Tapia, T. (2015). Empowering K-12 students with disabilities to learn computational thinking and computer programming. TEACHING Exceptional Children, 48(1), 45-53. doi:10.1177/0040059915594790
Meyer, A., Rose, D. H., & Gordon, D. T. (2014). Universal design for learning: theory and practice. Wakefield, MA: CAST Professional Publishing.
Papert, S. (1980). Mindstorms: children, computers, and powerful ideas. New York: Basicbooks.
Resnick, M., Silverman, B., Kafai, Y., Maloney, J., Monroy-Hernández, A., Rusk, N., . . . Silver, J. (2009). Scratch. Communications of the ACM, 52(11), 60-67. doi:10.1145/1592761.1592779
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2015). Defining Computational Thinking for Mathematics and Science Classrooms. Journal of Science Education and Technology, 25(1), 127-147. doi:10.1007/s10956-015-9581-5
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
Israel, M., Wherfel, Q. M., Pearson, J., Shehab, S., & Tapia, T. (2015). Empowering K-12 students with disabilities to learn computational thinking and computer programming. TEACHING Exceptional Children, 48(1), 45-53. doi:10.1177/0040059915594790
Meyer, A., Rose, D. H., & Gordon, D. T. (2014). Universal design for learning: theory and practice. Wakefield, MA: CAST Professional Publishing.
Papert, S. (1980). Mindstorms: children, computers, and powerful ideas. New York: Basicbooks.
Resnick, M., Silverman, B., Kafai, Y., Maloney, J., Monroy-Hernández, A., Rusk, N., . . . Silver, J. (2009). Scratch. Communications of the ACM, 52(11), 60-67. doi:10.1145/1592761.1592779
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2015). Defining Computational Thinking for Mathematics and Science Classrooms. Journal of Science Education and Technology, 25(1), 127-147. doi:10.1007/s10956-015-9581-5