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Teaching Computational Thinking to Exceptional Learners: Lessons From Two Diverse Classrooms

Listen and learn

Listen and learn : Research paper
Lecture presentation


Sunday, November 29, 9:00–9:45 am PST (Pacific Standard Time)
Presentation 2 of 2
Other presentations:
Investigating Design and Applications for Equity Maps in a History Course

Sharin Jacob  
Yenda Prado  
Dr. Mark Warschauer  

Computational thinking is a skill all students should learn. However, strategies for teaching computational thinking to exceptional learners are under-studied. This session presents lessons learned in two divergent fourth grade Special Education and GATE classrooms – regarding the effective, yet diverse, ways computational thinking was taught to exceptional learners.

Audience: Curriculum/district specialists, Principals/head teachers, Technology coordinators/facilitators
Attendee devices: Devices useful
Attendee device specification: Smartphone: Windows, Android, iOS
Laptop: Chromebook, Mac, PC
Tablet: Android, iOS, Windows
Participant accounts, software and other materials: Attendees may access the Scratch visual programming environment by visiting https://scratch.mit.edu/ on their electronic devices.
Topic: Computer science & computational thinking
Grade level: PK-5
Subject area: Special education, Computer science
ISTE Standards: For Educators:
Facilitator
  • Create learning opportunities that challenge students to use a design process and computational thinking to innovate and solve problems.
For Students:
Computational Thinker
  • Students break problems into component parts, extract key information, and develop descriptive models to understand complex systems or facilitate problem-solving.
Additional detail: Session recorded for video-on-demand, Graduate student

Proposal summary

Framework

Our study draws from Engestrom’s (1987) model of activity theory, grounded in (Leont’ev & Luria, 1972; Vygotsky, 1973), including the work of Greeno and Engestrom (2006), Lave and Wenger (1991), and Rogoff (2008). The activity theory model identifies three factors influencing learning: patterns of human interaction characterizing learning environments; tools mediating and transforming activity; and sociocultural contexts. Kuutti (1996) defines activity theory as “a philosophical and cross-disciplinary framework for studying different forms of human practices as development processes, both individual and social levels interlinked at the same time.” (pg. 532).”

According to activity theory, cognition is analyzed within broader patterns of interaction geared towards achieving specific goals. As a result, successful teacher moves facilitate the realization of shared goals, practices, and transformation of learning environments (Engestrom, 1987). At the level of curricular implementation, these moves could include using open ended questions to build student participation and guide students toward a shared understanding of lesson concepts. As a result, the moves teachers make to conduct activity in practice are critical to promoting active, student centered learning environments.

Methods

Study Design: A comparative case study design is used to examine the meaning-making moves Esther and Carla’s classrooms make as they learn to code with Scratch.

Data: Primary sources include transcriptions of seventeen audio-recorded computer science classroom lessons, field notes taken during classroom observations, and student and teacher interviews.

Analysis: A constant comparative method of analysis (Miles, Huberman, & Saldana, 2013) utilizing Hatch’s (2002) inductive frame was utilized to examine the strategies students and teachers used to engage with Scratch. First and second cycles of coding were used to identify four thematic categories: Strategies for Learning, Strategies for Teaching, Strategies for Making Connections, and Strategies for Managing Behavior. Noticing and clustering of patterns (Saldana, 2016) were used to analyze and map these relationships within the data.

Results

Esther’s Classroom: Using Scaffolding and Whole-Class Discussion of “Big Ideas” to Develop Computational Thinking

Esther’s classroom is similar in many ways to the typical 4th grade classroom: a white board surrounded by posters and graphic organizers reminding students of academic facts and classroom routines, student desks arranged in clusters of four facing the whiteboard, Esther’s desk is to the right of the whiteboard, and supply cupboards and windows to the left. However, a different aspect of this setting offers affordances that facilitate student preferences for learning: a well organized sitting area, complete with a rug similar to what one might see in a kindergarten reading corner and alternative seating (i.e. balance balls), accommodate students’ varying sensory needs. Esther encourages her students to engage in strategies for managing behavior, including sensory check-ins. As a result, students can choose to sit on the floor, in a chair, on a balance ball, or stand, depending on their sensory needs. Esther also uses sensory check-ins to redirect students’ attention, for example asking students to notice where their eye contact is during a classroom discussion of algorithms.

Students also utilize multiple strategies for learning to facilitate their computational thinking. These strategies include engaging in choral response to actively connect with the Scratch programming environment. During class discussion, Esther intentionally uses call and response to get her students to state the properties of sequences. Choral response as a strategy for learning allows Esther’s students to test students’ understanding of computational concepts and encourages students to take an agentive role in constructing their learning experiences in the classroom (Rufo, 2012).

Esther’s instruction rests on her positioning as the principal agent of instruction with her students as audience. Esther’s instructional objective is to utilize strategies for teaching to impart computational thinking skills to her students. Esther signals a shift into this arena when she beckons her students to sit in the floor space in front of the whiteboard. Once there, all eyes are on Esther as she projects content on the whiteboard and engages in direct instruction. These instructional moves principally focus on scaffolding instruction, asking questions, and prompting student response.

Esther’s heavy use of scaffolding is critical to accommodating the diverse ability levels present in her class and ensuring that students are afforded multiple opportunities to access the Scratch programming environment. Scaffolding, the breaking down of an activity or objective into smaller easier-to-follow steps, allows Esther to simplify complex computational concepts and promote understanding. For example, Esther uses physical scaffolds, such as acting like a robot moving through a series of steps to denote sequence, to augment student’s learning and help them access the Scratch programming environment.

Esther’s use of scaffolding requires her students to engage physically and verbally with computational concepts, as in the example above. In one conversation, Esther pointed out to us that because she has special education students, it was very important that she include sensory movement in her teaching. Esther’s commentary aligns with the incorporation of computational thinking-specific instructional supports to promote students’ understanding, production, and retention of content (Snodgrass, Israel, & Reese, 2016.)
Finally, discussion of “big ideas” is co-constructed by Esther and her students, together, as a strategy for making connections to highlight computational concepts and engage with the Scratch programming environment. In one conversation, for example, it becomes clear that Esther is not trying to get her students to define an algorithm, but rather, tap into their pre-existing sources of knowledge to identify key elements to building an algorithm. This more nuanced intention indicates Esther’s interest in developing her students ability to make interdisciplinary connections between pre-existing knowledge and lesson content - effective practices for engaging learners in STEM subjects (NASEM, 2018).

Carla’s Classroom: Using One-To-One Assistance and Facilitation to Foster Independence In Computational Thinking

Carla’s classroom looks similar to Esther’s, with rich wall displays depicting content across multiple subjects, a carpet for students to sit on the floor at the front of the classroom, and a collaboration table adjacent to Carla’s desk. Unlike Esther’s classroom, the desks do not face the whiteboard but are arranged perpendicular along the sidewalls so that students face one another, placing the focus on each other, rather than Carla. This arrangement places students’ focus on each other, rather than Carla. Like Esther’s class, there are no assigned seats in Carla’s class, and students are invited to sit wherever they see fit. This open seating arrangement promotes student choice and facilitates a comfortable, collaborative learning environment.

Unlike Esther’s class, Carla’s instructional objective is to accommodate students through teaching strategies that facilitate independent learning. As a result, a predominant teaching strategy for Carla involves providing one-to-one assistance to students to extend their independent learning. For example, Carla frequently encourages her students to practice Scratch at home and provides them with multiple independent study options should they finish work early in class. Coupled with Carla’s use of one-to-one assistance as a strategy for teaching, her students use multiple strategies for learning that support independent thinking such as independent interaction with materials, problem solving, and requests for clarification.

While the block-based features of the Scratch programming environment provides a low floor for novice programmers in Estelle’s class, Carla leverages Scratch’s high ceiling to extend her students’ learning by reviewing underlying code in projects developed by the greater Scratch community. Providing a low floor with high ceilings affords varied opportunities for participation and facilitates the development of more complex projects over time (Papert, 1980, Resnick et al., 2009). When her students face challenges, Carla encourages them to problem solve and clarify tasks with peers, thereby promoting a student centered learning environment where students view themselves as capable and knowledgeable (Hannafin, 1992).

Carla views her lessons as time for interactive student exploration of the Scratch programming environment. She disrupts traditional views of teacher/student roles and characterizes herself as a learner alongside her students, frequently sharing her insights as a learner with the class. This type of think-aloud is characteristic of Carla’s approach to interacting with her students and uses reflection of her own learning to models practices and perspectives that support successful computational thinkers. For example, as Carla facilitates peer feedback on students’ Scratch projects, she calls attention to the purpose of the linguistic sentence frames for facilitating peer feedback.

Importance

A growing body of research on computational thinking demonstrates benefit for students, however little research specifically focuses on instructional strategies tailored to develop exceptional students’ emergent computational thinking skills (Goode & Margolis, 2011; Kelleher & Pausch, 2007; Ladner & Israel, 2016). This study offers a view into the diverse strategies two teachers used to leverage the low floor, high ceiling features afforded by Scratch to provide an entry point to computational thinking for students with varying ability levels and needs. While each teacher used different strategies to teach computational thinking, they were both effective in getting their students to code successfully: explicit instruction provided Esther’s students with needed structure for the abstract and complex tasks inherent to computing (Israel et al., 2015), and open ended activities facilitated independent exploration and problem solving practices for Carla’s students (Israel et al., 2015; Kafai & Burke, 2014). Examining the activity systems within these two distinct learning environments uncovered how Esther and Carla designed efficient learning opportunities for their exceptional students. This study highlights ways diverse teaching approaches can be leveraged to successfully maximize learning and increase access to computational science curricula for students with diverse exceptional needs.

References

Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Journal of
Educational Technology & Society, 19(3).

Engestrom, Y. (1987). Learning by expanding: An activity theoretical approach to developmental research. Helsinki, Finland: Orienta-Konsultit Oy.

Goode, J., & Margolis, J. (2011). Exploring computer science: A case study of school reform. ACM Transactions on Computing Education (TOCE), 11(2), 12.

Greeno, J. G., & Engeström, Y. (2006). Learning in activity. Cambridge, UK: Cambridge

Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38-43. https://doi.org/10.3102/0013189X12463051

Hannafin, M. J. (1992). Emerging technologies, ISD, and learning environments: Critical perspectives. Educational technology research and development, 40(1), 49-63.

Hatch, J. A. (2002). Doing qualitative research in education settings. Albany, NY: State University of New York Press.
Israel, M., Pearson, J., Tapia, T., Wherfel, Q., Reese, G. (2015). Supporting all learners in school-wide computational thinking: A cross case analysis. Computers & Education, 82, 263-279.

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.

Kafai, Y. B., Burke, Q. (2014). Connected code: Why children need to learn programming. Cambridge, MA: MIT Press.

Kelleher, C., & Pausch, R. (2007). Using storytelling to motivate programming. Communications of the ACM, 50(7), 58-64.

Kuutti, K. (1996). Activity theory as a potential framework for human-computer interaction research. In B.A. Nardi (Ed.), Context and consciousness: Activity theory and human-computer interaction. Cambridge, MA: MIT Press.

Kumar, D. (2014). Digital playgrounds for early computing education. ACM Inroads, 5(1), 20-21.

Ladner, R. E., & Israel, M. (2016). For all in computer science for all. Communications of the ACM, 59(9), 26-28.

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.

Leont'ev, A. (1972). The problem of activity in psychology. Voprosyfilosofii, 9, 95-108.

Miles, M.B, Huberman, A.M., Saldana, J. (2013). Qualitative data analysis: A methods sourcebook. SAGE Publications, Incorporated.

Mitchell, I., Keast, S., Panizzon, D., & Mitchell, J. (2017). Using ‘big ideas’ to enhance teaching and student learning. Teachers and Teaching, 23(5), 596-610.

National Academies of Sciences, Engineering, and Medicine (NASEM). (2018). English Learners in STEM Subjects: Transforming Classrooms, Schools, and Lives. Retrieved from
https://www.nap.edu/catalog/25182/english-learners-in-stem-subjects-transforming-classrooms-schools- and-lives

Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc..

Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., ... & Kafai, Y. B. (2009). Scratch: Programming for all. Communications of the. Acm, 52(11), 60-67.

Rufo, D. (2012). Building forts and drawing on walls: Fostering student-initiated creativity inside and outside the elementary classroom. Art Education, 65(3), 40-47.

Saldaña, J. (2016). The coding manual for qualitative researchers (3rd Ed). Thousand Oaks, CA: Sage.

Selby, C. C. (2014). How can the teaching of programming be used to enhance computational thinking skills? (Unpublished doctoral dissertation). University of Southampton, Southampton, UK.

Snodgrass, M. R., Israel, M., & Reese, G. C. (2016). Instructional supports for students with disabilities in K-5 computing: Findings from a cross-case analysis. Computers & Education, 100, 1-17.

Vygotsky, L. (1973). Thought and Language. Cambridge: MIT Press

Wing, J. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.

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Presenters

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Sharin Jacob, University of California, Irvine
Graduate student

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Yenda Prado, University of California, Irvine
Graduate student

Yenda Prado studies inclusive education models and best practices at the intersection of language, literacy, and technology. She is a Community Research Fellow and doctoral candidate at University of California, Irvine through the Orange County Education Advancement Network. Her work currently centers on the ways and means that communities and schools support inclusion across remote, hybrid, and in-person instructional formats. Ms. Prado holds a Master in Education from Harvard University Graduate School of Education.

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