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How Do Educators Apply Computational Thinking to Instructional Planning?

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Research papers are a pairing of two 18 minute presentations followed by 18 minutes of Discussion led by a Discussant, with remaining time for Q & A.
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Presenters

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Associate Professor
National Louis University
@elkorda
Dr. Angela Elkordy is an Associate Professor at the National College of Education, National Louis University, Chicago, IL. She is the Founding Director of the Learning Sciences graduate program and served as the Director of Learning Technologies for many years. Dr. Elkordy loves her work teaching in-service teachers and school leaders about cognition and learning, teaching as a design science, instructional technologies, leadership, and research methods. She is the lead author of Design Ed: Connecting Learning Sciences Research to Practice, an ISTE publication (2019) that makes impactful findings of the learning sciences accessible for educators to use in their practice.
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Associate Professor
National Louis University
@Jack W. Denny
@Jack W. Denny
Dr. Denny joined the NLU faculty in 2010 as an assistant professor in the secondary eduation and educational leadership programs. During his career in public eduation, he has served as a classroom teacher of German, world languages department chair, fine arts division head, and assistant superintendent for curriclum and instruction. Dr. Denny has provided staff development across the country and abroad for teachers of world languages, specializing on the relationship of curriculum content to standards and assessments.
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Professor
National Louis University
Dr. Ayn Keneman is a Professor and Program Chair of Early Childhood Education at National Louis University in Chicago. She has 25 years of teaching experience in public and private schools in Trenton, N,J.. Atlanta Ga. and Winnetka,IL. as an ECE teacher and Reading/Learning Disability specialist. She presents annually at ISTE and ICS conferences and is a former SIG President of the International Literacy Association ( ILA). She attended the National Technology Leadership Summit in Washington, DC for the past five years. Her latest book, Design Ed: Connecting Learning Science to Research was published by ISTE in 2019.
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Associate Professor
National Louis University
Dr. Donna Wakefield is a professor of special education at National Louis University in Chicago, Illinois where she teaches graduate courses in language, literacy, assistive technology and differentiation. She worked in the K-12 setting as a speech-language pathologist, special education teacher, assistive technology facilitator, inclusion facilitator and special education administrator before joining the university. Donna teaches the meaningful integration of technology into academics . Donna has presented at numerous national and international conferences to teachers, administrators and tech enthusiasts and is an ISTE Certified Educator, a Google for Education Certified Trainer and an Apple Teacher.

Session description

Planning for impactful learning experiences is essential for educators but it can be challenging. The complex tasks associated with design and planning include deep thinking skills such as analysis, evaluation and synthesis. In this study, we investigate the impact of teaching computational thinking skills as a tool for learning design.

Framework

Key to our process will be focused attention on Computational Thinking (CT). This important twenty-first-century skill is typically characterized by four distinct cognitive skills that have clear parallels to instructional planning tasks and skills:
Deconstruction (learning objectives.
Algorithmic thinking (task analysis).
Pattern recognition –(e.g., student misconceptions, assessing for blocks of enabling knowledge key points).
Abstraction – (e.g., modifying instruction in the moment using formative assessment).
These skills are aligned with the Universal Design for Learning (UDL) framework.
These four phrases of CT are associated with critical thinking necessary for problem-solving skills that are crucial for instructional planning.

Our exploratory study at the intersection of the learning sciences, teaching as a design science, and professional development will inform educator preparation for the design and planning of impactful learning experiences. This study examines how teachers’ mental models of learning and instructional design planning change through participation in instruction or professional development of Computational Thinking (CT), a key 21st-century skill. Participants will learn instructional design frameworks - UDL - LITE, and apply the four sub-skills (Decomposition, Algorithmic thinking, pattern recognition, and Abstraction) as a framework for thinking in the context of learning design for instructional planning and decision making. [3]
Using micro-lessons on CT, UDL, just-in-time learning, lesson study/ critique, and chunking the work of UDL leveraging cognitive load theory will support students’ understanding of the needed application of the intersecting skill sets.
Current research and evidence base related to the problem/idea
The relevant literature base for this project falls into several main areas:
1. Cognitive processes/ thinking of teachers for planning instruction
2. Computational thinking / UDL
3. Teacher beliefs about instructional planning
4. Common problems/ issues with lesson planning - focus on early childhood

Universal Design for Learning:
The concept of universal design for learning (UDL) pertains to “proactively addressing barriers to learning” (Israel, Wherfel, et al., 2015, p. 46). The goal of UDL is to develop methods that enable all students to be successful in learning. By making use of this method, students are given more flexibility when it comes to accessing, engaging with, and demonstrating their knowledge of the material they are studying. It is important to note that lesson plans created this way benefit all students, but they have been shown to be especially effective for students with learning and attention problems. It is the goal of UDL to eliminate any barriers to learning and provide all students with an equal opportunity to succeed when utilized in a variety of teaching methods. The point is to provide flexibility that can be adjusted according to the strengths and needs of each student. It is this flexibility that is intended to benefit all students.

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Methods

Our mixed methods study explores the following questions:

Research Questions
Phase 1:

1. How do educators (including pre-service teachers) conceptualize the instructional planning process?
a) How does the use of computational thinking impact teacher perceptions of the instructional planning process?
2. How do educators apply computational thinking to instructional planning?
a) Are key challenges of lesson planning ameliorated through explicitly teaching computational thinking? design frameworks (e.g. LITE or UDL)?
3. How can teacher educators teach computational thinking effectively to teacher-learners?

Unit of Analysis/ Study
Our unit of analysis is be a cohort or class.

Participants
Participants in the study are pre-service or in-service teacher-learners in educator preparation contexts, primarily early childhood, secondary education or professional development courses.

Data sources / tools
Our sources of data are learning artifacts such as concept maps, task analysis documentation, lesson plans, analytic memos (researchers), a post-course survey and focus groups.

Data collection and analysis

•  pre and post-course learner artifacts to understand how educators initially conceptualize the lesson design and planning process. Each instructor will work individually to assess learners’ understanding by developing mental models – either visual representations or written descriptions of the instructional planning processes. We will analyze these data for patterns and trends.
• survey learners at the end of each course to collect perceptual data on the experience of using computational thinking, confidence, and self-efficacy in instructional planning.
•  a small number of focus groups to hear directly from the teacher-learners about their experiences, questions, concerns and understanding.
• research team own analytic memos about the process of teaching computational thinking, jointly analyzing and comparing ideas, questions and findings.

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Results

Our preliminary findings suggest that educators have very different conceptualizations of the instructional planning process. We will continue to work with our students through the Fall term, which ends at the beginning of December. We expect that we will have an array of learning artifacts to from which we can discern patterns - particularly misconceptions or misunderstandings of learning experience design. We also expect that teachers' awareness of thinking skills that are core to the design process - eg task analysis, pattern recognition (eg. analyzing assessments), algorithmic thinking, and abstraction will improve. We may see a corresponding increase in self-efficacy for the design process. Using CT as a cognitive tool, we expect evidence of the educators' thinking about the design process to change. Our data collection from the courses will be complete at the end of the term, followed by focus groups. Data analysis will be complete by early Spring 2023.

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Importance

Designing impactful learning experiences requires intentional planning of the learning outcomes, determining the rationale and sequence of activities as well as making decisions about formative and summative assessments. In addition to content knowledge, this kind of planning and organization requires a range of pedagogical skills and thinking, beginning with articulating the learning objectives. Teachers, particularly pre-service teachers, often struggle with developing the necessary skills. Our exploratory study tests the impact of direct instruction of computational thinking as a way to promote lesson design sub-skills by enhancing the quality and clarity of educators’ thinking and planning skills.
K-12 impact
If educators are better prepared to apply higher-order thinking skills when designing learning experiences and planning lessons, the clarity, and quality of instruction will be significantly improved. Our relatively simple but powerful strategies could potentially transform how educators in K-12 contexts think about their teaching – with a focus on student learning.

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References

Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 832–835. https://doi.org/10.1093/comjnl/bxs074
Burke, Q., Bailey, C. S., & Ruiz, P. (2019). CIRCL primer: Assessing computational thinking. In CIRCL Primer Series. Retrieved from http://circlcenter.org/assessing-computational-thinking
Courey, S. J., Tappe, P., Siker, J., & LePage, P. (2013). Improved lesson planning with Universal Design for Learning (UDL). Teacher Education and Special Education: The Journal of the Teacher Education Division of the Council for Exceptional Children, 36(1), 7–27. https://doi.org/10.1177/0888406412446178
Ellen MCGuire-Schwartz, M., & Arndt, J. S. (2007). Transforming Universal Design for Learning in early childhood teacher education from college classroom to early childhood classroom. Journal of Early Childhood Teacher Education, 28(2), 127–139. https://doi.org/10.1080/10901020701366707
Gabriele, L., Bertacchini, F., Tavernise, A., Vaca-Cárdenas, L., Pantano, P., & Bilotta, E. (2019). Lesson planning by computational thinking skills in Italian pre-service teachers. Informatics in Education, 18(1), 69–104. https://doi.org/10.15388/infedu.2019.04
Gafoor, D. K. A. (n.d.). Ways to improve lesson planning: A student teacher perspective. 12.
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
Harrison, B. (2007). Lesson design and planning. 7.
Haslip, M.J., Terry, N. A mixed methods study of the relationship between Individualized lesson planning and social-emotional outcomes in young children. Early Childhood Educ J (2022). https://doi.org/10.1007/s10643-022-01347
John, P. D. (2006). Lesson planning and the student teacher: Re‐thinking the dominant model. Journal of Curriculum Studies, 38(4), 483–498. https://doi.org/10.1080/00220270500363620
Jones, K. A., Jones, J., & Vermette, P. J. (n.d.).Six common lesson planning pitfalls: Recommendations for novice educators. 21.
Jones, M. G. (2022). Putting practice into theory: Changes in the organization of preservice teachers’ pedagogical knowledge. 28.
Kale, U., Akcaoglu, M., Cullen, T., Goh, D., Devine, L., Calvert, N., & Grise, K. (2018). Computational what? Relating computational thinking to teaching. TechTrends, 62(6), 574–584. https://doi.org/10.1007/s11528-018-0290-9
Kaleli̇Oğlu, F., Gülbahar, Y., & Kukul, V. (n.d.). A framework for computational thinking based on a systematic research review. 14.
Ko, E. K. (2012). What is your objective?: Preservice teachers’ views and practice of instructional planning. The International Journal of Learning: Annual Review, 18(7), 89–100. https://doi.org/10.18848/1447-9494/CGP/v18i07/47677
Lumbreras, Jr., R., & Rupley, W. H. (2020). Pre-service teachers’ application of understanding by design in lesson planning. International Journal of Evaluation and Research in Education (IJERE), 9(3), 594. https://doi.org/10.11591/ijere.v9i3.20491
Milkova, S. (n.d.). Strategies for effective lesson planning. 8.
Peters-Burton, E., Rich, P. J., Kitsantas, A., Laclede, L., & Stehle, S. M. (2021). High school science teacher use of planning tools to integrate computational thinking. Journal of Science Teacher Education, 1–23. https://doi.org/10.1080/1046560X.2021.1970088
Román-González, M., Pérez-González, J.-C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72, 678–691. https://doi.org/10.1016/j.chb.2016.08.047
Sahin-Taskin, C. (2017). Exploring pre-service teachers’ perceptions of lesson planning in primary education. Journal of Education and Practice, 7.
Santoyo, C., & Zhang, S. (n.d.). Secondary teacher candidates’ lesson planning Learning. 25.
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/j.edurev.2017.09.003
Spiteri, J. (2021). Quality early childhood education for all and the Covid-19 crisis: A viewpoint. Prospects https://doi.org/10.1007/s11125-020-09528-4
Straessle, J. (n.d.). Teachers’ perspectives of effective lesson planning: A comparative analysis. https://doi.org/10.25774/W4-8SWA-7371
Westerman, D. A. (1991). Expert and novice teacher decision making. Journal of Teacher Education, 42(4), 292–305. https://doi.org/10.1177/002248719104200407
Yadav, A., Stephenson, C., & Hong, H. (2017). Computational thinking for teacher education. Communications of the ACM, 60(4), 55–62. https://doi.org/10.1145/2994591
Yoder, N. (2014). Teaching the whole child: Instructional practices that support social-emotional learning in three teacher evaluation frameworks. Research-to-practice brief. Revised. Report prepared for the center on great teachers and leaders at American institutes for research. Retrieved October 23, 2021, https://eric.ed.gov/?id=ED581718

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Session specifications

Topic:
Innovation in higher education
Grade level:
Community college/university
Audience:
Professional developers, Teachers, Teacher education/higher ed faculty
Attendee devices:
Devices not needed
Subject area:
Preservice teacher education
ISTE Standards:
For Educators:
Facilitator
  • Create learning opportunities that challenge students to use a design process and computational thinking to innovate and solve problems.
Learner
  • Stay current with research that supports improved student learning outcomes, including findings from the learning sciences.
Designer
  • Design authentic learning activities that align with content area standards and use digital tools and resources to maximize active, deep learning.