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Exploring Student Behavior Using the TIPP&SEE Learning Strategy

Published: 07 August 2020 Publication History

Abstract

With the rise of Computational Thinking (CT) instruction at the elementary level, it is imperative for elementary computing instruction to support a variety of learners. TIPP&SEE is a meta-cognitive learning strategy that scaffolds student learning when learning from example code. Results from a previous study show statistically-significant performance differences favoring students using the TIPP&SEE strategy on a written assessment. In this work, our goal is gain insight as to it why such dramatic learning differences may have occurred. We analyze the students' computational artifacts and TIPP&SEE worksheets. Artifact analysis reveals that students in the TIPP&SEE group are more thorough in their work, completing more elements of the required tasks. In addition, they build open-ended projects with longer scripts that utilize more learned blocks. Worksheet analysis shows that students were highly accurate on some types of questions but largely skipped others. Despite these positive behaviors, there was little statistical correlation between student worksheet correctness, project completion, and written assessment performance. Therefore, while students in the TIPP&SEE group performed actions we believe lead to more success, no individual actions directly explain the results. Like other meta-cognitive strategies, the value of TIPP&SEE may lie in cognitive processes not directly observable, and may vary based upon individual student differences.

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cover image ACM Conferences
ICER '20: Proceedings of the 2020 ACM Conference on International Computing Education Research
August 2020
364 pages
ISBN:9781450370929
DOI:10.1145/3372782
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 August 2020

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  1. computational thinking
  2. elementary education
  3. learning strategy
  4. scratch

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ICER '20
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ICER '20: International Computing Education Research Conference
August 1 - 5, 2020
Virtual Event, New Zealand

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  • (2024)Observing Students' Behavior During Problem Solving: Determinants of SuccessProceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3677619.3678109(1-10)Online publication date: 16-Sep-2024
  • (2023)Castle and Stairs to Learn Iteration: Co-designing a UMC Learning Module with TeachersProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588793(222-228)Online publication date: 29-Jun-2023
  • (2023)Optimizing learning return on investment: Identifying learning strategies based on user behavior characteristic in language learning applicationsEducation and Information Technologies10.1007/s10639-023-12078-929:6(6651-6681)Online publication date: 8-Aug-2023
  • (2022)Development and Use of Domain-specific Learning Theories, Models, and Instruments in Computing EducationACM Transactions on Computing Education10.1145/353022123:1(1-48)Online publication date: 29-Dec-2022
  • (2022)Investigating the Use of Planning Sheets in Young Learners’ Open-Ended Scratch ProjectsProceedings of the 2022 ACM Conference on International Computing Education Research - Volume 110.1145/3501385.3543972(247-263)Online publication date: 3-Aug-2022
  • (2022)Metacognition and Self-Regulation in Programming Education: Theories and Exemplars of UseACM Transactions on Computing Education10.1145/348705022:4(1-31)Online publication date: 15-Sep-2022
  • (2022)Integrating online meta-cognitive learning strategy and team regulation to develop students’ programming skills, academic motivation, and refusal self-efficacy of Internet use in a cloud classroomUniversal Access in the Information Society10.1007/s10209-022-00958-923:1(395-410)Online publication date: 3-Dec-2022
  • (2022)Abstracting the Understanding and Application of Cognitive Load in Computational Thinking and Modularized LearningAugmented Cognition10.1007/978-3-031-05457-0_22(273-286)Online publication date: 26-Jun-2022
  • (2021)Investigating the Role of Cognitive Abilities in Computational Thinking for Young LearnersProceedings of the 17th ACM Conference on International Computing Education Research10.1145/3446871.3469746(2-17)Online publication date: 16-Aug-2021

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