Treffer: Effects of a Block-Based Scaffolded Tool on Students’ Introduction to Hierarchical Data Structures.

Contribution: In this article, the authors present findings and insights on the efficacy of using an educational block-based programming (BBP) environment—Blocks4DS, to teach the binary search tree (BST). Background: For a decade, BBP environments have been a hot topic in the computer science education (CSEd) community to promote interactive active learning of programming. However, little attention has been paid to BBP environments’ efficacy on nonintroductory courses like data structures and algorithms (DS&A). DS&A courses are challenging to students due to levels of abstraction that could be reduced by syntax-free features existing in BBP interfaces. Research Questions: 1) Can undergraduate computing-major students learn about the BST using Blocks4DS? 2) Do undergraduate computing-major students understand better BSTs when learning with a BBP environment? and 3) How do undergraduate computing-major students perceive Blocks4DS for nonintroductory computer science (CS) topics? Methodology: A mixed-method study was designed, using a control and intervention group with 83 first and second-year CS students, to evaluate the efficacy of Blocks4DS compared to traditional instructional methods (e.g., whiteboard and pseudocode). The authors evaluated its efficacy based on students’ conceptual understanding and perceptions. Findings: It was found that, regardless of prior experience with text-based programming languages and instructional approaches, students introduced to the BST with Blocks4DS gained significant conceptual understanding, and performed as well as peers instructed with pseudocode. Also, 83.3% of students found the tool engaging and 72.3% found it useful to learn DS&A. This suggests that Blocks4DS can be used to teach DS&A. [ABSTRACT FROM AUTHOR]

Copyright of IEEE Transactions on Education is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)