2026-09-19T14:27:50Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1063952026-06-06T00:49:33Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Harold Abelson.
Xie, Benjamin Xiang-Yu
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2017-01-12T18:19:02Z
2017-01-12T18:19:02Z
2016
2016
http://hdl.handle.net/1721.1/106395
967668152
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 81-83).
analyze skill progression in MIT App Inventor, an open, online learning environment with over 4.7 million users and 14.9 million projects/apps created. My objective is to understand how people learn computational thinking concepts while creating mobile applications with App Inventor. In particular, I am interested in the relationship between the development of sophistication in using App Inventor functionality and the development of sophistication in using computational thinking concepts as learners create more apps. I take steps towards this objective by modeling the demonstrated sophistication of a user along two dimensions: breadth and depth of capability. Given a sample of 10,571 random users who have each created at least 20 projects, I analyze the relationship between demonstrating domain-specific skills by using App Inventor functionality and generalizable skills by using computational thinking concepts. I cluster similar users and compare differences in using computational concepts. My findings indicate a common pattern of expanding breadth of capability by using new skills over the first 10 projects, then developing depth of capability by using previously introduced skills to build more sophisticated apps. From analyzing the clustered users, I order computational concepts by perceived complexity. This concept complexity measure is relative to how users interact with components. I also identify differences in learning computational concepts using App Inventor when compared to learning with a text-based programming language such as Java. In particular, statements (produce action) and expressions (produce value) are separate blocks because they have different connections with other blocks in App Inventor's visual programming language. This may result in different perceptions of computational concepts when compared to perceptions from using a text-based programming language, as statements are used more frequently in App Inventor than expressions. This work has implications to enable future computer science curriculum to better leverage App Inventor's blocks-based programming language and events-based model to offer more personalized guidance and learning resources to those who learn App Inventor without an instructor.
by Benjamin Xiang-Yu Xie.
M.Eng.
83 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Progression of computational thinking skills demonstrated by App Inventor users
Thesis
application/pdf
Publication
unknown
unknown
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<Abstract>analyze skill progression in MIT App Inventor, an open, online learning environment with over 4.7 million users and 14.9 million projects/apps created. My objective is to understand how people learn computational thinking concepts while creating mobile applications with App Inventor. In particular, I am interested in the relationship between the development of sophistication in using App Inventor functionality and the development of sophistication in using computational thinking concepts as learners create more apps. I take steps towards this objective by modeling the demonstrated sophistication of a user along two dimensions: breadth and depth of capability. Given a sample of 10,571 random users who have each created at least 20 projects, I analyze the relationship between demonstrating domain-specific skills by using App Inventor functionality and generalizable skills by using computational thinking concepts. I cluster similar users and compare differences in using computational concepts. My findings indicate a common pattern of expanding breadth of capability by using new skills over the first 10 projects, then developing depth of capability by using previously introduced skills to build more sophisticated apps. From analyzing the clustered users, I order computational concepts by perceived complexity. This concept complexity measure is relative to how users interact with components. I also identify differences in learning computational concepts using App Inventor when compared to learning with a text-based programming language such as Java. In particular, statements (produce action) and expressions (produce value) are separate blocks because they have different connections with other blocks in App Inventor's visual programming language. This may result in different perceptions of computational concepts when compared to perceptions from using a text-based programming language, as statements are used more frequently in App Inventor than expressions. This work has implications to enable future computer science curriculum to better leverage App Inventor's blocks-based programming language and events-based model to offer more personalized guidance and learning resources to those who learn App Inventor without an instructor.</Abstract>
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