Programming by choice: urban youth learning programming with scratch

By John H. Maloney, K. Peppler, Yasmin B. Kafai, Mitchel Resnick, Natalie Rusk. Technical Symposium on Computer Science Education, 2008

507 citations recorded by the source corpus.

Subfields: Computer Science Applications, Developmental and Educational Psychology, Information Systems

View Paper: https://www.semanticscholar.org/paper/24dd04071a135f305b7da8c2c65679e0b50c26d5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Uri Wilensky
2. David Weintrop
3. Stephen Cooper
4. Roy D. Pea
5. Shuchi Grover
6. Linda L. Werner
7. Jill Denner
8. Jodi Asbell-Clarke

In Developmental and Educational Psychology

1. Stephen Cooper
2. David Weintrop
3. Uri Wilensky
4. Shuchi Grover
5. Roy D. Pea

In Information Systems

1. Uri Wilensky
2. Stephen Cooper
3. Linda L. Werner
4. Roy D. Pea
5. David Weintrop
6. Shuchi Grover

Citations in the corpus

• Learning computer science concepts with scratch (2010, background): “The researchers did not investigate if the use of concurrency demonstrates understanding of this concept.”
• Designing for deeper learning in a blended computer science course for middle school students (2015, methodology): “(Campe et al., 2013) Other research with middle school age children has been conducted using Scratch (Maloney, Peppler, Kafai, Resnick, & Rusk, 2008).”
• Computer games created by middle school girls: Can they be used to measure understanding of computer science concepts? (2012, methodology): “Maloney et al. (2008) also found certain concepts were used less often in their students’ projects made using the Scratch programming language, such as random numbers, variables, and Boolean logic.”
• Comparing Block-Based and Text-Based Programming in High School Computer Science Classrooms (2017, background): “The signi fi cant change in con fi dence for the blocks condition is consistent with other noncom-parative studies that suggest that the block-based programming interface is e ff ective in increasing students’ con fi dence in their own programming ability (Maloney et al. 2008; Smith et al. 2014).”
• Computational Thinking in K–12 (2013, background): “The activities of abstracting pertinent behaviors into agents, applying rules, and evaluating the results via modeling and simulation are key ways of engaging in CT. Blikstein (2010) demonstrates leveraging Netlogo computational models for science learning in secondary-level classrooms.”