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Students may prefer visual programming languages like Alice, but learning outcomes in computer science education are not always tied to preference. Text-based languages like Jython can also be highly effective for learning.

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Area of Science:

Computer Science Education
Programming Language Pedagogy

Background:

Effective computer science curricula require understanding how different programming languages impact student learning.
Visual programming languages and text-based languages offer distinct pedagogical affordances.

Purpose of the Study:

To compare the learning effectiveness of a visual programming language (Alice) versus a text-based language (Jython) in computer science education.
To investigate the relationship between student language preference and actual learning gains.

Main Methods:

Comparative analysis of student learning outcomes.
Assessment of student preferences for visual (Alice) versus text-based (Jython) programming environments.

Main Results:

Student preference for a programming language did not consistently correlate with greater learning.
Both Alice and Jython facilitated learning, but the preferred language was not always the most beneficial for knowledge acquisition.

Conclusions:

Curriculum design should consider that student preference may not align with optimal learning in computer science.
Educators should evaluate the specific learning objectives when selecting between visual and text-based programming languages.