Work-in-Progress: Closing the Gaps: Diversity in Programming Education

Iris Groher, B. Sabitzer, Heike Demarle-Meusel, Lisa Kuka, Alexander Hofer
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Abstract

In programming education for non-computer science students, high diversity exists among our students, including gender, culture, age, educational background, and work experience. Introductory programming courses traditionally face high drop-out rates and poor performance, and students often perceive learning to program as difficult. Current research on diversity in programming education has primarily focused on gender differences, thus neglecting the influence of other diversity dimensions on students’ performance. The project Diversity in Programming Education (DIPE) aims to fill this gap by identifying how heterogeneous groups of students can be best supported. Based on our findings, we develop a didactic concept with accompanying teaching and learning material to support different diversity dimensions actively in programming education. The concept includes competence models for measuring competences and individual learning paths. To support a flat learning curve further in university programming courses in the future, we closely work with schools and educational centres for teachers to integrate algorithmic thinking into school curricula. We empirically evaluate our concept using a mixed methods approach. In particular, we will explore the effects of our concept in our programming course and will investigate the value of these effects as perceived by lecturers and students.
正在进行的工作:缩小差距:编程教育的多样性
在面向非计算机科学专业学生的编程教育中,我们的学生中存在着高度的多样性,包括性别、文化、年龄、教育背景和工作经验。传统的编程入门课程面临着高辍学率和较差的表现,学生经常认为学习编程是困难的。目前关于编程教育多样性的研究主要集中在性别差异上,而忽略了其他多样性维度对学生表现的影响。编程教育的多样性(DIPE)项目旨在通过确定如何最好地支持异质学生群体来填补这一空白。基于我们的研究结果,我们开发了一种教学概念,并附带了教学和学习材料,以积极支持编程教育中不同的多样性维度。该概念包括衡量能力的能力模型和个人学习路径。为了进一步支持未来大学编程课程的平坦学习曲线,我们与学校和教育中心密切合作,让教师将算法思维融入学校课程。我们使用混合方法对我们的概念进行实证评估。特别是,我们将在编程课程中探索我们的概念的影响,并将调查讲师和学生所感知到的这些影响的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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