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Recommender Systems and Scratch: An Integrated Approach for Enhancing Computer Programming Learning

机译：推荐系统和划痕：加强计算机编程学习的综合方法

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摘要

Learning computer programming is a challenging process. Among the current approaches for overcoming this challenge, visual programming languages (VPLs), such as Scratch, have shown very promising results for beginners. Interestingly, some higher education institutions have started to use VPLs to introduce basic programming concepts, mainly in CS1 courses. However, an important issue regarding Scratchs usage in higher education environments is that students may feel unmotivated being confronted by programming exercises that do not fulfill their individual expectations. To try and overcome this barrier, we propose CARAMBA, a Scratch extension including an exercise recommender system. Based on features, such as taste and complexity, CARAMBA is able to personalize student learning with Scratch by suitably suggesting exercises for students. An in-depth evaluation was conducted about the effects of our proposal on both the learning of basic concepts of CS1 and the overall performance of students. We adopted an equivalent pretest-posttest design with 88 college students at an Ecuadorian university. Results confirm that recommending exercises in Scratch had a positive effect on students programming learning abilities in terms of pass rates. In totality, the pass rate achieved by our proposal was over 52%, which is 8% higher than the rate achieved during a previous experience using only Scratch (without recommendation) and 21% higher than the historical results of traditional teaching (without Scratch). Furthermore, we analyzed the degree of exploitation of CARAMBA by students to portray two facts: students actually used CARAMBA and there was a significant, positive correlation between the utilization of CARAMBA and the scores obtained by the students.
机译：学习计算机编程是一个具有挑战性的过程。在目前克服这一挑战的方法中，视觉编程语言（VPLS），如划痕，为初学者表示了非常有希望的结果。有趣的是，一些高等教育机构已经开始使用VPLS引入基本的编程概念，主要是CS1课程。然而，关于高等教育环境中的划痕使用的重要问题是，学生可能会感到无耻地被编程练习面临，这些练习不符合他们的个人期望。为了克服这一障碍，我们提出了Caramba，包括运动推荐系统的划痕扩展。基于特征，如味道和复杂性，Caramba能够通过适当地向学生练习来个性化学生学习。关于我们对CS1基本概念的学习以及学生的整体表现的影响，进行了深入的评估。我们在厄瓜多尔大学采用了一个相当于88名大学生的预测试。结果证实，推荐术争对学生编程学习能力方面的练习效果积极影响。在完全方面，我们的提案所取得的通过率超过52％，比仅使用划痕（不建议）的经验中获得的速度高出8％，比传统教学的历史结果高21％（没有划痕） 。此外，我们通过学生分析了Caramba的剥削程度来描绘两个事实：学生实际使用Caramba，并且Caramba的利用与学生获得的分数之间存在显着肯定的相关性。

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