Visual recipes for slicing and dicing data: teaching data wrangling using subgoal graphics

Visual recipes for slicing and dicing data: teaching data wrangling using subgoal graphics
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切片和切块数据的可视化方法:使用子目标图形进行数据整理教学

DOI:
10.1145/3488042.3488063
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发表时间:
2021
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通讯作者:
Sundin L
Sundin L
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作者:
Sundin L

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当今全球市场对数据处理技能的需求不断上升,这给编程教育社区带来了新的挑战。非专业学生通常需要在学习其他科目的同时快速学习它。以前的研究表明,子目标标签提供了一个强大的脚手架策略,帮助新手分解问题。因为数据争吵本质上很容易用图形表示,我们想知道这样的标签是否可以用子目标图形来增强。为了测试这个想法,我们开发了一个在线教程,在编程和非编程的数据争论练习中都有子目标图形。按照随机对照试验的范例,对照组只被给予子目标标签,没有任何图形。该平台收集学习者的活动,以评估教学效益。参与者从多个机构招募(N=197,134)。我们的研究结果并没有显示出显着的差异,在各种学习者的表现指标,但主观反馈,我们的参与者表明,学习者认为图形是非常有帮助的。我们讨论了客观和主观数据之间的明显差异的可能原因。
The rising demand for data wrangling skills in today’s global market poses new challenges for the programming education community. Non-majors often need to learn it quickly alongside their other subjects. Previous research suggests that subgoal labels offer a powerful scaffolding strategy to help novices decompose problems. Because data wrangling is inherently easy to represent graphically, we wonder whether such labels could be augmented with subgoal graphics. To test this idea, we developed an online tutorial that features subgoal graphics in both programmatic and non-programmatic data wrangling exercises. Following an RCT paradigm, a control group is only given subgoal labels, without any graphics. The platform collects learner activity in order to evaluate the pedagogical benefits. Participants were recruited from multiple institutions (N=197, 134). Our results did not show a significant difference in various learner performance metrics, however subjective feedback from our participants suggest that learners perceive the graphics to be very helpful. We discuss possible reasons for the apparent disparity between objective and subjective data.
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