通过探索动态电影评分数据，向高中统计教师介绍预测建模:任务设计的重点

Q3 Social Sciences

Statistics Education Research Journal Pub Date : 2022-07-04 DOI:10.52041/serj.v21i2.49

Anna-Marie Fergusson, M. Pfannkuch

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引用次数: 3

摘要

随着数据科学的出现，对统计建模教学的建议包括更多地关注预测。然而，关于数据科学预测建模教学任务设计的研究很少。因此，基于设计的研究方法被用于开发一个新的基于web的任务，该任务探索:访问和使用来自API的动态电影评级数据;开发模型以生成预测区间;并在浏览器中修改和运行提供的R代码。该任务是在一个面对面的教学实验中实施的，涉及六位高中统计教师。对教师对任务的反应的分析确定了四个关键的任务设计特征，这些特征似乎刺激了与预测建模和api相关的统计和计算思想的发展。

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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INTRODUCING HIGH SCHOOL STATISTICS TEACHERS TO PREDICTIVE MODELLING BY EXPLORING DYNAMIC MOVIE RATINGS DATA: A FOCUS ON TASK DESIGN

With the advent of data science, recommendations for teaching statistical modelling include adopting a greater focus on prediction. However, there has been minimal research about the design of tasks for teaching predictive modelling from a data science. Therefore, a design-based research approach was used to develop a new web-based task that explored: accessing and using dynamic movie ratings data from an API; developing a model to generate prediction intervals; and modifying and running provided R code in the browser. The task was implemented within a face-to-face teaching experiment involving six high school statistics teachers. Analysis of the teacher responses to the task identified four key task design features that appeared to stimulate development of statistical and computational ideas related to predictive modelling and APIs.

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来源期刊

Statistics Education Research Journal Social Sciences-Education

CiteScore

1.30

自引率

0.00%

发文量

期刊介绍： SERJ is a peer-reviewed electronic journal of the International Association for Statistical Education (IASE) and the International Statistical Institute (ISI). SERJ is published twice a year and is free. SERJ aims to advance research-based knowledge that can help to improve the teaching, learning, and understanding of statistics or probability at all educational levels and in both formal (classroom-based) and informal (out-of-classroom) contexts. Such research may examine, for example, cognitive, motivational, attitudinal, curricular, teaching-related, technology-related, organizational, or societal factors and processes that are related to the development and understanding of stochastic knowledge. In addition, research may focus on how people use or apply statistical and probabilistic information and ideas, broadly viewed. The Journal encourages the submission of quality papers related to the above goals, such as reports of original research (both quantitative and qualitative), integrative and critical reviews of research literature, analyses of research-based theoretical and methodological models, and other types of papers described in full in the Guidelines for Authors. All papers are reviewed internally by an Associate Editor or Editor, and are blind-reviewed by at least two external referees. Contributions in English are recommended. Contributions in French and Spanish will also be considered. A submitted paper must not have been published before or be under consideration for publication elsewhere.