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使用非参数多维尺度分析评估学习方法。

Assessing approaches to learning with nonparametric multidimensional scaling.

作者信息

Knezek Gerald, Gibson David, Christensen Rhonda, Trevisan Ottavia, Carter Morgan

机构信息

University of North Texas Denton Texas USA.

Curtin University Bentley Western Australia Australia.

出版信息

Br J Educ Technol. 2023 Jan;54(1):126-141. doi: 10.1111/bjet.13275. Epub 2022 Sep 5.

Abstract

This article reports on a trace-based assessment of approaches to learning used by middle school aged children who interacted with NASA Mars Mission science, technology, engineering and mathematics (STEM) games in , an online game environment with 8 million registered young learners. The learning objectives of two games included awareness and knowledge of NASA missions, developing knowledge and skills of measurement and scaling, applying measurement for planetary comparisons in the solar system. Trace data from 1361 interactions were analysed with nonparametric multidimensional scaling methods, which permitted visual examination and statistical validation, and provided an example and proof of concept for the multidimensional scaling approach to analysis of time-based behavioural data from a game or simulation. Differences in approach to learning were found illustrating the potential value of the methodology to curriculum and game-based learning designers as well as other creators of online STEM content for pre-college youth. The theoretical framework of the method and analysis makes use of the Epistemic Network Analysis toolkit as a post hoc data exploration platform, and the discussion centres on issues of semantic interpretation of interaction end-states and the application of evidence centred design in post hoc analysis.

摘要

本文报告了一项基于痕迹的评估,该评估针对的是在一个拥有800万注册年轻学习者的在线游戏环境中,与美国国家航空航天局(NASA)火星任务科学、技术、工程和数学(STEM)游戏互动的中学生所采用的学习方法。两款游戏的学习目标包括对NASA任务的认识和了解、培养测量和缩放的知识与技能、运用测量进行太阳系内行星比较。使用非参数多维缩放方法分析了来自1361次互动的痕迹数据,该方法允许进行可视化检查和统计验证,并为从游戏或模拟中分析基于时间的行为数据的多维缩放方法提供了一个示例和概念验证。研究发现了学习方法上的差异,这说明了该方法对于课程和基于游戏的学习设计师以及其他面向大学前青少年的在线STEM内容创作者的潜在价值。该方法和分析的理论框架利用认知网络分析工具包作为事后数据探索平台,讨论集中在互动结束状态的语义解释问题以及事后分析中以证据为中心的设计应用上。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9264/10078274/5848bd7618df/BJET-54-126-g002.jpg

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