Learning edge momentum: a new account of outcomes in CS1
Learning edge momentum: a new account of outcomes in CS1
复制标题
学习优势动力：CS1 成果的新描述
DOI：
10.1080/08993401003612167
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发表时间：
2010
影响因子：
2.7
通讯作者：
A. Robins
中科院分区：
文献类型：
--
作者：
A. Robins
与其他科目相比，典型的入门编程(CS1)课程的不及格率和优等率都高于正常水平，形成了典型的双峰分数分布。在本文中，我将探讨两种可能的解释。传统的解释是，学习者自然而然地分为程序员和非程序员。然而，回顾几十年的研究，几乎没有证据支持这一说法。我提出了另一种解释，学习边缘动量(LEM)效应。这一假设是通过年级分布的模拟模型引入的，然后建立在心理学和教育学文献的基础上。LEM的运作方式是，成功地获得一个概念会让学习其他密切相关的概念变得更容易(而失败会让学习变得更难)。人们的学习方式和构成编程语言的概念的紧密结合的本质之间的这种相互作用，在CS1中造成了固有的结构性偏见，从而驱使学生走向极端的结果。
Compared to other subjects, the typical introductory programming (CS1) course has higher than usual rates of both failing and high grades, creating a characteristic bimodal grade distribution. In this article, I explore two possible explanations. The conventional explanation has been that learners naturally fall into populations of programmers and non-programmers. A review of decades of research, however, finds little or no evidence to support this account. I propose an alternative explanation, the learning edge momentum (LEM) effect. This hypothesis is introduced by way of a simulated model of grade distributions, and then grounded in the psychological and educational literature. LEM operates such that success in acquiring one concept makes learning other closely linked concepts easier (whereas failure makes it harder). This interaction between the way that people learn and the tightly integrated nature of the concepts comprising a programming language creates an inherent structural bias in CS1, which drives students towards extreme outcomes.