Improving Learning by Imitation in Online Courses using Memorization, Learning by Doing and Lecture Architecture for Naive Programmers

Siddharth Srivastava, Shalini Lamba, T. Prabhakar
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引用次数: 2

Abstract

Learning by Imitation (LBI) is the most natural way of learning natural languages. The designers of online courses focus on approaches like learning by doing, adaptive learning, and so on for designing online education systems like Massive Open Online Courses (MOOC) and Intelligent Tutoring System (ITS). They don’t consider LBI as an essential parameter while designing courses and MOOC/ITS for naive programmers. The purpose of this research is to arrive at a framework that helps in designing pedagogically effective MOOC/ITS for naive programmers which reflects LBI approach. We conducted an online survey where 130 students participated. Online lectures were designed using our reference framework. A desktop App was developed for naive programmers allowing them to code at different levels of abstractions. The Lectures plus App provides a learning environment reflecting LBI. We conducted pre and post-tests and found a remarkable increase in programming performance of these participants.
利用记忆、实践学习和面向初级程序员的课堂结构提高在线课程的模仿学习
模仿学习(LBI)是学习自然语言最自然的方法。在线课程的设计者在设计大规模在线开放课程(MOOC)和智能辅导系统(ITS)等在线教育系统时,关注的是边做边学、自适应学习等方法。在为幼稚的程序员设计课程和MOOC/ITS时,他们不认为LBI是一个必要的参数。本研究的目的是得出一个框架,该框架有助于为初级程序员设计教学上有效的MOOC/ITS,反映了LBI方法。我们进行了一项有130名学生参与的在线调查。在线课程是根据我们的参考框架设计的。为初级程序员开发了一个桌面应用程序,允许他们在不同的抽象层次上进行编码。讲座+ App提供了一个反映LBI的学习环境。我们进行了前后测试,发现这些参与者的编程性能有了显著提高。
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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