自动检测以前的编程知识,从新手程序员的代码编译历史

IF 2.1 Q1 EDUCATION & EDUCATIONAL RESEARCH
Erno Lokkila, Athanasios Christopoulos, M. Laakso
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引用次数: 0

摘要

学生先前的编程知识对编程入门课程有很大的影响。那些有经验的人似乎经常轻松地通过课程。那些没有先前经验的人看到其他人轻松地通过课程,并从材料中脱离或退出。本研究的目的是为了证明初学者的编程行为可以被建模为一个马尔可夫过程。由此产生的转移矩阵可以用于机器学习算法,以创建行为相似的学生群。详细描述了马尔可夫过程中使用的状态机以及如何计算转移矩阵。我们计算了665名学生的转移矩阵,并使用k-means聚类算法对他们进行聚类。在对数据集进行分析的基础上,选择聚类个数为3个。我们表明,当以1-5的李克特量表测量时,所创建的集群对学生编程的先验知识具有统计上不同的意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatically detecting previous programming knowledge from novice programmer code compilation history
Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5.
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来源期刊
Informatics in Education
Informatics in Education EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
6.10
自引率
3.70%
发文量
20
审稿时长
20 weeks
期刊介绍: INFORMATICS IN EDUCATION publishes original articles about theoretical, experimental and methodological studies in the fields of informatics (computer science) education and educational applications of information technology, ranging from primary to tertiary education. Multidisciplinary research studies that enhance our understanding of how theoretical and technological innovations translate into educational practice are most welcome. We are particularly interested in work at boundaries, both the boundaries of informatics and of education. The topics covered by INFORMATICS IN EDUCATION will range across diverse aspects of informatics (computer science) education research including: empirical studies, including composing different approaches to teach various subjects, studying availability of various concepts at a given age, measuring knowledge transfer and skills developed, addressing gender issues, etc. statistical research on big data related to informatics (computer science) activities including e.g. research on assessment, online teaching, competitions, etc. educational engineering focusing mainly on developing high quality original teaching sequences of different informatics (computer science) topics that offer new, successful ways for knowledge transfer and development of computational thinking machine learning of student''s behavior including the use of information technology to observe students in the learning process and discovering clusters of their working design and evaluation of educational tools that apply information technology in novel ways.
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