• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 教学法与教学组织 • 上一篇    下一篇

基于学习行为数据的操作系统教学效果量化分析

王雷,甄子琦,沃天宇,姜博,孙海龙,龙翔   

  1. (北京航空航天大学计算机学院,北京100083)
  • 收稿日期:2018-07-03 修回日期:2018-09-16 出版日期:2018-11-26 发布日期:2018-11-25
  • 基金资助:

    国家自然科学基金(61672073);北京航空航天大学“凡舟”教育基金

Quantitative analysis of  the teaching effect  of
operating system  courses based on learning behavior data

WANG Lei,ZHEN Ziqi,WO Tianyu,JIANG Bo,SUN Hailong,LONG Xiang   

  1. (School of Computer Science and Engineering,Beihang University,Beijing 100083,China)
  • Received:2018-07-03 Revised:2018-09-16 Online:2018-11-26 Published:2018-11-25

摘要:

“系统能力”是计算机专业本科生的核心能力之一,而操作系统课程是培养学生系统能力的关键。传统教学模式存在一些普遍性问题:
(1)难以细粒度地区分学生能力差异性,无法提供精细的指导;
(2)教学活动设计过于宏观和粗粒度,缺乏学期内和学期间的迭代反馈机制;
(3)缺乏细粒度的学习行为数据,无法在微观层次分析和发现能力形成的规律。针对这些问题,
设计了一个操作系统实验集成环境,不仅实现了实验发布、提交、评判等工作自动化,同时收集和分析实验过程中学生的行为数据,从而及时发现教学过程中的问题。使用实验集成环境前,在2014年只有38%的学生完成了4个实验,而使用实验集成环境的2015年这个完成率达到了62%。
通过分析2015年实验中学习行为数据,
发现了代码复制、预备知识不足、代码阅读以及lab2现象等问题。针对这些问题,
采取了本学期与长期两方面改进措施。最后,对2015年、2016年和2017年学习行为数据的量化分析表明,教学过程的改进措施取得了明显的效果。
 

关键词: 学习行为, 操作系统, 量化分析, 系统能力

Abstract:

System capability is one of the core capabilities of the students majored in computer science, and the operating system courses are the key to cultivating it. There are some typical problems  in the traditional teaching process. Firstly, it is difficult to distinguish the ability gap among students in a fine grained manner, so we cannot provide the students detailed guidance. Secondly, teaching activity design is too general and coarsegrained, and there is lack of iterative feedback mechanism during and after each semester. Thirdly, without finegrained learning behavior data, the rules of competence acquisition cannot be analyzed and discovered at the micro level. We design an integrated experiment environment for operating systems, which can not only realize automatic experiment releasing, code committing and evaluation, but also collect and analyze the behavior data of students during the experimentation process. Thus, we can discover the problems in the teaching process in time. Only 38% of students completed at least 4 labs before the experiment environment  came into use in 2014, which reached 62% in 2015. However, by analyzing the learning behavior data in 2015, we found problems such as code copying, lack of preliminary knowledge, inadequate code reading and the lab2 phenomenon. In response to these problems, we take measures both in the current semester and in the longterm period. Finally, quantitative analysis of learning behavior data for 2015, 2016 and 2017 shows that, these improvement measures  achieved significant effect.

Key words: learning behavior, operating system, quantitative analysis, system capability