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截止日期:12月23日

34?1442652654
发帖时间:12/25/2016 23:57
更新时间:12/25/2016 23:59
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( 1.09 MB) 16BaiY, 12/25/2016 23:56
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  • 用户头像
    16BaiY 2年前
    摘要

    Online learning platform, which have taken higher education by storm, are an opportunity to observe student behavior and knowledge. 
    The vast majority of educational data mining research is based on the online learning platform in Europe and America and within a small-scale data. In this work, we collected 17,854 code submissions in CS1 from Trustie, which is a famous domestic online education platform. Then, we perform a preliminary exploratory inspect for code quality by SonarQube from the original data. Numerical experiments suggest that, logical training is more important than grammar training and "code smell" will seriously affect the quality of the program. Our understanding of educational data mining may also shed light on improving teaching quality and deserves additional study.

0?1470885445
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34?1442652654
发帖时间:12/14/2016 03:00
更新时间:12/14/2016 08:17

Predictive compilation results from student code smells with ELM

ABSTRACT

Code smells are not usually bugs, but it  refers to any symptom in the source code  that indicate weaknesses in design.In other word, It is directly related to the stability and robustness of the program. Therefore, new programmer should pay attention to these potential risks in particular,because a good programming style (avoiding code smell as much as possible) will lay a solid foundation for advanced programming. Over the past decade, we have observed significant growth of work in students' program data mining. However, the detailed course data is so rare that the majority of the studies focus on simplistic statistical analysis and are conducted within a small scale data set (less than 10000). In this work, we collected 17,854 code submissions in CS1, and use an open source platform SonarQube automatically inspect the code quality from the raw data. We then proposed an Extreme Learning Machine (ELM) based method to predict compilation results from processed data and educational platform data. In order to verify the accuracy of the forecast, we use the BP network as a comparison. The results show that our method is more accurate.



I. INTRODUCTION 

II. METHODS

    A. BP NETWORK 

    B. ELM NETWORK 

III. PREDICTION BASED ON ELM

    A. Dataset  (平台介绍,数据来源,数据规模

    B. Data Preprocessing (经过sonar处理的流程,主要code smell举例)

    C. Evaluation Metrics (预测精确度定义)

    D. Parameter Optimization (参数优化)

    E. Results (结果与对比)

IV. CONCLUSIONS

REFERENCES 


回复 ︿ (5)
  • 用户头像
    尹刚 3年前
    5?1460204756
    尹刚 3年前
    另外,标题也有语法错误。

    34?1442652654
    16BaiY 3年前
    好嘞,我再改改

    另外,题目可以不用elm,使用deep learning更好吧?

  • 用户头像
  • 用户头像
    尹刚 3年前

    很好!修改一下:


    I. INTRODUCTION

    II. RESEARCH PROBLEM

        说明你要解决的教学问题,一般2-3个即可

        这个问题的解决程度,回答,要在III、IV、V中反复呼应

    III. METHOD SELECTION

        A. BP NETWORK

        B. ELM NETWORK

        这一节要结合具体案例说明,为何要选择ELM,BP和ELM的关系,优缺点等等。

    IV. PREDICTION BASED ON ELM

        A. Dataset  (平台介绍,数据来源,数据规模)

        B. Data Preprocessing (经过sonar处理的流程,主要code smell举例)

        C. Evaluation Metrics (预测精确度定义)

        D. Parameter Optimization (参数优化)

    V. RESULTS (结果与对比)

    VI. RELATED WORK

    VII. CONCLUSIONS

    REFERENCES

        请补充参考文献

0?1470885445
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3051?1565316234
【支持】 论文题目修改 正常
指派给   未指派
发布时间: 12/13/2016 22:06
更新时间:12/14/2016 06:34

毛老师,尹老师,白羽,

现在想的题目是:Using Pull-Based Collaborative Development Model in Software Engineering Courses: an Exploratory Study

大家看有什么可改进的地方?谢谢

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34?1442652654
创建时间:12/13/2016 10:52
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