• DocumentCode
    3079257
  • Title

    Evaluation of Academic Plans of Study Using Data Mining Techniques

  • Author

    Siddiqui, Muazzam Ahmed ; Gemalel-Din, Shehab

  • Author_Institution
    Dept. of Inf. Syst., King Abdulaziz Univ., Jeddah, Saudi Arabia
  • fYear
    2013
  • fDate
    15-18 July 2013
  • Firstpage
    224
  • Lastpage
    228
  • Abstract
    A plan of study enumerates the courses recommended by an academic program along with a time frame to complete the requirements of a degree or credential. A general plan of study can be recommended by an academic department or college to all of its students or, as an alternative, personalized plans of study can be created for each student by his/her academic advisor. This paper presents a case study of assessing the recommended as well as personalized plans of study in terms of their affect on students´ performance using data mining techniques. The study was conducted at the Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University. We computed the degree to which each student followed the recommended plan of study and correlated this to the students GPA to assess the impact of the plan of study to the academic performance. Our results showed a statistically significant, moderate positive correlation indicating that following the recommended plan of study has a positive impact on the academic performance. To assess the personalized plans of study proposed by the academic advisor, we built and compared three models to predict the GPA resulting from these plans. Given a proposed plan of study, our model was able to predict the correct GPA with a 0.44 root mean squared error.
  • Keywords
    computer aided instruction; data mining; further education; GPA student; academic advisor; academic college; academic department; academic performance; academic plans evaluation; academic program; data mining techniques; information systems department; Computational modeling; Data mining; Data models; Educational institutions; Predictive models; Regression tree analysis; Support vector machines; GPA prediction; educational data mining; plan of study assessment; predictive modeling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Learning Technologies (ICALT), 2013 IEEE 13th International Conference on
  • Conference_Location
    Beijing
  • Type

    conf

  • DOI
    10.1109/ICALT.2013.70
  • Filename
    6601913