Annotating Exam Questions Through Automatic Learning Concept Classification

被引：0
|
作者：
Begusic, Domagoj [1 ]
Pintar, Damir [1 ]
Skopljanac-Macina, Frano [1 ]
Vranic, Mihaela [1 ]
机构：
[1] Univ Zagreb, Fac Elect Engn & Comp, Zagreb, Croatia
来源：
2018 26TH INTERNATIONAL CONFERENCE ON SOFTWARE, TELECOMMUNICATIONS AND COMPUTER NETWORKS (SOFTCOM) | 2018年
关键词：
educational data mining; exam queries; learning concepts; classification; e-learning;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Educational data mining (or EDM) is an emerging interdisciplinary research field concerned with developing methods for exploring the specific and diverse data encountered in the field of education. One of the most valuable data sources in the educational domain are repositories of exam queries, which are usually designed for evaluating how efficient the learning process was in transferring knowledge about certain taught concepts, but which commonly do not contain any additional information about concepts they are related to beyond the text of the query and offered answers. In this paper we present our novel approach of using text mining methods to automatically annotate pre-existing exam queries with information about concepts they relate to. This enables automatic categorization of exam queries as well as easier reporting of learning concept adoption after these queries are used in an exam. We apply this approach to real-life exam questions from a high education university course and show validation of our results performed in consultation with experts from the educational domain.
引用
收藏
页码：123 / 128
页数：6
相关论文
共 50 条
  • [41] AUTOMATIC CLASSIFICATION OF ELECTRONIC HEALTH RECORDS FOR A VALUE-BASED PROGRAM THROUGH MACHINE LEARNING
    Zanotto, B.
    Etges, A. P.
    Dal Bosco, A.
    Cortes, E. G.
    Ruschel, R.
    Martins, S. O.
    Souza, A. C.
    Valiense, C.
    Viegas, F.
    Canuto, S.
    Luiz, W.
    Vieira, R.
    Goncalves, M.
    Polanczyk, C. A.
    VALUE IN HEALTH, 2021, 24 : S76 - S76
  • [42] Automatic Particle Classification Through Deep Learning Approaches for Increasing Productivity in the Technical Cleanliness Laboratory
    Zwinkau, Ronny
    Frentrup, Simon
    Moehle, Roman
    Deuse, Jochen
    ADVANCES IN HUMAN FACTORS AND SYSTEMS INTERACTION, 2020, 959 : 34 - 44
  • [43] Automatic bug localization using a combination of deep learning and model transformation through node classification
    Leila Yousofvand
    Seyfollah Soleimani
    Vahid Rafe
    Software Quality Journal, 2023, 31 : 1045 - 1063
  • [44] Improving radial basis function kernel classification through incremental learning and automatic parameter selection
    Renjifo, Carlos
    Barsic, David
    Carmen, Craig
    Norman, Kevin
    Peacock, G. Scott
    NEUROCOMPUTING, 2008, 72 (1-3) : 3 - 14
  • [45] Concept-Based Semi-Automatic Classification of Drugs
    Gurulingappa, Harsha
    Kolarik, Corinna
    Hofmann-Apitius, Martin
    Fluck, Juliane
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2009, 49 (08) : 1986 - 1992
  • [46] Synthetic aperture radar automatic target classification processing concept
    Woollard, M.
    Bannon, A.
    Ritchie, M.
    Griffiths, H.
    ELECTRONICS LETTERS, 2019, 55 (24) : 1301 - 1302
  • [47] Automatic classification of Web pages based on the concept of domain ontology
    Song, MH
    Lim, SY
    Kang, DJ
    Lee, SJ
    12th Asia-Pacific Software Engineering Conference, Proceedings, 2005, : 645 - 651
  • [48] Classification of tetraplegics through automatic movement evaluation
    Maksimovic, R
    Popovic, M
    MEDICAL ENGINEERING & PHYSICS, 1999, 21 (05) : 313 - 327
  • [49] Automatic Object Classification through Semantic Analysis
    Li, Xiaokun
    Zhu, Zhigang
    20TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, VOL 2, PROCEEDINGS, 2008, : 497 - 504
  • [50] IMPROVING THE QUALITY OF LEARNING THROUGH THE QUESTIONS OF TEXTS
    Sula, Artur
    Lama, Irena Ndoci
    Gjokutaj, Mimoza
    PROBLEMS OF EDUCATION IN THE 21ST CENTURY, 2011, 36 : 106 - 115
12345