Time-Dependent Performance Prediction System for Early Insight in Learning Trends

被引：15

作者：

Villagra-Arnedo, Carlos J. ^{[1
]}

Gallego-Duran, Francisco J. ^{[2
]}

Llorens-Largo, Faraon ^{[1
]}

Satorre-Cuerda, Rosana ^{[1
]}

Compan-Rosique, Patricia ^{[3
]}

Molina-Carmona, Rafael ^{[1
]}

机构：

[1] Univ Alicante, Comp Sci & Artificial Intelligence, Alicante, Spain

[2] Univ Alicante, Alicante, Spain

[3] Univ Alicante, Dept Comp Sci & Artificial Intelligence, Alicante, Spain

来源：

INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE | 2020年 / 6卷 / 02期

关键词：

E-learning; Education; Learning Analytics; Learning Management Systems; Prediction; Support Vector Machine;

D O I：

10.9781/ijimai.2020.05.006

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Performance prediction systems allow knowing the learning status of students during a term and produce estimations on future status, what is invaluable information for teachers. The majority of current systems statically classify students once in time and show results in simple visual modes. This paper presents an innovative system with progressive, time-dependent and probabilistic performance predictions. The system produces by-weekly probabilistic classifications of students in three groups: high, medium or low performance. The system is empirically tested and data is gathered, analysed and presented. Predictions are shown as point graphs over time, along with calculated learning trends. Summary blocks are with latest predictions and trends are also provided for teacher efficiency. Moreover, some methods for selecting best moments for teacher intervention are derived from predictions. Evidence gathered shows potential to give teachers insights on students' learning trends, early diagnose learning status and selecting best moment for intervention.

引用

页码：112 / 124

页数：13

共 24 条

[1] Developing an early-warning system for spotting at-risk students by using eBook interaction logs [J].

Akcapinar, Gokhan ;

Hasnine, Mohammad Nehal ;

Majumdar, Rwitajit ;

Flanagan, Brendan ;

Ogata, Hiroaki .

SMART LEARNING ENVIRONMENTS, 2019, 6 (01)

[2]

[Anonymous], 2007, P 37 ASEE IEEE FRONT, DOI DOI 10.1109/FIE.2007.4417993

[3]

[Anonymous], 2008, PROC

[4]

[Anonymous], P 3 INT C LEARN COLL

[5] SUPPORT-VECTOR NETWORKS [J].

CORTES, C ;

VAPNIK, V .

MACHINE LEARNING, 1995, 20 (03) :273-297

[6]

Dekker G. W., 2009, ED DATA MINING 2009

[7] An introduction to ROC analysis [J].

Fawcett, Tom .

PATTERN RECOGNITION LETTERS, 2006, 27 (08) :861-874

[8]

Freund Y., 1996, Machine Learning. Proceedings of the Thirteenth International Conference (ICML '96), P148

[9]

Hamalainen Wilhelmiina., 2010, Handbook of Educational Data Mining, (January 2010), P57

[10] Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis [J].

Hamoud, Alaa Khalaf ;

Hashim, Ali Salah ;

Awadh, Wid Aqeel .

INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE, 2018, 5 (02) :26-31

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