An Assessment System Adapted to Differentiated Learning within Massive Open Online Courses Using Psychometric Testing

被引：0

作者：

Sophia Tahiri, Jihane ^{[1
]}

Bennani, Samir ^{[1
]}

Khalidi Idrissi, Mohammed ^{[1
]}

机构：

[1] Univ Mohammed V Rabat, EMI Mohammadia Sch Engn, RIME Team Networking Modeling & E Learning, LRIE Lab,Res Comp Sci & Educ Lab, Rabat, Morocco

来源：

2016 15TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY BASED HIGHER EDUCATION AND TRAINING (ITHET) | 2016年

关键词：

Massive Open Online Courses; MOOC; differentiated instruction; differentiation; psychometric testing; learner's path; assessment model; dropout rate; learner needs; heterogeneity; generalizability theory; G theory; item response theory; IRT; MOOCS;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper aims to present an assessment system adapted to differentiated learning using psychometric testing as a potential element in the diversification of learners' path. This system allows reducing the proportion of learners in a situation of failure and thereby improving the success rate. The assessment design is based on the generalizability theory (G theory) and the item response theory (IRT). These theories provide information to determine learner's profile. The purpose of the system is to identify learners' needs and preferences, which allows the instructor to suggest corrective measures adapted to each case of learner.

引用

页数：7

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