Question Item Quality Analysis in Learning Content Management Systems (LCMS): A Case Study Based Approach

Question Item Quality Analysis in Learning Content Management Systems (LCMS): A Case Study Based Approach

Nilay Kr. NagNabamita Majumder Anjan Goswami 

Department of Master of Computer Application, MCKV Institute of Engineering, Howrah, India

Department of Master of Computer Application, MCKV Institute of Engineering, Howrah, India

Department of Master of Computer Application, MCKV Institute of Engineering, Howrah, India

Corresponding Author Email: 
nilaynag@yahoo.com; naba_majumder@yahoo.com; anjgos@gmail.com
Page: 
89-101
|
DOI: 
https://doi.org/10.18280/mmc_d.380108
Received: 
April 2017
| |
Accepted: 
15 November 2017
| | Citation

OPEN ACCESS

Abstract: 

Nowadays Learning Content Management System (LCMS) has taken a major role in e-Learning evolution.Along many proprietory LCMS , Moodle, as an Open Source Software(OSS) based LCMS established itself as a major player in E-Learning deployment. Besides managing many activities under LCMS, assessment module is very important and crucial component. Most of the time we focus on performance issues of the Learners, implicitly ignoring the effectiveness of the assessment questions. Question Item analysis encompasses the process of collecting, summarizing the information from student’s responses to assess the quality of multiple-choice questions (MCQs). This article focuses on available features in LCMS along with a case study based approach towards assessing quality of MCQs in the light of few statistical parameters as Facility index (Fp) and Discriminative Efficiency (DEp), Discrimination efficiency (Dp) etc. So here we are to get interpretation of quality parameters to evaluate overall standard of multiple-choice questions (MCQs). As in near future standardization of web based learning management systems along with question item analysis will significantly influence assessment practices.

Keywords: 

LCMS, E-learning, VLE, facility index, discriminative efficiency, discrimination index

1. Introduction
2. Dataset Specification (Source)
3. Method
4. Statistical Analysis of Question [15] [16]
5. Result
6. Observations
7. Conclusion
  References

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