Deep Analytics for Content Management System (CMS)
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Authors
Zhao, Ying
Subjects
Advisors
Date of Issue
2018-04
Date
Presented April 10-12, 2018
Period of Performance: 10/01/2017-09/30/2018
Period of Performance: 10/01/2017-09/30/2018
Publisher
Monterey, California: Naval Postgraduate School
Language
en_US
Abstract
Project Summary: The researchers investigated the needs of the College of Distance Education and Training (CDET) Content Management System (CMS) and identified the significant capabilities and measures of effectiveness (MoEs) these systems utilized. Our research examined which capabilities and MoEs may enhance the training and education platform/environment offered within the Marine Corps Distance Learning Network (MarineNet) learning ecosystem. The research identified which data should be captured and which metrics should be analyzed. Since actual CMS data was not available, other proxy data sources were identified such as KDD Cup data, NIH UK, and NPS thesis data. Once we can access as the CMS pilot data, we identified various big data and deep learning tools such as Tableau, D3, Python SciPy, NetworkX, RapidMiner, R, Octave, WeKa, and Google Analytics that would be useful in analyzing the data. CDET will use this information to determine and define appropriate electronic learning (or distance/distributed learning) MoEs.
Type
Report
Description
NPS NRP Executive Summary
Report Type: Final Report
Report Type: Final Report
Series/Report No
Naval Research Program (NRP) Project Documents
Department
Information Sciences (IS)
Organization
Naval Research Program (NRP)
Naval Research Program
Identifiers
NPS Report Number
NPS-18-M021-A
Sponsors
CDET
MCU/EDCOM
MCU/EDCOM
Funder
NPS-18-M021-A
Format
7 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.