Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

Virtual learning environments contain valuable data about students that can be correlated and analyzed to optimize learning. Modern learning environments based on data mashups that collect and integrate data from multiple sources are relevant for learning analytics systems because they provide insig...

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Bibliographic Details
Published in:Applied sciences 2021-09, Vol.11 (18), p.8506
Main Authors: Rodriguez-Garcia, Mercedes, Balderas, Antonio, Dodero, Juan Manuel
Format: Article
Language:English
Subjects:
Access control
Big Data
data mashup
data privacy
Datasets
Interoperability
k-anonymity
Learning
learning analytics
Learning management systems
Metadata
Online instruction
Preempting
Preservation
Privacy
privacy-preserving data publishing
Protocol
R&D
Research & development
Statistical analysis
Students
Virtual environments
Web mashups
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Summary:Virtual learning environments contain valuable data about students that can be correlated and analyzed to optimize learning. Modern learning environments based on data mashups that collect and integrate data from multiple sources are relevant for learning analytics systems because they provide insights into students’ learning. However, data sets involved in mashups may contain personal information of sensitive nature that raises legitimate privacy concerns. Average privacy preservation methods are based on preemptive approaches that limit the published data in a mashup based on access control and authentication schemes. Such limitations may reduce the analytical utility of the data exposed to gain students’ learning insights. In order to reconcile utility and privacy preservation of published data, this research proposes a new data mashup protocol capable of merging and k-anonymizing data sets in cloud-based learning environments without jeopardizing the analytical utility of the information. The implementation of the protocol is based on linked data so that data sets involved in the mashups are semantically described, thereby enabling their combination with relevant educational data sources. The k-anonymized data sets returned by the protocol still retain essential information for supporting general data exploration and statistical analysis tasks. The analytical and empirical evaluation shows that the proposed protocol prevents individuals’ sensitive information from re-identifying.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11188506