LEA in Private: A Privacy and Data Protection Framework for a Learning Analytics Toolbox
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| Title: | LEA in Private: A Privacy and Data Protection Framework for a Learning Analytics Toolbox |
|---|---|
| Language: | English |
| Authors: | Steiner, Christina M., Kickmeier-Rust, Michael D., Albert, Dietrich |
| Source: | Journal of Learning Analytics. 2016 3(1):66-90. |
| Availability: | Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/ |
| Peer Reviewed: | Y |
| Page Count: | 25 |
| Publication Date: | 2016 |
| Document Type: | Journal Articles Reports - Descriptive |
| Descriptors: | Privacy, Guidelines, Ethics, Information Security, Correlation, Data Analysis, Laws, Research and Development, Program Descriptions, Informed Consent, Foreign Countries, Civil Rights, Information Management, Classification, Ownership, Trust (Psychology), Best Practices |
| Geographic Terms: | Europe |
| ISSN: | 1929-7750 |
| Abstract: | To find a balance between learning analytics research and individual privacy, learning analytics initiatives need to appropriately address ethical, privacy, and data protection issues. A range of general guidelines, model codes, and principles for handling ethical issues and for appropriate data and privacy protection are available, which may serve the consideration of these topics in a learning analytics context. The importance and significance of data security and protection are also reflected in national and international laws and directives, where data protection is usually considered as a fundamental right. Existing guidelines, approaches, and regulations served as a basis for elaborating a comprehensive privacy and data protection framework for the LEA's BOX project. It comprises a set of eight principles to derive implications for ensuring ethical treatment of personal data in a learning analytics platform and its services. The privacy and data protection policy set out in the framework is translated into the learning analytics technologies and tools that were developed in the project and may be used as best practice for other learning analytics projects. |
| Abstractor: | As Provided |
| Number of References: | 55 |
| Entry Date: | 2017 |
| Accession Number: | EJ1126798 |
| Database: | ERIC |
| Abstract: | To find a balance between learning analytics research and individual privacy, learning analytics initiatives need to appropriately address ethical, privacy, and data protection issues. A range of general guidelines, model codes, and principles for handling ethical issues and for appropriate data and privacy protection are available, which may serve the consideration of these topics in a learning analytics context. The importance and significance of data security and protection are also reflected in national and international laws and directives, where data protection is usually considered as a fundamental right. Existing guidelines, approaches, and regulations served as a basis for elaborating a comprehensive privacy and data protection framework for the LEA's BOX project. It comprises a set of eight principles to derive implications for ensuring ethical treatment of personal data in a learning analytics platform and its services. The privacy and data protection policy set out in the framework is translated into the learning analytics technologies and tools that were developed in the project and may be used as best practice for other learning analytics projects. |
|---|---|
| ISSN: | 1929-7750 |
