Juan Carlos Farah
EPFL SCI STI DG
ELG 139 (Bâtiment ELG)
Station 11
CH-1015 Lausanne
Publications
Infoscience
A Teacher Survey on Educational Data Management Pr
| Home > A Teacher Survey on Educational Data Management Practices: Tracking and Storage of Activity Traces |
A Teacher Survey on Educational Data Management Practices: Tracking and Storage of Activity Traces
When students participate in computer-mediated learning, their activities are often recorded for learning analytics and educational data mining purposes. While handling student data has associated privacy concerns and is often subject to legal regulations, there is a limited understanding of how educational data is currently managed in practice. To clarify this question, we conducted a survey of over 100 teachers, examining their experience with storing and analyzing student data. The survey identified the wide variety of platforms used to track and store student activity traces, highlighting the necessity to develop common data exchange standards and enforce data management policies that are consistent across platforms. In addition, the responses also revealed a mismatch between platform affordances regarding data tracking and storage, and teacher awareness of such affordances. This disconnect could be mitigated by reinforcing the transparency policies and usability of educational platforms, as well as by improving teacher data management literacy.
- Preprint: 2017___EC_TEL___Ethics___Privacy_in_Learning_Analytics_Workshop - PDF;
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A Blueprint for a Blockchain-Based Architecture to
| Home > A Blueprint for a Blockchain-Based Architecture to Power a Distributed Network of Tamper-Evident Learning Trace Repositories |
A Blueprint for a Blockchain-Based Architecture to Power a Distributed Network of Tamper-Evident Learning Trace Repositories
The need to ensure privacy and data protection in educational contexts is driving a shift towards new ways of securing and managing learning records. Although there are platforms available to store educational activity traces outside of a central repository, no solution currently guarantees that these traces are authentic when they are retrieved for review. This paper presents a blueprint for an architecture that employs blockchain technology to sign and validate learning traces, allowing them to be stored in a distributed network of repositories without diminishing their authenticity. Our proposal puts participants in online learning activities at the center of the design process, granting them the option to store learning traces in a location of their choice. Using smart contracts, stakeholders can retrieve the data, securely share it with third parties and ensure it has not been tampered with, providing a more transparent and reliable source for learning analytics. Nonetheless, a preliminary evaluation found that only 56% of teachers surveyed considered tamper-evident storage a useful feature of a learning trace repository. These results motivate further examination with other end users, such as learning analytics researchers, who may have stricter expectations of authenticity for data used in their practice.
- Fulltext: 2018-icalt - PDF;
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