TY - GEN
T1 - Learning analytics' privacy on the blockchain
AU - Forment, Marc Alier
AU - Filvà, Daniel Amo
AU - García-Peñalvo, Francisco José
AU - Escudero, David Fonseca
AU - Casañ, María José
N1 - Funding Information:
This research work has been completed within the Ph.D. program in Education in the Knowledge Society of the University of Salamanca, Spain [21-23].
Publisher Copyright:
© 2018 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
PY - 2018/10/24
Y1 - 2018/10/24
N2 - Learning Analytics collect sensitive data from students. In some cases, the ethics behind the use or access by third parties are not clear. This situation raises adverse reactions and feelings of fear that generates a negative perception towards the use of Learning Analytics. In consequence, it is questioned whether privacy and security can be preserved when collecting educational data. As a result, some policies and good practices are set to frame how student data should be used in the application of this analytical approach. Its objective is to increase confidence in the application of Learning Analytics. However, some of these legal actions, which are limited to the areas in which they are originated, are the result of allegations of data leakage. Hence, these initiatives can do little to insure the use of sensitive data of students in unknown situations. To ensure continuity and an increase of confidence in the application of Learning Analytics is necessary to bind to legality a new approach to safeguard privacy of students' data in its current and future uses. Considering the above, it is possible to add a technological layer above these policies that ensures its viability. Some emerging technologies such as blockchain and smart contracts are strong candidates to ensure privacy and secure sensible data of students. Th e use of smart contracts allows the automation of legal actions so that they are executed as soon as irregularities in the use or data collection are detected. In this work, we propose a series of actions to preserve the identity of students and secure their data with emerging technologies such as blockchain.
AB - Learning Analytics collect sensitive data from students. In some cases, the ethics behind the use or access by third parties are not clear. This situation raises adverse reactions and feelings of fear that generates a negative perception towards the use of Learning Analytics. In consequence, it is questioned whether privacy and security can be preserved when collecting educational data. As a result, some policies and good practices are set to frame how student data should be used in the application of this analytical approach. Its objective is to increase confidence in the application of Learning Analytics. However, some of these legal actions, which are limited to the areas in which they are originated, are the result of allegations of data leakage. Hence, these initiatives can do little to insure the use of sensitive data of students in unknown situations. To ensure continuity and an increase of confidence in the application of Learning Analytics is necessary to bind to legality a new approach to safeguard privacy of students' data in its current and future uses. Considering the above, it is possible to add a technological layer above these policies that ensures its viability. Some emerging technologies such as blockchain and smart contracts are strong candidates to ensure privacy and secure sensible data of students. Th e use of smart contracts allows the automation of legal actions so that they are executed as soon as irregularities in the use or data collection are detected. In this work, we propose a series of actions to preserve the identity of students and secure their data with emerging technologies such as blockchain.
KW - Blockchain
KW - Data privacy
KW - Data security management
KW - Digital identity
KW - Learning analytics
UR - http://www.scopus.com/inward/record.url?scp=85058567215&partnerID=8YFLogxK
U2 - 10.1145/3284179.3284231
DO - 10.1145/3284179.3284231
M3 - Conference contribution
AN - SCOPUS:85058567215
T3 - ACM International Conference Proceeding Series
SP - 294
EP - 298
BT - Proceedings - TEEM 2018
A2 - Garcia-Penalvo, Francisco Jose
PB - Association for Computing Machinery
T2 - 6th International Conference on Technological Ecosystems for Enhancing Multiculturality, TEEM 2018
Y2 - 24 October 2018 through 26 October 2018
ER -