The SHEILA Framework: Informing Institutional Strategies and Policy Processes of Learning Analytics

Tsai, Yi‐Shan; Moreno-Marcos, Pedro Manuel; Jivet, Ioana; Scheffel, Maren; Tammets, Kairit; Kollom, Kaire; Gašević, Dragan

doi:10.18608/jla.2018.53.2

Cited by 88 publications

(90 citation statements)

References 16 publications

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“…In short, this form of leadership in HEIs is commonly responsible for organisational stability, policy and efficiency. The adoption of LA can be resource demanding and requires new forms of infrastructure, system processes and expertise (Tsai et al , ). When implementing LA, the need for stability and consistency generates tensions with entrepreneurial leadership, as noted earlier.…”

Section: Resultsmentioning

confidence: 99%

Complexity leadership in learning analytics: Drivers, challenges and opportunities

Tsai¹,

Poquet²,

Gašević³

et al. 2019

Brit J Educational Tech

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Learning analytics (LA) has demonstrated great potential in improving teaching quality, learning experience and administrative efficiency. However, the adoption of LA in higher education is often beset by challenges in areas such as resources, stakeholder buy‐in, ethics and privacy. Addressing these challenges in a complex system requires agile leadership that is responsive to pressures in the environment and capable of managing conflicts. This paper examines LA adoption processes among 21 UK higher education institutions using complexity leadership theory as a framework. The data were collected from 23 interviews with institutional leaders and subsequently analysed using a thematic coding scheme. The results showed a number of prominent challenges associated with LA deployment, which lie in the inherent tensions between innovation and operation. These challenges require a new form of leadership to create and nurture an adaptive space in which innovations are supported and ultimately transformed into the mainstream operation of an institution. This paper argues that a complexity leadership model enables higher education to shift towards more fluid and dynamic approaches for LA adoption, thus ensuring its scalability and sustainability.

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Section: Resultsmentioning

confidence: 99%

Complexity leadership in learning analytics: Drivers, challenges and opportunities

Tsai¹,

Poquet²,

Gašević³

et al. 2019

Brit J Educational Tech

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“…3 Current solutions are delivered as policies, such as General Data Protection Regulation (GDPR) laws and regulations [10]. There are also research results in form of frameworks, good practice guides or principles [11][12][13][14]. Policies, regulations or guidelines do not correct the problem from its root because they do not prevent data leakages or misuses in real time.…”

Section: Methodsmentioning

confidence: 99%

Personal Data Broker Instead of Blockchain for Students’ Data Privacy Assurance

Fonseca

Alier

García-Peñalvo

et al. 2019

Advances in Intelligent Systems and Computing

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Data logs about learning activities are being recorded at a growing pace due to the adoption and evolution of educational technologies (Edtech). Data analytics has entered the field of education under the name of learning analytics. Data analytics can provide insights that can be used to enhance learning activities for educational stakeholders, as well as helping online learning applications providers to enhance their services. However, despite the goodwill in the use of Edtech, some service providers use it as a means to collect private data about the students for their own interests and benefits. This is showcased in recent cases seen in media of bad use of students' personal information. This growth in cases is due to the recent tightening in data privacy regulations, especially in the EU. The students or their parents should be the owners of the information about them and their learning activities online. Thus they should have the right tools to control how their information is accessed and for what purposes. Currently, there is no technological solution to prevent leaks or the misuse of data about the students or their activity. It seems appropriate to try to solve it from an automation technology perspective. In this paper, we consider the use of Blockchain technologies as a possible basis for a solution to this problem. Our analysis indicates that the Blockchain is not a suitable solution. Finally, we propose a cloud-based solution with a central personal point of management that we have called Personal Data Broker.

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“…Un ejemplo de estas preocupaciones se puede encontrar en el cierre de inBloom (Herold, 2014), debido a los problemas de privacidad planteados por los padres y los grupos de presión sobre sus prácticas de Learning Analytics y Big Data en la educación (Laveti, Kuppili, Ch, Pal, & Babu, 2017;Singer, 2014). En consecuencia, muchas situaciones se adhieren a marcos y políticas propuestos por investigadores para generar soluciones ad-hoc en las propias instituciones educativas (Tsai, Moreno-Marcos, Tammets, Kollom, & Gašević, 2018).…”

Section: Ii43 Social Network Analyticsunclassified

“…• Socios externos: Asegúrese de que las aplicaciones externas proporcionen los más altos estándares de seguridad de datos. SHEILA (Tsai et al, 2018) es otro proyecto destinado a ayudar a integrar Learning Analytics en instituciones educativas. Proponen un marco de desarrollo de políticas que junto a las instituciones permita integrar la analítica del aprendizaje de forma segura.…”

Section: Ii43 Social Network Analyticsunclassified

“…Se concluye que una implementación de la tecnología blockchain puede funcionar como puerta de acceso a datos y facilitar la interoperabilidad entre herramientas, pero no almacenar datos personales de estudiantes y ser una solución al problema de la investigación. Surgen muchas dudas, muchos temores y muchas incógnitas en el tratamiento de datos que un único proyecto o iniciativas, como SHEILA o DELICATE (Drachsler & Greller, 2016;Tsai et al, 2018), no pueden resolver, y menos con políticas o marcos de comportamiento ético (ver sección II.4.4 Miedos y recelos: una cuestión delicada). Por añadidura, España vive convulsa en un cambio continuo de leyes de protecciones de datos.…”

Section: Iv1 Conclusionesunclassified

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Privacidad y gestión de la identidad en procesos de analítica de aprendizaje

Fonseca¹

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Analytics in the educational context (Academic Analytics / Learning Analytics) involves an analytical procedure that aims to potentially improve the teaching/learning process by collecting, analyzing, and creating visualizations of data from educational processes and/or students. During the process, students' and teachers' data and metadata are stored, shared, transformed, and used at the discretion of educational institutions and third-party services, a treatment that may be against the students' interests or will.Without proper control or management, the privacy, confidentiality, and security of students' data are exposed on a daily basis, which is a particularly delicate issue when it comes to underage students. Therefore, the use of analytics in education generates an environment of mistrust regarding the processing of data of the persons involved.As a result of the above, there is a serious problem of fragility, or lack of maturity in the state of the question, concerning the protection of personal data and student identity in educational processes where educational analytical procedures intervene. As the results of this research will demonstrate, there is low protection of privacy, confidentiality, and data security in digital repositories, affecting not only the use, but also the transfer and custody of student-generated data.It should be noted that the terms of use and privacy, policies impose conditions that students and teachers must validate to use the service. However, during the use of the service, it is often not clear to students, teachers, or institutions where the limits of data use, access, management, or processing lie. Data protection laws grant several rights that can be exercised by students and teachers, even in exceptional and personal situations. These rights offer a margin of autonomy in the configuration of profiles, which learning platforms must consider as functionalities by design and by default. In the research carried out, it is easy to find examples of the shortcomings in the current platforms.This thesis focuses on an analytical investigation of the state of the matter, with the main objective of proposing solutions to resolve this fragile situation in the processing

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The SHEILA Framework: Informing Institutional Strategies and Policy Processes of Learning Analytics

Cited by 88 publications

References 16 publications

Complexity leadership in learning analytics: Drivers, challenges and opportunities

Complexity leadership in learning analytics: Drivers, challenges and opportunities

Personal Data Broker Instead of Blockchain for Students’ Data Privacy Assurance

Privacidad y gestión de la identidad en procesos de analítica de aprendizaje

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