Data literacy for learning analytics

Wolff, Annika; Moore, J.; Zdráhal, Zdeněk; Hlosta, Martin; Kužílek, Jakub

doi:10.1145/2883851.2883864

Cited by 17 publications

(19 citation statements)

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“…In a complex educational system, the introduction of a subtle change can meet substantial resistance because of the perceived increase in workload for staff . As LA makes use of data from various sources, institutions not only need data experts to obtain and analyze good quality data, but they also need the users (e.g., administrators, teaching staff, and students) to have basic data interpretation skills and the ability to reflect on data critically, in order that LA may have positive impact on informing decisions and changing behaviour (Arnold et al, 2014;Pardo & Siemens, 2014;Wolff, Moore, Zdrahal, Hlosta, & Kuzilek, 2016). This has been identified as a common gap between needs and solutions in institutional analytics capacity (Norris & Baer, 2013;Siemens et al, 2013).…”

Section: Demand On Resourcesmentioning

confidence: 99%

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

et al. 2018

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This paper introduces a learning analytics policy and strategy framework developed by a cross-European research project team -SHEILA 1 (Supporting Higher Education to Integrate Learning Analytics), based on interviews with 78 senior managers from 51 European higher education institutions across 16 countries. The framework was developed adapting the RAPID Outcome Mapping Approach (ROMA), which is designed to develop effective strategies and evidence-based policy in complex environments. This paper presents four case studies to illustrate the development process of the SHEILA framework and how it can be used iteratively to inform strategic planning and policy processes in real world environments, particularly for large-scale implementation in higher education contexts. To this end, the selected cases were analyzed at two stages, each a year apart, to investigate the progression of adoption approaches that were followed to solve existing challenges, and identify new challenges that could be addressed by following the SHEILA framework. Notes for Practice This paper presents a framework that can be used to assist with strategic planning and policy processes for learning analytics. This research builds on the RAPID Outcome Mapping Approach (ROMA) and adapts it by including elements of actions, challenges, and policy prompts. The proposed framework was developed based on the experiences of learning analytics adoption at 51 European higher education institutions. The proposed framework will enhance systematic adoption of learning analytics on a wide scale.

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Section: Demand On Resourcesmentioning

confidence: 99%

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

et al. 2018

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“…in the form of visualizations. Although attention on visualization literacy [15], [16] continues to grow in different disciplines [17] there is still an ongoing discussion of the relative merits of different visualization platforms [18], [19]. Several studies [20], [21] illustrate people's limited knowledge in understanding data visualizations, indicating the urgency to address the problem.…”

Section: Introductionmentioning

confidence: 99%

Open Data Visualization in Danish Schools: A case Study

Saddiqa¹,

Larsen

Magnussen

et al. 2019

Computer Science Research Notes

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Increasingly public bodies and organizations are publishing Open Data for citizens to improve their quality of life and solving public problems. But having Open Data available is not enough. Public engagement is also important for successful Open Data initiatives. There is an increasing demand for strategies to actively involve the public exploiting Open Data, where not only the citizens but also school pupils and young people are able to explore, understand and extract useful information from the data, grasp the meaning of the information, and to visually represent findings. In this research paper, we investigate how we can equip our younger generation with the essential future skills using Open Data as part of their learning activities in public schools. We present the results of a survey among Danish school teachers and pupils. The survey focuses on how we can introduce Open Data visualizations in schools, and what are the possible benefits and challenges for pupils and teachers to use Open Data in their everyday teaching environment. We briefly review Copenhagen city's Open Data and existing open source software suitable for visualization, to study which open source software pupils can easily adapt to visualize Open Data and which data-sets teachers can relate to their teaching themes. Our study shows that introducing Open Data visualizations in schools make everyday teaching interesting and help improving pupils learning skills and that to actively use Open Data visualizations in schools, teachers and pupils need to boost their digital skills.

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“…Por último, destacar la necesidad de que para la verdadera implementación y sistematización de analíticas de aprendizaje en educación es crítico alcanzar la involucración de las instituciones educativas y de la generación de políticas educacionales nacionales, y esto genera problemáticas que inicialmente podrían no ser obvias (Macarini et al, 2019). Diversos trabajos han analizado las políticas que instituciones están promoviendo para desarrollar capacidades y competencias para implementar analíticas de aprendizaje de forma sistemática (Arnold et al, 2014), así como para resaltar cuáles son los principales desafíos que las instituciones afrontan (Tsai y Gasevic, 2017), como por ejemplo los programas de reciclaje profesional e incentivos hacia educadores para conseguir que empiecen a adquirir las habilidades necesarias para ser capaces de entender y usar en sus clases analíticas de aprendizaje (Wolff, Moore, Zdrahal, Hlosta y Kuzilek, 2016).…”

Section: Tecnología Ciencias Del Aprendizaje Privacidad Instituciounclassified

El Proceso de Implementación de Analíticas de Aprendizaje

Ruipérez‐Valiente

2020

RIED

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Con el despegue de la popularidad del área de analítica de aprendizaje durante la última década, numerosas investigaciones han surgido y la opinión pública se ha hecho eco de esta tendencia. Sin embargo, la realidad es que el impacto que ha tenido en la práctica ha sido bastante bajo, y se está produciendo poca transferencia a las instituciones educativas. Una de las posibles causas es la elevada complejidad del campo, y que no existan procesos claros; por ello, en este trabajo, se propone un pragmático proceso de implementación de analíticas de aprendizaje en cinco etapas: 1) entornos de aprendizaje, 2) recolección de datos en crudo, 3) manipulación de datos e ingeniería de características, 4) análisis y modelos y 5) aplicación educacional. Además, se revisan una serie de factores transversales que afectan esta implementación, como la tecnología, ciencias del aprendizaje, privacidad, instituciones y políticas educacionales. El proceso que se detalla puede resultar de utilidad para investigadores, analistas de datos educacionales, educadores e instituciones educativas que busquen introducirse en el área. Alcanzar el verdadero potencial de las analíticas de aprendizaje requerirá de estrecha colaboración y conversación entre todos los actores involucrados en su desarrollo, que permita su implementación de forma sistemática y productiva.

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Data literacy for learning analytics

Cited by 17 publications

References 0 publications

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

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

Open Data Visualization in Danish Schools: A case Study

El Proceso de Implementación de Analíticas de Aprendizaje

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