Towards Open Data in Digital Education Platforms

Machado, Joana Soares; Farah, Juan Carlos; Gillet, Denis; Rodríguez‐Triana, María Jesús

doi:10.1109/icalt.2019.00048

Cited by 3 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The shift of open data movement has been recently addressed by Machado et al (2019), which also mentions the "limited support for researchers in education to generate, access, and share experimental data using openly available digital education platforms." Still, this review paper is focused on the process of conducting experiments and communicating results.…”

Section: Discussionmentioning

confidence: 99%

“…The shift of open data movement has been recently addressed by Machado et al (2019), which also mentions the “limited support for researchers in education to generate, access, and share experimental data using openly available digital education platforms.” Still, this review paper is focused on the process of conducting experiments and communicating results. We conclude that the presented limitations of EDM publicly available datasets in conjunction with our conclusions regarding actions that need to be taken on public data sources and repositories represent a valuable road‐map for the next generation datasets.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Review on publicly available datasets for educational data mining

Mihăescu

Popescu

2021

WIREs Data Min & Knowl

View full text Add to dashboard Cite

The availability of a dataset represents a critical component in educational data mining (EDM) pipelines. Once the dataset is at hand, the next steps within the research methodology regard proper research issue formulation, data analysis pipeline design and implementation and, finally, presentation of validation results. As the EDM research area is continuously growing due to the increasing number of available tools and technologies, one of the critical issues that constitute a bottleneck regards a properly documented review on publicly available datasets. This paper aims to present a succinct, yet informative, description of the most used publicly available data sources along with their associated EDM tasks, used algorithms, experimental results and main findings. We have found that there are three types of data sources: well‐known data sources, datasets used in EDM competitions and standalone EDM datasets. We conclude that the success of the future of EDM data sources will rely on their ability to manage proposed approaches and their experimental results as a dashboard of benchmarked runs. Under these circumstances, the reproducibility of data analysis pipelines and benchmarking of proposed algorithms becomes at hand for the research community such that progress in the EDM domain may be much more easily acquired. The most crucial outcome regards the possibility of continuously improving existing data analysis pipelines by tackling EDM tasks that rely on publicly available datasets and benchmarking data analysis pipelines that use open‐source implementations. This article is categorized under: Application Areas > Education and Learning Fundamental Concepts of Data and Knowledge > Big Data Mining

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Review on publicly available datasets for educational data mining

Mihăescu

Popescu

2021

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…We build on our review of related work and on a previous requirements elicitation process to guide the design considerations for our pipeline. Outcomes from this elicitation process showed that researchers in education were most interested in 19 features across five different processes related to LA data collection, management, and dissemination [5]. These features were to be supported by a toolkit comprising tools for (i) data exploration, visualization, and analysis, (ii) data management and sharing, (iii) managing data privacy, (iv) interacting with datasets, (v) importing and exporting data in multiple formats, and (vi) certifying data authenticity.…”

Section: Design Considerationsmentioning

confidence: 99%

“…Given that flexibility is often required for such implementations, we focus both on how our pipeline's components can be used as part of an integrated system and individually as standalone units. Building on a previous requirements elicitation process [5], we begin by reviewing related work and motivating the need for such a pipeline. We then introduce the design considerations and architecture of our data pipeline in Section III and Section IV, respectively.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Data Pipeline for Managing Learning Analytics

Farah

Machado

Cunha

et al. 2021

2021 19th International Conference on Information Technology Based Higher Education and Training (ITHET)

Self Cite

View full text Add to dashboard Cite

Despite the importance of learning analytics in digital education, there is limited support for researchers in education to generate, access, and share experimental data while complying with ethical and privacy legislation. We propose a set of related tools that support researchers with these tasks and present a blueprint for how these tools can be integrated with existing platforms, enabling researchers to run studies within learning environments, adhere to legal and ethical privacy frameworks, and share their anonymous or anonymized data with a wider audience. We demonstrate the integration of these features into an existing online learning platform.

show abstract

“…Data privacy management is an essential part of ethical LA practice. There is a growing base of research on the measurement and mitigation of privacy risks to address ethical challenges presented by the collection and use of learner data for analytics (Chicaiza et al., 2020; Corrin et al., 2019; Drachsler & Greller, 2016; Ferguson, 2019; Gursoy et al., 2017; Hoel & Chen, 2016; Khalil & Ebner, 2016; Machado et al., 2019; Pardo & Siemens, 2014; Steiner et al., 2016). There is also some work addressing the cross‐disciplinary nature of learning analytics, including privacy concerns (eg, Teasley, 2019).…”

Section: Introductionmentioning

confidence: 99%

Implementing next generation privacy and ethics research in education technology

Marshall¹,

Pardo

Smith

et al. 2022

Brit J Educational Tech

View full text Add to dashboard Cite

For the developers of next‐generation education technology (EdTech), the use of Learning Analytics (LA) is a key competitive advantage as the use of some form of LA in EdTech is fast becoming ubiquitous. At its core LA involves the use of Artificial Intelligence and Analytics on the data generated by technology‐mediated learning to gain insights into how students learn, especially for large cohorts, which was unthinkable only a few decades ago. This LA growth‐spurt coincides with a growing global “Ethical AI” movement focussed on resolving questions of personal agency, freedoms, and privacy in relation to AI and Analytics. At this time, there is a significant lack of actionable information and supporting technologies, which would enable the goals of these two communities to be aligned. This paper describes a collaborative research project that seeks to overcome the technical and procedural challenges of running a data‐driven collaborative research project within an agreed set of privacy and ethics boundaries. The result is a reference architecture for ethical research collaboration and a framework, or roadmap, for privacy‐preserving analytics which will contribute to the goals of an ethical application of learning analytics methods. What is already known about this topic Privacy Enhancing Technologies, including a range of provable privacy risk reduction techniques (differential privacy) are effective tools for managing data privacy, though currently only pragmatically available to well‐funded early adopters. Learning Analytics is a relatively young but evolving field of research, which is beginning to deliver tangible insights and value to the Education and EdTech industries. A small number of procedural frameworks have been developed in the past two decades to consider data privacy and other ethical aspects of Learning Analytics. What this paper adds This paper describes the mechanisms for integrating Learning Analytics, Data Privacy Technologies and Ethical practices into a unified operational framework for Ethical and Privacy‐Preserving Learning Analytics. It introduces a new standardised measurement of privacy risk as a key mechanism for operationalising and automating data privacy controls within the traditional data pipeline; It describes a repeatable framework for conducting ethical Learning Analytics. Implications for practice and/or policy For the Learning Analytics (LA) and Education Technology communities the approach described here exemplifies a standard of ethical LA practice and data privacy protection which can and should become the norm. The privacy risk measurement and risk reduction tools are a blueprint for how data privacy and ethics can be operationalised and automated. The incorporation of a standardised privacy risk evaluation metric can help to define clear and measurable terms for inter‐ and intra‐organisational data sharing and usage policies and agreements (Author, Ruth Marshall, is an Expert Contributor on ISO/IEC JTC 1/SC 32/WG 6 "Data usage", due for publication in earl...

show abstract

Towards Open Data in Digital Education Platforms

Cited by 3 publications

References 11 publications

Review on publicly available datasets for educational data mining

Review on publicly available datasets for educational data mining

An End-to-End Data Pipeline for Managing Learning Analytics

Implementing next generation privacy and ethics research in education technology

Contact Info

Product

Resources

About