Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics

Educational institutions need to formulate a well-established data-driven plan to get long-term value from their learning analytics (LA) strategy. By tracking learners’ digital traces and measuring learners’ performance, institutions can discern consequential learning trends via use of predictive mo...

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Autores principales:	Anuradha Mathrani, Teo Susnjak, Gomathy Ramaswami, Andre Barczak
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Learning analytics Generalizability Interpretability Feature extraction Transparency Ethics protocol Electronic computers. Computer science QA75.5-76.95 Theory and practice of education LB5-3640
Acceso en línea:	https://doaj.org/article/c04675352bdc46508b114a69a833db8b
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spelling	oai:doaj.org-article:c04675352bdc46508b114a69a833db8b2021-12-04T04:36:06ZPerspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics2666-557310.1016/j.caeo.2021.100060https://doaj.org/article/c04675352bdc46508b114a69a833db8b2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666557321000318https://doaj.org/toc/2666-5573Educational institutions need to formulate a well-established data-driven plan to get long-term value from their learning analytics (LA) strategy. By tracking learners’ digital traces and measuring learners’ performance, institutions can discern consequential learning trends via use of predictive models to enhance their instructional services. However, questions remain on how the proposed LA system is suitable, meaningful, and justifiable. In this concept paper, we examine generalizability and transparency of the internals of predictive models, alongside the ethical challenges in using learners’ data for building predictive capabilities. Model generalizability or transferability is hindered by inadequate feature representation, small and imbalanced datasets, concept drift, and contextually un-related domains. Additional challenges relate to trustworthiness and social acceptance of these models since algorithmic-driven models are difficult to interpret by themselves. Further, ethical dilemmas are faced in engaging with learners’ data while developing and deploying LA systems at an institutional level. We propose methodologies for apprehending these challenges by establishing efforts for managing transferability and transparency, and further assessing the ethical standing on justifiable use of the LA strategy. This study showcases underlying relationships that exist between constructs pertaining to learners’ data and the predictive model. We suggest the use of appropriate evaluation techniques and setting up research ethics protocols, since without proper controls in place, the model outcome would not be portable, transferable, trustworthy, or admissible as a responsible outcome. This concept paper has theoretical and practical implications for future inquiry in the burgeoning field of learning analytics.Anuradha MathraniTeo SusnjakGomathy RamaswamiAndre BarczakElsevierarticleLearning analyticsGeneralizabilityInterpretabilityFeature extractionTransparencyEthics protocolElectronic computers. Computer scienceQA75.5-76.95Theory and practice of educationLB5-3640ENComputers and Education Open, Vol 2, Iss , Pp 100060- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Learning analytics Generalizability Interpretability Feature extraction Transparency Ethics protocol Electronic computers. Computer science QA75.5-76.95 Theory and practice of education LB5-3640
spellingShingle	Learning analytics Generalizability Interpretability Feature extraction Transparency Ethics protocol Electronic computers. Computer science QA75.5-76.95 Theory and practice of education LB5-3640 Anuradha Mathrani Teo Susnjak Gomathy Ramaswami Andre Barczak Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics
description	Educational institutions need to formulate a well-established data-driven plan to get long-term value from their learning analytics (LA) strategy. By tracking learners’ digital traces and measuring learners’ performance, institutions can discern consequential learning trends via use of predictive models to enhance their instructional services. However, questions remain on how the proposed LA system is suitable, meaningful, and justifiable. In this concept paper, we examine generalizability and transparency of the internals of predictive models, alongside the ethical challenges in using learners’ data for building predictive capabilities. Model generalizability or transferability is hindered by inadequate feature representation, small and imbalanced datasets, concept drift, and contextually un-related domains. Additional challenges relate to trustworthiness and social acceptance of these models since algorithmic-driven models are difficult to interpret by themselves. Further, ethical dilemmas are faced in engaging with learners’ data while developing and deploying LA systems at an institutional level. We propose methodologies for apprehending these challenges by establishing efforts for managing transferability and transparency, and further assessing the ethical standing on justifiable use of the LA strategy. This study showcases underlying relationships that exist between constructs pertaining to learners’ data and the predictive model. We suggest the use of appropriate evaluation techniques and setting up research ethics protocols, since without proper controls in place, the model outcome would not be portable, transferable, trustworthy, or admissible as a responsible outcome. This concept paper has theoretical and practical implications for future inquiry in the burgeoning field of learning analytics.
format	article
author	Anuradha Mathrani Teo Susnjak Gomathy Ramaswami Andre Barczak
author_facet	Anuradha Mathrani Teo Susnjak Gomathy Ramaswami Andre Barczak
author_sort	Anuradha Mathrani
title	Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics
title_short	Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics
title_full	Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics
title_fullStr	Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics
title_full_unstemmed	Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics
title_sort	perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/c04675352bdc46508b114a69a833db8b
work_keys_str_mv	AT anuradhamathrani perspectivesonthechallengesofgeneralizabilitytransparencyandethicsinpredictivelearninganalytics AT teosusnjak perspectivesonthechallengesofgeneralizabilitytransparencyandethicsinpredictivelearninganalytics AT gomathyramaswami perspectivesonthechallengesofgeneralizabilitytransparencyandethicsinpredictivelearninganalytics AT andrebarczak perspectivesonthechallengesofgeneralizabilitytransparencyandethicsinpredictivelearninganalytics
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Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics

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