Current stance on predictive analytics in higher education: opportunities, challenges and future directions

Umer, Rahila; Sušnjak, Teo; Mathrani, Anuradha; Lim, Suriadi

doi:10.1080/10494820.2021.1933542

Cited by 16 publications

(10 citation statements)

References 64 publications

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“…Existen cuatro formas de AA informadas en la literatura que permiten el análisis descriptivo, análisis de diagnóstico, análisis predictivo y análisis prescriptivo (Hernández-de-Menéndez et al, 2022). En definitiva, la AA son una nueva herramienta tecnológica considerada como una práctica de minería de conjunto de datos de las instituciones de Educación Superior (en adelante ES) para generar datos procesables a través de técnicas de modelado estadístico y predictivo con el propósito de comprender y optimizar el aprendizaje y los entornos en los que se produce, mejorar la toma de decisiones, el resultado y éxito de los estudiantes, los que son determinantes para la gestión educativa eficaz (Mella-Norambuena et al, 2022;Umer et al, 2021).…”

Section: Analíticas De Aprendizaje: Definiciónunclassified

Analíticas de aprendizaje y su potencial para una educación de calidad sostenible

Mella-Norambuena¹,

Sáez-Delgado²,

López-Angulo³

et al. 2023

Ciencia Latina

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Este ensayo plateó que las analíticas de aprendizaje han mostrado beneficios, pero no todo su potencial, se requiere usar los datos que éstas proporcionan con énfasis en el aprendizaje para una gestión educativa y toma de decisiones efectivas y así avanzar hacia el logro del ODS4 sobre la calidad educativa en las universidades. Una educación de calidad sostenible es un gran reto incluso para países desarrollados y aunque las analíticas se están expandiendo como solución en respuesta a la demanda para retroalimentar el ecosistema educativo, aún existe una gran brecha para aprovechar sus múltiples beneficios. Se discute la aceptación de analítica por el profesorado, ausencia de un modelo de gestión, diferentes motivaciones institucionales para su uso, la falsa dicotomía “objetividad-subjetividad” de los datos, mucha ciencia de datos y poca ciencia de aprendizaje, y paradigmas de enseñanza, como posibles factores a mejorar para lograr el máximo potencial de las analíticas.

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Section: Analíticas De Aprendizaje: Definiciónunclassified

Analíticas de aprendizaje y su potencial para una educación de calidad sostenible

Mella-Norambuena¹,

Sáez-Delgado²,

López-Angulo³

et al. 2023

Ciencia Latina

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“…The study found that not only the accuracy of the existing models has room for improvement, but more robust testing for its validity, portability (or generic models) and overall generalizability needs to be conducted. In a recent study, Umer et al (2021) concluded that many datasets used to build predictive models in this domain were small, often having less than 10% of the overall data points for certain class labels, leading to unreliable predictive accuracies especially when course-tailored predictive models are being created. The study also calls for enhancing the scope of engagement data to cover learner interaction data from forum messages, identifying pedagogically meaningful features and developing dashboard visualizations that have some underlying pedagogical intent.…”

Section: Representation and Actionsmentioning

confidence: 99%

Learning analytics dashboard: a tool for providing actionable insights to learners

Sušnjak

Ramaswami

Mathrani

2022

Int J Educ Technol High Educ

Self Cite

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This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our study finds that most LA dashboards merely employ surface-level descriptive analytics, while only few go beyond and use predictive analytics. In response to the identified gaps in recently published dashboards, we propose a state-of-the-art dashboard that not only leverages descriptive analytics components, but also integrates machine learning in a way that enables both predictive and prescriptive analytics. We demonstrate how emerging analytics tools can be used in order to enable learners to adequately interpret the predictive model behavior, and more specifically to understand how a predictive model arrives at a given prediction. We highlight how these capabilities build trust and satisfy emerging regulatory requirements surrounding predictive analytics. Additionally, we show how data-driven prescriptive analytics can be deployed within dashboards in order to provide concrete advice to the learners, and thereby increase the likelihood of triggering behavioral changes. Our proposed dashboard is the first of its kind in terms of breadth of analytics that it integrates, and is currently deployed for trials at a higher education institution.

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“…Despite the large‐scale adoption of learning analytics—as a research endeavour and less so as a practice—several challenges remain (Ifenthaler & Yau, 2020; Kizilcec et al, 2020; Romero & Ventura, 2020; Umer et al, 2021). One of such challenges is how to use digital data to predict student performance proactively and consistently.…”

Section: Introductionmentioning

confidence: 99%

“…Predictive models created using data from individual courses tend, on average, to show a rather optimistic view of the possibilities of learning analytics (Ifenthaler & Yau, 2020; Shafiq et al, 2022; Umer et al, 2021). However, the predictive models created with several courses tell a grim story about predictive power and generalizability.…”

Section: Introductionmentioning

confidence: 99%

Modelling within‐person idiographic variance could help explain and individualize learning

Saqr

2023

Brit J Educational Tech

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Learning analytics is a fast‐growing discipline. Institutions and countries alike are racing to harness the power of using data to support students, teachers and stakeholders. Research in the field has proven that predicting and supporting underachieving students is worthwhile. Nonetheless, challenges remain unresolved, for example, lack of generalizability, portability and failure to advance our understanding of students' behaviour. Recently, interest has grown in modelling individual or within‐person behaviour, that is, understanding the person‐specific changes. This study applies a novel method that combines within‐person with between‐person variance to better understand how changes unfolding at the individual level can explain students' final grades. By modelling the within‐person variance, we directly model where the process takes place, that is the student. Our study finds that combining within‐ and between‐person variance offers a better explanatory power and a better guidance of the variables that could be targeted for intervention at the personal and group levels. Furthermore, using within‐person variance opens the door for person‐specific idiographic models that work on individual student data and offer students support based on their own insights. Practitioner notesWhat is already known about this topic Predicting students' performance has commonly been implemented using cross‐sectional data at the group level. Predictive models help predict and explain student performance in individual courses but are hard to generalize. Heterogeneity has been a major factor in hindering cross‐course or context generalization. What this paper adds Intra‐individual (within‐person) variations can be modelled using repeated measures data. Hybrid between–within‐person models offer more explanatory and predictive power of students' performance. Intra‐individual variations do not mirror interindividual variations, and thus, generalization is not warranted. Regularity is a robust predictor of student performance at both the individual and the group levels. Implications for practice The study offers a method for teachers to better understand and predict students' performance. The study offers a method of identifying what works on a group or personal level. Intervention at the personal level can be more effective when using within‐person predictors and at the group level when using between‐person predictors.

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Current stance on predictive analytics in higher education: opportunities, challenges and future directions

Cited by 16 publications

References 64 publications

Analíticas de aprendizaje y su potencial para una educación de calidad sostenible

Analíticas de aprendizaje y su potencial para una educación de calidad sostenible

Learning analytics dashboard: a tool for providing actionable insights to learners

Modelling within‐person idiographic variance could help explain and individualize learning

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