e-Analytics for e-Learning

Miteva, Dafinka; Stefanov, Krassen

doi:10.4018/ijhcitp.2017100101

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…HEIs' leadership is applying data analytics tools to identify at-risk students and reduce drop-out rate [3,[54][55][56][57][58][59], provide better feedback [60], identify effective teaching strategies [32], track student engagement and predict student success [32,56,[61][62][63], improve student success and graduation rate [61,[64][65][66][67][68], improve HEI evaluation results [61], outline realistic targets to strategically tackle inefficiencies and solve declining student enrolment problems [56]. There are also examples of successful experiments for using data analysis tools to help HEIs' leadership make datadriven decisions on quality assurance, improving institutional processes and student achievement, and reducing drop-outs [69][70][71][72].…”

Section: Application Areas and Benefitsmentioning

confidence: 99%

Data-driven Decision Making in Higher Education Institutions: State-of-play

Gaftandzhieva¹,

Hussain²,

Hilčenko³

et al. 2023

IJACSA

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The paper highlights the importance of using datadriven decision-making tools in Higher Education Institutions (HEIs) to improve academic performance and support sustainable development. HEIs must utilize data analytics tools, including educational data mining, learning analytics, and business intelligence, to extract insights and knowledge from educational data. These tools can help HEIs' leadership monitor and improve student enrolment campaigns, track student performance, evaluate academic staff, and make data-driven decisions. Although decision support systems have many advantages, they are still underutilized in HEIs, leaving field for further research and implementation. To address this, the authors summarize the benefits of applying data-driven decision approaches in HEIs and review various frameworks and methodologies, such as a course recommendation system and an academic prediction model, to aid educational decision-making. These tools articulate pedagogical theories, frameworks, and educational phenomena to establish mainstay significant components of learning to enable the scheming of superior learning systems. The tools can be utilized by the placement agencies or companies to find out their probable trainees/ recruitees. They can help students in course selection, and educational management in being more efficient and effective.

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Section: Application Areas and Benefitsmentioning

confidence: 99%

Data-driven Decision Making in Higher Education Institutions: State-of-play

Gaftandzhieva¹,

Hussain²,

Hilčenko³

et al. 2023

IJACSA

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“…Las AA son consideradas como un campo de estudio emergente ISN 2707-2207/ISSN 2707-2215 (en línea), enero-febrero, 2023, Volumen 7, Número 1 p 5456 y merece una mayor exploración (Guzmán-Valenzuela et al, 2021). El desarrollo de un sistema que funcione como eje entre los diferentes actores que integran las comunidades universitarias y los procesos de aprendizaje, que retroalimente al ecosistema educativo al servicio de los estudiantes, los profesores, la administración y los gestores de la educación (Miteva, Stefanov & Stefanova, 2016). Tradicionalmente, las decisiones tomadas por el profesorado y los administradores de las IES se basan en presunciones e hipótesis.…”

Section: Contextualización De Las Aa a Nivel Marco Global Y Marco Localunclassified

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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“…Learning analytics involves data analysis to guide decision making in education systems (Czerkawski,2014).Centraltothedecision-makingoutcomesistheapproachtoleveragestudent dataforthedeliveryofmorepersonalisedandadaptiveteachingandlearning (Miteva,Stefanov,& Stefanova,2016).Intermsofprocess,astatisticalanalysisofstudentdataisundertakentodevelop adeeperlevelofunderstandingofthestudentexperience.Theresultsofthedataanalysisarethen usedtoinformstrategiestoprovidemoreindividualisedinstructiontomeeteachstudent'sunique learning needs (Miteva et al, 2016). All university learning management systems (LMS) have reportingmetrics(e.g.timeofaccess,timespentonpages,numberofposts)embeddedwithinthem.…”

Section: Introductionmentioning

confidence: 99%

New Discoveries for User Acceptance of E-Learning Analytics Recommender Systems in Saudi Arabia

Alharbi

Sandhu

2019

International Journal of Innovation in the Digital Economy

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This article adopts e-learning analytics principles to provide a new model to explain the acceptance behaviour of recommender systems adoption with e-learning in the Saudi Arabian context and reflects the increasing focus of the Saudi Arabian Ministry of Education on delivering online educational services. This focus has come at the necessity to improve overall access to the education system, and higher education and has been driven with evidence of improving learning outcomes with electronic learning (e-learning) information and instructional technology with the use of e-learning analytics recommender systems. This review utilises the technology acceptance model as a theoretical framework to generate a set of interlocked hypotheses that go to explaining student behaviours towards technological acceptance and continued usage intention of recommender systems.

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e-Analytics for e-Learning

Cited by 3 publications

References 6 publications

Data-driven Decision Making in Higher Education Institutions: State-of-play

Data-driven Decision Making in Higher Education Institutions: State-of-play

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

New Discoveries for User Acceptance of E-Learning Analytics Recommender Systems in Saudi Arabia

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