Construction on Learning Analytics Object for Sharing and Interoperation of Educational Big Data

2016 ◽  
pp. 186-195
Author(s):  
Yonghe Wu ◽  
Wei Zhong ◽  
Chun Zhou ◽  
Xiaoling Ma
Keyword(s):  
Big Data ◽  
Learning Analytics

Learning Analytics Considered Harmful

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Laurie P Dringus
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Research Community ◽  
Current Status ◽  
Online Course ◽  
Full Potential

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.

scholarly journals Desenhar e implementar um sistema de learning analytics no ensino superior

2013 ◽  
pp. 123-146
Author(s):  
Sérgio André Ferreira ◽  
António Andrade
Keyword(s):  
Big Data ◽  
Management System ◽  
Learning Analytics ◽  
Content Management ◽  
Content Management System ◽  
Learning Content ◽  
Academic Analytics ◽  
Learning Content Management

A utilização de plataformas tecnológicas com base de funcionamento online, com destaque para os Learning Content Management System(LCMS), tem ganho uma importância crescente nas Instituições de Ensino Superior (IES). Da atividade dos alunos e professores nestas plataformas resulta um imenso trilho de cliques, que se traduz no registo de um enorme volume de dados – Big Data – no sistema. A ideia do Learning Analytics (LA) é simples e tem associado um potencial transformativo muito elevado: o aproveitamento destes dados permite um processo de tomada de decisão mais informada, abrindo as portas a um novo modelo na gestão das IES nos campos pedagógico e da eficiência organizacional. Contudo, a abordagem à temática dos LA ainda está na infância e a operacionalização eficaz exige respostas a grandes desafios no domínio tecnológico, educacional e das políticas. O trabalho aqui apresentado insere-se neste contexto. Na Universidade Católica Portuguesa -Porto está em curso o desenvolvimento de um sistema LA alimentado com dados do LCMS institucional - Blackboard – que tem como objetivo posicionar cada unidade curricular (UC) e faculdade numa matriz de cinco níveis de integração do LCMS no processo formativo. A matriz foi construída com base em modelos internacionais e considerou-se as funcionalidades oferecidas pelo LCMS. Para dar resposta aos requisitos desta matriz, desenhou-se todo o backoffice do sistema de extração e análise de dados no LCMS. Adicionalmente, foi construída e validada uma escala que contempla as mesmas dimensões, para aferição da opinião dos estudantes sobre a integração e a importância do LCMS no seu processo de ensino e aprendizagem. Depois de concluída a construção deste LA é objetivo articular esta informação comos resultados académicos dos estudantes (Sistema de Gestão Académica) e avaliação dos docentes/ disciplinas (SIGIQ) - dando-se passos na construção de um Academic Analytics.

Leveraging Big Data to Help Each Learner and Accelerate Learning Science

2017 ◽  
Vol 119 (3) ◽  
pp. 1-24 ◽  
Author(s):  
Philip H. Winne
Keyword(s):  
Randomized Controlled Trial ◽  
Big Data ◽  
Gold Standard ◽  
Learning Analytics ◽  
Controlled Trial ◽  
Raw Material ◽  
Learning Science ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
What Works

Background Today's gold standard for identifying what works, the randomized controlled trial, poorly serves each and any individual learner. Elements of my argument provide grounds for proposed remedies in cases where software can log extensive data about operations each learner applies to learn and each bit of information to which a learner applies those operations. Purpose of Study Analyses of such big data can produce learning analytics that provide raw material for self-regulating learners, for instructors to productively adapt instructional designs, and for learning scientists to advance learning science. I describe an example of such a software system, nStudy. Research Design I describe and analyze features of nStudy, including bookmarks, quotes, notes, and note artifacts that can be used to generate trace data. Results By using software like nStudy as they study, learners can partner with instructors and learning scientists in a symbiotic and progressive ecology of authentic experimentation. Conclusion I argue that software technologies like nStudy offer significant value in supporting learners and advancing learning science. A rationale and recommendations for this approach arise from my critique of pseudo-random controlled trials.

A Pedagogical Perspective on Big Data and Learning Analytics: A Conceptual Model for Digital Learning Support

2019 ◽  
Vol 24 (4) ◽  
pp. 599-619 ◽  
Author(s):  
Sabine Seufert ◽  
Christoph Meier ◽  
Matthias Soellner ◽  
Roman Rietsche
Keyword(s):  
Big Data ◽  
Conceptual Model ◽  
Learning Analytics ◽  
Learning Support ◽  
Digital Learning

The skinny on big data in education: Learning analytics simplified

TechTrends ◽  
2015 ◽  
Vol 59 (2) ◽  
pp. 75-80 ◽  
Author(s):  
Jacqueleen A. Reyes
Keyword(s):  
Big Data ◽  
Learning Analytics

scholarly journals Big Data Characterization of Learner Behaviour in a Highly Technical MOOC Engineering Course

2016 ◽  
Vol 3 (3) ◽  
pp. 170-192 ◽  
Author(s):  
Kerrie Anna Douglas ◽  
Peter Bermel ◽  
Md Monzurul Alam ◽  
Krishna Madhavan
Keyword(s):  
Big Data ◽  
Learning Analytics ◽  
Statistical Test ◽  
Future Research ◽  
User Behaviour ◽  
Significant Difference ◽  
Nonparametric Statistical Test ◽  
Nonparametric Statistical

MOOCs attract a large number of users with unknown diversity in terms of motivation, ability, and goals. To understand more about learners in a MOOC, the authors explored clusters of user clickstream patterns in a highly technical MOOC, Nanophotonic Modelling through the algorithm k-means++.  Five clusters of user behaviour emerged: Fully Engaged, Consistent Viewers, One-Week Engaged, Two-Week Engaged, and Sporadic users. Assessment behaviours and scores are then examined within each cluster, and found different between clusters. Nonparametric statistical test, Kruskal-Wallis yielded a significant difference between user behaviour in each cluster. To make accurate inferences about what occurs in a MOOC, a first step is to understand the patterns of user behaviour. The latent characteristics that contribute to user behaviour must be explored in future research. Keywords: MOOCs, Learning Analytics, Assessment

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