Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs

2019 ◽  
Vol 28 (2) ◽  
pp. 206-230 ◽  
Author(s):  
Anna Y. Q. Huang ◽  
Owen H. T. Lu ◽  
Jeff C. H. Huang ◽  
C. J. Yin ◽  
Stephen J. H. Yang
Keyword(s):  
Big Data ◽  
Academic Performance ◽  
Learning Analytics ◽  
Classification Methods ◽  
Learning Logs

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.

Research on the university intelligent learning analysis system based on AI

2021 ◽  
pp. 1-10
Author(s):  
Meng Huang ◽  
Shuai Liu ◽  
Yahao Zhang ◽  
Kewei Cui ◽  
Yana Wen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Academic Performance ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Three Dimensional ◽  
Training Model ◽  
Future Trend ◽  
Artificial Intelligence Technology ◽  
Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

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 Detailed Study on Classification Algorithms in Big Data

2020 ◽  
pp. 30-46
Author(s):  
Saranya N. ◽  
Saravana Selvam
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Linear Regression ◽  
Comprehensive Evaluation ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Classification Methods ◽  
Computing Science ◽  
Data Collections

After an era of managing data collection difficulties, these days the issue has turned into the problem of how to process these vast amounts of information. Scientists, as well as researchers, think that today, probably the most essential topic in computing science is Big Data. Big Data is used to clarify the huge volume of data that could exist in any structure. This makes it difficult for standard controlling approaches for mining the best possible data through such large data sets. Classification in Big Data is a procedure of summing up data sets dependent on various examples. There are distinctive classification frameworks which help us to classify data collections. A few methods that discussed in the chapter are Multi-Layer Perception Linear Regression, C4.5, CART, J48, SVM, ID3, Random Forest, and KNN. The target of this chapter is to provide a comprehensive evaluation of classification methods that are in effect commonly utilized.

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