A systematic meta-Review and analysis of learning analytics research

Du, Xu; Yang, Juan; Shelton, Brett E.; Hung, Jui-Long; Zhang, Mingyan

doi:10.1080/0144929x.2019.1669712

Cited by 63 publications

(31 citation statements)

References 26 publications

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“…To facilitate a more in‐depth analysis, the selected papers were classified by a similar multidimensional model used in dos Santos et al (2017) and adapted from Moissa, Gasparini, and Kemczinski (2015). The model was first proposed by Chatti, Dyckhoff, Schroeder, and Thüs (2013) and has been largely used as a reference inside the LA community (eg, Dawson et al , 2014; Du, Yang, Shelton, Hung, & Zhang, 2019). This is a simple‐to‐understand model that consisted initially of four dimensions: data sources (what), the goals of the analysis (why), techniques employed (how), and stakeholders (who).…”

Section: Categorizationmentioning

confidence: 99%

Mapping Learning Analytics initiatives in Latin America

Cechinel¹,

Ochôa²,

Santos³

et al. 2020

Brit J Educational Tech

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The growth of Learning Analytics (LA) as a research field has been extensively documented since its beginnings. This paper provides a broad overview of the publications that Latin American authors have published in the last years by performing a quantitative review of the literature (from 2011 to 2019). A total of 282 papers were collected and categorized according to a multidimensional model composed of seven dimensions (data collected, stakeholders targeted, goals of the paper, techniques employed, maturity of the tool, research approaches and ethical issues). The paper describes how the field has evolved over the years in Latin America, presenting the main characteristics of the papers from different perspectives (year, type of venue, language) together with the descriptives of the categorized dimensions and the relations between them. At last, papers are presented in number by countries and institutions, and we perform an authorship and coauthorship analysis with information extracted from the selected papers.

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Section: Categorizationmentioning

confidence: 99%

Mapping Learning Analytics initiatives in Latin America

Cechinel¹,

Ochôa²,

Santos³

et al. 2020

Brit J Educational Tech

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“…Nowadays, the learning analysis and educational data mining play an important role in organizing the progress of the educational process where this data concentrates on explaining the way of getting a precious knowledge in order to apply the learning automatically, and make judicial interventions for the process and to use it for expecting various decisive educational results such as the performances ( [9], [10]). When it comes to the learning analysis, it focuses on the way of having optimism in applying the process of learning by the help of data analysis [9].…”

Section: Introductionmentioning

confidence: 99%

“…When it comes to the learning analysis, it focuses on the way of having optimism in applying the process of learning by the help of data analysis [9]. To illustrate the learning analysis, it is concentrating on the data that is specialized in making and giving some decisions related to both learning and teaching processes [10]. Thus, data analysis may have a big effect on all stakeholders starting from teachers, to students, to the trainers of leadership, till the educational administration.…”

Section: Introductionmentioning

confidence: 99%

Guiding the Students in High School by Using Machine Learning

Ababneh¹,

Aljarrah²,

Karagozlu³

et al. 2021

TEM Journal

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Machine learning is considered the most significant technique that processes and analyses educational big data. In this research paper, many previous papers related to analysing the educational big data that uses a lot of artificial intelligence techniques were studied. The purpose of the study is to identify weaknesses and gaps in previous researches. The results showed that many researches highlighted early expectations for academic performance. Unfortunately, no one thought of finding an effective way to guide high schooled students to reach their appropriate majors that can be suitable to their abilities. Those students need to be guided to pass this sensitive phase with high efficiency and good results. Thus, this school level is considered as the starting point for students’ academic lives, professional, and future success.

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“…Thanks to the capability of online learning systems to track and store students' online activities, a feasible way to tackle the above issues is to get deep insights by analyzing the logs of online courses and then constructing the models for supporting instruction-related decision-making, which has attracted many research efforts [4]- [6]. Performance prediction or early warning prediction is one of the most important and interesting research topics among them [7], [8]. However, there are still some research gaps in performance prediction that remain unanswered.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

Yang

Hung

2020

IEEE Access

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The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education. However, the traditional prediction algorithms are originally designed for balanced dataset, while the educational dataset typically belongs to highly imbalanced dataset, which makes it more difficult to accurately identify the at-risk students. In order to solve this dilemma, this study proposes an integrated framework (LVAEPre) based on latent variational autoencoder (LVAE) with deep neural network (DNN) to alleviate the imbalanced distribution of educational dataset and further to provide early warning of at-risk students. Specifically, with the characteristics of educational data in mind, LVAE mainly aims to learn latent distribution of at-risk students and to generate at-risk samples for the purpose of obtaining a balanced dataset. DNN is to perform final performance prediction. Extensive experiments based on the collected K-12 dataset show that LVAEPre can effectively handle the imbalanced education dataset and provide much better and more stable prediction results than baseline methods in terms of accuracy and F 1.5 score. The comparison of t-SNE visualization results further confirms the advantage of LVAE in dealing with imbalanced issue in educational dataset. Finally, through the identification of the significant predictors of LVAEPre in the experimental dataset, some suggestions for designing pedagogical interventions are put forward. INDEX TERMS Performance prediction, early warning prediction, latent variational autoencoder, resampling methods, deep neural network, t-SNE.

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A systematic meta-Review and analysis of learning analytics research

Cited by 63 publications

References 26 publications

Mapping Learning Analytics initiatives in Latin America

Mapping Learning Analytics initiatives in Latin America

Guiding the Students in High School by Using Machine Learning

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

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