Who, when, and why

Aguiar, Everaldo; Lakkaraju, Himabindu; Bhanpuri, Nasir H.; Miller, David P.; Yuhas, Ben P.; Addison, Kecia L.

doi:10.1145/2723576.2723619

Cited by 42 publications

(18 citation statements)

References 8 publications

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“…For instance, Wandera et al (2019) proposed several models to predict school pass rate in order to support higher-level decision making. Similarly, Aguiar et al (2015) developed performance prediction models to help schools more efficiently allocate limited resources by prioritizing students who are most in need of help and target intervention programs to match those students’ particular needs.…”

Section: Resultsmentioning

confidence: 99%

“…The model predicting dropout achieved an area under the ROC curve (AUC) of 0.76 and the authors identified that the total number of non-correctible dress code violations, the number of in-school suspensions and the standard deviation of grades in the current semester were the most predictive features. Using a similar approach, Aguiar et al (2015) used random forest and logistic regression models for early prediction of students at risk of not graduating from high school. The authors suggested that these predictions can be used to inform targeted interventions for these students, hopefully leading to better outcomes ( Aguiar et al, 2015 ).…”

Section: Resultsmentioning

confidence: 99%

“…Using a similar approach, Aguiar et al (2015) used random forest and logistic regression models for early prediction of students at risk of not graduating from high school. The authors suggested that these predictions can be used to inform targeted interventions for these students, hopefully leading to better outcomes ( Aguiar et al, 2015 ).…”

Section: Resultsmentioning

confidence: 99%

“…The techniques listed in this section are highly related to the educational goals presented in section 5.2.1 . The papers related to predicting and enhancing students learning outcomes in general adopted traditional machine learning algorithms such as decision trees, naïve Bayes, support vector machines, logistic regression, and neural networks ( Aguiar et al, 2015 ; Lakkaraju et al, 2015 ; Blasi, 2017 ).…”

Section: Resultsmentioning

confidence: 99%

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Applications of Learning Analytics in High Schools: A Systematic Literature Review

Sousa

Alexandre

Mello

et al. 2021

Front. Artif. Intell.

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Learning analytics aims to analyze data from students and learning environments to support learning at different levels. Although learning analytics is a recent field, it reached a high level of maturity, especially in its applications for higher education. However, little of the research in learning analytics targets other educational levels, such as high school. This paper reports the results of a systematic literature review (SLR) focused on the adoption of learning analytics in high schools. More specifically, the SLR followed four steps: the search, selection of relevant studies, critical assessment, and the extraction of the relevant field, which included the main goals, approaches, techniques, and challenges of adopting learning analytics in high school. The results show that, in this context, learning analytics applications are focused on small-scale initiatives rather than institutional adoption. Based on the findings of this study, in combination with the literature, this paper proposes future directions of research and development in order to scale up learning analytics applications in high schools.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Sousa

Alexandre

Mello

et al. 2021

Front. Artif. Intell.

View full text Add to dashboard Cite

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“…Other works related to predict students at risk correspond to: Aguiar, Chawla, Brockman, Ambrose, and Goodrich () which measure engagement from learners' electronic portfolios to predict student' attrition, and Aguiar et al () that tailor an incremental approach to choose and prioritize learners who may be at risk to not graduate on time, and to propose the predictors of a given student going off‐track.…”

Section: Applicationsmentioning

confidence: 99%

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Peña-Ayala

2018

WIREs Data Min & Knowl

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Before the emergence of computer‐based educational systems (CBES) whose aims of providing teaching and learning experiences to hundreds even thousands of users, an explosion of information (e.g., students' log data) demands sophisticated methods to gather, analyze, and interpret learners' traces to regulate and enhance education. Thus, learning analytics (LA) arises as a knowledge discovery paradigm that provides valuable findings and facilitates stakeholders to understand the learning process and its implications. Therefore, a landscape of the LA nature, its underlying factors, and applications achieved is outlined in this paper according to a suggested LA Taxonomy that classifies the LA duty from a functional perspective. The aim is to provide an idea of the LA toil, its research lines, and trends to inspire the development of novel approaches for improving teaching and learning practices. Furthermore, the scope of this review covers recently published papers in prestigious journals and conferences, where the works dated from 2016 are summarized and those corresponding to 2014–2015 are cited according to the proposed LA taxonomy. A glimpse is sketched of LA, where underlying elements frame the field foundations to ground the approaches. Moreover, LA strengths, weaknesses, challenges, and risks are highlighted to advice how the LA arena could be enhanced and empowered. In addition, this review offers an insight of the recent LA labor, as well as motivates readers to enrich the LA achievements. This work promotes the LA practice giving an account of the job being achieved and reported in literature, as well as a reflection of the state‐of‐the‐art and an acumens vision to inspire future labor. This article is categorized under: Application Areas > Education and Learning Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

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