What and when

Brown, Michael G.; DeMonbrun, Robert Matthew; Lonn, Steven; Aguilar, Stephen J.; Teasley, Stephanie D.

doi:10.1145/2883851.2883907

Cited by 18 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, Marbouti, Diefes-Dux, and Madhavan [21] highlighted the importance of minimizing false negatives to ensure that students at risk are warned about their situation. Generally, students may or may not be at risk, but other approaches have considered more states with a view to taking corrective measures, such as performance at or above (or below) the course mean, or trending towards underperformance [22].…”

Section: Prediction In Educationmentioning

confidence: 99%

Prediction in MOOCs: A Review and Future Research Directions

Moreno-Marcos

Alario-Hoyos

Muñoz‐Merino

et al. 2019

IEEE Trans. Learning Technol.

113

View full text Add to dashboard Cite

This paper surveys the state of the art on prediction in MOOCs through a Systematic Literature Review (SLR). The main objectives are: (1) to identify the characteristics of the MOOCs used for prediction, (2) to describe the prediction outcomes, (3) to classify the prediction features, (4) to determine the techniques used to predict the variables, and (5) to identify the metrics used to evaluate the predictive models. Results show there is strong interest in predicting drop-outs in MOOCs. A variety of predictive models are used, though regression and Support Vector Machines stand out. There is also wide variety in the choice of prediction features, but clickstream data about platform use stands out. Future research should focus on developing and applying predictive models that can be used in more heterogeneous contexts (in terms of platforms, thematic areas, and course durations), on predicting new outcomes and making connections among them (e.g., predicting learners' expectancies), on enhancing the predictive power of current models by improving algorithms or adding novel higher-order features (e.g., efficiency, constancy, etc.).

show abstract

Section: Prediction In Educationmentioning

confidence: 99%

Prediction in MOOCs: A Review and Future Research Directions

Moreno-Marcos

Alario-Hoyos

Muñoz‐Merino

et al. 2019

IEEE Trans. Learning Technol.

113

View full text Add to dashboard Cite

show abstract

“…Such systems consider that the influence of time and individual, disciplinary, and organizational factors are closely related to students' performances. Under this context, Brown, DeMonbrun, Lonn, Aguilar, and Teasley () exploit a data set of weekly performance classifications for first year students in a pre‐major advising program. As a finding, they know student academic performance is tailored by disciplinary rules in the timing of assessments.…”

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

View full text Add to dashboard Cite

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

show abstract

“…The activities that were most influential in the final grade were those related to exam preparation. • Brown et al performed multiple analytics studies with the help of Student Explorer, the first of which involved determining the reasons why students fall into a medium or high risk category [28]. This was done by using event history analysis techniques to determine the probability that a student enters one of the at-risk levels.…”

mentioning

confidence: 99%

Systematic Literature Review of Predictive Analysis Tools in Higher Education

et al. 2019

View full text Add to dashboard Cite

The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. They have applications in multiple contexts, education being an important one of them. Focusing on higher education scenarios, most notably universities, predictive analysis techniques are present in studies that estimate academic outcomes using different kinds of student-related data. Furthermore, predictive algorithms are the basis of tools such as early warning systems (EWS): applications able to foresee future risks, such as the likelihood of students failing or dropping out of a course, and alert of such risks so that corrective measures can be taken. The purpose of this literature review is to provide an overview of the current state of research activity regarding predictive analytics in higher education, highlighting the most relevant instances of predictors and EWS that have been used in practice. The PRISMA guidelines for systematic literature reviews were followed in this study. The document search process yielded 1382 results, out of which 26 applications were selected as relevant examples of predictors and EWS, each of them defined by the contexts where they were applied and the data that they used. However, one common shortcoming is that they are usually applied in limited scenarios, such as a single course, evidencing that building a predictive application able to work well under different teaching and learning methodologies is an arduous task.

show abstract

What and when

Cited by 18 publications

References 18 publications

Prediction in MOOCs: A Review and Future Research Directions

Prediction in MOOCs: A Review and Future Research Directions

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

Systematic Literature Review of Predictive Analysis Tools in Higher Education

Contact Info

Product

Resources

About