Creating a Recommender System to Support Higher Education Students in the Subject Enrollment Decision

Fernández-García, Antonio Jesús; Rodríguez-Echeverría, Roberto; Preciado, Juan Carlos; Conejero, José María

doi:10.1109/access.2020.3031572

Cited by 24 publications

(14 citation statements)

References 30 publications

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“…The work described in [75] focused on data obtained from over 11,000 students enrolled in several online courses in the National Distance Education University. The study referred in [106] considered data obtained from 323 students enrolled, from 2010 to 2018, in the computer sciences and information course of a public unidentified university.…”

Section: Where Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

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How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Oliveira

Sobral

Ferreira

et al. 2021

BDCC

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Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.

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Section: Where Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

“…Another example is the work described in [98], where EDM is used to warn students of poor performance. In [106], students are provided with recommended subjects based on historic data. In [112], a tool identifies the urgency in student posts so that a tutor can prioritise the answers.…”

Section: How Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Oliveira

Sobral

Ferreira

et al. 2021

BDCC

View full text Add to dashboard Cite

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“…Beyond enrolment time, after one semester of college, we ourselves have worked on a decision support system with the challenge of addressing the construction of a reliable Recommender System on the basis of data which are both sparse and few in quantity, as well as being imbalanced, thus hampering the anticipatation of students' academic achievement [10]. We have also studied the importance of Feature Selection methods in Academic Data to create easy-to-explain predictive models for shorter periods of time, reducing overfitting, and avoiding the sparsity of data which these kind of datasets usually possess [11].…”

Section: Related Workmentioning

confidence: 99%

“…There are many encoding techniques that can be employed, the best known being Label Encoding and One-Hot Encoding. The former assigns an integer value to each of the feature values, while the latter consists of creating n new binary features [10], for each given feature with n classes.…”

Section: ) Encodingmentioning

confidence: 99%

A Real-Life Machine Learning Experience for Predicting University Dropout at Different Stages Using Academic Data

et al. 2021

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High levels of school dropout are a major burden on the educational and professional development of a country's inhabitants. A country's prosperity depends, among other factors, on its ability to produce higher education graduates capable of moving a country forward. To alleviate the dropout problem, more and more institutions are turning to the possibilities that artificial intelligence can provide to predict dropout as early as possible. The difficulty of accessing personal data and privacy issues that it entails force the institutions to rely on the Academic Data of their students to create accurate and reliable predictive systems. This work focuses on creating the best possible predictive model based solely on academic data, and accordingly, its capacity to infer knowledge must be maximised. Thus, Feature Engineering and Instance Engineering techniques such as dealing with redundancy, significance of the features, correlation, cardinality features, missing values, creation or elimination of features, data fusion, removal of unuseful instances, binning, resampling, normalisation, or encoding are applied in detail before the construction of well-known models such as Gradient Boosting, Random Forest, and Support Vector Machine along with an Ensemble of them at different stages: prior to enrolment, at the end of the first semester, at the end of the second semester, at the end of the third semester, and at the end of the fourth semester. Through the construction of these predictive models that serve as inputs to a decision support system, the application of effective dropout prevention policies can be applied.

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“…Universities are being urged to adopt data-driven, evidence-based approaches to improve student retention, reduce drop-out rates, and improve the quality of education by removing bias from grading and evaluation of students (Nuutila et al, 2018;Gabriele et al, 2016). Many researchers have addressed this call for implementing predictive models capable of identifying key performance determinants of students (Barik et al, 2020;Deo et al, 2020;Fernández-García et al, 2020). There seems to be a need to improve overall transparency in student evaluation to benefit both students and educational institutions.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Classification Models in Predicting Graduate Admission Decision

2021

JHETP

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While the decision process of graduate admissions remains elusive, specific criteria are decidedly significant in determining admission outcome. Prospective students applying to graduate programs experience a real predicament of selecting the right schools to invest limited resources for the application. This paper presents comparisons of various machine learning classification models, including Naïve Bayes, Logistic Regression, Multilayer Perceptron and Decisions Tree models, in predicting the admission outcome of candidates with a set of known parameters using a dataset of 400 applicant records. By comparing the performance metrics of these methods, the study finds Naïve Bayes to be the most accurate model for this type of dataset. Predictive models such as the ones discussed in this paper can be a valuable tool for prospective students in shortlisting universities in their application process. The study also proposes a framework that incorporates machine learning-based classification into the admissions decision process. Implementing such methods may help support graduate admissions committees in streamlining large pools of applications or observing and understanding trends in their past admission decisions.

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Creating a Recommender System to Support Higher Education Students in the Subject Enrollment Decision

Cited by 24 publications

References 30 publications

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

A Real-Life Machine Learning Experience for Predicting University Dropout at Different Stages Using Academic Data

A Comparison of Classification Models in Predicting Graduate Admission Decision

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