scholarly journals Machine Learning para la mejora de la experiencia con MOOC: el caso de la Universitat Politècnica de València

2021 ◽  
pp. 91-104
Author(s):  
Jorge Angel Martinez Navarro ◽  
Ignacio Despujol Zabala
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
User Experience ◽  
Data Analytics ◽  
Dropout Rate ◽  
Student Dropout ◽  
Pedagogical Decisions ◽  
Revisión Sistemática ◽  
Literature Reviews ◽  
The University

El trabajo que se presenta tiene como objetivo el diseño de una propuesta de mecanismos automatizados fundamentados en machine learning para la mejora de la experiencia de los participantes en los cursos MOOC de la Universitat Politécnica de Valencia y la reducción de las tasas de abandono. Siguiendo una estrategia de investigación basada en el diseño IBD, en la que se ha priorizado siempre las decisiones pedagógicas por encima de las propias analíticas de datos, se han realizado tres iteraciones con distintos patrones metodológicos (revisión sistemática de literatura, machine learning basado en los datos de 260 cursos y más de 700.000 estudiantes, y creación de mecanismos automatizados) que siempre finalizan con la presentación de resultados y la realimentación por parte del equipo de la universidad. Las principales conclusiones de este trabajo indican que, de los veinticinco indicadores pedagógicos de abandono referidos por las revisiones bibliográficas en la iteración 1, solo se validan diez de ellos con los cursos de la UPV (no se tienen datos automáticos ni automatizables de los otros), y de esos finalmente solo seis de ellos son posibles predictores del abandono del alumnado, con los datos utilizados. Se proponen finalmente un conjunto de mecanismos automatizados que se aplicarán en la plataforma EdX de la universidad, para la mejora de la experiencia de los usuarios y la reducción de la tasa de abandonos en los cursos. The aim of this paper is to design a proposal for automated mechanisms based on machine learning to improve the experience of participants in MOOC courses at the Universitat Politécnica de Valencia and reduce dropout rates. Following a desing based research DBR design, in which pedagogical decisions have always been prioritised over data analytics, three iterations have been carried out with different methodological patterns (systematic literature review, machine learning based on data from 260 courses and 700.000 students, and creation of automated mechanisms) that always end with the presentation of results and feedback from the university team. The main conclusions of this work indicate that, of the twenty-five pedagogical dropout indicators referred to by the literature reviews in iteration 1, only ten of them are validated with UPV courses (no automated or automatable data are available for the others), and of those finally only six of them are possible predictors of student dropout, with the data used. Finally, a set of automated mechanisms are proposed to be applied in the university's EdX platform to improve the user experience and reduce the dropout rate in the courses.

scholarly journals Managing alarm systems for quality and safety in the hospital setting

2018 ◽  
Vol 7 (3) ◽  
pp. e000202 ◽  
Author(s):  
Tita Alissa Bach ◽  
Lars-Martin Berglund ◽  
Eva Turk
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Grey Literature ◽  
Hospital Setting ◽  
Integrated Approach ◽  
Clinical Environment ◽  
Alarm Systems ◽  
Literature Reviews ◽  
Technical Factors ◽  
Quality In Healthcare

ObjectiveTo provide an overview of documented studies and initiatives that demonstrate efforts to manage and improve alarm systems for quality in healthcare by human, organisational and technical factors.MethodsA literature review, a grey literature review, interviews and a review of alarm-related standards (IEC 60601-1-8, IEC 62366-1:2015 and ANSI/Advancement of Medical Instrumentation HE 75:2009/2013) were conducted. Qualitative analysis was conducted to identify common themes of improvement elements in the literature and grey literature reviews, interviews and the review of alarm-related standards.Results21 articles and 7 publications on alarm quality improvement work were included in the literature and grey literature reviews, in which 10 themes of improvement elements were identified. The 10 themes were categorised into human factors (alarm training and education, multidisciplinary teamwork, alarm safety culture), organisational factors (alarm protocols and standard procedures, alarm assessment and evaluation, alarm inventory and prioritisation, and sharing and learning) and technical factors (machine learning, alarm configuration and alarm design). 26 clinicians were interviewed. 9 of the 10 themes were identified from the interview responses. The review of the standards identified 3 of the 10 themes. The study findings are also presented in a step-by-step guide to optimise implementation of the improvement elements for healthcare organisations.ConclusionsImproving alarm safety can be achieved by incorporating human, organisational and technical factors in an integrated approach. There is still a gap between alarm-related standards and how the standards are translated into practice, especially in a clinical environment that uses multiple alarming medical devices from different manufacturers. Standardisation across devices and manufacturers and the use of machine learning in improving alarm safety should be discussed in future collaboration between alarm manufacturers, end users and regulators.

A Machine Learning Approach to Detect Student Dropout at University

2021 ◽  
Vol 10 (6) ◽  
pp. 3101-3107
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Dropout Rate ◽  
Random Forest Classifier ◽  
Drop Out ◽  
Dropout Rates ◽  
Learning Technology ◽  
Student Dropout ◽  
High Dropout Rate ◽  
Academic Plan

In universities, student dropout is a major concern that reflects the university's quality. Some characteristics cause students to drop out of university. A high dropout rate of students affects the university's reputation and the student's careers in the future. Therefore, there's a requirement for student dropout analysis to enhance academic plan and management to scale back student's drop out from the university also on enhancing the standard of the upper education system. The machine learning technique provides powerful methods for the analysis and therefore the prediction of the dropout. This study uses a dataset from a university representative to develop a model for predicting student dropout. In this work, machine- learning models were used to detect dropout rates. Machine learning is being more widely used in the field of knowledge mining diagnostics. Following an examination of certain studies, we observed that dropout detection may be done using several methods. We've even used five dropout detection models. These models are Decision tree, Naïve bayes, Random Forest Classifier, SVM and KNN. We used machine-learning technology to analyze the data, and we discovered that the Random Forest classifier is highly promising for predicting dropout rates, with a training accuracy of 94% and a testing accuracy of 86%.

A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Roberto Salazar-Reyna ◽  
Fernando Gonzalez-Aleu ◽  
Edgar M.A. Granda-Gutierrez ◽  
Jenny Diaz-Ramirez ◽  
Jose Arturo Garza-Reyes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Research Area ◽  
Engineering Systems ◽  
Content Type ◽  
Healthcare Engineering

PurposeThe objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining and machine learning to healthcare engineering systems.Design/methodology/approachA systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors and content.FindingsFrom the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field.Research limitations/implicationsThe use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors' previous knowledge and the nature of the publications were used to select different platforms.Originality/valueTo the best of the authors' knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining and machine learning applied to healthcare engineering systems.

scholarly journals Smart literature review: a practical topic modelling approach to exploratory literature review

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Claus Boye Asmussen ◽  
Charles Møller
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Topic Modelling ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Literature Reviews ◽  
Modelling Approach

Abstract Manual exploratory literature reviews should be a thing of the past, as technology and development of machine learning methods have matured. The learning curve for using machine learning methods is rapidly declining, enabling new possibilities for all researchers. A framework is presented on how to use topic modelling on a large collection of papers for an exploratory literature review and how that can be used for a full literature review. The aim of the paper is to enable the use of topic modelling for researchers by presenting a step-by-step framework on a case and sharing a code template. The framework consists of three steps; pre-processing, topic modelling, and post-processing, where the topic model Latent Dirichlet Allocation is used. The framework enables huge amounts of papers to be reviewed in a transparent, reliable, faster, and reproducible way.

scholarly journals User Experience in Institutional Repositories

2018 ◽  
Vol 9 (1) ◽  
pp. 70-86 ◽  
Author(s):  
Laura Icela González-Pérez ◽  
María-Soledad Ramírez-Montoya ◽  
Francisco J. García-Peñalvo
Keyword(s):  
Open Access ◽  
Literature Review ◽  
User Experience ◽  
Business Models ◽  
Academic Community ◽  
Educational Context ◽  
User Centered Design ◽  
Institutional Repositories ◽  
Custom Made ◽  
The University

Disruptive ideas and innovative business models take shape from observing and investigating the needs and demands of potential users and measuring their success based on the acceptance by users and their satisfaction. In an educational context, a new mission of the university has emerged, supported by the transfer of open access knowledge through Institutional Repositories (IR); it is important to know the motivations and needs of the academic community to promote scientific dissemination using these platforms. The present article uses the method of systematic literature review: using 29 studies from SCOPUS and WoS, involving the topics User-Centered Design (UCD) and repositories. The results show that two of the three UCD phases—evaluation and requirements—are closely linked and are the reiterative focus of UCD; thus, it is desirable to promote the design of custom-made prototypes according to the users' motivations. It is necessary to redefine methodologies for IR development within open-access ecosystems to guide them towards meeting their potential users' needs and motivations.

scholarly journals User Experience in Institutional Repositories

2020 ◽  
pp. 423-440
Author(s):  
Laura Icela González-Pérez ◽  
María-Soledad Ramírez-Montoya ◽  
Francisco J. García-Peñalvo
Keyword(s):  
Open Access ◽  
Literature Review ◽  
Present Article ◽  
User Experience ◽  
Systematic Literature Review ◽  
Business Models ◽  
Academic Community ◽  
Institutional Repositories ◽  
Custom Made ◽  
The University

Disruptive ideas and innovative business models take shape from observing and investigating the needs and demands of potential users and measuring their success based on the acceptance by users and their satisfaction. In an educational context, a new mission of the university has emerged, supported by the transfer of open access knowledge through Institutional Repositories (IR); it is important to know the motivations and needs of the academic community to promote scientific dissemination using these platforms. The present article uses the method of systematic literature review: using 29 studies from SCOPUS and WoS, involving the topics User-Centered Design (UCD) and repositories. The results show that two of the three UCD phases—evaluation and requirements—are closely linked and are the reiterative focus of UCD; thus, it is desirable to promote the design of custom-made prototypes according to the users' motivations. It is necessary to redefine methodologies for IR development within open-access ecosystems to guide them towards meeting their potential users' needs and motivations.

scholarly journals Predicting Dropout in Higher Education: a Systematic Review

2021 ◽  
Author(s):  
Jailma Januário da Silva ◽  
Norton Trevisan Roman
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Systematic Review ◽  
Literature Review ◽  
Decision Tree ◽  
Systematic Literature Review ◽  
Machine Learning Technique ◽  
Student Dropout ◽  
Learning Technique ◽  
Research Questions

In this article, we present a systematic literature review, carried out from February to March 2020, on the application of a machine learning technique to predict student dropout in higher education institutions. Besides describing the protocol followed during our research, which includes the research questions, searched databases and query strings, along with criteria for inclusion and exclusion of articles, we also present our main results, in terms of the attributes used by current research on this theme, along with adopted approaches, specific algorithms, and evalution metrics. The Decision Tree technique is the most used for the construction of models, and accuracy and recall and precision being the most used metric for evaluating models.

Impact of Postgraduate Students Dropout and Delay in University: Analysis Using Machine Learning Algorithms

2021 ◽  
Vol 10 (3) ◽  
pp. 1821-1826
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Prediction Model ◽  
Learning Algorithms ◽  
Dropout Rate ◽  
Machine Learning Algorithms ◽  
Accuracy Class ◽  
Postgraduate Students ◽  
Student Dropout ◽  
Postgraduate Studies

Cost of education and economic background are some factors that influence student dropout from postgraduate studies. However, high dropouts do not affect the students only, but also impact university revenue. This research analyzes various literature on machine learning algorithms and applies suitable algorithm to produce a prediction model. This study indicates that decision tree and Random Forest algorithms have better accuracy, class recall, and class precision than Naïve Bayes. Therefore, the prediction model uses the Decision Tree algorithm to provide various approaches to maximize revenue in universities. The findings indicate high dropout rates negatively impact university revenue, while low rates influence revenue positively. Other aspects like grants received by students, the number of research publications, anddegree level also positively or negatively impact revenue if the dropout rate is medium. A complete understanding of this prediction model can identify and minimize the risk of early withdrawal or delayed graduation and improve revenue generation by universities.

scholarly journals The Investigation of Student Dropout Prediction Model in Thai Higher Education Using Educational Data Mining: A Case Study of Faculty of Science, Prince of Songkla Uni-versity

2019 ◽  
Vol 27 (1) ◽  
pp. 356-367 ◽  
Author(s):  
Jarutas Pattanaphanchai ◽  
Koranat Leelertpanyakul ◽  
Napa Theppalak
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Prediction Model ◽  
Retention Rate ◽  
Dropout Rate ◽  
Drop Out ◽  
Data Mining Technique ◽  
Student Dropout ◽  
The University

The student’s retention rate is one of the challenging issues that representing the quality of the university. A high dropout rate of students affects not only the reputation of the university but also the students’ career in the future. Therefore, there is a need of student dropout analysis in order to improve the academic plan and management to reduce students drop out from the university as well as to  enhance the quality of the higher education system. Data mining technique provides powerful methods for analysis and the prediction the dropout. This paper proposes a model for predicting students’ dropout using the dataset from the representative of the largest public university in the Southen part of Thailand. In this study, data from Faculty of Science, Prince of Songkla University was collected from academic year of 2013 to 2017. The experiment result shows that JRip rule induction is the best technique to generate a prediction model receiving the highest accuracy value of 77.30%. The results highlight the potential prediction model that can be used to detect the early state of dropping out of the student which the university can provide supporting program to improve the student retention rate

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