Predicting at-risk university students in a virtual learning environment via a machine learning algorithm

2020 ◽  
Vol 107 ◽  
pp. 105584 ◽  
Author(s):  
Kwok Tai Chui ◽  
Dennis Chun Lok Fung ◽  
Miltiadis D. Lytras ◽  
Tin Miu Lam
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Environment ◽  
University Students ◽  
Learning Algorithm ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Machine Learning Algorithm

SCREENING OF PATIENTS AT RISK FOR WILD TYPE ATTR-CM USING A COMPUTATIONAL MACHINE LEARNING ALGORITHM

2021 ◽  
Vol 37 (10) ◽  
pp. S65
Author(s):  
C Willis ◽  
K Kawamoto ◽  
A Watanabe ◽  
J Biskupiak ◽  
K Nolen ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Wild Type ◽  
Patients At Risk

IMPLEMENTING MACHINE LEARNING ALGORITHM TO IDENTIFY PATIENTS AT RISK OF DEVELOPING ELEVATED LEFT SIDED CARDIAC FILLING PRESSURES

2018 ◽  
Vol 71 (11) ◽  
pp. A727
Author(s):  
Kinjan Patel ◽  
Marton Tokodi ◽  
Partho P. Sengupta ◽  
Ashok Runkana ◽  
Sirish Shrestha ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Patients At Risk ◽  
Cardiac Filling

scholarly journals SCREENING OF PATIENTS AT RISK FOR WILD TYPE ATTR-CM USING A COMPUTATIONAL MACHINE LEARNING ALGORITHM

2021 ◽  
Vol 77 (18) ◽  
pp. 677
Author(s):  
Connor Willis ◽  
Kensaku Kawamoto ◽  
Alexandre Watanabe ◽  
Joseph Biskupiak ◽  
Kim Nolen ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Wild Type ◽  
Patients At Risk

Machine Learning Algorithm Based Bandwidth Prediction for Internet Usage

2020 ◽  
Vol 17 (8) ◽  
pp. 3749-3753
Author(s):  
J. Rajaram ◽  
M. Nalini ◽  
N. Vadivelan
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Arima Model ◽  
Internet Usage ◽  
Machine Learning Algorithm ◽  
Framework Structure ◽  
The Past ◽  
Basic Activity ◽  
Time Plan

The applicability of framework structure and affiliation arranging recognize a basic activity in the bandwidth prediction. The procedure for predicting the framework use is to see the basic transmission limit with respect to future periods. This prediction helps with utilizing the techniques workplaces in the saint way. Thinking about the fundamental cost of bandwidth, at top hours of a framework traffic we can follow an amazing sort of plan to purchase. In this paper, the past use data of FWDR organize centers is at risk to univariate direct time plan ARIMA model after precise change is used to calculate necessary bandwidth limit concerning future needs. The anticipated data is veered from the obvious data gained from a for all intents and purposes indistinguishable framework and the foreseen data has been viewed as inside ten percent MAPE. This design reduction the MAPE by eleven point seventy-one percentage and fifteen point forty-two percent of self-rulingly when stood separated from the non-able changed ARIMA model at ninety-nine percent CI. The outcome show that the suitably changed ARIMA design has improved show when meandered from non-intentionally changed ARIMA model. Increasingly significant dataset can be passed on with season alterations and thought of expanded length groupings, for dynamically unequivocal and longer term needs.

scholarly journals Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment

2019 ◽  
Vol 11 (24) ◽  
pp. 7238 ◽  
Author(s):  
Naif Radi Aljohani ◽  
Ayman Fayoumi ◽  
Saeed-Ul Hassan
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Learning Environment ◽  
Short Term Memory ◽  
At Risk Students ◽  
Virtual Learning ◽  
Classification Problem ◽  
Virtual Learning Environment ◽  
Clickstream Data ◽  
Education Decision

In higher education, predicting the academic performance of students is associated with formulating optimal educational policies that vehemently impact economic and financial development. In online educational platforms, the captured clickstream information of students can be exploited in ascertaining their performance. In the current study, the time-series sequential classification problem of students’ performance prediction is explored by deploying a deep long short-term memory (LSTM) model using the freely accessible Open University Learning Analytics dataset. In the pass/fail classification job, the deployed LSTM model outperformed the state-of-the-art approaches with 93.46% precision and 75.79% recall. Encouragingly, our model superseded the baseline logistic regression and artificial neural networks by 18.48% and 12.31%, respectively, with 95.23% learning accuracy. We demonstrated that the clickstream data generated due to the students’ interaction with the online learning platforms can be evaluated at a week-wise granularity to improve the early prediction of at-risk students. Interestingly, our model can predict pass/fail class with around 90% accuracy within the first 10 weeks of student interaction in a virtual learning environment (VLE). A contribution of our research is an informed approach to advanced higher education decision-making towards sustainable education. It is a bold effort for student-centric policies, promoting the trust and the loyalty of students in courses and programs.

scholarly journals Dinâmica da Digitação Aplicada à Autenticação Periódica de Usuários em Ambientes Virtuais de Aprendizagem

2017 ◽  
Vol 25 (02) ◽  
pp. 36
Author(s):  
Marco Aurélio Silva Cruz ◽  
Julio Cesar Duarte ◽  
Ronaldo Ribeiro Goldschmidt
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Learning Environment ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Machine Learning Techniques ◽  
Keystroke Dynamics ◽  
Learning Techniques

The authentication of users on a Virtual Learning Environment (VLE) is, in general, punctual and intrusive, occurring when the user connects to the environment, by typing his password. Such approach allows, after the initial login, that unauthenticated users take the role of authenticated users and perform tasks in the environment, causing, among other things, distortions in the perception about the academic performance of students. The objective of this work is, thus, to propose a mechanism to execute periodic and non-intrusive authentications of users in VLEs. The proposed mechanism uses machine learning techniques to build recognition models based on the keystroke dynamics of users and it is also independent of the used VLE. A prototype of the proposed mechanism, integrated with Moodle, was implemented and applied to a postgraduate course with seventeen users. The recognition models generated by the prototype in the case study showed a performance above 92% of accuracy, which is a positive indication about the viability of the utilization of the proposed mechanism.

Virtual Learning Environments as a Continuous Assessment Tool in University Students

2019 ◽  
pp. 229-252
Author(s):  
Piero Espino Román ◽  
Eugenia Olaguez Torres ◽  
Ricardo López Hernández
Keyword(s):  
Learning Environment ◽  
University Students ◽  
Virtual Environment ◽  
Assessment Tool ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Academic Program ◽  
Continuous Assessment ◽  
Teacher Student ◽  
The University

This chapter presents the development of a virtual learning environment through the use of e-assessment based on web technologies. This virtual environment development consists in the use of Code Igniter developed by EllisLab. Bootstrap was also simultaneously used, which is a framework developed within Twitter, with the objective of standardizing the tools that are used in the development. This virtual environment allowed to assess and keep track of the work of a selected group of students from the Mechatronics Engineering academic program of the Universidad Politécnica de Sinaloa in Mexico, which, throughout the course, allowed teachers to manage the students' assessments. As a conclusion, it was found that the virtual learning environment using e-assessment contributes to the teacher-student interaction within a virtual environment and in an online modality. Finally, it was reported that time spans in different areas were optimized, such as the areas of elaboration, design, application, and feedback of the university students' assessment.

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