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Big Data in Educational Data Mining and Learning Analytics
English
2014
International Journal of Innovative Research in Computer and Communication Engineering
English
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Educational data mining and learning analytics are used to research and build models in several areas that can influence learning systems. ...
Key educational applications of relationship mining include discovery of associations between student performance and course sequences and discovering which pedagogical strategies lead to more effective ...
, and integration of psychometric modeling frameworks into machine-learned models. ...
doi:10.15680/ijircce.2014.0212044
fatcat:grxjrjggnvbibiritlajwizn7e
Predictive Model of Postgraduate Student's Dropout and Delay Using Machine Learning Algorithms
2021
International Journal of Advanced Trends in Computer Science and Engineering
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Age, money management skills, number of children, and health expenses are the other factors that contribute to higher dropout or delay at the university. ...
Therefore, many scholars have used the Decision Tree to predict student performance with greater success. ...
Machine learning is an emerging trend in education, where it is applied in learning historical data and using it to predict learners' future behavior [6] . ...
doi:10.30534/ijatcse/2021/591022021
fatcat:adunrwkecnd63ivpqdwzf6jcly
Academic libraries as active contributors to student wellness
2018
College & Undergraduate Libraries
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Colleges and universities have come to realize that student wellness is a factor in student retention and success. ...
in this essential form of outreach. ...
In implementing Hettler's model and others like it, colleges are creating learning opportunities for wellbeing that may impact students for the rest of their lives. ...
doi:10.1080/10691316.2018.1517433
fatcat:gflq7spacvem3hdjhcun5g7r7m
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
2017
Annual Review of Clinical Psychology
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We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. ...
Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. ...
ACKNOWLEDGEMENTS This work was supported by the National Institutes of Mental Health with grants P20MH090318, R01MH100482, and R01MH095753 to Dr. Mohr, and K08MH102336 to Dr. Schueller. ...
doi:10.1146/annurev-clinpsy-032816-044949
pmid:28375728
pmcid:PMC6902121
fatcat:rxu55uc7jvaeplcrzaeotkmv6i
COVID-19 Tweets Textual Analytics Using Machine Learning Classification for Fear Sentiment
2020
International Journal of Advanced Trends in Computer Science and Engineering
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With the huge number of messages seem in user interfaces, it obstruct user accessibility to useful information hide in disorganized, incomplete and unstructured text message. ...
The volume of COVID 19 microblogging messages is increasing exponentially with the popularity of COVID 19 microblogging services. ...
FUTURE STUDIES Corporations and small businesses can also benefit through such analyses and machine learning models to better understand consumer sentiment and expectations. ...
doi:10.30534/ijatcse/2020/221952020
fatcat:tvlslsmkubfsnnbov7ox3pmma4
Assessment of Academic Performance with The E-mental Health Interventions in Virtual Learning Environment Using Machine Learning Techniques: A Hybrid Approach
2021
Journal of Engineering Education Transformations
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There is a need and evaluation for the assessment and estimation of the impact of e-mental health interventions with the students learning through the virtual learning environment. ...
The act of virtual learning is defined through learning and practicing in an environment using digital/electronic content for self-paced through online teaching and mentoring. ...
A Virtual Learning Environment is a platform to students for delivering learning materials through the web. ...
doi:10.16920/jeet/2021/v34i0/157109
fatcat:nqyt727fhvbnrcxazrb2kx3fcu
Machine learning and medical devices: The next step for tissue engineering
2021
Engineering
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Acknowledgements The authors acknowledge support from the National Institutes of Health (P41 EB023833 and R01 AR068073). ...
Through machine learning, the technology builds an individual profile to understand the wearer's cardiovascular behaviors that fall within a normal and healthy range for that person. ...
Harnessing the power of machine learning is the next step in the evolution of medical device development and is key f or the continued success of tissue engineering. ...
doi:10.1016/j.eng.2021.05.014
fatcat:m3klaxs74ffxrfmq5rat3crjxy
The Future of Work: What It Is and How Our Resilience in the Face of It Matters
2021
Kinesiology Review
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Preparing students for future success in this environment requires educators to think more broadly and holistically about their roles. ...
Beyond this, the potential for future pandemics and other transformational events and trends mean that work is in a state of permanent flux. ...
And with machine learning, more jobs in health care and other fields will be automated and permanently disappear from the labor market (Selingo, 2017) . ...
doi:10.1123/kr.2021-0044
fatcat:u5sdyxri25gfrclbr6ds6b3jmy
Un análisis bibliométrico sobre el uso y la adopción de la educación en línea en la enseñanza superior
2021
Educación Superior
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metadata obtained and present the state, structure, and trends in the study of the adoption and use of e-learning in higher education. ...
This bibliometric analysis identifies and presents the articles that have given shape to the current research trends on the adoption and use of e-learning in higher education using structural equation ...
There is an increased interest in
Microsoft Academic through machine learning understanding the impact of psychological
parsing of accessible text strings (metadata + aspects, as drivers ...
doi:10.53287/ibsf4872ts39b
fatcat:gjyug23wmzgpdaarv7ggmjbnr4
Improving the Field's Understanding of Suicide Protective Factors and Translational Suicide Prevention Initiatives
2021
International Journal of Environmental Research and Public Health
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World Health Organization data show that approximately 800,000 persons die by suicide each year [...] ...
This investigation through the lens of the Integrated Motivational Volitional model of suicide (IMV) [6] provides a useful framework of understanding how upstream suicide prevention efforts that foster ...
The use of natural language processing and machine learning techniques may enhance the identification of the level of care needed.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/ijerph18031027
pmid:33503803
fatcat:dbupxp6hxbazjbyi6u66ntpwh4
Artificial Intelligence in Health, Human Service Delivery and Education: A Brief Conceptual Overview
2019
Journal of Health Science
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However, there are emerging applications of AI in the improvement of human welfare, health, and educational attainment. Each of these will be addressed in the conceptual review to follow. ...
Artificial intelligence has been the subject of substantial optimism and being of service to improving human lifestyles. ...
Machine learning statistical and predictive models of group and individual behaviors could be enhanced to improve community interventions. ...
doi:10.17265/2328-7136/2019.02.002
fatcat:ky2n6dz25naelhmuntjhlq6lq4
Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers
2020
Sustainability
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Throughout this research, we concentrate on predictive measures to identify and forecast procrastinator students by using ensemble machine learning models (i.e., Logistic Regression, Decision Tree, Gradient ...
Ubiquitous online learning is continuing to expand, and the factors affecting success and educational sustainability need to be quantified. ...
The approach we build through machine learning allows for a fine-grained study of procrastination and its connection with learning outcomes that can help understand more successful rearrangements of online ...
doi:10.3390/su12156074
fatcat:ponzclqabveqrlog77znuehpme
Recent Advances in Computational Epidemiology
2013
IEEE Intelligent Systems
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This approach has been tremendously successful in informing public health policy. Nevertheless, a potential weakness is its inability to capture the complexity of human interactions and behaviors. ...
and college students. ...
Health scientists have developed verbal or conceptual behavioral models to understand the role of behaviors in public health. 24, 25 But these models are typically informal and it's quite demanding to ...
doi:10.1109/mis.2013.114
pmid:25505373
pmcid:PMC4258713
fatcat:6psjez7lfvdmbik64g2i4xdtbu
Incorporating Health Education in the Curriculum: The Kenyan Experience
2018
International Journal of Trend in Scientific Research and Development
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The most recent challenges in health could be tackled by incorporating health education at all levels of learning. ...
It is based on health promoting social models. The young person is the heart of these models in a dynamic environment. ...
It demonstrates the usefulness of educational and preventive action in studies have shown a positive correlation between health behavior and with multiple students' success. ...
doi:10.31142/ijtsrd18832
fatcat:kplbv32jhfd2bbgkjm76uzmf4u
My Teacher Is a Machine: Understanding Students' Perceptions of AI Teaching Assistants in Online Education
2020
International Journal of Human-Computer Interaction
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Based on the present study's findings, more research is needed to better understand the nuances associated with the learning experience one may have from an AI teaching assistant. ...
Her research focuses on strategic instructional communication in a variety of contexts including risk, crisis, and health. ...
behavioral learning through various ways. ...
doi:10.1080/10447318.2020.1801227
fatcat:qfwuyhpbb5cr3ll3b7r7phjsze
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