scholarly journals Early Dropout Prediction in MOOCs through Supervised Learning and Hyperparameter Optimization

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1701
Author(s):  
Theodor Panagiotakopoulos ◽  
Sotiris Kotsiantis ◽  
Georgios Kostopoulos ◽  
Omiros Iatrellis ◽  
Achilles Kameas
Keyword(s):  
Online Education ◽  
Online Courses ◽  
Early Stage ◽  
Activity Patterns ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Educational Institutions ◽  
Student Dropout ◽  
Wide Range

Over recent years, massive open online courses (MOOCs) have gained increasing popularity in the field of online education. Students with different needs and learning specificities are able to attend a wide range of specialized online courses offered by universities and educational institutions. As a result, large amounts of data regarding students’ demographic characteristics, activity patterns, and learning performances are generated and stored in institutional repositories on a daily basis. Unfortunately, a key issue in MOOCs is low completion rates, which directly affect student success. Therefore, it is of utmost importance for educational institutions and faculty members to find more effective practices and reduce non-completer ratios. In this context, the main purpose of the present study is to employ a plethora of state-of-the-art supervised machine learning algorithms for predicting student dropout in a MOOC for smart city professionals at an early stage. The experimental results show that accuracy exceeds 96% based on data collected during the first week of the course, thus enabling effective intervention strategies and support actions.

Web and Education as Disruptors of Traditional Education and Development of Future Students and Workers

2020 ◽  
pp. 170-192
Author(s):  
Marie-Line Germain
Keyword(s):  
Online Education ◽  
Online Courses ◽  
Traditional Education ◽  
Educational Institutions ◽  
Digital Platforms ◽  
Disruptive Technologies ◽  
Wide Range ◽  
Education And Development ◽  
The Web

Over the past 30 years, the internet has evolved from being the web of content to being the web of thoughts and the web of things in business, communication, entertainment, and education. To stay competitive, higher education institutions have had to train students on the wide range of skills and experiences and to move to digital platforms to better meet the needs of students, employees, and organizations. This chapter provides an overview of the development of online education, the 1.0 to 5.0 phases of web development, and how the field of education has adapted to these phases. Particular emphasis is placed on the use of mobile learning such as MOOCs, course collaboration software, and how smartphones can be used in courses to interact with peers and faculty. This chapter then presents a case study illustrating how online courses can successfully integrate Web 4.0 and 5.0 technology. It concludes by discussing the benefits and challenges of adopting some disruptive technologies and on how educational institutions can meet the needs of the next generation of students.

scholarly journals Epigenetic Classifiers for Precision Diagnosis of Brain Tumors

2019 ◽  
Vol 12 ◽  
pp. 251686571984028 ◽  
Author(s):  
Javier IJ Orozco ◽  
Ayla O Manughian-Peter ◽  
Matthew P Salomon ◽  
Diego M Marzese
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Brain Tumors ◽  
Brain Metastases ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Malignant Neoplasms ◽  
Cancer Breast ◽  
Wide Range ◽  
Breast Cancer Brain Metastases

DNA methylation profiling has proven to be a powerful analytical tool, which can accurately identify the tissue of origin of a wide range of benign and malignant neoplasms. Using microarray-based profiling and supervised machine learning algorithms, we and other groups have recently unraveled DNA methylation signatures capable of aiding the histomolecular diagnosis of different tumor types. We have explored the methylomes of metastatic brain tumors from patients with lung cancer, breast cancer, and cutaneous melanoma and primary brain neoplasms to build epigenetic classifiers. Our brain metastasis methylation (BrainMETH) classifier has the ability to determine the type of brain tumor, the origin of the metastases, and the clinical-therapeutic subtype for patients with breast cancer brain metastases. To facilitate the translation of these epigenetic classifiers into clinical practice, we selected and validated the most informative genomic regions utilizing quantitative methylation-specific polymerase chain reaction (qMSP). We believe that the refinement, expansion, integration, and clinical validation of BrainMETH and other recently developed epigenetic classifiers will significantly contribute to the development of more comprehensive and accurate systems for the personalized management of patients with brain metastases.

Development of a Complication- and Treatment-Aware Prediction Model for Favorable Functional Outcome in Aneurysmal Subarachnoid Hemorrhage Based on Machine Learning

Neurosurgery ◽  
2020 ◽  
Author(s):  
Nicolai Maldaner ◽  
Anna M Zeitlberger ◽  
Marketa Sosnova ◽  
Johannes Goldberg ◽  
Christian Fung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Subarachnoid Hemorrhage ◽  
Functional Outcome ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Early Model ◽  
Test Dataset ◽  
Wide Range ◽  
Favorable Functional Outcome

Abstract BACKGROUND Current prognostic tools in aneurysmal subarachnoid hemorrhage (aSAH) are constrained by being primarily based on patient and disease characteristics on admission. OBJECTIVE To develop and validate a complication- and treatment-aware outcome prediction tool in aSAH. METHODS This cohort study included data from an ongoing prospective nationwide multicenter registry on all aSAH patients in Switzerland (Swiss SOS [Swiss Study on aSAH]; 2009-2015). We trained supervised machine learning algorithms to predict a binary outcome at discharge (modified Rankin scale [mRS] ≤ 3: favorable; mRS 4-6: unfavorable). Clinical and radiological variables on admission (“Early” Model) as well as additional variables regarding secondary complications and disease management (“Late” Model) were used. Performance of both models was assessed by classification performance metrics on an out-of-sample test dataset. RESULTS Favorable functional outcome at discharge was observed in 1156 (62.0%) of 1866 patients. Both models scored a high accuracy of 75% to 76% on the test set. The “Late” outcome model outperformed the “Early” model with an area under the receiver operator characteristics curve (AUC) of 0.85 vs 0.79, corresponding to a specificity of 0.81 vs 0.70 and a sensitivity of 0.71 vs 0.79, respectively. CONCLUSION Both machine learning models show good discrimination and calibration confirmed on application to an internal test dataset of patients with a wide range of disease severity treated in different institutions within a nationwide registry. Our study indicates that the inclusion of variables reflecting the clinical course of the patient may lead to outcome predictions with superior predictive power compared to a model based on admission data only.

scholarly journals Supervised Machine Learning Algorithms For Early Diagnosis Of Alzheimer’s Disease

2019 ◽  
Vol 8 (3) ◽  
pp. 7964-7967
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Memory Loss ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Diagnostic Tools ◽  
Longitudinal Mri ◽  
Permanent Brain Damage

Alzheimer’s is a neurodegenerative disease which can eventually leads to dementia. Mostly occurring in elderly people over the age of 65, it is hard to detect and diagnose correctly. Most common symptoms include memory loss and slow deterioration of cognitive functions. Given that these symptoms are seen often in old people, this hinders the detection of Alzheimer’s disease (AD). Alzheimer’s is currently incurable, but detection of the disease during its early stage is often beneficial to the patient, since there are treatments which can considerably improve the quality of life of the patient. However this can only be done if the patient has been diagnosed at a stage before any permanent brain damage has been done. Most of the current methods for detecting and diagnosing AD are not good enough. It is the need of the hour to develop better and early diagnostic tools. With the improvements in the field of machine learning, we now have the tools needed to drastically improve detection of Alzheimer’s. We examine various machine learning methods and algorithms to find a method which can boost the chances of detecting the disease. We will use the following algorithms: Decision Tree, SVM, Random Forest and Adaboost. The dataset being used is the longitudinal MRI data available included in the OASIS dataset. We will use the aforementioned algorithms on the dataset and compare the accuracies achieved to find an optimal.

scholarly journals Considering Performance in the Automated and Manual Coding of Sociolinguistic Variables: Lessons From Variable (ING)

2021 ◽  
Vol 4 ◽  
Author(s):  
Tyler Kendall ◽  
Charlotte Vaughn ◽  
Charlie Farrington ◽  
Kaylynn Gunter ◽  
Jaidan McLean ◽  
...  
Keyword(s):  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Language Variation And Change ◽  
The Public ◽  
Acoustic Correlates ◽  
Wide Range ◽  
Automated Coding ◽  
Automated Procedures ◽  
The Impact

Impressionistic coding of sociolinguistic variables like English (ING), the alternation between pronunciations like talkin' and talking, has been a central part of the analytic workflow in studies of language variation and change for over a half-century. Techniques for automating the measurement and coding for a wide range of sociolinguistic data have been on the rise over recent decades but procedures for coding some features, especially those without clearly defined acoustic correlates like (ING), have lagged behind others, such as vowels and sibilants. This paper explores computational methods for automatically coding variable (ING) in speech recordings, examining the use of automatic speech recognition procedures related to forced alignment (using the Montreal Forced Aligner) as well as supervised machine learning algorithms (linear and radial support vector machines, and random forests). Considering the automated coding of pronunciation variables like (ING) raises broader questions for sociolinguistic methods, such as how much different human analysts agree in their impressionistic codes for such variables and what data might act as the “gold standard” for training and testing of automated procedures. This paper explores several of these considerations in automated, and manual, coding of sociolinguistic variables and provides baseline performance data for automated and manual coding methods. We consider multiple ways of assessing algorithms' performance, including agreement with human coders, as well as the impact on the outcome of an analysis of (ING) that includes linguistic and social factors. Our results show promise for automated coding methods but also highlight that variability in results should be expected even with careful human coded data. All data for our study come from the public Corpus of Regional African American Language and code and derivative datasets (including our hand-coded data) are available with the paper.

LEARNERS PREFERENCE TOWARDS ONLINE COURSES- A STUDY

2021 ◽  
pp. 43-45
Author(s):  
G. Anuradha ◽  
G. A. Hema
Keyword(s):  
Online Education ◽  
Formal Education ◽  
Online Courses ◽  
Weighted Average ◽  
Vital Role ◽  
Educational Institutions ◽  
Average Score ◽  
Web Browser ◽  
Brick And Mortar

Online courses are revolutionizing formal education and have opened a new genre of outreach on cultural and scientic topics. These courses deliver a series of lessons to a web browser or mobile device, to be conveniently accessed anytime, any place. Nowadays People are interested in enriching their knowledge with the help of growing technologies, like e-books, mobile library etc. Among them online courses play a vital role. It helps people with authorized certications, employment etc. without age barrier. For the purpose of study 200 respondents were taken from Coimbatore city. The tools used for the research are simple percentage analysis, and weighted average score. From the study it is found that majority of the respondents have indicated their priority for course cost as highly satised feature of the online course when compared to others. The study suggests that an online education is preferred by individuals who may not be able to make it for classes in a traditional brick and mortar kind of college due to various reasons, one can gain more professional knowledge in these online courses if they use it effectively. The educational institutions can also motivate the learners by waving atleast the part of the fees so that more number of students can be enrolled. The study concluded that, the quality of education has improved by online courses and even it has become easy for students to refer the content as per their leisure. In the era of digitalization the scope of online education increases even more and will be benecial for students, professionals and also institutions.

scholarly journals Experiences of online learning during COVID-19 pandemic lockdown period: a cross-sectional survey among the college students in India

2021 ◽  
Author(s):  
Nikita Saraf ◽  
Prakash S. Doss ◽  
Sanskruti Tahakik ◽  
Kowshik B. Reddy ◽  
Arun Rangasamy ◽  
...  
Keyword(s):  
Online Learning ◽  
Online Education ◽  
Scale Up ◽  
Educational Institutions ◽  
Higher Educational Institutions ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Wide Range ◽  
Technical Issues ◽  
Higher Educational

Background:  The COVID-19 pandemic forced educational institutions across the world to shut down their campuses adhering to the lockdown protocols. As a result, higher educational institutions seek to deliver education through online platforms. This sudden shift created a huge impact on students learning capabilities.Methods: We conducted an online cross-sectional survey to assess the college student’s perspective about the online learning and also the most common problems faced in this online learning. A 17 item Google form questionnaire was structured by the authors. We received responses 1834 students from different higher educational institutions across India.Results: The results revealed that 71.7% of students rated the pandemic online learning below moderate on a 5-point scale. The students felt that they learn better in offline classrooms (78.9%) and reported few technical issues which hinder their learning capacity.  The students appreciated the flexibility of learning from home, availability of more personal space, accessibility of abundant online learning resources, and wide range of online learning platforms in this online learning.  However, most of the students felt that online learning is stressful and affects their mental health in particular.Conclusions: The study reveals the importance of online education in the near future. As we scale up to the future, this survey findings warrant a prompt action to improve digital infrastructure to resolve the technical issues, reduce cost involvement, well-structured training to improve pedagogical skills and technical skills of the faculty members.

scholarly journals Análisis de un curso en línea masivo y abierto (MOOC) con una eficiencia terminal atípica / Analysis of a Massive Open Online Course (MOOC) with an Atypical Terminal Efficiency

2016 ◽  
Vol 5 (1) ◽  
pp. 91-101 ◽  
Author(s):  
Lorena Aleman de la Garza ◽  
Teresa Sancho-Vinuesa ◽  
Marcela Georgina Gómez Zermeño
Keyword(s):  
Online Education ◽  
Latin American ◽  
Online Courses ◽  
Academic Staff ◽  
Completion Rate ◽  
Educational Institutions ◽  
Great Expectations ◽  
American University ◽  
Massive Open Online Course ◽  
Management Of Technology

ABSTRACTMassive Open Online Courses (MOOCs) have generated great expectations since they empower online education by providing students, teachers and the community in general, a new way of building knowledge. However, when measuring the efficiency of the MOOCs there is no consensus on the methodology to calculate how successful they are. This document presents a comparative analysis between 12 MOOCs from different academic areas taught by one Latin-American University, who is a pioneer in offering education through the Coursera platform. Within the analysis, we highlight a MOOC with a completion rate exceeding 20%. In order to identify the factors that influenced the atypical completion rate the results include a description of the participants’ characteristics, their access and management of technology as well as the strategies implemented by the instructors and academic staff to generate, for participants, a motivating virtual environment of learning. Results suggest that educational institutions must establish criteria for the design and implementation of MOOCs aiming to offer participants qualitycontent and enriching experiences.RESUMENLos cursos en línea, masivos y abiertos (MOOC) han generado grandes expectativas debido a que potencializan la educación en línea al ofrecer a estudiantes, docentes y a la comunidad en general, una nueva manera de construir conocimiento. Sin embargo, en el momento de medir la eficiencia de los MOOC no existe consenso en la metodología a seguir para calcular su éxito. Este documento presenta un análisis comparativo entre 12 cursos MOOC de distintas áreas académicas impartidos por una de las universidades latinoamericanas pioneras en ofrecer educación a través de la plataforma Coursera.  Dentro del análisis, destaca un MOOC con tasa de eficiencia terminal superior al 20%. Con la finalidad de identificar los factores que influyeron en la tasa de eficiencia terminal atípica se describen las características de los participantes, su competencia en el uso e incorporación a la tecnología así como las estrategias implementadas por el equipo docente y administrativo del curso para generar un ambiente virtual de aprendizaje que sea motivador para los participantes. Los resultados sugieren que las institucio-nes educativas establezcan criterios en el diseño e implementación de los cursos MOOC con la finalidad de ofrecer a los partici-pantes contenidos de calidad y experiencias de aprendizaje enriquecedoras.

Education in the Pandemic

2021 ◽  
pp. 80-97
Author(s):  
V. M. NOVIKOV
Keyword(s):  
Online Education ◽  
General Education ◽  
Education System ◽  
Digital Libraries ◽  
Transition Period ◽  
Educational Institutions ◽  
Epidemic Period ◽  
Wide Range ◽  
The World ◽  
The Future

The education sector in Ukraine, as in most other countries of the world, is facing a global challenge due to the spread of the coronavirus SARS-CoV-2. Educational institutions are in an emergency situation and are forced to switch to distance or mixed learning. As a result, there were difficulties with the implementation of a new alternative model, according to which educational institutions have started to work and provide a wide range of their services. The purpose and novelty of the article is a systematic generalization of the results of the extreme mode of operation of educational institutions during the pandemic to adapt them to the extraordinary situation of functioning and further development. The method of research is the systematization of empirical data on individual practices of different types of educational institutions and, on this ground, determination of the possibility of combining in a holistic operation mechanism in terms of the social justice and effi ciency principles. Th e article analyzes the readiness of the education system for distance learning in the context of a pandemic, determines positions for the regulation and sustainable adjustment of educational institutions, ensuring fair access for various segments of society to online resources, the formation of positive scenarios for the education functioning in the epidemic and post-epidemic period. The paper uses the main theoretical positions formulated in the monograph “COVID 19. Great Overload” by Klaus Schwab, economist, founder and president of the World Economic Forum in Davos since 1971, and his co-author Thierry Muller, a publicist and researcher. One of the main provisions concerning education and its long-term development is that the current crisis is forcing society to realize that most decisions are based on moral and fair choices, and that in the future it is possible to move away from personal interests and create a more harmonious society. The UN materials on the analysis of the COVID-19 consequences for the education system and its reconstruction in the future are also used. The study identifies the dynamics of processes throughout the pandemic. Positive and problematic practices are given. The findings obtained from research and experience can be the basis for developing measures to help education institutions to overcome the “transition period”. The key factors of their stability are the general education services (digital libraries, online education platforms), mobilization of teaching and student contingents to work in new conditions, compliance of financial and distribution mechanisms with the requirements of the time.

Use of MOOCs for Technology and Business Education

2021 ◽  
pp. 137-154
Author(s):  
William Philip Wall ◽  
Bilal Khalid
Keyword(s):  
Developing Countries ◽  
Online Education ◽  
Business Education ◽  
Online Courses ◽  
Higher Learning ◽  
Alternative Form ◽  
Access To Education ◽  
Wide Range ◽  
Participation In Higher Education

Over the past decade, massive open online courses (MOOCs) as a new idea have been a highly debatable topic in online education. MOOCs were created to provide unlimited and free participation in higher education and made available to a wide range of recipients from all the corners of the world. Many developing countries rely on this alternative form of learning, which is totally different from the conventional classrooms, to increase access to education and improve the quality of higher learning. This has made educationalists in many developing countries express more interest in looking into how MOOCs can fit in and be implemented. This chapter explores the use of MOOCs in technology and business education.

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