scholarly journals MODEL THEORY AND MACHINE LEARNING

2019 ◽  
Vol 25 (03) ◽  
pp. 319-332
Author(s):  
HUNTER CHASE ◽  
JAMES FREITAG
Keyword(s):  
Machine Learning ◽  
Online Learning ◽  
Model Theory ◽  
Pac Learning ◽  
Combinatorial Property ◽  
Online Setting ◽  
Dividing Line

AbstractAbout 25 years ago, it came to light that a single combinatorial property determines both an important dividing line in model theory (NIP) and machine learning (PAC-learnability). The following years saw a fruitful exchange of ideas between PAC-learning and the model theory of NIP structures. In this article, we point out a new and similar connection between model theory and machine learning, this time developing a correspondence between stability and learnability in various settings of online learning. In particular, this gives many new examples of mathematically interesting classes which are learnable in the online setting.

Pandemic-Driven Online Teaching - the Natural Setting for a Flipped Classroom?

2021 ◽  
Author(s):  
Victor K. Lai
Keyword(s):  
Online Learning ◽  
Student Engagement ◽  
Online Teaching ◽  
Flipped Classroom ◽  
Teaching And Learning ◽  
Chemical Engineering ◽  
Natural Setting ◽  
Traditional Lectures ◽  
Online Setting ◽  
Method Of Instruction

Abstract As the COVID-19 pandemic forced a sudden shift to online teaching and learning in April 2020, one of the more significant challenges faced by instructors is encouraging and maintaining student engagement in their online classes. This paper describes my experience of flipping an online classroom for a core Chemical Engineering Fluid Mechanics class to promote student engagement and collaboration in an online setting. Comparing exam scores with prior semesters involving in-person, traditional lecture-style classes suggests students need a certain degree of adjustment to adapt to this new learning mode. A decrease in Student Rating of Teaching (SRT) scores indicates that students largely prefer in-person, traditional lectures over an online flipped class, even though written comments in the SRT contained several responses favorable to flipping the class in an online setting. Overall, SRT scores on a department level also showed a similar decrease, which suggests students were less satisfied with the quality of teaching overall throughout the department, with this flipped method of instruction neither improving nor worsening student sentiment towards online learning. In addition, whereas most students liked the pre-recorded lecture videos, they were less enthusiastic about using breakout rooms to encourage student collaboration and discussion. Further thought and discussion on best practices to facilitate online student interaction and collaboration are recommended, as online learning will likely continue to grow in popularity even when in-person instruction resumes after the pandemic.

Predicting Online Learning Student Results Using Machine Learning

2021 ◽  
Author(s):  
Leon Fernandy Chandra ◽  
Juanrico Alvaro ◽  
Aurelius Vannes Leander ◽  
Derwin Suhartono
Keyword(s):  
Machine Learning ◽  
Online Learning

Online Machine Learning

2013 ◽  
pp. 27-54 ◽  
Author(s):  
Óscar Fontenla-Romero ◽  
Bertha Guijarro-Berdiñas ◽  
David Martinez-Rego ◽  
Beatriz Pérez-Sánchez ◽  
Diego Peteiro-Barral
Keyword(s):  
Machine Learning ◽  
Online Learning ◽  
Mathematical Models ◽  
General Framework ◽  
Current Application ◽  
Data Set

Machine Learning (ML) addresses the problem of adjusting those mathematical models which can accurately predict a characteristic of interest from a given phenomenon. They achieve this by extracting information from regularities contained in a data set. From its beginnings two visions have always coexisted in ML: batch and online learning. The former assumes full access to all data samples in order to adjust the model whilst the latter overcomes this limiting assumption thus expanding the applicability of ML. In this chapter, we review the general framework and methods of online learning since its inception are reviewed and its applicability in current application areas is explored.

scholarly journals ONLINE LEARNING FOR IMAGE PROCESSING IN NETWORKED SETTING

2017 ◽  
Vol 10 (13) ◽  
pp. 284
Author(s):  
Ankush Rai ◽  
Jagadeesh Kannan R
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Online Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
The Past ◽  
Slow Development ◽  
Online Learning Algorithm

In the past decade development of machine learning algorithm for network settings has witnessed little advancements owing to slow development of technologies for improving bandwidth and latency.  In this study we present a novel online learning algorithm for network based computational operations in image processing setting

scholarly journals Online learning behavior analysis based on machine learning

2019 ◽  
Vol 14 (2) ◽  
pp. 97-106
Author(s):  
Ning Yan ◽  
Oliver Tat-Sheung Au
Keyword(s):  
Machine Learning ◽  
Online Learning ◽  
Correlation Analysis ◽  
Prediction Accuracy ◽  
Classification Models ◽  
Limited Data ◽  
Learning Models ◽  
Learning Behavior ◽  
Content Type ◽  
Machine Learning Models

Purpose The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data. Design/methodology/approach The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues. Findings Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper. Originality/value This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

scholarly journals Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz96 case study (v1.0)

2020 ◽  
Author(s):  
Stephan Rasp
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Online Learning ◽  
High Resolution ◽  
Machine Learning Algorithms ◽  
Training Dataset ◽  
The Neural Network ◽  
Training Stage ◽  
Lorenz 96 ◽  
Resolution Simulation

Abstract. Over the last couple of years, machine learning parameterizations have emerged as a potential way to improve the representation of sub-grid processes in Earth System Models (ESMs). So far, all studies were based on the same three-step approach: first a training dataset was created from a high-resolution simulation, then a machine learning algorithms was fitted to this dataset, before the trained algorithms was implemented in the ESM. The resulting online simulations were frequently plagued by instabilities and biases. Here, coupled online learning is proposed as a way to combat these issues. Coupled learning can be seen as a second training stage in which the pretrained machine learning parameterization, specifically a neural network, is run in parallel with a high-resolution simulation. The high-resolution simulation is kept in sync with the neural network-driven ESM through constant nudging. This enables the neural network to learn from the tendencies that the high-resolution simulation would produce if it experienced the states the neural network creates. The concept is illustrated using the Lorenz 96 model, where coupled learning is able to recover the "true" parameterizations. Further, detailed algorithms for the implementation of coupled learning in 3D cloud-resolving models and the super parameterization framework are presented. Finally, outstanding challenges and issues not resolved by this approach are discussed.

Online Learning for Linear Programming with Time-Dependent Cost

2011 ◽  
Vol 230-232 ◽  
pp. 793-797
Author(s):  
Wei Li ◽  
Chong Yang Deng
Keyword(s):  
Machine Learning ◽  
Linear Programming ◽  
Online Learning ◽  
Real World ◽  
Time Dependent ◽  
Programming Models ◽  
Intelligent Optimization ◽  
Learning Parameter ◽  
Real World Problems

Machine learning and linear programming with time dependent cost are two popular intelligent optimization tools to handle uncertainty in real world problems. Thus, combining these two technologies is quite attractive. This paper proposed an effective framework to deal with uncertainty in practice, based on combing introducing learning parameter into linear programming models.

scholarly journals Chinese College Students Have Higher Anxiety in New Semester of Online Learning During COVID-19: A Machine Learning Approach

2020 ◽  
Vol 11 ◽  
Author(s):  
Chongying Wang ◽  
Hong Zhao ◽  
Haoran Zhang
Keyword(s):  
Machine Learning ◽  
Online Learning ◽  
Undergraduate Students ◽  
Psychological Health ◽  
Hubei Province ◽  
Anxiety Level ◽  
Chinese College Students ◽  
Applied Machine Learning ◽  
Machine Learning Model ◽  
Administrative Units

The COVID-19 pandemic has caused tremendous loss starting from early this year. This article aims to investigate the change of anxiety severity and prevalence among non-graduating undergraduate students in the new semester of online learning during COVID-19 in China and also to evaluate a machine learning model based on the XGBoost model. A total of 1172 non-graduating undergraduate students aged between 18 and 22 from 34 provincial-level administrative units and 260 cities in China were enrolled onto this study and asked to fill in a sociodemographic questionnaire and the Self-Rating Anxiety Scale (SAS) twice, respectively, during February 15 to 17, 2020, before the new semester started, and March 15 to 17, 2020, 1 month after the new semester based on online learning had started. SPSS 22.0 was used to conduct t-test and single factor analysis. XGBoost models were implemented to predict the anxiety level of students 1 month after the start of the new semester. There were 184 (15.7%, Mean = 58.45, SD = 7.81) and 221 (18.86%, Mean = 57.68, SD = 7.58) students who met the cut-off of 50 and were screened as positive for anxiety, respectively, in the two investigations. The mean SAS scores in the second test was significantly higher than those in the first test (P < 0.05). Significant differences were also found among all males, females, and students majoring in arts and sciences between the two studies (P < 0.05). The results also showed students from Hubei province, where most cases of COVID-19 were confirmed, had a higher percentage of participants meeting the cut-off of being anxious. This article applied machine learning to establish XGBoost models to successfully predict the anxiety level and changes of anxiety levels 4 weeks later based on the SAS scores of the students in the first test. It was concluded that, during COVID-19, Chinese non-graduating undergraduate students showed higher anxiety in the new semester based on online learning than before the new semester started. More students from Hubei province had a different level of anxiety than other provinces. Families, universities, and society as a whole should pay attention to the psychological health of non-graduating undergraduate students and take measures accordingly. It also confirmed that the XGBoost model had better prediction accuracy compared to the traditional multiple stepwise regression model on the anxiety status of university students.

Brain Based Learning Environments in an Online Setting

2011 ◽  
pp. 90-98
Author(s):  
Silvia Braidic
Keyword(s):  
Online Learning ◽  
Online Education ◽  
Learning Environment ◽  
Learning Communities ◽  
Course Design ◽  
Online Learning Environment ◽  
Online Programs ◽  
Online Setting ◽  
Brain Based Learning ◽  
The University

This chapter introduces the reader on how to foster successful learning communities to meet the diverse needs of university students by creating a brain based online learning environment. Students come in all shapes and sizes. At the university level, students enrolled in online programs, have made a choice to do so. Today, online education is a unique and important venue for many students wishing to continue (or start) their education. It is part of a new culture with many distinct characteristics (Farrell, 2001). For instructors, online instruction creates its own set of challenges in terms of the course design and implementation. The author hopes that developing an understanding of how to create a brain based online learning environment will inform the reader of ways to foster successful learning communities to most effectively meet the diverse needs of the students it serves.

Differentiating Instruction to Meet the Needs of Online Learners

2011 ◽  
pp. 114-132
Author(s):  
Silvia Braidic
Keyword(s):  
Online Learning ◽  
Learning Communities ◽  
Instructional Strategies ◽  
Teaching And Learning ◽  
Online Learners ◽  
Online Learning Communities ◽  
Face To Face ◽  
Differentiating Instruction ◽  
Online Setting ◽  
The One

This chapter introduces how to differentiate instruction in an online environment. Fostering successful online learning communities to meet the diverse needs of students is a challenging task. Since the “one size fits all” approach is not realistic in a face-to-face or online setting, it is essential as an instructor to take time to understand differentiation and to work in creating an online learning environment that responds to the diverse needs of learners. It is our responsibility to ensure that the teaching and learning that takes place online is not only accessible, but of quality. The author hopes that developing an understanding of differentiation and specific instructional strategies to differentiate online will inform the learner of ways to maximize learning by addressing the diverse needs of students.

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