scholarly journals Do machine learning platforms provide out-of-the-box reproducibility?

2021 ◽  
Author(s):  
Odd Erik Gundersen ◽  
Saeid Shamsaliei ◽  
Richard Juul Isdahl
Keyword(s):  
Machine Learning ◽  
Learning Platforms

scholarly journals Wave physics as an analog recurrent neural network

2019 ◽  
Vol 5 (12) ◽  
pp. eaay6946 ◽  
Author(s):  
Tyler W. Hughes ◽  
Ian A. D. Williamson ◽  
Momchil Minkov ◽  
Shanhui Fan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Recurrent Neural Network ◽  
Audio Signals ◽  
Digital Implementation ◽  
Analog Machine ◽  
Learning Platforms ◽  
Complex Features ◽  
Hardware Platforms

Analog machine learning hardware platforms promise to be faster and more energy efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate for building analog processors for time-varying signals. Here, we identify a mapping between the dynamics of wave physics and the computation in recurrent neural networks. This mapping indicates that physical wave systems can be trained to learn complex features in temporal data, using standard training techniques for neural networks. As a demonstration, we show that an inverse-designed inhomogeneous medium can perform vowel classification on raw audio signals as their waveforms scatter and propagate through it, achieving performance comparable to a standard digital implementation of a recurrent neural network. These findings pave the way for a new class of analog machine learning platforms, capable of fast and efficient processing of information in its native domain.

Performance Comparison of Machine Learning Platforms

2019 ◽  
Vol 31 (2) ◽  
pp. 207-225 ◽  
Author(s):  
Asim Roy ◽  
Shiban Qureshi ◽  
Kartikeya Pande ◽  
Divitha Nair ◽  
Kartik Gairola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Comparison ◽  
Learning Platforms

scholarly journals Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University Courses

2021 ◽  
Vol 11 (13) ◽  
pp. 5800
Author(s):  
Giacomo Nalli ◽  
Daniela Amendola ◽  
Andrea Perali ◽  
Leonardo Mostarda
Keyword(s):  
Machine Learning ◽  
Group Formation ◽  
Clustering Algorithms ◽  
Homogeneous Groups ◽  
University Courses ◽  
Heterogeneous Groups ◽  
E Learning ◽  
Learning Platforms ◽  
The Creation ◽  
Collaborative Activities

Online learning environments such as e-learning platforms are often used to encourage collaborative activities amongst students. In this context, group work is often used to improve the learning outcomes. Group formation is often performed randomly since university courses can be composed of a large number of students. While random formation saves time and resources, the student heterogeneity in terms of learning capabilities is not guaranteed. Although advanced e-learning platforms such as Moodle are widely used, they lack plugins that allow the automatic formation of heterogeneous groups of students. This work proposes a novel intelligent plugin for Moodle that allows the creation of heterogeneous groups by using Machine Learning. This intelligent application can be used in order to improve the students’ performance in collaborative activities. Our machine learning approach first uses clustering algorithms on Moodle data to identify homogeneous groups that are composed of students having similar behavior. Heterogeneous groups are then created by combining students selected from different homogeneous groups. To this end, a novel algorithm and the corresponding software, which allow the creation of heterogeneous groups, have been developed. We have implemented our approach by realizing a Moodle plugin where teachers can create heterogeneous groups.

scholarly journals Survey of Machine Learning Techniques for Student Profile Modeling

2021 ◽  
Vol 16 (04) ◽  
pp. 136
Author(s):  
Touria Hamim ◽  
Faouzia Benabbou ◽  
Nawal Sael
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Machine Learning Techniques ◽  
Student Profile ◽  
Learning Techniques ◽  
Survey Results ◽  
Learning Platforms ◽  
Recruitment System ◽  
State Of Art ◽  
Profile Modeling

Developments in information technology have led to the emergence of several online platforms for educational purposes, such as e-learning platforms, e-recommendation systems, e-recruitment system, etc. These systems exploit advances in Machine Learning to provide services tailored to the needs and profile of students. In this paper, we propose a state of art on student profile modeling using machine learning techniques during last four years. We aim to analyze the most used and most efficient machine learning techniques in both online and face-to-face education context, for different objectives such as failure, dropout, orientation, academic performance, etc. and also analyze the dominant features used for each objective in order to achieve a global view of the student profile model. Decision Tree is the most used and the most efficient by most of research studies. And academic, personal identity and online behavior are the top characteristics used for the student profile. To strengthen the survey results, an experiment was carried out, based on the application of machine learning techniques extracted from the state of art analysis, on the same datasets. Decision tree gave the highest performance, which confirms the survey results.

scholarly journals Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach

10.2196/24032 ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. e24032
Author(s):  
Iman Akour ◽  
Muhammad Alshurideh ◽  
Barween Al Kurdi ◽  
Amel Al Ali ◽  
Said Salloum
Keyword(s):  
Machine Learning ◽  
Technology Acceptance ◽  
United Arab Emirates ◽  
Mobile Learning ◽  
Teaching And Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Equation Modeling ◽  
Research Model ◽  
Learning Platforms

Background Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of the COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical and face-to-face contact teaching practices, thereby requiring many students to actively use mobile technologies for learning. Mobile learning technologies offer viable web-based teaching and learning platforms that are accessible to teachers and learners worldwide. Objective This study investigated the use of mobile learning platforms for instruction purposes in United Arab Emirates higher education institutions. Methods An extended technology acceptance model and theory of planned behavior model were proposed to analyze university students’ adoption of mobile learning platforms for accessing course materials, searching the web for information related to their disciplines, sharing knowledge, and submitting assignments during the COVID-19 pandemic. We collected a total of 1880 questionnaires from different universities in the United Arab Emirates. Partial least squares-structural equation modeling and machine learning algorithms were used to assess the research model, which was based on the data gathered from a student survey. Results Based on our results, each hypothesized relationship within the research model was supported by our data analysis results. It should also be noted that the J48 classifier (89.37% accuracy) typically performed better than the other classifiers when it came to the prediction of the dependent variable. Conclusions Our study revealed that teaching and learning could considerably benefit from adopting remote learning systems as educational tools during the COVID-19 pandemic. However, the value of such systems could be lessened because of the emotions that students experience, including a fear of poor grades, stress resulting from family circumstances, and sadness resulting from a loss of friends. Accordingly, these issues can only be resolved by evaluating the emotions of students during the pandemic.

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