scholarly journals Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rongrong Wang ◽  
Zhengjie Shi
Keyword(s):  
Online Education ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Student Behavior ◽  
Educational Process ◽  
Classification Model ◽  
Massive Data ◽  
Emotion Classification ◽  
Clickstream Data ◽  
Instructional Videos

Due to the epidemic, online course learning has become a major learning method for students worldwide. Analyzing its massive data from the massive online education platforms becomes a challenge because most learners watch online instructional videos. Thus, analyzing learners’ learning behaviors is beneficial to implement personalized online learning strategies with sentiment classification models. To this end, we propose a context-aware network model based on transfer learning that aims to predict learner performance by solving learners’ problems and improving the educational process, contributing to a comprehensive analysis of such student behavior and exploring various learning models in MOOC video interactions. In addition, we visualize and analyze MOOC video interactions, enabling course instructors and education professionals to analyze clickstream data generated by learners interacting with course videos. The experimental results show that, in the process of “massive data mining,” personalized learning strategies of this model can efficiently enhance students’ interest in learning and enable different types of students to develop personalized online education learning strategies.

scholarly journals Educational analytics. A new approach to improving the quality of the educational process

2020 ◽  
Author(s):  
D. Oskin ◽  
◽  
A. Oskin ◽  
Keyword(s):  
Online Education ◽  
Programming Language ◽  
Learning Analytics ◽  
Educational Process ◽  
Classification Model ◽  
New Approach ◽  
High Level ◽  
Student Classification

This article describes the trends in online education caused by the COVID-19 pandemic. The introduction of learning analytics into the educational process is substantiated. The main methods and tools of educational analytics are considered. Using a specific example, we will understand the construction and assessment of a student classification model using the high-level programming language Python.

scholarly journals Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shu-tong Xie ◽  
Qiong Chen ◽  
Kun-hong Liu ◽  
Qing-zhao Kong ◽  
Xiu-juan Cao
Keyword(s):  
Online Education ◽  
Behavior Analysis ◽  
Student Behavior ◽  
Feature Selection Method ◽  
Multiclass Classification ◽  
Classification Model ◽  
Learning Behavior ◽  
Teaching Activities ◽  
Education Data ◽  
Output Code

In recent years, online and offline teaching activities have been combined by the Small Private Online Course (SPOC) teaching activities, which can achieve a better teaching result. Therefore, colleges around the world have widely carried out SPOC-based blending teaching. Particularly in this year’s epidemic, the online education platform has accumulated lots of education data. In this paper, we collected the student behavior log data during the blending teaching process of the “College Information Technology Fundamentals” course of three colleges to conduct student learning behavior analysis and learning outcome prediction. Firstly, data collection and preprocessing are carried out; cluster analysis is performed by using k-means algorithms. Four typical learning behavior patterns have been obtained from previous research, and these patterns were analyzed in terms of teaching videos, quizzes, and platform visits. Secondly, a multiclass classification framework, which combines a feature selection method based on genetic algorithm (GA) with the error correcting output code (ECOC) method, is designed for training the classification model to achieve the prediction of grade levels of students. The experimental results show that the multiclass classification method proposed in this paper can effectively predict the grade of performance, with an average accuracy rate of over 75%. The research results help to implement personalized teaching for students with different grades and learning patterns.

Pentingnya Mendesain Pembelajaran Menuju Pendidikan Berkualitas

2020 ◽  
Vol 3 (2) ◽  
pp. 304-318
Author(s):  
I Made Putra Aryana
Keyword(s):  
Learning Strategies ◽  
Student Development ◽  
Human Life ◽  
Educational Process ◽  
Learning Design ◽  
Literature Study ◽  
Learning Program ◽  
Personal Lives ◽  
Teacher Needs

This article aims to put forward the learning design so that learning runs well, accompanied by anticipatory steps to minimize the gaps that occur so that learning activities achieve the goals set. The writing of this article uses the literature study method taken from various sources about learning. A teacher needs to have the ability to design and implement a variety of learning strategies that are considered suitable with the interests, talents and in accordance with the level of student development, including utilizing various sources and learning media to ensure the effectiveness of learning. The essence of learning design is the determination of optimal learning methods to achieve the stated goals. There is no learning model that can provide the most effective recipe for developing a learning program. The determination of the design model to develop a learning program depends on the designer's consideration of the model to be used or chosen. The educational process is a series of efforts to guide, direct the potential of human life in the form of basic abilities and personal lives as individual and social creatures and in their relationship with the natural surroundings to become responsible individuals.

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

scholarly journals A comparison of three interactive examination designs in active learning classrooms for nursing students

BMC Nursing ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Linda Ahlstrom ◽  
Christopher Holmberg
Keyword(s):  
Nursing Education ◽  
Mixed Methods ◽  
Nursing Students ◽  
Active Learning ◽  
Online Education ◽  
Learning Strategies ◽  
Free Text ◽  
Course Content ◽  
Undergraduate Nursing ◽  
Chi Squared

Abstract Background Despite the advantages of using active learning strategies in nursing education, researchers have rarely investigated how such pedagogic approaches can be used to assess students or how interactive examinations can be modified depending on circumstances of practice (e.g., in online education). Aims The aim was to compare three interactive examination designs, all based on active learning pedagogy, in terms of nursing students’ engagement and preparedness, their learning achievement, and instructional aspects. Methods A comparative research design was used including final-year undergraduate nursing students. All students were enrolled in a quality improvement course at a metropolitan university in Sweden. In this comparative study to evaluate three course layouts, participants (Cohort 1, n = 89; Cohort 2, n = 97; Cohort 3, n = 60) completed different examinations assessing the same course content and learning objectives, after which they evaluated the examinations on a questionnaire in numerical and free-text responses. Chi-squared tests were conducted to compare background variables between the cohorts and Kruskal–Wallis H tests to assess numerical differences in experiences between cohorts. Following the guidelines of the Good Reporting of a Mixed Methods Study (GRAMMS), a sequential mixed-methods analysis was performed on the quantitative findings, and the qualitative findings were used complementary to support the interpretation of the quantitative results. Results The 246 students who completed the questionnaire generally appreciated the interactive examination in active learning classrooms. Among significant differences in the results, Cohort 2 (e.g., conducted the examination on campus) scored highest for overall positive experience and engagement, whereas Cohort 3 (e.g., conducted the examination online) scored the lowest. Students in Cohort 3 generally commended the online examination’s chat function available for use during the examination. Conclusions Interactive examinations for nursing students succeed when they are campus-based, focus on student preparation, and provide the necessary time to be completed.

scholarly journals Research of Water Body Turbidity Classification Model for Aquiculture Based on Transfer Learning

2021 ◽  
Vol 1757 (1) ◽  
pp. 012004
Author(s):  
Jianhua Zheng ◽  
Gaolin Yang ◽  
Yanxuan Huang ◽  
Leian Liu ◽  
Guihuang Hong ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Water Body ◽  
Classification Model

Transfer learning based convolution neural net for authentication and classification of emotions from natural and stimulated speech signals

2021 ◽  
pp. 1-12
Author(s):  
Mukul Kumar ◽  
Nipun Katyal ◽  
Nersisson Ruban ◽  
Elena Lyakso ◽  
A. Mary Mekala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Research Work ◽  
Oral Communication ◽  
Neural Net ◽  
Emotion Classification ◽  
Style Transfer ◽  
Conventional Machine ◽  
Neural Network Algorithm

Over the years the need for differentiating various emotions from oral communication plays an important role in emotion based studies. There have been different algorithms to classify the kinds of emotion. Although there is no measure of fidelity of the emotion under consideration, which is primarily due to the reason that most of the readily available datasets that are annotated are produced by actors and not generated in real-world scenarios. Therefore, the predicted emotion lacks an important aspect called authenticity, which is whether an emotion is actual or stimulated. In this research work, we have developed a transfer learning and style transfer based hybrid convolutional neural network algorithm to classify the emotion as well as the fidelity of the emotion. The model is trained on features extracted from a dataset that contains stimulated as well as actual utterances. We have compared the developed algorithm with conventional machine learning and deep learning techniques by few metrics like accuracy, Precision, Recall and F1 score. The developed model performs much better than the conventional machine learning and deep learning models. The research aims to dive deeper into human emotion and make a model that understands it like humans do with precision, recall, F1 score values of 0.994, 0.996, 0.995 for speech authenticity and 0.992, 0.989, 0.99 for speech emotion classification respectively.

Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes

2021 ◽  
Author(s):  
Yun Zhang ◽  
Ling Wang ◽  
Xinqiao Wang ◽  
Chengyun Zhang ◽  
Jiamin Ge ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Deep Learning ◽  
Drug Discovery ◽  
Research And Development ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Chemical Reactions ◽  
Data Augmentation ◽  
Learning Method ◽  
Reaction Prediction

An effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery.

scholarly journals Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2910
Author(s):  
Kei Suzuki ◽  
Tipporn Laohakangvalvit ◽  
Ryota Matsubara ◽  
Midori Sugaya
Keyword(s):  
Feature Selection ◽  
Single Channel ◽  
Feature Selection Method ◽  
Classification Model ◽  
Features Selection ◽  
Selection Methods ◽  
Emotion Classification ◽  
Model Based ◽  
Physiological Indexes ◽  
Emotion Estimation

In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal–Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications.

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