scholarly journals A Hierarchical Learning Model based on Deep Learning and its Application in a SPOC and Flipped Classroom

2021 ◽  
Vol 16 (09) ◽  
pp. 76
Author(s):  
Xiangming An ◽  
Chengliang Qu
Keyword(s):  
Deep Learning ◽  
Flipped Classroom ◽  
Computational Thinking ◽  
Hierarchical Classification ◽  
Learning Model ◽  
Teaching Model ◽  
Hierarchical Learning ◽  
Content Organization ◽  
Computer Teaching ◽  
Integration Degree

In accordance with the progressive knowledge-to-ability transformation laws, a hierarchical learning model composed of cognitive layer, application layer, and design layer was created and applied to college computer teaching. This model was used to facilitate the deep learning among students through the association establishment, step-by-step understanding, and comprehensive application of new and old knowledge. In the teaching design process, the “5-problem” teaching, which centered on “student–problem–activity–resource,” was conducted and applied to the “Small Private Online Course (SPOC) + flipped classroom.” The teaching result was assessed using the proposed hierarchical classification method. Results demonstrate that the improved teaching model remarkably enhances the ability of noncomputer major students to solve the practical problems encountered in their specialties by virtue of computational thinking through the data analysis of evaluation results and students’ survey feedback. The students obviously speak more highly of the improved teaching model than the traditional blended teaching in the aspects of teaching content organization, learning effect, integration degree with the specialty and satisfaction. The degree of their participation in the flipped classroom reached as high as 90%.

scholarly journals Does the acceptance of hybrid learning affect learning approaches in France?

2017 ◽  
Vol 14 ◽  
pp. 24 ◽  
Author(s):  
Lionel Di Marco ◽  
Alain Venot ◽  
Pierre Gillois
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Information Search ◽  
Flipped Classroom ◽  
Hybrid Learning ◽  
Learning Model ◽  
Intention To Use ◽  
Learning Technology ◽  
Learning Approaches ◽  
Time Utilization

Purpose: Acceptance of a learning technology affects students’ intention to use that technology, but the influence of the acceptance of a learning technology on learning approaches has not been investigated in the literature. A deep learning approach is important in the field of health, where links must be created between skills, knowledge, and habits. Our hypothesis was that acceptance of a hybrid learning model would affect students’ way of learning.Methods: We analysed these concepts, and their correlations, in the context of a flipped classroom method using a local learning management system. In a sample of all students within a single year of study in the midwifery program (n= 38), we used 3 validated scales to evaluate these concepts (the Study Process Questionnaire, My Intellectual Work Tools, and the Hybrid E-Learning Acceptance Model: Learner Perceptions).Results: Our sample had a positive acceptance of the learning model, but a neutral intention to use it. Students reported that they were distractible during distance learning. They presented a better mean score for the deep approach than for the superficial approach (P< 0.001), which is consistent with their declared learning strategies (personal reorganization of information; search and use of examples). There was no correlation between poor acceptance of the learning model and inadequate learning approaches. The strategy of using deep learning techniques was moderately correlated with acceptance of the learning model (r<sub>s</sub>= 0.42, P= 0.03).Conclusion: Learning approaches were not affected by acceptance of a hybrid learning model, due to the flexibility of the tool. However, we identified problems in the students’ time utilization, which explains their neutral intention to use the system.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model
Sign in / Sign up

Export Citation Format

Share Document