scholarly journals Research on Blending learning Design for Promoting Deep Learning

2021 ◽  
Vol 9 (7) ◽  
pp. 173-178
Author(s):  
Huan Chenglin ◽  
◽  
Chen Jianwei ◽  

Under the background of further promoting the construction of "golden class" in universities, how to use teaching innovation and promote the cultivation of learners is the focus of educational researchers. Guided by the theory of deep learning, based on MOOC resource platform and teaching management platform, this study constructs a "3-stage-2 platform" blended learning mode for deep learning in three stages: before class, in class and after class, puts forward teaching strategies to promote deep learning, and based on this mode, takes the computer network course as an example to carry out the blended teaching design and practice. The results show that the model has a certain role in promoting learners' knowledge mastery and ability training, and good emotional experience is conducive to learners' deep learning. Keywords: Deep learning, mixed teaching, teaching design.

2014 ◽  
Vol 556-562 ◽  
pp. 4986-4989
Author(s):  
Qiu Ping Gao

This paper designs multi-core and multi-channel physical education (PE) teaching innovation management platform and introduces TIM theory, so as to collect the transmission speed of video signal for different number of nuclei and the number of channels' teaching management platform. It has found from the calculating that, when the nuclear number is 1, and the channel number is 2, the speed of data transmission signal is the lowest, and the theoretical value is 5.6, the measured value is 5.5, namely the theoretical value is consistent with the measured value. When the nuclear number is 4, and the channel number is 8, the speed of data transmission signal is the highest, and the theoretical value is 9.6, the measured value is 9.7, namely the theoretical value also consistent with the measured value. So in the design of PE teaching innovation management platform, it needs to synthetically consider the nuclear number of DSP and the channels number, so as to achieve the effects of optimization design for the teaching management platform.


2021 ◽  
Author(s):  
◽  
Kameron Christopher

<p>In this thesis I develop a robust system and method for predicting individuals’ emotional responses to musical stimuli. Music has a powerful effect on human emotion, however the factors that create this emotional experience are poorly understood. Some of these factors are characteristics of the music itself, for example musical tempo, mode, harmony, and timbre are known to affect people's emotional responses. However, the same piece of music can produce different emotional responses in different people, so the ability to use music to induce emotion also depends on predicting the effect of individual differences. These individual differences might include factors such as people's moods, personalities, culture, and musical background amongst others. While many of the factors that contribute to emotional experience have been examined, it is understood that the research in this domain is far from both a) identifying and understanding the many factors that affect an individual’s emotional response to music, and b) using this understanding of factors to inform the selection of stimuli for emotion induction. This unfortunately results in wide variance in emotion induction results, inability to replicate emotional studies, and the inability to control for variables in research.  The approach of this thesis is to therefore model the latent variable contributions to an individual’s emotional experience of music through the application of deep learning and modern recommender system techniques. With each study in this work, I iteratively develop a more reliable and effective system for predicting personalised emotion responses to music, while simultaneously adopting and developing strong and standardised methodology for stimulus selection. The work sees the introduction and validation of a) electronic and loop-based music as reliable stimuli for inducing emotional responses, b) modern recommender systems and deep learning as methods of more reliably predicting individuals' emotion responses, and c) novel understandings of how musical features map to individuals' emotional responses.  The culmination of this research is the development of a personalised emotion prediction system that can better predict individuals emotional responses to music, and can select musical stimuli that are better catered to individual difference. This will allow researchers and practitioners to both more reliably and effectively a) select music stimuli for emotion induction, and b) induce and manipulate target emotional responses in individuals.</p>


2010 ◽  
Vol 108-111 ◽  
pp. 33-38
Author(s):  
E Ming Jie ◽  
Zhou Jin Shu

The application of technologies of virtual reality, network and sensors makes the construction of Distance Opening Experiment Teaching Platform (DOETP) become reality, which fully utilizes the experimental resources of institutions, institutes and enterprises. Based on the analysis on characteristics of virtual experiment and distance control experiment, the paper designed the basic infracture of DOETP. The logical structure of distance education platform was brought out, which integrates DOETP and Distance Opening Teaching Management Platform (DOTMP). The detail funcions design of distance education platform were also provided, which is of significance for the realization of the education platform.


2021 ◽  
Author(s):  
Jinran Qie ◽  
Erfan Khoram ◽  
Dianjing Liu ◽  
Ming Zhou ◽  
Li Gao

Sign in / Sign up

Export Citation Format

Share Document