scholarly journals Project-based Learning: Connotation, Characteristics and Misunderstandings

2021 ◽  
Vol 3 (2) ◽  
pp. 22
Author(s):  
Peigeng Guo
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Teaching Quality ◽  
Project Based Learning ◽  
Learning Problems ◽  
Teaching Process ◽  
Teaching Mode ◽  
Innovation Ability ◽  
Reading Teaching ◽  
Actual Teaching

As a kind of teaching mode, project-based learning starts from the driving problems in the real world, aiming to solve learning problems, improve students' innovation ability and practical ability, as well as promote students' deep learning. It makes a vital difference in solving the current difficulties in reading teaching, which is characterized by process, authenticity and development. Due to the reading teaching is still in exploration stage from the perspective of project-based learning, there are inevitable problems in the actual teaching process, including improper time arrangement and lack of planning. Besides, learning is a mere formality, lacking of effectiveness. So teachers need to be more careful to improve the teaching quality and efficiency of project-based learning.

Research on Online Scene Teaching Mode of Tobacco Picking Decision Tree Construction Process Integrating Deep Learning

2021 ◽  
Vol 7 (5) ◽  
pp. 3076-3086
Author(s):  
Zhang Shuili ◽  
Zhao Yi ◽  
Zheng Kexin ◽  
Zhang Jun ◽  
Zheng Fuchun
Keyword(s):  
Deep Learning ◽  
Decision Tree ◽  
Online Teaching ◽  
Information Gain ◽  
Teaching Evaluation ◽  
Teaching Process ◽  
Teaching Mode ◽  
Teaching Interaction ◽  
Tree Construction ◽  
Information And Communication

Objectives: In view of the characteristics of online teaching during the coronavirus pandemic and the importance of practical teaching in training students’ skills in the process of graduate education, this paper proposes an online scene teaching mode that takes projects as the carrier and integrates with deep learning. In order to meet the demand for information and communication engineering professionals in the big data context, the whole teaching process is divided into four stages: Topic selection, Teaching project setting, online teaching interaction and teaching evaluation. In the teaching process of Python Data Analysis Foundations, the project “establishment process of tobacco picking decision tree based on information gain” is taken as the teaching case. Prior knowledge and references are pushed through the cloud platform before class, and The scene of tobacco picking affected by the weather is set in the online classroom to guide students to seek solutions to problems, and the results are presented with graphics to assist students to summarize, and then reset the scene to promote knowledge transfer, so as to integrate deep learning into the teaching process, and modify the corresponding stages according to the teaching evaluation results. The content of the scene is gradually increased from easy to difficult, from simple to complex, and from least to most, gradually increasing the difficulty, which enhances students’ learning interest and sense of achievement. Meanwhile, students’ initiative to participate in curriculum research further strengthens the effectiveness of the course in serving scientific research, which has a certain value of popularization and application.

scholarly journals Research on IT English Flipped Classroom Teaching Model Based on SPOC

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lili Liu
Keyword(s):  
Flipped Classroom ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Classroom Teaching ◽  
Teaching Process ◽  
Teaching Mode ◽  
English Teaching ◽  
Specific Teaching ◽  
English Classroom

In order to improve the existing problems in the teaching process of IT English courses and improve the quality of IT English, this paper conducts a research on the flipped classroom teaching mode of IT English based on SPOC. The present situation of IT English teaching is analyzed, and the problems existing in the teaching process are also analyzed. On the basis of the above thought, with the support of SPOC platform build IT turn English classroom teaching mode, namely, by setting the course target, learning for class in advance, choose high-quality class, learning information collection, upload the related resources, and do a good job in teaching design complete teacher preparation, and design the specific teaching unit of teaching process, teaching quality evaluation model was constructed. In this way, the teaching quality evaluation results of IT English flipped classroom are obtained, in order to further improve the teaching quality. The experimental results show that the flipped English teaching mode based on SPOC can effectively improve students’ performance and increase students’ average daily learning time and course satisfaction, and the practical application effect is good.

scholarly journals A Blended Collaborative Teaching Mode in Language Learning Based on Recommendation Algorithm

2021 ◽  
Vol 16 (23) ◽  
pp. 111-126
Author(s):  
Ting Wang
Keyword(s):  
Foreign Language ◽  
Language Learning ◽  
Language Teaching ◽  
Foreign Language Learning ◽  
Collaborative Teaching ◽  
Teaching Quality ◽  
Foreign Language Teaching ◽  
Recommendation Algorithm ◽  
Teaching Process ◽  
Teaching Mode

With the continuous expansion of economic globalization, English, as the official language in the world, has a high popularity. It can be related to a better development in the future. Meanwhile, with the continuous progress of technology in modern society, multimedia online teaching has become the main means in colleges and universities, which especially brings great changes to foreign language teaching. However, foreign language teaching is facing many difficulties such as large number of contents, less lessons and low participation of students in class. Therefore, the reform of college foreign language teaching is imperative. Based on blended collaborative teaching mode, the paper has designed a “Three Classes” composed of “Communication Class”, “Famous Teacher Class” and “Elite Online Class”. Teaching process has been designed according to the cognitive process of “Previewing before class, consolidating in class and expanding after class”. In addition, recommendation algorithm has been used to collect data on four dimensions, namely, involvement in foreign language learning, content completion of foreign language learning, interaction in foreign language learning and effectiveness of foreign language learning. A performance evaluation system for foreign language learning supported by big data has been established. The results of teaching practice have proved that the blended collaborative teaching mode implemented in the study can be integrated into the whole teaching process through intelligent data analysis, information sharing and technology application, improving teaching quality, enhancing the interaction between teachers and students, which is worthy of promotion and application.

scholarly journals Mobile Multimedia Classroom Construction for Rhythmic Gymnastics Based on APT Teaching Model

2017 ◽  
Vol 12 (07) ◽  
pp. 79
Author(s):  
Youguo Shi ◽  
Shuqin Chen ◽  
Haitao Wang
Keyword(s):  
Learning Ability ◽  
Teaching Model ◽  
Mobile Multimedia ◽  
Teaching Process ◽  
Teaching Mode ◽  
Rhythmic Gymnastics ◽  
Promising Application ◽  
Learning Interest ◽  
Assessment Pedagogy ◽  
Actual Teaching

APT teaching model is an information-based teaching model which includes Assessment, Pedagogy and Technology. It can improve students’ learning initiative and autonomous learning ability. In this study, mobile multimedia classroom based on APT teaching model was designed for Rhythmic Gymnastics. With APT teaching model, learners’ learning is no longer limited to time and place. It can motivate learners’ learning interest and enhance learning efficiency. The mobile multimedia classroom was constructed from three aspects: assessment, pedagogy and technology. The final teaching test shows that, learners are very interested in the teaching mode of mobile multimedia classroom and express a strong wish for such teaching mode. Mobile multimedia classroom for Rhythmic Gymnastics based on APT teaching model has a promising application prospect in actual teaching process.

scholarly journals The diversified teaching reform of The mixed flipped classroom Python Foundation course is aimed at deep learning

2020 ◽  
Vol 218 ◽  
pp. 02001
Author(s):  
Xiaoxu Xiao
Keyword(s):  
Deep Learning ◽  
Learning Community ◽  
Flipped Classroom ◽  
Teaching Quality ◽  
Independent Learning ◽  
Classroom Teaching ◽  
Teaching Mode ◽  
Teaching Resources ◽  
Teaching Reform ◽  
Teachers And Students

Under the background of deep learning, the reform of diversified teaching mode is related to the community of teachers and students, and the traditional one-way independent learning of students is transformed into interactive independent learning of teachers and students. Through the MOOC, SPOC, such as teaching case analysis, to explore how to build a hybrid flip classroom teaching mode, use of the advantages of various platforms together, fully applied to the classroom teaching, the reasonable using and sharing high quality teaching resources, to ensure the ability of the students’ learning and literacy, conducive to the students’ active learning and teacher’s guidance and the guidance of thinking, to increase the interaction between students and teachers, the construction of teachers and students “learning community” hybrid flip diversified teaching mode. This mode is applied in the teaching of Python Foundation course to promote the teaching reform of the course, help teachers improve the teaching quality, and provide reference for solving the problems faced by classroom teaching in colleges and universities as well as the teaching research of “MOOC+SPOC+ Flipped classroom” teaching mode.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

scholarly journals Deep Learning Classification of Canine Behavior Using a Single Collar-Mounted Accelerometer: Real-World Validation

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1549
Author(s):  
Robert D. Chambers ◽  
Nathanael C. Yoder ◽  
Aletha B. Carson ◽  
Christian Junge ◽  
David E. Allen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
Learning Algorithm ◽  
Drinking Behavior ◽  
True Positive Rate ◽  
Training Dataset ◽  
Activity Levels ◽  
Accelerometer Data ◽  
Activity Monitors

Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here, we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial pet activity monitors. We built machine learning training databases from more than 5000 videos of more than 2500 dogs and ran the algorithms in production on more than 11 million days of device data. We then surveyed project participants representing 10,550 dogs, which provided 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking (0.772, 0.990), petting (0.305, 0.991), rubbing (0.729, 0.996), scratching (0.870, 0.997), and sniffing (0.610, 0.968). We show that the devices’ position on the collar had no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1514 users), and of 94.9% for drinking (among 1491 users). The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

Demystifying Deep Learning: Developing and Evaluating a User-Centered Learning App for Beginners to Gain Practical Experience

i-com ◽  
2020 ◽  
Vol 19 (3) ◽  
pp. 201-213
Author(s):  
Sven Schultze ◽  
Uwe Gruenefeld ◽  
Susanne Boll
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Practical Experience ◽  
Learning Problems ◽  
User Evaluation ◽  
Classification Problems ◽  
Human Centered Design ◽  
Technical Evaluation ◽  
Programming Skills ◽  
Barrier To Entry

Abstract Deep Learning has revolutionized Machine Learning, enhancing our ability to solve complex computational problems. From image classification to speech recognition, the technology can be beneficial in a broad range of scenarios. However, the barrier to entry is quite high, especially when programming skills are missing. In this paper, we present the development of a learning application that is easy to use, yet powerful enough to solve practical Deep Learning problems. We followed the human-centered design approach and conducted a technical evaluation to identify solvable classification problems. Afterwards, we conducted an online user evaluation to gain insights on users’ experience with the app, and to understand positive as well as negative aspects of our implemented concept. Our results show that participants liked using the app and found it useful, especially for beginners. Nonetheless, future iterations of the learning app should step-wise include more features to support advancing users.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

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