Crowdsourced Peer Learning Activity for Internet of Things Education: A Case Study

Hussein, Ahmed Taha; Barhamgi, Mahmoud; Vecchio, Massimo; Perera, Charith

doi:10.1109/iotm.0001.1900040

Cited by 13 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, translation fragments are collected from translation memory lists [18]. It collects translation fragments (accumulating up to 4-grams) from the retrieved target sentences Y m as possible translation fragments G m x of X [19,20].…”

Section: Nn Machine Translationmentioning

confidence: 99%

Application of Improved Image Restoration Algorithm and Depth Generation in English Intelligent Translation Teaching System

Ren

2022

Mobile Information Systems

View full text Add to dashboard Cite

Deep generative technology has seen a lot of success in the speech and image fields, thanks to its vigorous development and widespread application. The goal of this paper is to use the improved image restoration and depth generation algorithms to improve the English intelligent translation teaching system. This will make a bigger difference in the classroom when it comes to teaching intelligent translation in English. To address the problem of data noise, this paper proposes a data augmentation method that can efficiently exploit large-scale monolingual data in semi-supervised scenarios, as well as a data augmentation method that exploits the robustness of statistical machine translation in unsupervised scenarios. The CNN-Dueling-DQN-NMT model outperforms the CNN machine translation baseline model on the WMT14 English-French dataset by 1.92. The BLEU value of the Transformer machine translation baseline model is improved by 1.63 when using the Transformer-Dueling-DQN-NMT model. With a BLEU value of 44.63, the Transformer-Dueling-DQN-NMT model performs best.

show abstract

Section: Nn Machine Translationmentioning

confidence: 99%

Application of Improved Image Restoration Algorithm and Depth Generation in English Intelligent Translation Teaching System

Ren

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…We conducted two different user surveys to confirm that the tool may be the best choice for IoT training. eir method guides students to think more deeply about IoT products and their design decisions [3]. However, their experimental procedures are not over, and the experimental results are inconsistent.…”

Section: Related Workmentioning

confidence: 99%

Online and Offline Mixed Intelligent Teaching Assistant Mode of English Based on Mobile Information System

Yanfei

2021

Mobile Information Systems

View full text Add to dashboard Cite

In order to actively explore the English teaching methods in the “Internet+” era, by exploring the teaching effects of online and offline mixed teaching English courses, it is found that the online and offline mixed teaching methods are very popular among students. Autonomous learning can enable students to acquire knowledge with greater flexibility and efficiency and make teacher guidance more targeted and interactive in offline classrooms, thereby improving teaching effects. In the new era, the rapid development and progress of Internet technology, information technology, and multimedia provide a good opportunity for China to continue to promote the reform of education and teaching. This article aims to study the online and offline mixed intelligent teaching assistant mode of English based on the mobile information system, expounds the overall design strategy of the online and offline mixed teaching mode, and proposes to strengthen the online and offline English mixed intelligent teaching assistant mode. And this article proposes an English hybrid intelligent teaching assistant model based on mobile information system development, which integrates massive English teaching resources. According to each student’s learning progress, tailor-made online learning courses and materials, combined with offline auxiliary teaching, achieve double education guarantee. The experimental results in this article show that if 30 people in the exam refer to the results, the teacher wants to analyze the results of these 10 questions. Please enter these 10 questions and corresponding rules of the user interface into the database, and use the system for reasoning and analysis, and the knowledge points involved in selecting 10 questions on the test paper are divided into 9 different grammatical points. When the entire class has conquered them and they are all deterministic answers, the weighted value is the current lowest value, which is 0. This shows that the system is very convenient.

show abstract

“…In [12], a proposal for a platform to teach the concept of IoT is presented. This platform is also based on the concept of crowdsourcing and aims at passing down initial concepts of IoT to students, without addressing professional or technical aspects.…”

Section: Related Workmentioning

confidence: 99%

A TpM‐based collaborative system to teach IoT

Ferreira

Yamaguti

Branquinho

et al. 2021

Comput Appl Eng Educ

View full text Add to dashboard Cite

The Internet of Things (IoT) is a multidisciplinary field involving different areas of knowledge. The development of IoT solutions depends on the integration and collaborative work between professionals from all of these areas. This has led to the development of methodologies to teach IoT, such as the three‐phase methodology (TpM). This study presents a collaborative system that operationalizes the use of the TpM, aiding in the process of teaching IoT. This system guides and integrates teams developing IoT solutions while providing easy and structured access to information about the project. The system has been used in short and graduation courses. The results have demonstrated its efficiency in assisting students to develop solutions while applying the TpM.

show abstract

Crowdsourced Peer Learning Activity for Internet of Things Education: A Case Study

Cited by 13 publications

References 7 publications

Application of Improved Image Restoration Algorithm and Depth Generation in English Intelligent Translation Teaching System

Application of Improved Image Restoration Algorithm and Depth Generation in English Intelligent Translation Teaching System

Online and Offline Mixed Intelligent Teaching Assistant Mode of English Based on Mobile Information System

A TpM‐based collaborative system to teach IoT

Contact Info

Product

Resources

About