Reform of English interactive teaching mode based on cloud computing artificial intelligence – a practice analysis

2020 ◽  
pp. 1-13
Author(s):  
Xiufang Liang ◽  
Lv Haiping ◽  
Jie Liu ◽  
Lin Lin
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Identity Management ◽  
Learning Experience ◽  
Teaching Model ◽  
Interactive Teaching ◽  
Good Learning ◽  
Function Module ◽  
And Function ◽  
Global And Local

Based on the cloud computing artificial intelligence model, the English interactive teaching model summarized and analyzed in an in-depth manner, the characteristics of the smart classroom explored, and the interactive teaching model reform practiced. This article has studied and analyzed the classic teaching model. Finally, based on constructivism, the advantages of the constructivism teaching model, cooperative teaching model, and mastering learning model selected to construct the teaching model of artificial intelligence courses. Through the questionnaire survey of the current teaching status of artificial intelligence courses, and the investigation of each link of the constructed model, according to the results of the survey to optimize the construction of artificial intelligence courses teaching model to make it more perfect. Based on the cloud computing technology, the system architecture and function module division of the network open class platform designed based on the overall needs, and developed and implemented on this basis. Through global and local two-level authentication, user information synchronization, and interconnection between homogeneous clouds, the identity management function realized. With the help of the e-schoolbag function, the learning results continuously and accurately evaluated, so that every learner can get a good learning experience.

Design of diversified teaching platform of college aerobics course based on artificial intelligence

2021 ◽  
pp. 1-13
Author(s):  
Fengping Huang
Keyword(s):  
Artificial Intelligence ◽  
Design Process ◽  
Experimental Results ◽  
Average Score ◽  
Network Teaching ◽  
Teaching Platform ◽  
Student Grouping ◽  
Function Module ◽  
And Function

In order to improve the diversified teaching effect of a college aerobics course, effectively improve the accuracy of student grouping on the teaching platform, a diversified teaching platform of college aerobics course based on artificial intelligence is designed. First of all, it puts forward the construction idea and design process of the network teaching platform, then designs the interface and function module of the teaching platform, and finally designs the grouping function of teaching objects, so as to complete the design of the diversified teaching platform of a college aerobics course based on artificial intelligence. The experimental results show that the grouping accuracy of students on the diversified teaching platform of college aerobics course based on artificial intelligence is greater than 75%, and the average score of students studying on the platform is 74.66. This explains why the designed platform can effectively provide the accuracy of grouping and the students’ performance.

CONTENT OF TEACHING THE ELEMENTS OF ARTIFICIAL INTELLIGENCE IN A SCHOOL INFORMATICS COURSE

2020 ◽  
pp. 3-10
Author(s):  
I. V. Levchenko
Keyword(s):  
Artificial Intelligence ◽  
General Education ◽  
Extracurricular Activities ◽  
Information Technologies ◽  
Learning Experience ◽  
9Th Grade ◽  
Modern Information ◽  
Further Development ◽  
Form Variant ◽  
Theoretical Foundations

The article considers the feasibility of integrating artificial intelligence technologies into school education and identifies a problem in identifying didactic elements in the field of artificial intelligence, which must be mastered in a school informatics course. The purpose of the article is to propose variant of the content of teaching the elements of artificial intelligence for the general education of schoolchildren as part of the curricular and extracurricular activities in informatics. An analysis of the psychological, pedagogical and scientific-methodical literature in the field of artificial intelligence made it possible to identify the appropriateness of teaching schoolchildren the elements of artificial intelligence in the framework of a comprehensive informatics course, as the theoretical foundations of modern information technologies. Summarizing and systematizing the learning experience of schoolchildren in the field of artificial intelligence made it possible to form variant of the content of teaching the elements of artificial intelligence, which can be implemented in a compulsory informatics course for 9th grade, as well as in elective classes. The results of the study are the theoretical basis for the further development of the components of the methodological system of teaching the elements of artificial intelligence in a school informatics course. The research materials may be useful to specialists in the field of teaching informatics and to informatics teachers.

scholarly journals Success factors in the realization of large ice projects in education

2021 ◽  
Vol 36 (1) ◽  
pp. 4-12
Author(s):  
Arno Pronk ◽  
Peng Luo ◽  
Qingpeng Li ◽  
Fred Sanders ◽  
Marjolein overtoom ◽  
...  
Keyword(s):  
Success Factors ◽  
Learning Experience ◽  
Learning By Doing ◽  
University Of Alberta ◽  
The North ◽  
3D Printed ◽  
Good Learning ◽  
The University ◽  
Ice Shell ◽  
Motivation And Learning

There has been a long tradition in making ice structures, but the development of technical improvements for making ice buildings is a new field with just a handful of researchers. Most of the projects were realized by professors in cooperation with their students as part of their education in architecture and civil engineering. The following professors have realized ice projects in this setting: Heinz Isler realized some experiments since the 1950s; Tsutomu Kokawa created in the past three decades several ice domes in the north of Japan with a span up to 25 m; Lancelot Coar realized a number of fabric formed ice shell structures including fiberglass bars and hanging fabric as a mold for an ice shell in 2011 and in 2015 he produced an fabric-formed ice origami structure in cooperation with MIT (Caitlin Mueller) and VUB (Lars de Laet). Arno Pronk realized several ice projects such as the 2004 artificially cooled igloo, in 2014 and 2015 dome structures with an inflatable mold in Finland and in 2016–2019, an ice dome, several ice towers and a 3D printed gridshell of ice in Harbin (China) as a cooperation between the Universities of Eindhoven & Leuven (Pronk) and Harbin (Wu and Luo). In cooperation between the University of Alberta and Eindhoven two ice beams were realized during a workshop in 2020. In this paper we will present the motivation and learning experiences of students involved in learning-by-doing by realizing one large project in ice. The 2014–2016 projects were evaluated by Sanders and Overtoom; using questionnaires among the participants by mixed cultural teams under extreme conditions. By comparing the results in different situations and cultures we have found common rules for the success of those kinds of educational projects. In this paper we suggest that the synergy among students participating in one main project without a clear individual goal can be very large. The paper will present the success factors for projects to be perceived as a good learning experience.

Reinforcement Learning for Cloud Computing Digital Library

2014 ◽  
Vol 571-572 ◽  
pp. 105-108
Author(s):  
Lin Xu
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Reinforcement Learning ◽  
Digital Library ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Current Status ◽  
Self Learning ◽  
New Framework

This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

Guest Editorial: Artificial Intelligence in the E&P Industry: What Have We Learned So Far and Where to Next?

2021 ◽  
Vol 73 (01) ◽  
pp. 12-13
Author(s):  
Manas Pathak ◽  
Tonya Cosby ◽  
Robert K. Perrons
Keyword(s):  
Artificial Intelligence ◽  
Science Fiction ◽  
Oil Recovery ◽  
Oil And Gas ◽  
Autonomous Systems ◽  
Oil And Gas Industry ◽  
Human Judgment ◽  
Gas Industry ◽  
The Media ◽  
And Function

Artificial intelligence (AI) has captivated the imagination of science-fiction movie audiences for many years and has been used in the upstream oil and gas industry for more than a decade (Mohaghegh 2005, 2011). But few industries evolve more quickly than those from Silicon Valley, and it accordingly follows that the technology has grown and changed considerably since this discussion began. The oil and gas industry, therefore, is at a point where it would be prudent to take stock of what has been achieved with AI in the sector, to provide a sober assessment of what has delivered value and what has not among the myriad implementations made so far, and to figure out how best to leverage this technology in the future in light of these learnings. When one looks at the long arc of AI in the oil and gas industry, a few important truths emerge. First among these is the fact that not all AI is the same. There is a spectrum of technological sophistication. Hollywood and the media have always been fascinated by the idea of artificial superintelligence and general intelligence systems capable of mimicking the actions and behaviors of real people. Those kinds of systems would have the ability to learn, perceive, understand, and function in human-like ways (Joshi 2019). As alluring as these types of AI are, however, they bear little resemblance to what actually has been delivered to the upstream industry. Instead, we mostly have seen much less ambitious “narrow AI” applications that very capably handle a specific task, such as quickly digesting thousands of pages of historical reports (Kimbleton and Matson 2018), detecting potential failures in progressive cavity pumps (Jacobs 2018), predicting oil and gas exports (Windarto et al. 2017), offering improvements for reservoir models (Mohaghegh 2011), or estimating oil-recovery factors (Mahmoud et al. 2019). But let’s face it: As impressive and commendable as these applications have been, they fall far short of the ambitious vision of highly autonomous systems that are capable of thinking about things outside of the narrow range of tasks explicitly handed to them. What is more, many of these narrow AI applications have tended to be modified versions of fairly generic solutions that were originally designed for other industries and that were then usefully extended to the oil and gas industry with a modest amount of tailoring. In other words, relatively little AI has been occurring in a way that had the oil and gas sector in mind from the outset. The second important truth is that human judgment still matters. What some technology vendors have referred to as “augmented intelligence” (Kimbleton and Matson 2018), whereby AI supplements human judgment rather than sup-plants it, is not merely an alternative way of approaching AI; rather, it is coming into focus that this is probably the most sensible way forward for this technology.

scholarly journals On being examined: do students and faculty agree?

2015 ◽  
Vol 39 (4) ◽  
pp. 320-326 ◽  
Author(s):  
Andrew Perrella ◽  
Joshua Koenig ◽  
Henry Kwon ◽  
Stash Nastos ◽  
P. K. Rangachari
Keyword(s):  
Conceptual Understanding ◽  
Correct Answer ◽  
Transfer Of Learning ◽  
Learning Experience ◽  
Health Sciences ◽  
Faculty Members ◽  
Point Scale ◽  
New Information ◽  
Good Learning ◽  
Level Of Agreement

Students measure out their lives, not with coffee spoons, but with grades on examinations. But what exams mean and whether or not they are a bane or a boon is moot. Senior undergraduates (A. Perrella, J. Koenig, and H. Kwon) designed and administered a 15-item survey that explored the contrasting perceptions of both students ( n = 526) and faculty members ( n = 33) in a 4-yr undergraduate health sciences program. A series of statements gauged the level of agreement on a 10-point scale. Students and faculty members agreed on the value of assessing student learning with a variety of methods, finding new information to solve problems, assessing conceptual understanding and logical reasoning, having assessments with no single correct answer, and having comments on exams. Clear differences emerged between students and faculty members on specific matters: rubrics, student choice of exam format, assessing creativity, and transfer of learning to novel situations. A followup questionnaire allowed participants to clarify their interpretation of select statements, with responses from 71 students and 17 faculty members. All parties strongly agreed that exams should provide a good learning experience that would help them prepare for the future (students: 8.64 ± 1.71 and faculty members: 8.03 ± 2.34).

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