Toward data privacy preservation with ciphertext update and key rotation for IoT

Shen, Shuaiyong; Yang, Yang; Liu, Ximeng

doi:10.1002/cpe.6729

Cited by 26 publications

(12 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of the measuring of student engagement, Fredricks et al (2004) proposed that student engagement is a meta-construct consisting of behavioral, emotional and cognitive engagement. Learning outcomes generally include knowledge increase, improved ability, and attitude change (Anderson and Lawton, 2009;Shen et al, 2021;Wang et al, 2021). In the current study, learning outcomes refer to the knowledge increase and improved ability of students in the course that integrated business simulation games (Cheng et al, 2019b).…”

Section: Hierarchical Teaching Methods and Business Simulation Gamesmentioning

confidence: 97%

The effect of student self-efficacy on learning outcomes in a business simulation mobile game: a quasi-experimental study

Yang

Chen

Wang

et al. 2022

LHT

View full text Add to dashboard Cite

PurposeThis study has two objectives: to explore the factors that influence student self-efficacy regarding engagement and learning outcomes in a business simulation game course and to compare the difference between hierarchical and general teaching methods.Design/methodology/approachFrom September 2021 to May 2022, a questionnaire was administered to 126 students in a business simulation game course at the Zhongshan Institute, University of Electronic Science and Technology of China. Data were analyzed using nonparametric paired samples tests and linear regression.FindingsThe results showed that student self-efficacy, engagement and learning outcomes were significantly higher with the hierarchical teaching method than with the general teaching method. There were also differences in the factors that influenced self-efficacy regarding learning outcomes between the two teaching methods. With the general teaching method, student self-efficacy did not directly affect learning outcomes, but did so indirectly by mediating the effect of engagement. However, with the hierarchical teaching method, self-efficacy directly and significantly affected learning outcomes, in addition to indirectly affecting learning outcomes through student engagement.Research limitations/implicationsCompared with the control group experimental research method, the quasi-experimental research method can eliminate the influence of sample heterogeneity itself, but the state of the same sample may change at different times, which is not necessarily caused by the hierarchical teaching design.Practical implicationsBased on the results of this study, teachers can apply hierarchical teaching according to student ability levels when integrating business simulation games. The results of this study can inspire teachers to protect student self-confidence and make teaching objectives and specific requirements clear in the beginning of the course, and also provide an important practical suggestion for students on how to improve their course performance.Social implicationsThe research results can be extended to other courses. Teachers can improve students' self-efficacy through hierarchical teaching design, thus improving students' learning performance and also provide reference value for students to improve their learning performance.Originality/valueThis study built a model based on self-system model of motivational development (SSMMD) theory, comparing factors that affect student self-efficacy regarding learning outcomes under different teaching methods. The model enriches the literature on SSMMD theory as applied to business simulation game courses and adds to our understanding of hierarchical teaching methods in this field. The results provide a valuable reference for teachers that can improve teaching methods and learning outcomes.

show abstract

Section: Hierarchical Teaching Methods and Business Simulation Gamesmentioning

confidence: 97%

The effect of student self-efficacy on learning outcomes in a business simulation mobile game: a quasi-experimental study

Yang

Chen

Wang

et al. 2022

LHT

View full text Add to dashboard Cite

show abstract

“…The Feature Pyramid Networks' multi-level prediction architecture improves the detection ability of small objects. The maximum number of output bounding boxes is 3,549, the loss function is changed from the original sum-squared error to binary cross-entropy, the output activation function is changed from the original softmax to logistic, and the basic architecture is changed from the original VGG-16 to ResNet (Cheng et al, 2019b ; Zheng et al, 2019 ; Lu et al, 2020 ; Shen et al, 2021 ; Yu et al, 2021 ).…”

Section: Relative Workmentioning

confidence: 99%

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

Liu

Chen²,

Yao³

2022

Front. Psychol.

View full text Add to dashboard Cite

With the emergence of big data, cloud computing, and other technologies, artificial intelligence (AI) technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional experiences. It also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of “artificial intelligence + education.”

show abstract

“…In the animation game, players use both hands to control two s at the same time and transport the apple back to the bag [26][27][28][29]. Completing the animation game level requires not only good control ability but also excellent hand-eye coordination [30][31][32].…”

Section: Animation Gamementioning

confidence: 99%

Automatic Generation of Real-Time Animation Game Learning Levels Based on Artificial Intelligence Assistant

Zhang

2022

Scientific Programming

View full text Add to dashboard Cite

With the development of computer animation games, now many animation games use the automatic generation of animation game levels method to generate animation game content. Using the automatic generation levels of the animation game method can quickly generate many different levels, saving labor costs in manual production. And with the combination of other fields, more accurate results can be obtained. In this paper, the goal is to automatically generate the level of the animation game. This research proposed animation game objects and victory conditions to derive the concept of the clearance path and uses artificial intelligence genetic algorithms to obtain the clearance path that best meets the needs of the developer to recombine new levels. In addition, since the generated levels are regenerated from the clearance path, there are still too many blank areas. In order to make full use of these blank areas, this research provides a level complete system. The system has formulated its placement rules for existing animation game objects. These rules fill in these blank areas and produce a more complete level. The contributions of this search proved that the genetic algorithm can converge quickly due to the correction effect in the mutation. In the complete system, it has successfully achieved a complete and playable level. In the system running time, this research adjusted the method of selecting elites and the use of permutations and combinations to mate, so that the system’s operation can be stabilized on an average value.

show abstract

Toward data privacy preservation with ciphertext update and key rotation for IoT

Cited by 26 publications

References 41 publications

The effect of student self-efficacy on learning outcomes in a business simulation mobile game: a quasi-experimental study

The effect of student self-efficacy on learning outcomes in a business simulation mobile game: a quasi-experimental study

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

Automatic Generation of Real-Time Animation Game Learning Levels Based on Artificial Intelligence Assistant

Contact Info

Product

Resources

About