Student Performance Prediction Based on Blended Learning

Xu, Zhuojia; Yuan, Hua; Liu, Qi-Shan

doi:10.1109/te.2020.3008751

Cited by 82 publications

(34 citation statements)

References 20 publications

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“…In the application of association rules, literature [19] uses association rules to analyze the key factors that affect students' performance in universities for nationalities and finds out useful rules, which can provide a reference for decisionmakers to determine the future direction of teaching management. In the application of DT (decision tree), literature [20] is based on the DT model, mining the student attributes that affect the teaching effect of computer courses. Mining the data of online tools used to support collaborative learning can effectively help improve the effect of team 2…”

Section: Related Workmentioning

confidence: 99%

Construction of College Chinese Writing Practice Teaching Platform under the Background of Information Technology

Wu¹

2022

Mobile Information Systems

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Writing has always been one of the difficulties in teaching Chinese as a foreign language. With the advent of the information society, modern information technology, represented by networking, digitalization, multimedia, and intelligence, has become a creative tool to expand human capabilities, changing people’s traditional way of life, study, and work. Guided by the theory of integration of information technology and curriculum, combined with the characteristics of Chinese writing, this paper puts forward that the goal of integration of information technology and Chinese writing is to change the traditional teaching structure, create an ideal environment for students to learn Chinese and improve their Chinese information literacy. A practical teaching platform for college Chinese writing is constructed, which is designed from the user role and based on user access control. The design includes the roles of graduate students, foreign teachers, administrators, and users. Each user has different operation rights, module rights, and management rights. According to a small amount of online information and the results of a previous course, we can make a good prediction. The results show that this method is effective, and we can predict students’ academic performance through their online behavior.

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Section: Related Workmentioning

confidence: 99%

Construction of College Chinese Writing Practice Teaching Platform under the Background of Information Technology

Wu¹

2022

Mobile Information Systems

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“…It is based on a pay-per-use model and can be provisioned with minimal management effort. With the emergence of the IoT and big data analytics applications in various domains such as healthcare [ 125 , 126 , 127 ], education [ 128 , 129 , 130 ], transportation [ 131 ], banking [ 132 , 133 ], energy utilities [ 134 , 135 ], and entertainment [ 136 , 137 ], cloud computing provides a sandbox for data processing and storage, enabling the deployment of compute-intensive smart city applications [ 138 ]. However, considering the distance between the IoT devices and the remote cloud servers, the latency requirements of time-critical applications may be violated.…”

Section: Taxonomy Of Technology-enabled Smart City Applications In 6g...mentioning

confidence: 99%

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Ismail

Buyya

2022

Sensors

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The recent upsurge of smart cities’ applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged the design of the upcoming 6G network generation, due to their stringent requirements in terms of the quality of services (QoS), availability, and dependability to satisfy a Service-Level-Agreement (SLA) for the end users. Industries and academia have started to design 6G networks and propose the use of AI in its protocols and operations. Published papers on the topic discuss either the requirements of applications via a top-down approach or the network requirements in terms of agility, performance, and energy saving using a down-top perspective. In contrast, this paper adopts a holistic outlook, considering the applications, the middleware, the underlying technologies, and the 6G network systems towards an intelligent and integrated computing, communication, coordination, and decision-making ecosystem. In particular, we discuss the temporal evolution of the wireless network generations’ development to capture the applications, middleware, and technological requirements that led to the development of the network generation systems from 1G to AI-enabled 6G and its employed self-learning models. We provide a taxonomy of the technology-enabled smart city applications’ systems and present insights into those systems for the realization of a trustworthy and efficient smart city ecosystem. We propose future research directions in 6G networks for smart city applications.

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“…Based on SPOC and flipped classes, Xu et al also launched research on online platforms. ey created experiments on the computer network mixed classes, stating that students' online behavior data can be used to predict their results and the results are more stable and reliable as classes advance [4]. Guided by Moore's trading distance, Hew et al defined MOOC success as student satisfaction with the course [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Grade Prediction in Blended Learning Using Multisource Data

Chen

Li-fang

et al. 2021

Scientific Programming

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Today, blended learning is widely carried out in many colleges. Different online learning platforms have accumulated a large number of fine granularity records of students’ learning behavior, which provides us with an excellent opportunity to analyze students’ learning behavior. In this paper, based on the behavior log data in four consecutive years of blended learning in a college’s programming course, we propose a novel multiclassification frame to predict students’ learning outcomes. First, the data obtained from diverse platforms, i.e., MOOC, Cnblogs, Programming Teaching Assistant (PTA) system, and Rain Classroom, are integrated and preprocessed. Second, a novel error-correcting output codes (ECOC) multiclassification framework, based on genetic algorithm (GA) and ternary bitwise calculator, is designed to effectively predict the grade levels of students by optimizing the code-matrix, feature subset, and binary classifiers of ECOC. Experimental results show that the proposed algorithm in this paper significantly outperforms other alternatives in predicting students’ grades. In addition, the performance of the algorithm can be further improved by adding the grades of prerequisite courses.

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Student Performance Prediction Based on Blended Learning

Cited by 82 publications

References 20 publications

Construction of College Chinese Writing Practice Teaching Platform under the Background of Information Technology

Construction of College Chinese Writing Practice Teaching Platform under the Background of Information Technology

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Grade Prediction in Blended Learning Using Multisource Data

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