Personalized Learning Resource Recommendation Algorithm of Mobile Learning Terminal

Liu, Hui; Kuan-na, Huang; Jia, Liping

doi:10.1109/cis.2019.00037

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Course recommendation systems can provide users with course resources that meet both their characteristics and their interests and preferences. Therefore, the course recommendation system has become a new and mainstream form of personalized information service and (Liu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Personalized Course Resource Recommendation Algorithm Based on Deep Learning in the Intelligent Question Answering Robot Environment

Sun¹

2023

International Journal of Information Technologies and Systems Approach

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Aiming at the problems of difficult-to-extract effective information and insufficient feature extraction in the existing intelligent question answering robot environment, a personalized course resource recommendation algorithm based on deep learning is proposed. Firstly, the potential preferences of users are obtained through course-related data. Secondly, the authors use one-hot coding and embedding to convert word vectors into low-dimensional, dense real-valued vectors and input them into the CIN-GRU model. Finally, the attention mechanism is used to improve the attention of some words and the accuracy of personalized course recommendation. The experiment shows that when the recommended list is 35, the precision, recall, and F1 value of the proposed personalized course recommendation method are 0.862, 0.851, and 0.857, respectively, which are higher than those of the comparison method. Therefore, the performance of the proposed method in sustainable personalized course resource recommendation is better than that of the comparison method.

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Section: Introductionmentioning

confidence: 99%

Personalized Course Resource Recommendation Algorithm Based on Deep Learning in the Intelligent Question Answering Robot Environment

Sun¹

2023

International Journal of Information Technologies and Systems Approach

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“…The algorithm was utilized to optimize the resource allocation strategy immediately after the arrival of users. Liu et al [22] investigated the personalized recommendation algorithm of learning resources, and developed a personalized learning model based on the recommendation algorithm. Their algorithm can analyze the historical learning data of users in mobile learning terminals, and recommend learning resources accurately according to the learning level and personal preference of users.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Mobile Learning Resources Based on a Complex Network

Liu¹,

Zhao²

2022

Int. J. Emerg. Technol. Learn.

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Currently, centralized online learning can no longer meet the fragmented learning needs of learners. It is a hot topic in mobile learning to allocate reasonable mobile learning resources (MLRs) for user terminals and servers. However, the existing studies have rarely discussed the matching relationship between the MLR features of user terminals and servers. To fill up the gap, this paper tries to optimize the allocation of MLRs based on the theory of mobile knowledge complex network. Firstly, a local bidirectional fitness model was established to optimize MLR allocation, and the core nodes were mined from the complex network of MLRs. Next, the authors clarified the causality between the density of MLR complex network and resource integration, constructed an evaluation index system (EIS) for MLR integration ability, and evaluated the overall resource integration ability of MLR network resources. The proposed network was proved effective in optimizing the resource allocation of mobile learning networks through experiments.

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Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

Gligorea,

Cioca,

Oancea

et al. 2023

Education Sciences

122

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The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes. This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, retention, and performance. A comprehensive literature review was conducted, focusing on articles published from 2010 onwards, to document the integration of AI/ML in e-learning. The review analyzed 63 articles, employing a systematic approach to evaluate the deployment of adaptive learning algorithms and their educational implications. Findings reveal that AI/ML algorithms are instrumental in personalizing learning experiences. These technologies have been shown to optimize learning paths, enhance engagement, and improve academic performance, with some studies reporting increased test scores. The integration of AI/ML in e-learning platforms significantly contributes to the personalization and effectiveness of the educational process. Despite challenges like data privacy and the complexity of AI/ML systems, the results underscore the potential of adaptive learning to revolutionize education by catering to individual learner needs.

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Personalized Learning Resource Recommendation Algorithm of Mobile Learning Terminal

Cited by 3 publications

References 6 publications

Personalized Course Resource Recommendation Algorithm Based on Deep Learning in the Intelligent Question Answering Robot Environment

Personalized Course Resource Recommendation Algorithm Based on Deep Learning in the Intelligent Question Answering Robot Environment

Optimal Allocation of Mobile Learning Resources Based on a Complex Network

Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

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