An Effective Student Grouping and Course Recommendation Strategy Based on Big Data in Education

Guo, Yu; Chen, Yue; Xie, Yuanyan; Ban, Xiaojuan

doi:10.3390/info13040197

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…The targeting strategy methodology involves the process of collecting, analyzing and interpreting electoral data to identify segments of the electorate that are most relevant and likely to support a particular political message (Guo et al, 2022). The first step is data collection from various sources, followed by data cleaning and pre-processing to ensure data quality and consistency (Cho et al, 2023).…”

Section: Review Of Literatur Targeting Strategymentioning

confidence: 99%

Use of Big Data in Political Communication Analysis: Forming a Targeting and Electorate Segmentation Strategy

Dharta,

Purwosusanto,

Yodiansyah

et al. 2024

JIDC

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In this digital era, political communication has become increasingly complex with the emergence of various social media platforms and information technology. This allows for greater interaction between political leaders, parties and the electorate. In this context, the use of Big Data has become crucial in collecting, analyzing and understanding the political behavior patterns of the electorate. This research aims to explore the use of Big Data in the context of political communication analysis, especially in forming targeting and electorate segmentation strategies. Through this approach, it is hoped that an effective method can be found in understanding mass political preferences. This research uses a qualitative and quantitative approach by analyzing Big Data data from various social media platforms, online surveys and other digital data sources. Statistical analysis and machine learning techniques are also used to identify patterns of electoral behavior. The research results show that the use of Big Data in political communication analysis provides deep insight into the preferences and needs of the electorate. By utilizing available data, targeting and electorate segmentation strategies can be prepared more precisely and effectively. The conclusion of this research is the analysis of political communication, the use of Big Data has proven its value in forming targeting and electorate segmentation strategies. With an integrated approach between qualitative and quantitative data, political leaders and parties can better understand political dynamics and increase the effectiveness of communication with the electorate.

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Section: Review Of Literatur Targeting Strategymentioning

confidence: 99%

Use of Big Data in Political Communication Analysis: Forming a Targeting and Electorate Segmentation Strategy

Dharta,

Purwosusanto,

Yodiansyah

et al. 2024

JIDC

View full text Add to dashboard Cite

show abstract

“…Using the LSTM-based model, the most suitable music is selected based on the user's previous and current emotions. Guo et al [9] proposed personalized education to group students and recommended courses for the purpose of improving cooperative learning. The frequency of words and Word2Vec are used to extract student characteristics, and the K-means algorithm is used to form interest-based groups.…”

Section: Personalized Servicesmentioning

confidence: 99%

Recommending K-Wave Items Tailored for Small-Sized Exporters by Incorporating Dense and Sparse Vectors

Lee,

Na,

Han

et al. 2023

Sustainability

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As K-wave has been strengthened via recent K-contents, K-wave items such as cosmetics and electronic devices have also gained attention globally. For small-sized export sellers who purchased the items and exported them to different countries, it is significant to discover which K-wave items are trending in specific countries. To do so, we proposed an ensemble recommender system by producing the dense vector, which is generated by a variant of Bidirectional Encoder Representations from Transformers (BERT), and balancing the vector with a sparse vector in order to ensure the efficient execution speed and recommendation accuracy. Based on the data we have collected specifically for potential K-items, our experiment showed that the proposed model outperforms the various baselines, which are used for content-based filtering.

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“…However, the aspect of computing power (deployment in the cloud) has not been addressed. As for the research conducted in [44], the objective is to make it easier for students to access suitable courses according to their interests, and to facilitate a collaborative and efficient working environment. On the one hand, the adopted approach enables unsupervised classification to divide students into clusters based on term frequency and semantic feature extraction algorithm using an improved K-means algorithm, which aims to improve the selection of initial cluster centers.…”

Section: B Literary Reviewmentioning

confidence: 99%

Agent-based approach for the recommendation and unsupervised classification of enterprise services in the cloud

Bourenane¹,

Houari²,

Taghezout³

2023

IJCDS

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The main objective of this study is to create a collaborative parallel environment, which supports the unsupervised classification and the filtration of an important volume of information. The proposed approach consists in the integration of an agent-based system composed of five reactive agents to assist the recommendation of the stored services in the cloud and to cluster these services through a new improved K-means as well. The conducted experiments and evaluations of the different approaches and measures, such as: Euclidean distance, Manhattan distance and Cosine similarity, show that the proposed approach of the unsupervised classification improve the within cluster sum of squares (WCSS), which facilitates the access to personalized and relevant services requested in a very improved response time, especially through migration to the cloud using agents.

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An Effective Student Grouping and Course Recommendation Strategy Based on Big Data in Education

Cited by 7 publications

References 17 publications

Use of Big Data in Political Communication Analysis: Forming a Targeting and Electorate Segmentation Strategy

Use of Big Data in Political Communication Analysis: Forming a Targeting and Electorate Segmentation Strategy

Recommending K-Wave Items Tailored for Small-Sized Exporters by Incorporating Dense and Sparse Vectors

Agent-based approach for the recommendation and unsupervised classification of enterprise services in the cloud

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