From Transcripts to Insights for Recommending the Curriculum to University Students

Autor:	Nam Thoai, Minh Thanh Chung, Thong Le Mai, Van Thanh Le
Rok vydání:	2020
Předmět:	Harmony (color) Computer science 05 social sciences Spark (mathematics) 0202 electrical engineering electronic engineering information engineering 050301 education 020207 software engineering 02 engineering and technology Performance objective Raw data 0503 education Data science Curriculum
Zdroj:	SN Computer Science. 1
ISSN:	2661-8907 2662-995X
Popis:	Student data play an important role in evaluating the effectiveness of educational programs in the universities. All data are aggregated to calculate the education criteria by year, region, or organization. Remarkably, recent studies showed the data impacts when making exploration to predict student performance objectives. Many methods in terms of data mining were proposed to be suitable to extract useful information in regards to data characteristics. However, the reconciliation between applied methods and data characteristics still exists some challenges. Our paper will demonstrate the analysis of this relationship for a specific dataset in practice. The paper describes a distributed framework based on Spark for extracting information from raw data. Then, we integrate machine learning techniques to train the prediction model. The experiments results are analyzed through different scenarios to show the harmony between the influencing factors and applied techniques.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8a50ada40067ee2bc8f16d3caedd7a33 https://doi.org/10.1007/s42979-020-00332-7 Zobrazit plný text záznamu Full text from SpringerLink