Intelligent analysis system of college students' employment and entrepreneurship situation: Big data and artificial intelligence-driven approach

Yu, Haijia; Zhang, Ru; Kim, Cheonshik

doi:10.1016/j.compeleceng.2023.108823

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…The manifestations of these issues include a misalignment between the professional settings of vocational colleges and local industrial development, resulting in a stark disparity between talent supply and demand in the human resources market. This misalignment, quantifiable through data, has significant implications for the stability rate of student employment [7].…”

Section: Issues In Higher Vocational Education In Ethnic Regions With...mentioning

confidence: 99%

A Comprehensive Data-Driven Analysis of Talent Supply using Delphi Method in Higher Vocational Education and Ethnic Minority Regions

2024

J. Appl. Data Sci.

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This study delves into the principles of structural reforms on the supply side of talent in higher vocational education, specifically focusing on the context of Guangxi, China, and extending its applicability to diverse ethnic regions. Embracing a data science approach, the research aims to develop a model grounded in theoretical foundations and policy considerations, offering insights to enhance the higher vocational education system and facilitate a high-quality talent supply. The research sample comprises 28 experts who contributed 182 perspectives on the constituent elements of higher vocational education reform in ethnic minority areas. Leveraging the Delphi method, the study employs qualitative evaluation methods through anonymous questionnaire surveys to ensure reliable feedback. A comprehensive survey includes 391 participants representing various stakeholders, such as the education department, teachers, industry experts, and students. Utilizing mathematical statistics and SPSS AU22.0 for data analysis, the study confirms that adaptation indicators meet established standards, aligning the theoretical model with measured data. Descriptive analysis and correlation testing of model variables reveal moderate to high average values, indicating a significant positive correlation between the scales. The study explores the layout of universities, major settings, curriculum systems, and talent cultivation as independent variables, with a focus on their influence on vocational talent cultivation. Additionally, it covers the demand side of talents, incorporating perspectives from the government, society, students, and parents. The analysis assesses the satisfaction of the supply side of higher vocational education, exploring specific manifestations of the contradiction between talent supply and demand. Through attribution analysis, the study concludes by proposing considerations for the supply-side structural reform of higher vocational education talents in Guangxi and similar ethnic regions. This research, rooted in data science methodologies, provides valuable insights for educational policymakers and practitioners. It sets the stage for further exploration into the dynamic interplay between data-driven decision-making and structural reforms in the higher vocational education landscape.

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Section: Issues In Higher Vocational Education In Ethnic Regions With...mentioning

confidence: 99%

A Comprehensive Data-Driven Analysis of Talent Supply using Delphi Method in Higher Vocational Education and Ethnic Minority Regions

2024

J. Appl. Data Sci.

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“…There is no denying the fact that in recent years, the topic of digitalization has received a fairly high degree of theoretical elaboration on certain technological aspects of implementation in the national economy. This is reflected in a significant number of research articles (Arslanalp et al, 2019;Huan et al, 2023; Suzhen et al, 2020; Kuznyetsova, Sydorchenko, Zadvorna, Nikonenko, Khalina, (2021) that conceptualize new economic concepts and phenomena or rethink already known ones, such as data, data analysis. And in the context of working with data and analyzing it, the concept of big data emerges.…”

Section: Literature Reviewmentioning

confidence: 99%

Використання Big Data Для Оптимізації Економічних Процесів У Цифрову Епоху

Сапотніцька,

Овандер,

Гарькава

et al. 2023

ФКДПТП

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Великі дані за останні роки стали джерелом інформації про стан розвитку економічних процесів і систем. За умови належного аналізу та інтерпретації великі дані можна використовувати для підтримки ухвалення управлінських рішень і розробки стратегії компанії або організації. Для поглибленого вивчення обраної теми було досліджено понад 40 джерел наукової літератури, що дало змогу оцінити й теоретичні аспекти розвитку Big Data, і практичні можливості впровадження Big Data в розвиток сучасного бізнесу. З огляду на це, метою дослідження є визначення особливостей використання Big Data для оптимізації економічних процесів в епоху цифрових технологій з урахуванням потенційних можливостей аналізу великих масивів даних. Для досягнення мети використані й загальнонаукові методи (аналіз, синтез, індукція, дедукція), і спеціальні методи статистичного аналізу даних і прогнозування з використанням вбудованих алгоритмів Microsoft Excel. У результаті проведених досліджень та аналізу статистичних матеріалів було доведено, що значимість Big Data з часом зростатиме, а перед компаніями відкриються нові можливості для використання цього виду інформації. Наукова новизна роботи полягає у визначенні широкого спектру можливостей та передумов використання Big Data для підтримки ухвалених управлінських рішень для тактичного та стратегічного розвитку бізнесу. Отже, використання Big Data створить передумови для більш активного та стабільного розвитку бізнесу, оскільки дозволить більш системно й збалансовано підходити до аналізу різних типів даних про стан зовнішнього та внутрішнього середовища бізнесу, визначити потенційні можливості розвитку та напрями стратегічного просування на ринку.

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“…For ML-based image classification, this practical application is invaluable. Learners can work with diverse datasets, experiment with different algorithms, and gain experience in preprocessing and feature extraction [43]. This hands-on experience is essential for developing the skills required to implement ML models effectively in image classification tasks.…”

Section: Blended Learning Benefits Ml-based Image Classificationmentioning

confidence: 99%

Blended Learning for Machine Learning-based Image Classification

2023

EAI Endorsed Trans e-Learn

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The paper commences with an introduction to blended learning, an educational approach that amalgamates traditional face-to-face instruction with online learning, aiming to capitalize on the advantages of conventional classroom instruction and digital resources in order to enhance the overall learning experience. The incorporation of diverse technologies facilitates a personalized learning experience that caters to the needs and learning styles of individual students. Image classification entails training machine learning models to categorize or label images into predetermined classes or categories, empowering machines to recognize and comprehend crucial components of visual information, emulating humans' classification of objects in the real world. The crux of image classification relies on extracting meaningful features from images and distinguishing different categories by associating specific features with distinct classes through iterative optimization learning. Machine learning significantly aids image classification by endowing automated systems with the capability to discern patterns, features, and distinctions within datasets, ultimately achieving accurate image classification. The integration of hybrid learning methods can augment the training process for machine learning models used in image classification by providing a flexible and adaptive learning environment.

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Intelligent analysis system of college students' employment and entrepreneurship situation: Big data and artificial intelligence-driven approach

Cited by 7 publications

References 16 publications

A Comprehensive Data-Driven Analysis of Talent Supply using Delphi Method in Higher Vocational Education and Ethnic Minority Regions

A Comprehensive Data-Driven Analysis of Talent Supply using Delphi Method in Higher Vocational Education and Ethnic Minority Regions

Використання Big Data Для Оптимізації Економічних Процесів У Цифрову Епоху

Blended Learning for Machine Learning-based Image Classification

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