Educational Big Data: Predictions, Applications and Challenges

Huang

2022

Front. Environ. Sci.

Big data can improve production efficiency and optimize resource allocation, which is conductive to achieve sustainable development. This paper explores the impact of big data on green economic development. The global MINDW-MML model is used to measure green economic development and text analysis method is used to quantify the development of big data. An empirical study is conducted based on the panel data from 30 provinces in China from 2011 to 2018. Results show that, big data promotes the development of green economy and plays a greater role in facilitating technological progress than improving efficiency. As for sub-indicators of big data, cloud computing, Internet of things, artificial intelligence, and Hadoop positively affect technological progress, while blockchain can improve efficiency. In addition, the positive role of big data in promoting green technological progress and green efficiency will vary according to geographical location, the intensity of environmental governance and the development of digital financial inclusion. As moving into the good phase of the economy, big data is more inclined to enhance green technological progress, while in a sluggish phase, it improves green efficiency more. These findings point the way forward for sustainable development. The Chinese government can actively build information infrastructure and improve the technical level and application capacity of big data.

Section: Adding Control Variablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How big data drives green economic development: Evidence from China

Huang

2022

Front. Environ. Sci.

“…BDA has enormous potential to cut off the cost and provide instant digital-based solutions. Most organizations are now producing large amounts of data [8,9]. This data can be helpful for organizations to gain various competitive advantages and make profitable decisions [10].…”

Section: Introductionmentioning

confidence: 99%

“…Sustainability is important for the technology adoption today, as it is considered a key element that has the potential to improve organization performance as well as to offer a source of competitive advantage; the same applies for BDA [8,9]. The importance of BDA and sustainable development has recently highlighted by various studies like [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Model for Decision-Makers’ Adoption of Big Data in the Education Sector

2021

Big Data Adoption (BDA) has already gained tremendous attention from executives in various fields. However, it is still not well explored in the education sector, where a large amount of academic data is being produced. Therefore, integrating Technology Organization Environment (TOE) and Diffusion of Innovation (DOI), this study aims to develop a theoretical model to identify the factors that influence BDA in the higher education sector. To do so, significant technology-, organization-, and environment-related factors have been extracted from previous BDA studies. Meanwhile, the moderating effects of the university size and the university age are added into the developed model. A sample of 195 data was collected from the managerial side of virtual university (VU) campuses in Pakistan using an online survey questionnaire. Structural equation modeling (SEM) was used to test the research model and developed hypotheses. The results showed that relative advantage, complexity, compatibility, top management support, financial resources, human expertise and skills, competitive pressure, security and privacy, and government policies are significant determinants of BDA. However, the results did not support the influence of IT infrastructure on BDA. Based on the findings, this study provides guidelines for the successful adoption of big data in higher education sector. This study can serve as a piece of help to the ministry of education, administrators, and big data service providers for the smooth adoption of big data.

“…Dan banyak survey yang telah dilakukan dalam bidang EDM yang membahas tentang prediksi kelulusan mahasiswa [17], [19] yang sukses membangun model menggunakan metode machine learning. Selain itu juga ada beberapa penelitian yang menggunakan konsep big data kedalam EDM [20]. Sehingga sudah banyak sekali topik tentang EDM dengan prediksi kelulusan mahasiswa [21], [22] yang membuat topik ini menjadi salah satu tren para peneliti dan akademisi.…”

unclassified

Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa

Zeniarja¹,

Salam²,

Ma'ruf³

2022

JRE

Institusi pendidikan tinggi seperti universitas merupakan inti dari sistem pendidikan di mana penelitian dan pengembangan ekstensif dilakukan dalam lingkungan yang kompetitif. Misi utama dari lembagalembaga ini adalah untuk menghasilkan, mengumpulkan, dan berbagi pengetahuan. Secara khusus, universitas biasanya membutuhkan pengetahuan yang dikumpulkan dari kumpulan data masa lalu dan saat ini yang, setelah ditambang, dapat digunakan untuk mewakili dan menyampaikan informasi kepada pihak admin universitas untuk memantau kondisi dan mengambil tindakan dalam menyelesaikan masalah [1].