Big Data and Analytics Implementation in Tertiary Institutions to Predict Students Performance in Nigeria

• URL: http://arxiv.org/abs/2207.14677v1
• Date: Fri, 29 Jul 2022 13:52:24 GMT
• Title: Big Data and Analytics Implementation in Tertiary Institutions to Predict Students Performance in Nigeria
• Authors: Ozioma Collins Oguine, Kanyifeechukwu Jane Oguine, Hashim Ibrahim Bisallah
• Abstract summary: The term Big Data has been coined to refer to the gargantuan bulk of data that cannot be dealt with by traditional data-handling techniques. This paper explores the attributes of big data that are relevant to educational institutions. It investigates the factors influencing the adoption of big data and analytics in learning institutions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The term Big Data has been coined to refer to the gargantuan bulk of data that cannot be dealt with by traditional data-handling techniques. Big Data is still a novel concept, and in the following literature, we intend to elaborate on it in a palpable fashion. It commences with the concept of the subject in itself, along with its properties and the two general approaches to dealing with it. Big Data provides an opportunity for educational Institutions to use their Information Technology resources strategically to improve educational quality, guide students to higher completion rates and improve student persistence and outcomes. This paper explores the attributes of big data that are relevant to educational institutions, investigates the factors influencing the adoption of big data and analytics in learning institutions, and seeks to establish the limiting factors hindering the use of big data in Institutions of higher learning. A survey research design was adopted in conducting this research, and Questionnaires were the instrument employed for data collection.

Related papers

• Data-Centric AI in the Age of Large Language Models [51.20451986068925]
  This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs) We make the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs. We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization.
  arXiv  Detail & Related papers  (2024-06-20T16:34:07Z)
• A Survey on Data Selection for Language Models [151.6210632830082]
  Data selection methods aim to determine which data points to include in a training dataset. Deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive. Few organizations have the resources for extensive data selection research.
  arXiv  Detail & Related papers  (2024-02-26T18:54:35Z)
• Query of CC: Unearthing Large Scale Domain-Specific Knowledge from Public Corpora [104.16648246740543]
  We propose an efficient data collection method based on large language models. The method bootstraps seed information through a large language model and retrieves related data from public corpora. It not only collects knowledge-related data for specific domains but unearths the data with potential reasoning procedures.
  arXiv  Detail & Related papers  (2024-01-26T03:38:23Z)
• Unveiling the Potential of Big Data Analytics for Transforming Higher Education in Bangladesh; Needs, Prospects, and Challenges [0.0]
  Big Data Analytics (BDA) can be a piece of the answer to accomplishing Bangladesh Higher Education (BHE) objectives. This paper reviews the capacity of BDA, considers possible applications in BHE, gives an insight into how to improve the quality of education or uncover additional values from the data generated by educational institutions.
  arXiv  Detail & Related papers  (2023-10-10T23:27:27Z)
• Towards Generalizable Data Protection With Transferable Unlearnable Examples [50.628011208660645]
  We present a novel, generalizable data protection method by generating transferable unlearnable examples. To the best of our knowledge, this is the first solution that examines data privacy from the perspective of data distribution.
  arXiv  Detail & Related papers  (2023-05-18T04:17:01Z)
• Advanced Data Augmentation Approaches: A Comprehensive Survey and Future directions [57.30984060215482]
  We provide a background of data augmentation, a novel and comprehensive taxonomy of reviewed data augmentation techniques, and the strengths and weaknesses (wherever possible) of each technique. We also provide comprehensive results of the data augmentation effect on three popular computer vision tasks, such as image classification, object detection and semantic segmentation.
  arXiv  Detail & Related papers  (2023-01-07T11:37:32Z)
• Big Data and Education: using big data analytics in language learning [0.0]
  Working with big data using data mining tools is rapidly becoming a trend in education industry. We consider some basic concepts as well as most popular tools, methods and techniques regarding Educational Data Mining and Learning Analytics.
  arXiv  Detail & Related papers  (2022-07-19T19:17:10Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)
• "If we didn't solve small data in the past, how can we solve Big Data today?" [0.0]
  We aim to research terms such as'small' and 'big' data, understand their attributes, and look at ways in which they can add value. Based on the research, it can be inferred that, regardless of how small data might have been used, organizations can still leverage big data with the right technology and business vision.
  arXiv  Detail & Related papers  (2021-11-08T16:31:01Z)
• Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions [5.124701758921822]
  We present a survey on the use of federated learning for big data services and applications. We review the use of FL for key big data services, including big data acquisition, big data storage, big data analytics, and big data privacy preservation.
  arXiv  Detail & Related papers  (2021-10-08T14:36:43Z)
• From Data to Knowledge to Action: A Global Enabler for the 21st Century [26.32590947516587]
  A confluence of advances in the computer and mathematical sciences has unleashed unprecedented capabilities for enabling true evidence-based decision making. These capabilities are making possible the large-scale capture of data and the transformation of that data into insights and recommendations. The shift of commerce, science, education, art, and entertainment to the web makes available unprecedented quantities of structured and unstructured databases about human activities.
  arXiv  Detail & Related papers  (2020-07-31T19:19:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.