Software Technology / IT · BSc · REF. TA-0656
Evaluating the Role of Big Data Analytics in Fraud Detection Accuracy within Online Learning Management Systems
Abstract
This BSc study investigates the subject matter outlined in the title above through a structured research design appropriate to the BSc level. Using primary and/or secondary data collection methods, the research examines the underlying variables, tests relevant hypotheses, and presents findings with implications for practice and policy. This is placeholder abstract text generated for catalogue preview purposes; the full document contains a complete, topic-specific abstract, literature review, methodology, data analysis, and conclusion.
Chapter One — 1.1 Background to the Study
Organizations that depend on online learning management systems are under increasing pressure to modernize, and Big Data Analytics has emerged as one of the more promising avenues for doing so, given its demonstrated impact in related domains.
In practice, however, adoption of big data analytics within online learning management systems has been uneven, and its actual impact on fraud detection accuracy is not yet well understood in a rigorous, evaluable way — a gap this study is positioned to address.
1.2 Statement of the Problem
Existing approaches to fraud detection accuracy within online learning management systems remain largely reactive and fragmented, with little systematic use of big data analytics despite its demonstrated value elsewhere. This study addresses the resulting gap by designing and evaluating a solution built specifically around big data analytics.
1.3 Objectives of the Study
- To design and implement a big data analytics-based approach to improving fraud detection accuracy in online learning management systems.
- To evaluate the effectiveness of Big Data Analytics in enhancing fraud detection accuracy within online learning management systems.
- To identify the key requirements and constraints relevant to deploying big data analytics in this context.
- To assess user and stakeholder perception of the resulting system.
1.4 Research Questions
- How can big data analytics be applied to improve fraud detection accuracy in online learning management systems?
- How effective is Big Data Analytics at enhancing fraud detection accuracy within online learning management systems?
- What requirements and constraints are relevant to deploying big data analytics in this context?
- How do users and stakeholders perceive the resulting system?
1.5 Significance of the Study
Beyond its immediate technical contribution, this study offers value to organizations evaluating whether to invest in big data analytics for their own online learning management systems, and contributes to the broader literature on applied software technology / IT by documenting a concrete implementation and evaluation case.
1.6 Scope of the Study
As a BSc-level study, its scope is confined to designing and evaluating a big data analytics-based solution for online learning management systems, focused specifically on fraud detection accuracy; broader deployment considerations fall outside this scope.
Chapters Two through Five, references and appendices are available for a one-time fee of ₦50,000.
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