Nowadays, E-learning is dynamically changing the phase of educational system. It has played a big role in providing academic resources to distance and continuing educational systems that helped the students and Instructor in coping with the demand of the fast-driven learning setup. Because of its importance, the demand to enhance its capabilities in the searching and presentation of its learning resources is now the focus of some researches.
In this paper, the researcher presents a new framework for an E-learning system with enhanced searching capabilities. The proposed framework is mainly focused on the improvements of the search capability to obtain more refined search results including the functionalities to be able to handle failed searches. The main contribution of this research study is to present a new and efficient e-learning framework that will be used as a model for the development of an e-learning system, integrated with search auditing functionality.
Table of Contents
1. INTRODUCTION
1.1. Backgrounds of the study
1.2. Statement of the problem
1.3. Research Question
1.4. Objective of the study
1.4.1. General objective
1.4.2. Specific objectives
1.5. Scope and limitation of the study
1.6. Significance of the Research
1.7. Thesis Layout
2. Research Design and Methodology
2.1. Research Methodology
2.1.1. Literature review
2.1.2. Direct observation
2.1.3. Interview
2.1.4. Building the Framework
2.1.5. Research Strategy
2.2. Methods of Data Collection
2.2.1. Questionnaire
2.2.2. Source of Data
2.2.3. Teachers
2.2.4. E-learning administrators
2.2.5. Data Collection Procedure
2.2.6. Population, sample size and sampling technique
2.2.7. Research Instruments
2.2.8. Data Analysis procedure
3. Literature Review and Related Studies
3.1. Overview of E-learning system
3.1.1. Definition of E-learning
3.1.2. Evolution of E-learning
3.2. Types of E-learning
3.3. Advantage and disadvantage of E-learning
3.3.1. Advantages of E-learning
3.3.2. Disadvantage of E-learning
3.4. Current status of E-learning
3.5. Challenges of E-learning
3.6. Opportunities of E-learning
3.7. Trends in E-learning
3.8. Related review
4. Building the Framework
4.1. Designing the Learning Management System
4.2. Proposed E-learning framework
4.2.1. Pre-learning Process
4.2.2. Learning Process
4.2.3. Post-Learning Process
4.3. Contribution to the new e-learning framework
4.3.1. Frequently Searched Learning Resource (FSLR)
4.3.2. Pending Search (PS)
5. Evaluating the Framework
5.1. Selection of Evaluation Instrument
5.2. Literatures for e-learning evaluation
5.3. ISO 9126 model
5.4. Proposed Evaluation Instrument
6. Results and Discussion
6.1. Functionality
6.2. Reliability
6.3. Usability
6.4. Efficiency
7. Conclusion and Recommendation
7.1. Proposed Future works:
8. References
Research Objectives and Themes
This thesis aims to design an efficient e-learning framework that enhances search capabilities by integrating a search auditing functionality, specifically addressing the challenges of failed searches and search efficiency in educational platforms.
- Design and evaluation of an e-learning framework with advanced search features.
- Improvement of search mechanisms to provide refined and relevant results.
- Implementation of auditing agents, such as Frequently Searched Learning Resources (FSLR) and Pending Search (PS) agents.
- Validation of the proposed model using ISO 9126 quality characteristics.
Excerpt from the Book
4.3.1. Frequently Searched Learning Resource (FSLR)
The frequently searched learning resource (FSLR) agent records all the search keywords of all the learning resource that was searched by the users of the e-learning system. This process will help to decongest the traffic between the Learning Resource repositories and learning management system (LMS) which is the usual bottleneck of the e-learning system model.
To accomplish this, the e-learning system will adopt the concept of cache and Least Recently Used (LRU) algorithm:
Cache is a hardware or software component that stores data so future requests for that data can be served faster; the data stored in a cache might be the result of an earlier computation, or the duplicate of data stored elsewhere. A cache hit occurs when the requested data can be found in a cache, while a cache miss occurs when it cannot. Cache hits are served by reading data from the cache, which is faster than computing a result or reading from a slower data store; thus, the more requests can be served from the cache, the faster the system performs.
While the Least Recently Used algorithm keeps the recently used items near the top of cache. Whenever a new item is accessed, the LRU places it at the top of the cache. When the cache limit has been reached, items that have been accessed less recently will be removed starting from the bottom of the cache.
Summary of Chapters
1. INTRODUCTION: Provides an overview of e-learning, discusses current challenges in search mechanisms, and establishes the research objectives and scope.
2. Research Design and Methodology: Details the exploratory research approach, data collection via questionnaires and interviews, and the methodology for building and evaluating the framework.
3. Literature Review and Related Studies: Examines existing e-learning systems, their evolution, types, advantages, disadvantages, and current research trends in e-learning.
4. Building the Framework: Describes the design of the Learning Management System (LMS) and the integration of new agents, FSLR and PS, to enhance search capabilities.
5. Evaluating the Framework: Discusses the selection of the ISO 9126 model as the evaluation instrument to measure the quality of the proposed e-learning prototype.
6. Results and Discussion: Presents the analysis and interpretation of the evaluation data gathered from expert respondents regarding the functionality, reliability, usability, and efficiency of the model.
7. Conclusion and Recommendation: Summarizes the research findings, confirms the efficiency of the proposed enhancements, and suggests directions for future work.
Keywords
E-learning system, e-learning framework, search audit, Learning Management System, ISO 9126, Frequently Searched Learning Resource, Pending Search, Learner profiling, adaptive e-learning, search mechanism, educational technology.
Frequently Asked Questions
What is the fundamental goal of this research?
The study aims to design a new e-learning framework that improves search efficiency and capability by introducing specific auditing features, thereby better assisting instructors and students in managing educational resources.
What are the core research themes covered in this paper?
The work focuses on the intersection of e-learning technology, search algorithms (specifically caching and LRU mechanisms), and software quality evaluation based on international standards.
What is the primary research question?
The research asks how to overcome the lack of mechanism for failed searches and search performance issues in current e-learning platforms through the implementation of a new, audited framework.
Which scientific methods are applied in this work?
The researcher uses an exploratory and qualitative research approach, employing literature review, direct observation, interviews with stakeholders, and the design/evaluation of a software prototype.
What does the main body of the work address?
The body explains the architectural design of the proposed e-learning model, the implementation of search-auditing agents, and the systematic validation of these components using the ISO 9126 standard.
Which keywords best characterize the work?
Keywords include e-learning framework, search audit, Learning Management System (LMS), and ISO 9126.
What is the function of the Frequently Searched Learning Resource (FSLR) agent?
The FSLR agent uses an LRU cache-based algorithm to store frequently queried keywords, which helps reduce traffic and latency between the Learning Management System and the database repository.
What role does the Pending Search (PS) agent play?
The PS agent monitors keywords for which no learning resources are available, notifies the instructor to create relevant content, and informs the learner once that content is available in their account.
- Arbeit zitieren
- Mengistu Estifanos (Autor:in), 2017, E-Learning Framework for Auditing Search Resource, München, GRIN Verlag, https://www.grin.com/document/494200
