Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches
Abstract
:1. Introduction
- Applying the NLP techniques for text mining and enhancing the trainer mentoring;
- Using agent-based recommendation to solve the limited user ranking problems and improve the performance of the e-learning environment;
- Updating the generated recommendation for each cluster to improve the quality of recommendations based on the extracted information from user profiles and search histories;
- Identifying the learning style based on the learner’s characteristics and knowledge discovery on the collected information through users’ activity.
2. Materials
2.1. Strategies of Recommendation
2.2. Agent-Based Recommendation System
2.3. Knowledge-Dependent and Evaluation of Recommendation
2.4. Comparative Analysis of E-Learning Recommendation
3. Methods
3.1. Information Collection
3.2. Domain Knowledge
3.3. Recommendation Framework
Enhancing Trainer Mentoring Using Text Mining
Algorithm 1 Learners’ Similar Course Set. |
|
4. Predictive Analysis of Agent-Based Recommendation
5. Results
5.1. Dataset
5.2. Performance Evaluation of the Proposed Agent-Based Recommendation
5.3. Evaluation of Learning Experience
5.4. Comparison of the Agent-Based Recommendation Performance Metrics
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Applied Technique | Overview | Advantages | Limitations | Applications |
---|---|---|---|---|
Collaborative Filtering [48] | Highlighted different ways through which to enhance the information retrieval and suggested the proper recommendation regarding the improvement in performance and level of satisfaction. | The domain knowledge is not required. Information retrieval improves the effectiveness of the system. | The recommendation system effectiveness is not seen until compared with other domains | Novel Dynamic Evolution Mechanism. |
Hybrid Filtering [49] | Type of book recommendation that comprises content-based and collaborative-based filtering prediction | Using spark big data to enhance the utilization rate of personalized book recommendations. | It has a time complexity issue. | Self-Organizing Map Neural Network Technique |
Context-aware [50] | Gathering the data related to feedback and contexts from the learner module or sensors. | Recommendation regulated based on context. | Contextual information integration | - |
Ontology-Dependent [51] | The recommendation focus on the learning and hybrid approach without ontology. The ontology uses different aspects of the context. | Overcoming the traditional recommendation limitations. Gives better performance in terms of hybrid recommendation. | Domain knowledge required. Correct recommendation identification. Evaluating the performance is difficult. | ASTM Standard Method |
Cross Domain [52] | Comparing the potential item with the rated item in terms of specifying matching options. | Domain information not required. | Data sparsity, Scalability | PrefixSpan to generate sequential patterns. TopSeq rule mining to find frequent sequential rule. |
Demographic [53] | Modifying and improving the new ways for financial planning recommendation. | The history rating of the special learner is not required. | Personal attributes must be retrieved from the learner. | LDA Approach |
Notations | Inference | Description |
---|---|---|
Knowledge Point | ||
Level of importance | ||
Level of difficulty | ||
Size | is a digit | |
Attributes of media | ||
Attributes of content | ||
Learning time suggestion | is a digit | |
Constraint matrix | is matrix | |
Current state | ||
Ranking of similarity | ||
Marked labels | ||
Visited time | = | | |
Visited frequency | is a natural digit | |
Weight |
Subsystems | Process |
---|---|
Domain | Saving the various components and resources of learning |
Learner | Extracting the features and information of the learner |
Application | Identifying the learner’s requirements by adding the operational rules |
Adaption | Learner’s agent identification based on intelligent recommendation |
Session | Subsystems controlling the main system |
Components | Description |
---|---|
Operating System | Windows 10 64 bit |
CPU | Intel(R) Core(TM) i7-8700 CPU @3.20 GHz |
Memory | 32 GB |
Programming Language | WinPython 3.6.2, IDE Jupyter Notebook |
Browser | Google Chrome |
Recommendation Module | Agent-based Recommendation |
Library and Framework | Web Service |
OS Manufacturer | Microsoft Corporation |
OS Edition | Professional |
Features | Description |
---|---|
User Information | The details of the user profile |
Tweets | Contents shared by the user |
Photos | Photos shared by the user |
Shares | Links or contents shared on social media |
Retweets | Re-posting the published information |
Date | Date of published information |
Time | Time of published information |
Comments | Ideas about the shared contents |
Learner (L) | Course 1 | Course 2 | Course 3 | Course 4 |
---|---|---|---|---|
L1 | Yes | No | No | No |
L2 | Yes | Yes | Yes | No |
L3 | Yes | Yes | No | No |
Method | Positive | Negative | Neutral | |
---|---|---|---|---|
Data Mining | Sentiment Analysis | 10 | 4 | 5 |
Topic Modeling | 11 | 3 | 6 | |
Clustering | 8 | 3 | 4 |
Categories | Options | Tra | SI-Top | Prefix | SI-FL | SS-IFL | SSC-IFL | SI-IFL |
---|---|---|---|---|---|---|---|---|
Quality | Usefulness | 4.4 | 4.8 | 4.4 | 4.6 | 4.8 | 5.0 | 4.5 |
Evaluation | Satisfaction | 4.6 | 4.3 | 4.4 | 4.5 | 4.9 | 5.2 | 4.4 |
Personal | Difficulty | 4.8 | 4.5 | 4.3 | 4.4 | 4.8 | 4.7 | 4.9 |
Realization | Media | 4.4 | 4.9 | 4.0 | 4.7 | 4.5 | 4.8 | 4.7 |
Evaluation | Time | 4.9 | 4.2 | 5.2 | 4.9 | 5.0 | 5.0 | 4.5 |
Content | 4.5 | 4.6 | 4.5 | 4.7 | 5.0 | 5.0 | 5.0 | |
Experience | Attention Focus | 4.6 | 4.8 | 4.5 | 4.7 | 4.9 | 4.2 | 4.9 |
Flow | Control | 4.6 | 5.0 | 4.7 | 4.2 | 5.4 | 4.4 | 4.3 |
Evaluation | Curiosity | 3.7 | 4.4 | 4.2 | 4.0 | 4.3 | 4.6 | 4.8 |
Intrinsic Interest | 4.4 | 4.8 | 4.9 | 4.4 | 4.9 | 4.9 | 5.0 |
ID of Learner | Date | Access Path |
---|---|---|
1 | 1 December 2021 | Course 1 |
2 | 2 December 2021 | Course 2 |
3 | 3 December 2021 | Course 3 |
4 | 4 December 2021 | Course 1 |
5 | 5 December 2021 | Course 1 |
6 | 6 December 2021 | Course 2 |
7 | 7 December 2021 | Course 2 |
8 | 8 December 2021 | Course 3 |
9 | 9 December 2021 | Course 1 |
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Shahbazi, Z.; Byun, Y.-C. Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches. Mathematics 2022, 10, 1192. https://doi.org/10.3390/math10071192
Shahbazi Z, Byun Y-C. Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches. Mathematics. 2022; 10(7):1192. https://doi.org/10.3390/math10071192
Chicago/Turabian StyleShahbazi, Zeinab, and Yung-Cheol Byun. 2022. "Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches" Mathematics 10, no. 7: 1192. https://doi.org/10.3390/math10071192
APA StyleShahbazi, Z., & Byun, Y. -C. (2022). Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches. Mathematics, 10(7), 1192. https://doi.org/10.3390/math10071192