Utilize student interaction history
Utilize AI and machine learning algorithms to analyze student interaction history, self-assessment test scores, and final scores in a course. Create a system that dynamically ranks and classifies the relevance of different course content parts, such as subsections or modules.
This AI-driven system can identify which course sections are more frequently accessed, where students tend to perform better or worse, and which topics are most challenging. Instructors can then prioritize these areas for improvement or offer additional resources to help students with the content they find most relevant or challenging. This approach ensures that the course content is continually optimized based on student feedback and performance data.
This AI-driven system can identify which course sections are more frequently accessed, where students tend to perform better or worse, and which topics are most challenging. Instructors can then prioritize these areas for improvement or offer additional resources to help students with the content they find most relevant or challenging. This approach ensures that the course content is continually optimized based on student feedback and performance data.
Example:
Scenario: Adaptive Learning in a Programming Course
Data Collection:
The learning platform collects data on student interactions, including time spent on modules, scores in self-assessment quizzes, and final exam results.
Feature Extraction:
Relevant features include the time spent on each programming language module, the accuracy of self-assessment test scores, and the final grades.
Machine Learning Algorithms:
Machine learning algorithms analyze the data to identify patterns. For instance, the system may recognize that many students struggle with a specific programming concept, such as object-oriented programming.
Dynamic Ranking and Classification:
The system dynamically ranks course content based on relevance. It identifies that the module on object-oriented programming is frequently accessed but has lower average scores, suggesting it's a challenging area.
Instructor Feedback and Optimization:
Instructors receive insights indicating that the object-oriented programming module is a challenging area for many students. They decide to create additional resources, such as video tutorials and interactive exercises, to support students in mastering this concept.
Continuous Improvement:
The system continually updates its rankings as new data becomes available. If the additional resources lead to improved student performance, the system recognizes the positive impact and adapts its recommendations accordingly.
Student Support and Resources:
Students struggling with object-oriented programming receive targeted notifications recommending the new resources. The learning platform provides adaptive feedback and suggestions for additional practice to reinforce the challenging concepts.
Adaptive Learning Paths:
The system adapts learning paths based on individual student performance. If a student consistently excels in certain topics, the platform may offer more advanced challenges or provide opportunities for project-based learning in those areas.
Data Collection:
The learning platform collects data on student interactions, including time spent on modules, scores in self-assessment quizzes, and final exam results.
Feature Extraction:
Relevant features include the time spent on each programming language module, the accuracy of self-assessment test scores, and the final grades.
Machine Learning Algorithms:
Machine learning algorithms analyze the data to identify patterns. For instance, the system may recognize that many students struggle with a specific programming concept, such as object-oriented programming.
Dynamic Ranking and Classification:
The system dynamically ranks course content based on relevance. It identifies that the module on object-oriented programming is frequently accessed but has lower average scores, suggesting it's a challenging area.
Instructor Feedback and Optimization:
Instructors receive insights indicating that the object-oriented programming module is a challenging area for many students. They decide to create additional resources, such as video tutorials and interactive exercises, to support students in mastering this concept.
Continuous Improvement:
The system continually updates its rankings as new data becomes available. If the additional resources lead to improved student performance, the system recognizes the positive impact and adapts its recommendations accordingly.
Student Support and Resources:
Students struggling with object-oriented programming receive targeted notifications recommending the new resources. The learning platform provides adaptive feedback and suggestions for additional practice to reinforce the challenging concepts.
Adaptive Learning Paths:
The system adapts learning paths based on individual student performance. If a student consistently excels in certain topics, the platform may offer more advanced challenges or provide opportunities for project-based learning in those areas.
Reference:
1. Murtaza, Mir & Ahmed, Yamna & Shamsi, Jawwad & Sherwani, Fahad & Usman, Mariam. (2022 ). AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions. IEEE Access. 10. 1-1. 10.1109/ACCESS.2022.3193938.
2. Alruwais N, Zakariah M. Evaluating Student Knowledge Assessment Using Machine Learning Techniques. Sustainability. 2023; 15(7 ):6229. https://doi.org/10.3390/su15076229
3. M. Murtaza, Y. Ahmed, J. A. Shamsi, F. Sherwani and M. Usman, "AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions," in IEEE Access, vol. 10, pp. 81323-81342, 2022, doi: 10.1109/ACCESS.2022.3193938.
2. Alruwais N, Zakariah M. Evaluating Student Knowledge Assessment Using Machine Learning Techniques. Sustainability. 2023; 15(7 ):6229. https://doi.org/10.3390/su15076229
3. M. Murtaza, Y. Ahmed, J. A. Shamsi, F. Sherwani and M. Usman, "AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions," in IEEE Access, vol. 10, pp. 81323-81342, 2022, doi: 10.1109/ACCESS.2022.3193938.
Author of the tip:
Spiros Sirmakessis
University of Peloponnese