A recommendation system for effective learning strategies: An integrated approach using context-dependent DEA

• Structure
• Title Analysis
• Abstract Analysis
• Introduction Analysis
• Literature Review Analysis
• Conclusion Analysis
• Methods Analysis
• Results (Empirical) Analysis

Structure

• Article Info - Keywords
• Abstract
• 1. Introduction
• 2. Literature Review
• 3. Materials and methods
• 4. Empirical Analysis
• 5. Conclusions
• CRediT authorship contribution statement
• Declaration of Competing Interest
• Acknowledgement
• References

Title Analysis

• recommendation system
• learning strategies
• integrated approach
• context-dependent evaluation method
• data envelopment analysis (DEA)

Abstract Analysis

• Introduction / Background

  Universities have been focusing on increasing individualized training and providing appropriate education for students. The individual differences and learning needs of college students should be given enough attention. From the perspective of learning efficiency, we establish a clustering hierarchical progressive improvement model (CHPI), which is based on cluster analysis and context-dependent data envelopment analysis (DEA) methods.

• Methods

  The CHPI clusters students’ ontological features, employs the context-dependent DEA method to stratify students of different classes, and calculates measures, such as obstacles, to determine the reference path for individuals with inefficient learning processes. The learning strategies are determined according to the gap between the inefficient individual to be improved and the individuals on the reference path.

• Results

  By the study of college English courses as an example, it is found that the CHPI can accurately recommend targeted learning strategies to satisfy the individual needs of college students so that the learning of individuals with inefficient learning processes in a certain stage can be effectively improved.

• Conclusions

  In addition, CHPI can provide specific, efficient suggestions to improve learning efficiency comparing to existing recommendation systems, and has great potential in promoting the integration of education-related researches and expert systems.

Introduction Analysis

• Premise: Education a priority
• ILPs improve student performance
• Student Engagement Theory
• Low achievement is due
  • inappropriate arragement of students' investment in learning
  • lack of guidance on how to adjust learning efforts
• Expert systems
• Research framework proposed to measure effectiveness
• Efficient Learning is goal
  • Qualitative - pedagogy and psychology
  • Quantitative
• personality recommendation system - student performance and preferences
• Clustering Hierarchical Progressive Improvement model (CHPI)
• Provide new framework
• learning efficiency based on context-dependent DEA to measure students' learning status
• CHPI model proven to work well based on framework

Literature Review Analysis

• 2.1 Relevant studies on personalized learning
• 2.2 Systematized learning recommendation
• 2.3 Data envelopment analysis with applications

Conclusion Analysis

• PAM custering method and context-dependent DAE method = CHPI
  • 1. Quantization method across the data
  • 2. Cluster students according to ontological features
  • 3. Stratify students into different categories based on context-dependent DEA method
  • 4. define the measure of obstacles and select the reference path
• Recommendations can be extended to other disciplines and forms of education
• Limitations and Gaps
  • Garbage in, Garbage out
  • impacts of cycle of the learning process (semesters, years, etc)

Methods Analysis

• 3.1 Data pre-processing
• 3.2 Ontological feature clustering
• 3.3 Improved context-dependent DEA
  • 3.3.1 Definition of learning efficiency
  • 3.3.2 Context-dependent DEA stratification
• 3.4 Effective learning path recommendation

Results (Empirical) Analysis

• 4.1 Learning data collecting and pre processing
• 4.2 Clustering students with different characteristics
• 4.3 Generating stratifications and learning path