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Learning Performance in Adaptive Learning Systems: A Case Study of Web Programming Learning Recommendations.

Hsiao-Chi Ling¹, Hsiu-Sen Chiang²

¹Department of Marketing, Kainan University, Taoyuan, Taiwan.

Frontiers in Psychology

|February 14, 2022

Summary

This summary is machine-generated.

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This study introduces a personalized web programming recommendation system to address student learning challenges. The system, using C4.5 decision trees and adaptive instruction, improves learning effectiveness and confidence for beginner programmers.

Area of Science:

Computer Science Education
Human-Computer Interaction
Educational Technology

Background:

Learning computer programming presents challenges due to the discrepancy between programming logic and human cognition.
Mismatched course content and skill level lead to learner frustration, reduced motivation, and abandonment.
Personalized learning paths are crucial for fostering accomplishment and confidence in novice programmers.

Purpose of the Study:

To develop and evaluate a web programming recommendation system for personalized learning guidance.
To create tailored learning paths for front-end and back-end web development.
To enhance learner self-confidence and overall learning effectiveness.

Main Methods:

Study 1: Developed a recommendation system using C4.5 decision tree methods based on data from 41 professional web designers.

Keywords:

computer programming decision tree learning motivation personality traits personalization

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Study 1: Implemented and tested the system with 13 beginner programmers over 4 weeks, gathering feedback through interviews.

Study 2: Developed an online platform with real-time feedback and adaptive instruction, contrasting it with fixed instruction models.

Main Results:

Study 1: The recommendation system demonstrated acceptable effectiveness in guiding beginner programmers.
Study 2: Learners in the adaptive instruction group showed significantly better learning performance compared to the fixed instruction group.
The system successfully provided personalized learning paths, contributing to a sense of accomplishment.

Conclusions:

Personalized recommendation systems and adaptive instruction are effective in improving web programming education.
Addressing individual learning needs enhances student motivation, confidence, and programming skill acquisition.
The developed system offers a viable solution for overcoming common challenges in learning programming.