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MindMap Gallery Research on analysis of college students' programming learning behavior based on generative artificial intelligence

Research on analysis of college students' programming learning behavior based on generative artificial intelligence

This is a mind map about the analysis and research of college students' programming learning behavior based on generative artificial intelligence. The main content includes: research conclusions and inspirations, research findings, research design, research status, and research background.

Edited at 2024-03-10 17:37:32

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Research on analysis of college students' programming learning behavior based on generative artificial intelligence

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Outline

Research on analysis of college students' programming learning behavior based on generative artificial intelligence

Research Background

The efficient code generation capability of Chat GPT enables it to assist programmers in completing various tasks in the programming process.

In-depth analysis of learners' programming process can help teachers and learners adopt more effective teaching strategies and learning methods.

This study takes Chat GPT as an example, and analyzes learners' learning behavior and knowledge exploration issues in the process of using Chat GPT for programming through the perspective of behavioral analysis.

Use a variety of learning analysis methods to explore the behavioral patterns and knowledge inquiry quality of learners with different performance, and study the application effect of Chat GPT in university programming education, in order to provide new ideas for improving the teaching effect of programming courses.

Research status

Opinions remain on the effectiveness of Chat GPT in programming education

Advantage

limitation

Programming learning process analysis

The purpose of this study is to use learning analysis technology to conduct fine-grained collection, analysis, and mining of learners' programming behaviors and knowledge inquiry issues during the learning process, and to provide guidance on using Chat GPT to assist programming learning by exploring the reasons.

research problem

(1) What kind of behavior patterns do learners have when using Chat GPT to assist in programming learning?

(2) When using Chat GPT to assist programming learning, what are the characteristics of the programming behavior of learners with different performance?

(3) When using Chat GPT to assist programming learning, what is the quality of learners’ inquiry questions?

Research design

Research object

The course involves a total of five classes. In the first four classes, students learned the basic concepts of Python programming and experienced the application of Chat GPT. In the last class, students are required to use Chat GPT to create programming projects.

Data encoding

The research mainly collected the behavioral data of learners during the programming process and the inquiry questions communicated with Chat GPT, and conducted analysis based on this.

Use clickstream analysis to analyze

Analyze the quality of learners’ initial questions (knowledge exploration) and continued questions on Chat GPT feedback content (feedback content exploration) from three levels: shallow, medium and deep; All data are converted into the format required by cognitive network analysis [15], and the online cognitive network analysis tool (web ENA) is used for data analysis.

The study found

Programming learning behavior distribution analysis

Descriptive analysis of programming learning behavior

Sequence transformation analysis of programming learning behavior

Lag sequence analysis (Ls A) analyzes the coding results

Visualize Ls A results as a network using network visualization methods

Copy and paste the code in Chat GPT → Debug the code → Read the error message in the console

Copy and paste the Python code into Chat GPT → Copy the error message in the console to Chat GPT

Read the feedback information in Chat GPT→Copy the code in Chat GPT

Paste error message into Chat GPT → Encounter technical issues

Analysis of programming learning behavior of different performance groups

Sankey diagram visualization

Analysis of learners’ knowledge inquiry questions in Chat GPT

The average cognitive network distribution of the high, middle and low performance groups is presented.

Research conclusions and implications

Chat GPT can assist students in programming learning

There are potential problems in Chat GPT assisted programming learning

The limitations of "thinking for students" and "excessive reliance on technology"

make a suggestion

There are differences in behavioral patterns among different performance groups when utilizing Chat GPT

Strategies for learning programming using Chat GPT for learners of different performance levels

The quality of inquiry questions will affect the effectiveness of programming learning using Chat GPT

In the process of using Chat GPT to assist programming learning, learners should focus on the quality and depth of questions, and stimulate Chat GPT to provide more in-depth knowledge by asking challenging and open questions.