透過分析程式設計模式提供干預措施以提升學習成效;Interventions to enhance learning outcomes by analyzing coding patterns

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题名:	透過分析程式設計模式提供干預措施以提升學習成效;Interventions to enhance learning outcomes by analyzing coding patterns
作者:	董育汝;Tung, Yu-Ru
贡献者:	資訊工程學系
关键词:	程式設計模式;程式設計方式;干預措施;學習成效;自我調節學習;線上程式系統;Coding pattern;Coding mode;Intervention;Learning outcome;Self-regulated learning;Online programming system
日期:	2022-07-11
上传时间:	2022-10-04 11:58:18 (UTC+8)
出版者:	國立中央大學
摘要:	在現今的程式教學中，線上程式系統已經成為大家常用的方式，允許學生可以不受時間和地點限制的練習程式設計。此外，因應疫情的影響，遠距課程需求日益增加，學生需要在沒有教師面對面指導的情況下獨立調整自己的學習。在過去的研究中發現透過行為分析相較於考試成績預測自我調節學習的能力更好，程式設計行為分析也可以幫助教師更了解學生的學習狀況和程式設計的過程。除此之外，線上程式系統的應用分成教育中程式評量、線上編譯器等，在過去的研究中大多只分析單一應用的行為，且大多僅透過作答次數、錯誤次數、時間等特徵，其分析結果容易忽略程式內容。本研究將學生使用線上程式系統中的程式設計行為，定義出於練習(線上編譯)及程式評量中的各種狀態，生成程式設計行為序列並轉換為機率轉移矩陣，透過K-Means++分群演算法分析出9種程式設計方式。為了進一步研究程式設計方式，將學生停留於各個程式設計方式的機率與程式測驗成績進行相關性分析，發現邏輯及語法極為困擾模式停留機率高的學生與程式測驗成績有顯著的負相關。接著，透過學生在各個程式設計方式的停留機率區分出3種不同的程式設計模式(精熟群、困擾群、放棄群)的學生。為了了解各個程式設計模式的學生特性，本研究將各個程式設計模式的學生在程式測驗成績、自我調節學習能力各面向、各個程式設計方式的停留機率進行變異數分析。最後針對不同程式設計模式的學生特性，給予相對應的回饋，並推薦邏輯及語法極為困擾模式停留機率高的學生對應的練習題目。研究結果顯示各個程式設計方式的停留機率與程式測驗成績、自我調節學習能力具有不同程度關聯性，而程式設計模式不同的學生雖然在程式測驗成績上沒有顯著差異，但在各個程式設計方式的停留機率及自我調節學習之複誦、組織、控制信念能力上亦有不同程度之顯著的差異。在干預措施之成效上，實驗組的學生在程式語法上相較於干預前更為熟悉，且相較於控制組在程式測驗成績上的表現顯著較佳，但在自我調節學習各面向的能力上則較無顯著差異。;In recent programming teaching, the online programming system has become a common method, allowing students to practice coding regardless of time and place. In addition, with the increasing demand for distance courses in response to the impact of Covid-19, students need to independently adjust their learning without face-to-face guidance from teachers. In past studies, it has been found that behavioral analysis is better than test scores to predict the ability of self-regulated learning, and programming behavioral analysis can also help teachers better understand students′ learning status and coding process. In addition, the application of the online programming system is divided into program evaluation in education, online compiler, etc. In the past research, most of them only analyzed the behavior of a single application, and most of them only analyzed the characteristics of the number of answers, the number of errors, and the time, and the analysis results tend to ignore the program content. In this study, students use the programming behavior in the online programming system to define various states in practice and evaluation, generate the programming behavior sequence and convert it into a probability transition matrix, through the K-Means++ clustering algorithm separated 9 coding modes. In order to further study the coding modes, the correlation analysis was carried out between the probability of students staying in each coding mode and the scores of the programming test. Then, students with 3 different coding patterns (adept, struggle, and abandonment group) were distinguished by the students′ staying probability in each coding mode. In order to understand the characteristics of students of each coding pattern, we conducted an ANOVA analysis of the students of each coding pattern in the programming exam scores, self-regulated learning, and the probability of staying in each coding mode. Finally, according to the characteristics of students with different coding patterns, give corresponding feedback, and recommend the corresponding practice questions for students who are extremely troubled by logic and syntax and have a high probability of staying in the mode. The study results show that the probability of staying in each coding mode is related to the programming exam scores and self-regulation learning to varying degrees. Although there is no significant difference in the programming exam scores of students with different coding patterns, the probability of staying in each coding mode is significant differences in different degrees in the ability to rehearsal, organization, and control beliefs. In terms of the effect of the intervention, the students in the experimental group were more familiar with the syntax of the program than before the intervention, and were significantly better than the control group in the performance of the programming exam. In addition, there was no significant difference in all aspects of self-regulated learning.
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