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題名:運用專注力督導系統於國小高年級學生運算思維教學之成效分析
作者:吳佳娣
作者(外文):WU,CHIA-TI
校院名稱:國立臺北教育大學
系所名稱:課程與教學傳播科技研究所(課程與教學)
指導教授:劉遠楨
學位類別:博士
出版日期:2021
主題關鍵詞:人工智慧深度學習專注力督導系統運算思維程式設計能力Artificial intelligencedeep learningconcentration supervision systemcomputational thinkingprogramming ability
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學生學習的專注程度影響有效學習的深度,教師的教室經營都是追求學生專心於學習,到目前為止的大部份是以眼動儀來了解學生的專注力,但面臨準確度不足的疑慮,且也欠缺立即回饋以提升學生專心度的系統。本研究以電腦視覺和深度學習的技術發展一個專注力督導系統,當學生不專心時,這系統可以根據學生的學習進度給予學習即時回饋訊息,藉以提升學生的專注力。另外,本研究也透過這專注力督導系統監測輔助教學,了解學生運算思維、程式設計以及STEM職業傾向是否有顯著差異。
本研究先以類神經網路技術設計專注力督導系統,接著以準實驗研究法進行實驗,實驗參與者為200位國小五年級學生,並隨機將學生分為兩組,分別為實驗組100位學生與對照組100位學生。實驗課程為期六週,實驗組使用專注力督導系統,對照組沒有使用專注力督導系統,實驗數據收集後,以共變數分析了解專注力督導系統對於學生運算思維、程式設計能力與STEM職業興趣的教學成效。
實驗結果發現,在本研究開發的以深度學習為基礎之專注力督導系統輔助下,由於可以在學生分心時提供適時的回饋訊息,協助學生提高學習時之專注力,進而促進學生有效學習。實驗數據發現,實驗組學生在運算思維、程式設計能力較對照組學生有顯著進步,可以獲得較佳的學習成效。
The degree of concentration of students’ learning affects the depth of effective learning. Teachers’ classroom management pursues students’ concentration on learning. So far, most of them use eye trackers to understand students’ concentration, but they face doubts about insufficient accuracy. There is also a lack of a system for immediate feedback to improve students' concentration. This research uses computer vision and deep learning technology to develop a concentration supervision system. When students are not attentive, this system can give immediate feedback information to students according to their learning progress, thereby enhancing students' concentration. In addition, this research also uses this concentration supervision system to understand whether there are significant differences in students' computational thinking, programming, and STEM career orientation.
In this study, an artificial neural network technology was used to design a concentration supervision system, and then a quasi-experimental research method was used to conduct experiments. The participants in the experiment were 200 sixth grade students in elementary schools. There were 100 students in the experimental group and 100 students in the control group. The experimental course lasts for six weeks. The experimental group uses the concentration supervision system, and the control group does not use the concentration supervision system. After the experimental data is collected, use covariate analysis to understand the effects of the concentration supervision system on students' computational thinking, programming ability, and STEM career interest.
The experimental results found that with the aid of the deep learning-based concentration supervision system developed in this research, it can provide immediate feedback when students are distracted, help students improve their concentration during learning, and promote effective learning. The experimental data found that the students in the experimental group had significant improvement in computational thinking and programming ability compared with the students in the control group, and they could obtain better learning results.
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