政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/137669

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/137669

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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/137669

Title:	應用量質性整合框架於學習模式與表現之分析 Analysis of Learning Patterns and Performance – An Integrated Quantitative and Qualitative Approach
Authors:	吳怡潔 Wu, Yi-Chieh
Contributors:	廖文宏 Liao, Wen-Hung 吳怡潔 Wu, Yi-Chieh
Keywords:	學習模式學習成效評估 3D建模軟體 STEAM教案 K-12國民教育英語輔助學習智慧語音助理 Learning patterns Performance evaluation 3D modeling software Qmodel Creator STEAM lesson plan K-12 education Computer Assisted Language Learning (CALL) Intelligent Voice Assistant
Date:	2021
Issue Date:	2021-11-01 11:58:20 (UTC+8)
Abstract:	數位科技應用於教育的趨勢，不僅體現於學校軟硬體基礎建設的建置、強化，也有愈來愈多的教師樂於嘗試新科技，例如導入3D列印工具，設計整合式的創新教案。有別於傳統的數理教程，此種教案以「做中學」為核心精神，首要目標為激發學生的創造力、想像力、以及面對挑戰的應變能力。本論文主要關注學習過程中的兩大面向：1) 學習模式（與任務無關）、以及學習成效評估（與任務有關）。我們以3D建模軟體的學習過程，作為第一個研究案例。參與學生在課堂上學習多種建模軟體，過程中的操作記錄檔、螢幕錄影、以及建模成品，皆用以本研究案例之分析。對應於所關注的兩大面向，我們提出以下類型的指標：（一）學習行為特徵量化，其中涵蓋：有效操作期間 (Effective Operating Period, EOP)、試誤期間 (Trial-and-Error Period, TEP)、實作期間 (Implementation Period, IP) 等等；（二）學習成效評量，由於與任務相關，在此定義為3D模型之複>雜度評估，其中包括：細節程度 (Degree of Detail, DoD)、輪廓 (shape, Cf)、分割 (partition, Cp)、以及區塊比例 (block-ratio, Cr) 複雜度，等模型評估指標。基於上述提出的指標，我們可以分析參與者的學習體驗及模型完成度，進而錨定影響學習模式及學習成效之關鍵因素。教師亦可取得學生們更切實的學習狀態，作為回饋參考。在第二個研究案例中，我們著重於探討小學英語學習歷程，採用目前最熱門的智慧語音助理Amazon Echo Dot，根據當學期英語課本內容，實作專用之技能（skill）與參與者互動，並從使用紀錄中分析參與者之使用習慣。我們與政大實小英語教師合作、徵求學生及家長同意在家安裝Echo Dot後，進行為期一學期的實驗。我們主要採用語音和字詞的特徵，用以評估學生們的英文學習成效。為了瞭解持續與語音助理對話的關鍵因素，我們從分析活躍使用者的對話模式開始，並發掘那些在學習過程中可能的影響因素。我們期待從學習過程中的第一手資料及其分析結果，獲取有助於瞭解評估學生們的英語學習成效的線索。 As more schools incorporate technologies into their curriculum to stimulate the creativity of K-12 students with a learning-by-doing approach, it becomes crucial to understand how users work with the novel tools and to evaluate integrated lesson plans in the STEAM (Science, Technology, Engineering, Arts and Math) educational framework. Our work focuses on two perspectives during the learning process: learning patterns (task-independent), and the evaluation of the outcome (task-dependent). In the first case study of the thesis, we took the learning process of 3D modeling software as the case study. Participants operation logs, screen recordings, and finished work for respective 3D modeling software were recorded and analyzed. Two types of indicators have been developed. One is concerned with the quantification of learning behavior, including Effective Operating Period (EOP), Trial-and-Error Period (TEP), and Implementation Period (IP). The other has to do with the evaluation of learning outcome, i.e., the complexity of 3D models, including the Degree of Detail (DoD), shape (Cf), partition (Cp), and block-ratio (Cr) complexity. Based on the proposed features, we are able to identify the key factors that affect students` learning experiences and performance in terms of learning patterns and model completeness. Through these indicators, instructors can gain better insights into student`s learning status of 3D modeling software. In the second case study, we focus on exploring the English learning process for elementary school students. We employ Amazon Echo Dot, one of the most popular intelligent voice assistant nowadays, as a tool to facilitate language learning. We developed an Amazon Skill that incorporates the content from English textbooks for the participants to interact with using voice input. The operation logs from Echo Dot faithfully reveal student`s usage patterns and preferences. A semester-long experiment has been conducted with the assistance of the Affiliated Experimental Elementary School (AEES) of National Chengchi University. After data collection has been completed, we utilize acoustic and transcript evaluation metrics to examine the voice recordings and user logs. Our initial analysis focuses the active users, i.e., participants who have continued to engage in conversations with the voice assistant. Several questions regarding user behavior are prompted and responded to based on the collected and processed data. Analyzing the content of the conversation will help disclose more detailed information regarding the learning process. The progress of individual students can also be monitored to determine if further assistance is needed.
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Description:	博士國立政治大學資訊科學系 104753502
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0104753502
Data Type:	thesis
DOI:	10.6814/NCCU202101644
Appears in Collections:	[Department of Computer Science ] Theses

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