本研究之核心主要為數位科技學習環境開發，透過行動裝置(平板)與擴增實境的結合，使學生能將抽象概念可視化及運用不同的科技化表徵進行學習，為學生搭建良好的自主學習鷹架。本數位學習環境之設計以高中選修化學中較為困難且抽象之混成軌域概念作為主題，結合擴增實境影像、動畫、3D模型互動操作等功能，期盼能協助學生理解混成軌域之形成過程以及建立混成軌域與分子形狀之關聯性。本研究之研究對象為台灣北部一所公立高中，共33名受試對象參與實驗。透過混成軌域概念前後測驗以檢視學生在本數位學習環境的學習成效、並透過眼球追蹤技術來記錄學生的學習歷程，以及使用AGEC動機問卷及CTAS化學與科技態度量表、PSVT:R空間旋轉化視覺測驗、結構概念先備知識測驗來了解學習者特性。本研究針對概念前後測進行t檢定分析、視覺歷程分析，以及學習表現、視覺歷程、學習者特性三者間相關性分析，也依據後測表現進行分組以了解不同學習表現的學習者在視覺歷程與學習者特性上是否呈現差異。

本研究之主要發現如下：學習者在使用融入AR數位環境學習後，不論是學習混成軌域的基礎概念還是進階概念（如孤對電子、雙鍵參鍵分子），其概念後測成績相較於前測均顯著提升。視覺歷程部分，學習者在AR模型靜態模式下相比於互動模式，訊息處理時間、訊息整合時間和注意力分布更高，但認知心力較低。學習者在3D模型互動模式下相比靜態模式，訊息處理時間、訊息整合時間、和認知努力程度均更高。學習者對動畫類訊息有較長的訊息處理時間和注意力分布，尤其對軌域能量關係的動畫比形狀改變類型動畫更為關注。依據學習表現的分組分析結果發現，高學習表現者在面對複雜概念時，對2D的AR分子掃描圖、3D模型互動模式和動畫類訊息有更多的訊息處理時間與注意力分配。低學習表現者則普遍在文字區域分配了更多注意力。學習表現與學習者特性的分析發現，高學習表現組學生相對於低學習表現組，對化學學科有更好的態度、有更高的分子結構概念，且基礎概念高學習表現組尚有更高的空間概念。學習者特性與視覺歷程的相關性分析發現，學習者認為數位環境提供越高挑戰性時，會在較困難概念的動畫區花費越多時間處理和整合訊息，或是利用3D模型進行越多概念整合。學習者在數位環境內感受到越高注意力投入和越明確的學習目標會使學習者在進階概念的文字區域花費越少時間進行訊息檢索。對化學學科有越良好態度的學習者，能以越少的時間處理基礎和進階概念的路易士結構圖，並且在面對混成軌域進階概念時，會投入越多時間透過3D模型整合訊息。化學學科自信越高的學習者在觀看“軌域形成”過程的動畫時，能根據自身對內容的熟悉程度調整注意力分配。對運用科技學習化學有越好態度的學習者，未必喜歡所有的科技化表徵的呈現方式。空間能力越高的學習者，在複雜分子的3D模型互動模式下有越低的訊息處理時間和訊息整合時間，對路易士結構圖的訊息處理時間和認知努力也越少。具備越高分子結構先備知識的學習者，在處理基礎概念路易士結構圖時花費越少時間，但在複雜概念的3D互動模型和雙鍵參鍵形成的Pi鍵結動畫上會分配越多注意力。

本研究結果呈現了AR融入之數位環境對學習表現的影響以及視覺歷程分析結果，並提供了學習者特性的影響，期盼能提供了未來數位環境設計和應用之方向。

This study focuses on the development of an augmented reality (AR) integrated learning environment, enabling students to visualize abstract concepts and use various technological representations for learning, thereby providing a solid scaffold for autonomous learning. The design of this AR-integrated learning environment centers around the hybrid orbital related concepts, which are relatively difficult and abstract in high school elective chemistry. It incorporates AR images, animations, and interactive 3D models to help students understand the formation process of hybrid orbitals and establish the relationship between hybrid orbitals and molecular shapes. The participants in this study were 33 students from a public high school in northern Taiwan. The study utilized pre- and post-tests on hybrid orbital concepts to evaluate learning outcomes within the digital learning environment. Eye-tracking technology was employed to record students' visual attention during learning. Additional assessments including the AGEC motivation questionnaire, CTAS chemistry and technology attitude scale, PSVT spatial rotation test, and prior knowledge tests on structural concepts were used to understand learner characteristics.

The study conducted t-tests on pre- and post-test results, analyzed visual processes, and examined the correlations between learning performance, visual processes, and learner characteristics. Group analysis based on post-test performance was also carried out to determine if there were differences in visual processes and learner characteristics among learners with varying performance levels.

Key findings of this study include the following: Learners demonstrated significant improvement in both basic and advanced concepts of hybrid orbitals (such as lone pairs and double-bonded molecules) after learning in the AR-integrated digital environment. In terms of visual processes, learners exhibited higher information processing time, information integration time, and attention distribution in static AR models compared to interactive modes, while cognitive load was lower. Conversely, in the interactive 3D model mode, information processing time, information integration time, and cognitive effort were higher. Learners spent more time processing information and distributing attention to animations, particularly those illustrating orbital energy relationships over shape-changing animations. Group comparison based on learning performance revealed that high-performing learners allocated more time and attention to 2D AR molecular scans, interactive 3D models, and animation information when dealing with complex concepts, whereas low-performing learners tended to focus more on textual information.

The analysis of learning performance and learner characteristics showed that high-performing students had better attitudes towards chemistry, higher molecular structure concepts, and greater spatial concepts compared to low-performing students. The correlation analysis between learner characteristics and visual processes indicated that learners who perceived the digital environment as more challenging spent more time processing and integrating information in difficult concept animations or used 3D models for conceptual integration. Learners who felt more engaged and had clearer learning objectives spent less time on information retrieval in advanced concept text areas. Students with a positive attitude towards chemistry spent less time processing basic and advanced Lewis structure diagrams and more time integrating information through 3D models for advanced hybrid orbital concepts. Those with higher confidence in chemistry adjusted their attention distribution based on familiarity with content during orbital formation animations. Learners with a positive attitude towards using technology for learning chemistry did not necessarily favor all technological representations. Students with higher spatial ability had lower information processing and integration times in complex molecular 3D models and spent less cognitive effort on lewis structure diagrams. Learners with higher prior knowledge of molecular structures spent less time processing basic Lewis structure diagrams but allocated more attention to complex concept 3D interactive models and animations depicting pi-bond formation in double bonds.

The results of this study highlight the impact of AR-integrated digital environments on learning performance, present visual process during the AR-based learning, and also provide insights into the influence of learner characteristics. These findings aim to guide the future design and application of digital learning environments.

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