基於行動樣式評估視覺化系統之探索研究

Abstract

在資訊爆炸的時代裡，各種資訊在網路裡形成人類難以直接收集、管理和處理的巨量資料，就算經過整理，也較難以直觀的方式看出資料想表達出的訊息。巨量資料不只在收集與儲存很重要，資料分析與表現也十分重要，也就是如何幫助人們理解與分析大量的資料，並及時做出決策已成為重要議題，這也是近年視覺化表達研究與相關輔助工具在各領域廣受重視的原因。 本研究主要探討出視覺化工具對決策的輔助效益，研究主要以EDIFICEPVR（複雜的認知活動中人與訊息之間互動的認知與設計）架構之32 個知識行動樣式（Epistemic Action Patterns, EAPs），瞭解視覺化工具或者與知識行動樣式（EPAs）的關聯。研究採用兩個案，第一個案為工業電力視覺化系統，預計採用實驗室開發之視覺化輔助系統，其中包含數值曲線圖、符號化後曲線圖、熱圖、查詢表格與分群結果呈現，以幫助使用者對機器產生的電力資料可進行巨觀與微觀的分析與比較。第二個案為主題知識地圖，透過本實驗室開發之另一個視覺化輔助系統，其中包含主題地圖、路徑推薦、主題階層樹、搜索工具與字詞推薦，以幫助使用者在大量資訊中快速且正確地找到所需的訊息。個案一與二分別透過側錄軟體評估系統專家與新手的EPAs 與透過系統有較好與較差的任務表現學習者的EPAs，藉以分析系統操作行為的差異以了解視覺化系統對不同使用者的效益。 結果顯示，專家與任務表現較佳之受試者對於新手與任務表現較差之受試者，在進行任務時所採用的EPAs 與視覺輔助工具有所異同，顯見其搜尋策略並不相同，研究將於後續整理評估結果以了解視覺化系統對領域專家與新手在進行複雜決策任務的幫助，進而改善系統使之發揮更大的效益。In the era of information explosion, various kinds of information on the Internet form huge amounts of data that humans cannot directly collect, manage, and process. Even after sorting out, it is difficult to understand what the information wants to express intuitively. A huge amount of data is not only important for collection and storage, but also for data analysis and interpretation. How to assist people to understand and analyzea large amount of data and make timely decisions has become an important issue. This is also the reasons why the research on visual interpretation and related aid tools are widely recognized in various fields. This study mainly explores how the visualization tools benefits decision-making. The research contributes to use 32 epistemic action patterns of the EDIFICE-PVR (Epistemology and Design of human-Information Interaction in complex Cognitive Activities-Properties of Visual Representations), to understand visualization tools or associations with epistemic action patterns. The study used two cases. The first case was the industrial electricity consumption visualization system. It is expected to use laboratory-developed visual aid system that includes numerical graphs, symbolized graphs, heat maps, query tables, and clustered results. So that users are able to analyze and compare machine-generated power information in macro and micro aspects. The second case is the subject-oriented visualization tool, which includes topic maps, path recommendations, topic tree, search tools and term suggestion to help users find the information they need quickly and correctly in huge amounts of information. Case 1 and Case 2 respectively evaluate the EPAs of experts and novices through the recording software assessment system and the EPAs of learners with better and worse tasks through the system. It is used to analyze the differences in system operation behavior to understand the benefits of the visualization system for different users. The results show that the EPAs and visual aids used by experts and novices in their missions are different, and their search strategies are not the same. The research will sort out the results of the assessment to understand how the visualization system can help domain experts and novices in conducting complex decision-making tasks, thereby improving the system to make it more effective.