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系統識別號 U0026-2501201510035900
論文名稱(中文) 應用ACT-R認知模型評估行動運算裝置之人機介面-以iOS衛星導航行動應用程式為例
論文名稱(英文) Evaluation of HCI interaction on smart phone using ACT-R cognitive modeling based on iOS GPS navigation APP
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 103
學期 1
出版年 104
研究生(中文) 林琬真
研究生(英文) Wan-Chen Lin
學號 r76011082
學位類別 碩士
語文別 中文
論文頁數 91頁
口試委員 指導教授-林明毅
口試委員-許尚華
口試委員-蘇國瑋
中文關鍵字 ACT-R  認知模型  衛星導航 
英文關鍵字 ACT-R  cognitive model  GPS navigation APP 
學科別分類
中文摘要 現今行動運算裝置可視為人們生活的一部分,該裝置及相關應用越來越普及的情形下,此類裝置的介面設計成為了人機互動領域上新穎的議題。
本研究目的有二,其一為利用問卷和績效指標評估APP的易用性程度,探討介面與功能兩者之間的連結、易用性和使用者經驗;其二為以認知模型評估APP的介面,探討認知模型的評估數據是否符合真實數據。
本實驗招募18位受試者,分別操作3種衛星導航APP(Google Maps、Garmin Taiwan、Mio Map),在研究人員告知最佳操作步驟前後各操作一次,並分別填寫SMEQ量表(Subjective Mental Effort Question)、PSSUQ問卷(Post-Study System Usability Questionnaire)與本研究自製問卷,並取得完成任務的時間和動作次數。之後再分別對此三APP連續操作各50次,將所得之執行時間和過程與ACT-R認知模型所評估之數據相比較。
研究結果發現,在實驗一的部分,受試者會因為不同的衛星導航APP使用者介面與有無最佳提示而造成使用者經驗差異,給予提示後滿意度會提升,而且完成任務的時間與動作次數接降低;實驗二的部分,以平均絕對值誤差率(MAPE)作為評估模型預測準確的指標,而模型評估三個APP所得的MAPE值分別是,Google為9.4%(高準確預測)、Garmin為13.8%(優良預測)、Mio為20.9%(合理預測),若以操作行為點擊和滑動來看,模型評估結果為,點擊為19.7% (優良預測)、滑動為24.5%(合理預測)。
雖然模型可以評估操作時間,但是無法提供介面設計如何改善,因此實驗一的結果可以補足此部分的缺點,Google的評估指標為最佳,所得操作時間較短,但是其圖示涵義設計不佳;Garmin次佳,但是清單呈現的方式不佳,納入太多選項使得使用者需要更專注地尋找;雖然評估Mio的指標為最差,但是他也有設計良好的地方,例如圖示不會讓人混淆。
英文摘要 SUMMARY
Interface design of mobile computing devices has become a new issue on human-computer interaction(HCI) field. This study has two purposes, one is to explore usability, user experience and the link between interface and functionality. The other is to use the cognitive model evaluate mobile app interface, and explore if model data and subjects data are consistent or not. This study chooses the ACT-R cognitive model to evaluate that subjects operate GPS navigation APP by using a smart phone. The results of experiment 1, subjects have different user experience(UX) whether researcher give tips or not. The results of experiment 2, the prediction of touch action is good forecasting, but the prediction of sliding action is not.
Key words: ACT-R, cognitive model, GPS navigation APP.
INTRODUCTION
This study has two purposes, one is to explore usability, user experience and the link between interface and functionality. Subjects should evaluate the degree of usability of APP by writing the questionnaire subjectively. The other is to use the cognitive model evaluate mobile app interface, and explore if model data and subjects data are consistent or not. This study desired UI designers could use the model to evaluate draft. Designers could determine abandoning or continue developing through evaluating the draft by model. So it will reduce the risk of development failure. Therefore, this study chooses the ACT-R cognitive model to evaluate that subjects operate GPS navigation APP by using a smart phone.
In this study, 18 subjects were recruited. They should operate three GPS navigation APPs twice, and write SMEQ, PSSUQ and questionnaire this study made at each time. First time researcher would say nothing, and second time the researcher will tell subjects the best method. And then, subjects should repeat the best method 50 times for three GPS navigation APPs, and this data is used to compare to model data.
The results of experiment 1, subjects have different user experience(UX) whether researcher give tips or not. If researcher gives tips, subjects would get better UX. The results of experiment 2, this study uses MAPE to be as prediction accuracy. The MAPE of three APPs are 9.4%(high accuracy forecasting), 13.8%(good forecasting) and 20.9%(reasonable forecasting).
MATERIALS AND METHODS
Three APPs this study used are Google Maps, Garmin Taiwan and Mio Map. Subjects only could use right hand thumb to touch or slide screen. They couldn’t type or use two fingers. All the subjects should do both experiment.
This study used CogTool (1.2.2 version) based on ACT-R 6 to predict the execution time. CogTool is a prototyping tool, and it can predict the behavior of users. ACT-R model is developed by Anderson (1983). It is a cognitive architecture including visual attention, and motor movement and has been the basis for a number of models in HCI. So researcher load all interfaces of three APPs, and link them by setting touch or sliding action. CogTool has a step named “Think Step”, researcher set it 0.4 minutes. All the setting is already, then execute the prediction and get the prediction time.
In experiment 1, this study used MIXED procedure of SAS 9.3to run two-way ANOVA analysis, and selected α = 0.05 as statistical significance, and use the least significant difference procedure(LSD) as a post-test approach.
In experiment 2, this study used mean absolute error(MAE), root-mean-square error(RMSE) to be measures of error, and used mean absolute percentage error(MAPE), symmetric mean absolute percentage error(sMAPE), mean absolute scaled error(MASE) to be measures of error rate.
RESULTS AND DISCUSSION
The result of experiment 1, Figure 1 shows APPs have significant SMEQ scores differences in pre and post task. In pre section, Google Maps and Mio Map have significant SMEQ scores differences. Garmin Taiwan and Mio Map have significant SMEQ scores differences, too.
In pre section, subjects spend much time to find function which could complete tasks, but too much time let subjects get high mental workload. So SMEQ score is high in pre section.
Mio Map does not provide street view so subjects do not find the assigned street view. Because of that, Mio Map task is easier than Google Maps and Garmin Taiwan. So subjects feel lower mental workload when using Mio Map.

Figure 1. SMEQ score
The result of experiment 2, Figure 2 shows Model prediction and subjects data by Google Maps task. The MAE is 1102ms, RMSE is 1480ms, MAPE is 9.4%, sMAPE is 9.5%, and MASE is 29.1%

Figure 2. Model prediction and subjects data(Google Maps)
Figure 3 shows Model prediction and subjects data by Garmin Tiawan task. The MAE is 2192ms, RMSE is 2748ms, MAPE is 13.8%, sMAPE is 13%, and MASE is 60.4%.

Figure 3. Model prediction and subjects data(Garmin Taiwan)
Figure 4 shows Model prediction and subjects data by Garmin Tiawan task. The MAE is 2198ms, RMSE is 2602ms, MAPE is 20.9%, sMAPE is 18.6%, and MASE is 62.5%.

Figure 4. Model prediction and subjects data(Mio Map)
Model predicting Google Maps task is the best prediction of three APPs. The possible reason researcher thought are action types and action numbers. The mainly action type of Google Maps task is touch, of Mio is sliding. So researcher infers that model could predict touch action more exactly.
CONCLUSION
In this study, researcher has described a set of evaluation concepts and tools to predict using APPs time. Model has good forecasting in touch action, reasonable forecasting in sliding action. Prediction time could provide usability information so that researcher find out usability information from experiment 1. Google Maps gets the best prediction and the least time, but its icon desired not well. Garmin Taiwan gets the second best, but its items of lists is too much to find the goal. Mio gets the worst prediction and the second least time, but its icon is not bad to users.
論文目次 摘要 I
Extended Abstract II
誌謝 VII
目錄 VIII
圖目錄 XI
表目錄 XIV
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的與假設 2
1.3 研究範圍與限制 3
1.4 研究流程與架構 4
第二章 文獻探討 6
2.1 認知心理學 6
2.1.1 訊息處理 7
2.1.2 感官收錄 7
2.1.3 注意力 8
2.1.4 記憶 9
2.2 認知模型 10
2.2.1 費茨法則(Fitts’ Law) 10
2.2.2 GOMS模型 11
2.2.3 ACT-R模型 11
2.3 行動運算與裝置 13
2.4 人機介面 14
2.5 認知模型於介面評估之應用 15
2.5.1 繪圖板介面 15
2.5.2 自動提款機介面 15
2.5.3 車用衛星導航系統介面 17
2.5.4 手機介面 18
2.6 小結 21
第三章 研究方法與工具 22
3.1 實驗設計 22
3.1.1 實驗流程 22
3.1.2 研究變數 27
3.1.3 研究對象 28
3.1.4 實驗設備 29
3.2 行動應用程式介紹與操作步驟 31
3.3 實驗資料處理 35
3.4 認知模型建立 36
3.5 模型評估數據處理 43
3.6 統計分析與評估指標 44
第四章 結果 46
4.1 受試者基本資料 46
4.2 實驗一分析 46
4.2.1 SMEQ量表分數 46
4.2.2 PSSUQ問卷分數 48
4.2.3 自製問卷分數 50
4.2.4 任務時間與手部動作次數 53
4.2.5 任務失敗率 55
4.3 實驗二分析 56
4.3.1 Google模擬分析 56
4.3.2 Garmin模擬分析 59
4.3.3 Mio模擬分析 62
4.3.4 操作行為 65
4.3.5 線性回歸模型 66
第五章 討論 69
5.1 實驗一討論 69
5.1.1 SMEQ量表分數 69
5.1.2 PSSUQ問卷分數 70
5.1.3 自製問卷分數 71
5.1.4 任務時間、手部動作次數與失敗率 74
5.2 實驗二討論 74
5.2.1 Google 74
5.2.2 Garmin 76
5.2.3 Mio 78
5.2.4 APP間相互比較 80
5.2.5 操作行為比較 81
5.2.6 線性回歸模型比較 81
5.2.7 其他嘗試 82
第六章 結論與建議 83
6.1 實驗一的結論與建議 83
6.2 實驗二的結論與建議 83
參考文獻 85
中文文獻 85
英文文獻 85
附錄一 研究倫理審查通過證明 90
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