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應用行動科技紀錄與研究人們日常生活行為與脈絡

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應用行動科技紀錄與研究人們日常生活行為與脈絡

  1. 1. 應用行動科技紀錄與研究 人們日常生活行為與脈絡 張永儒 (Stanley Chang) ASIS&T 10.13.2017
  2. 2. 關於我… • 交大資工助理教授 • 密西根大學資訊博士 • 領域 – Human Computer Interaction (HCI) – Ubiquitous Computing (Ubicomp) – Information Behavior – Interaction Design 人機互動 普及運算 資訊行為 互動設計
  3. 3. 今天你不會聽到: • 行動科技一覽介紹 • 如何設計和製作行動科技 • 研究案例的細節 • 建議買哪一台來研究 廣告一下 啊?
  4. 4. 但你會聽到: • 傳統方法研究日常生活行為的 缺點 • 行動科技的研究方法,例子,和 趨勢 • 三個研究案例摘要 • 行動科技研究過程&注意事項
  5. 5. 日常生活
  6. 6. 食 衣 住 行 育 樂
  7. 7. 通訊 交友 購物 健康 心情 運動 上網 工作 其他你 自己想
  8. 8. 傳統方法
  9. 9. 問卷調查 (Survey) Facts, attitudes, opinion, tendency, general behaviors • Large and broad samples • Quantitative analysis
  10. 10. 質性研究 (Qualitative) Focused on why and how in addition to what, where, when, how often • Usually relies on small, in-depth samples • Requires interpretation
  11. 11. 日誌研究 (Diary Study) • Reporting experiences & events in context. • Hourly, daily, weekly, monthly • More burden
  12. 12. 調查時間
  13. 13. 你早上幾點出門? 花了多少時間吃午餐?
  14. 14. 你今天看了FB幾次? 看了多久? 什麼時候看? 看了什麼? 看完愉悅的程度?(1-5分) 中間切換到別的App幾次?
  15. 15. 現在請大家拿 出你的手機
  16. 16. 花一分鐘自拍 (歡迎跟旁邊一起,也歡迎打卡 J )
  17. 17. 你剛自拍了幾張? 存了幾張? 跟誰一起自拍? 有打卡嗎?
  18. 18. Retrospective vs. Introspective (回顧) (內省)
  19. 19. Encoding Problem Storage Problem Retrieval Failure Reconstruction Errors 為何Retrospective不 Reliable?
  20. 20. 當然可能還有其他原因
  21. 21. 可透過Memory Cues 來幫助回憶 (e.g. Prompted Recall Diary, Daily Reconstruction Method)
  22. 22. 行動科技 特性: • 你常常帶著它 • 離你很近 • 有感應器 • 有計算能力 • 有通訊能力
  23. 23. Real Time Data Capture Dr. Saul ShiffmanDr. Arthur Stone
  24. 24. Experience Sampling Method (ESM) 也稱為Ecological Momentary Assessment (EMA) Capture in situ experiences
  25. 25. 調查不同情況裡的行為
  26. 26. 調查不同族群的行為
  27. 27. 早期ESM工具
  28. 28. 早期ESM工具
  29. 29. 飲食,健康
  30. 30. 隱私感受 Patil et al. 2014
  31. 31. 心理狀態 Atz 2013 Weppner et al.2013
  32. 32. 活動情境 Dunton et al. 2011 Pejovic et al. 2015
  33. 33. Figure 1. An exemplary multi-device environment with a laptop, smart- phone, tablet and smartwatch.跨裝置通知接收度 Weber et al. 2016 mpact of interruptions caused by notifications, nly focused on detecting opportune moments, points, to notify the user. In a diary study, Cz- howed that returning to tasks after being inter- 4]. Fisher et al. investigated episodes of mobile as indicators of opportune moments to deliver ]. Iqbal and Bailey investigated effects of in- ation management on users and their tasks [8]. s built a system that uses statistical models to ons until breakpoints, resulting in reduced frus- ction time. Using a context-aware computing Intille detected activity transitions [7]. They sages delivered in this activity transitions were Figure 2. Screenshots of the ESM questionnaire app. UBICOMP '16, SEPTEMBER 12–16, 2016, HEIDELBERG, GERMANY act of interruptions caused by notifications, y focused on detecting opportune moments, ints, to notify the user. In a diary study, Cz- owed that returning to tasks after being inter- Fisher et al. investigated episodes of mobile indicators of opportune moments to deliver Iqbal and Bailey investigated effects of in- on management on users and their tasks [8]. built a system that uses statistical models to s until breakpoints, resulting in reduced frus- on time. Using a context-aware computing ntille detected activity transitions [7]. They ges delivered in this activity transitions were Figure 2. Screenshots of the ESM questionnaire app. UBICOMP '16, SEPTEMBER 12–16, 2016, HEIDELBERG, GERMANY
  34. 34. 資訊需求Every year since 2011 Google has run an annual study to learn what people really, really want to know, whether it’s something Google provides or not. It’s called Daily Information Needs, but the psychologists at Google involved with the project just call it DIN. Here’s how the DIN study works: Google recruits subjects who agree to report their information needs to Google on demand. Eight times a day Google randomly pings them, and they instantly respond with the questions they want answered at that moment. At the end of the day, subjects compile a summary of their needs, noting what, if anything, they did to get their questions answered and whether they were successful. The study began with 50 people in 2011, grew to 1200 in 2012, and this year has a similar number. In addition to those US numbers, Google runs the study in a number of other countries. 新聞來源: https://medium.com/backchannel/googles-secret-study-to-find-out-our-needs-eba8700263bf Kathy Baxter
  35. 35. Google I/O 2014 - Don't Listen to Users, Sample Their Experience! Video: https://www.youtube.com/watch?v=v1KKsLukIBE 相關ESM影片
  36. 36. 1. 沒有Recall Error的問題 2. 資料貼近現實生活的行為跟情境 3. 可研究較大量受試者 優點:
  37. 37. 1. 你需要肯配合的受試者(是個挑戰) 2. 可能中斷受試者目前活動 3. 不宜太長 (e.g. 一個月) 4. 不適合研究需要注意力的活動 5. 不適合研究太頻繁或太少發生的 行為 6. 不適合需要打字較多的問題 缺點:
  38. 38. Mobile Sensing
  39. 39. Quantified Self
  40. 40. Mobile Phone Sensing Health Monitoring Traffic Monitoring Environment Monitoring Social Interaction Special Purpose Human Behavior Commerce Khan et al. 2013 Mobile Sensing
  41. 41. App使用行為 TMT Do et al. 2011, Nokia
  42. 42. App使用行為 TMT Do et al. 2011, Nokia
  43. 43. 移動與通勤行為
  44. 44. 移動與通勤行為 Fan et al. 2012
  45. 45. 運動行為
  46. 46. 加上 Feedback Lane et al., 2011 Froehlich et al. 2009 越健康跟 越環保,動 物越多
  47. 47. 1. 耗電, 隱私及安全性上的顧慮 2. 可觀察趨勢, 但要靠自己腦補原因 3. 需要操作和分析資料的人材 缺點: 1. 大量觀察點, 可找趨勢, 相關性, 和作行 為預測 2. Log有雜訊, 但不會騙人 3. Scalable, 且有現成的App 優點:
  48. 48. 行動科技研究 近年趨勢
  49. 49. 把已記錄特定事件, 行為, 或情境放 到日誌幫助受試者回顧 趨勢一: 手機Logging + Diary (Event-based Diary, Prompted Recall Diary)
  50. 50. Tjondronegoro and Chua, 2012 旅行日誌
  51. 51. Lane et al., 2011 健康日誌
  52. 52. n a e t , M n r r . b f sensation, comfort sensation, current activity, indoor location, clothing level, and brief notes that might help them recall the reasons for their sensation and comfort report when completing the end-of-day diary entry. We Figure 1: (Left) The ESM interface; (Right) The Web-based 人體舒適度 Huang et al. 2015
  53. 53. • 可以針對特定事件發問 • 受試者回應可用來註釋該期間收集資料 • 可記錄不同形態資料來幫助受試者回憶 (問卷回 應, 照片, 聲音,影片,Location, Sensor) • 受試者回應可解釋記錄資料, 記錄資料可驗証受 試者回應 優點: 缺點: • Diary變得很耗電 • 可能要自己開發工具(App & Backend) • 整合不同形態Data給受試者檢視需要功夫
  54. 54. 透過穿戴式相機捕捉日常生活 趨勢二: Wearable Life-Logging
  55. 55. Lam et al. 2013 研究外出時間及活動
  56. 56. Chen et al. 2013 飲食行為
  57. 57. Gouveia & Karapanos, 2013 Wearable Logging + Diary
  58. 58. • 穿戴式相機或攝影機被認為較準確的方法 • 照片或影片比Sensor容易解讀,情境資料更豐富 • 可和手機Logging一起併用 • 可放在Diary幫助受試者記憶 優點: 缺點: • 受試者顧慮他人看法 • 可能需要大量時間人力整理及分析 • 多數照片沒有資訊性 • 目前沒有夠快拍攝速度的穿戴式相機可撐 >1天 – 電力限制, 容量限制
  59. 59. 針對偵測到特定事件, 行為, 或情境 發問 趨勢三: Context-Triggered ESM (event-contingent ESM)
  60. 60. 研究手機Micro-Usage Ferreira et al. 2014
  61. 61. 研究手機通知 Chang et al. 2017
  62. 62. Meschtscherjakov et al. 2011 研究開車行為
  63. 63. 透過ESM拿到的情境資料和受試者 回應來讓手機學習,偵測,跟預測 行為 趨勢四: ESM & Logging For Anticipatory Systems
  64. 64. 預測會不會 接電話 預測可不可 以打擾 Pejovic & Musolesi 2014 Smith et al. 2014
  65. 65. 透過ESM拿到的情境資料給予行為 回饋來改善使用者行為或狀態 趨勢五: ESM for Behavior Change Interventions (BCI)
  66. 66. SociableSense, Lathia et al. 2013 促進社交行為
  67. 67. 透過ESM回應來建立預測模型。之 後偵測到使用者可能狀態後給予回 饋來改善使用者行為或狀態 趨勢四 + 五: Anticipatory Behavior Change Interventions
  68. 68. 趨勢四 + 五: Anticipatory Behavior Change Interventions Pejovic et al. 2016
  69. 69. Happy Hour, Carmona et al. 2015 建議運動改善心情
  70. 70. 透過遊戲化機制來增加(ESM)資料 品質與數量 趨勢六: Gamification for ESM
  71. 71. 比較貢獻, 內容跟分數,增加限時機制 Geo Oulu, Van Berkel et al. 2017 one or study he list led by estions archer. value ve and . The in the her. aining ol that archer it can in a er 5.). rently, make a oup of an list t, e.g. on can n tool r the uously mobile of the et, but oblem that it le data opinion, especially if they do it for a good research purpose, they can do it anonymously and with little effort. On the other hand, in experience sampling studies in real world there is a risk that the participants will be too busy, too concerned or too tired to answer the questions systematically. Therefore we decided that we should provide the participants with additional motivation by including some gamification elements. The first element is based on assumption that the participants are curious about responses of other people and the overall results of the studies. The fact that they agreed to take part in a study suggests that they are interested in the case, so we believe our assumption is right. When a participant subscribes to a study, they have access to a summary of responses to one of the study’s questions. There is also an information that in order to unlock the summary of another question, the user has to give 10 responses themselves. With each unlocked question the amount of required responses shall increase – e.g. 10 – 25 – 50 – 80 – 120 responses. The participants that have given a big number of responses shall be allowed to add their own question to Figure 3. A summary of responses, described in Section 5. . ght Crowdpinion, Machnik et al, 2015 ‘survey’. Our participants were required to install our GeoOulu application on their personal An To assess the impact of gamification on the participants’ ESM responses, we developed two v application that only differ in their gamification elements. Our experimental design is between subjects, with two experimental conditions: gamified softw gamified software. The outcome variables include: ESM response rate, ESM response quality, a ESM responses. 3.1 GeoOulu Our Android application GeoOulu consists of four screens, as shown in Fig. 1. There are numero between the gamified and non-gamified versions of our game, as summarised in Table 1. Fig. 1. (1) Application start screen, including leaderboard. (2) Submission of new word. (3) Rating of words. (4) Feedback (+ score) and option to return to the start screen. • The start screen of the application welcomes the participant to the application and a participant to choose a nickname upon initial launch of the app. The gamified versi scoreboard in this screen. • The second screen asks participants to enter a keyword that describes their current ’. Our participants were required to install our GeoOulu application on their personal Android phones. ss the impact of gamification on the participants’ ESM responses, we developed two versions of the tion that only differ in their gamification elements. experimental design is between subjects, with two experimental conditions: gamified software, and non- d software. The outcome variables include: ESM response rate, ESM response quality, and number of sponses. GeoOulu droid application GeoOulu consists of four screens, as shown in Fig. 1. There are numerous differences n the gamified and non-gamified versions of our game, as summarised in Table 1. Fig. 1. (1) Application start screen, including leaderboard. (2) Submission of new word. (3) Rating of words. (4) Feedback (+ score) and option to return to the start screen. The start screen of the application welcomes the participant to the application and also allows the participant to choose a nickname upon initial launch of the app. The gamified version includes a scoreboard in this screen. The second screen asks participants to enter a keyword that describes their current location. The ‘survey’. Our participants were required to install our GeoOulu application on their personal To assess the impact of gamification on the participants’ ESM responses, we developed tw application that only differ in their gamification elements. Our experimental design is between subjects, with two experimental conditions: gamified so gamified software. The outcome variables include: ESM response rate, ESM response quality ESM responses. 3.1 GeoOulu Our Android application GeoOulu consists of four screens, as shown in Fig. 1. There are num between the gamified and non-gamified versions of our game, as summarised in Table 1. Fig. 1. (1) Application start screen, including leaderboard. (2) Submission of new word (3) Rating of words. (4) Feedback (+ score) and option to return to the start screen. • The start screen of the application welcomes the participant to the application and participant to choose a nickname upon initial launch of the app. The gamified ve scoreboard in this screen. • The second screen asks participants to enter a keyword that describes their curre
  72. 72. 透過不同裝置來發ESM問卷來提高 回答率 趨勢七: Pervasive/Cross-Device ESM
  73. 73. Hernandez et al. 2016 透過不同裝置(Glass, Watch, Phone) 來發送ESM 問卷 nces in the Experience Sampling oss Wearable Devices stian nte2 Pattie Maes1 Karen Quigley3 Rosalind Picard1 tute of Technology, Cambridge, United States1,2 tern University, Boston, United States3 media.mit.edu1 , cinfante@mit.edu2 , k.quigley@neu.edu3 sed for natural using a earable ove this tatively orn and to the s work, tion to devices Figure 1. We developed a novel ESM application that can be Figure 3. Example of daily scheduled prompts for the three devices of one of the participants. smartwatch, the volume down button of the phone, or swipe two fingers down on the Glass (see Figure 2). The application also records time stamps for every user interaction and other relevant events such as the time when the prompt is triggered, the time when the first user interaction occurs, and the time when the reports are provided. Table 1 provides an overview of the main interaction differences for each device. • Time Variability. The standard deviation of the triggering time for each device has to be at least 3 hours ensuring the prompts are distributed throughout the day. • Device Variability. No more than two consecutive prompts can happen on the same device in order to minimize the anticipation of users. Figure 3 shows an example of triggering times for one day. Each person had a different pattern on each different day.
  74. 74. 透過Bluetooth Low Energy (BLE) 裝置放在 主要活動位置來發送ESM問卷 Paruthi et al. 2017 ND IMPLEMENTATION situations populations research q gle purpose self-report devices to lower the burd reporting in specific contexts? SIGN AND IMPLEMENTATION es Limited coverage of situations Limited coverage of populations Limited coverage of research questions sign single purpose self-report devices to lower the burden of self- reporting in specific contexts? Study 1: Activity Tracking Study 2: Stress and Sleepiness or d rgy “ ” ty Tracking Study 2: Stress and Sleepiness TATION -report devices to lower the burd pecific contexts?
  75. 75. 各式各樣裝置,問卷機制, 跟 資料型態,跟額外效果 當然你可以全部加在 一起.. 代價就是: 1. 容易失焦 2. 要做得更多
  76. 76. 三個研究個案 • 使用科技 • 研究流程 • 部分記錄到的結果
  77. 77. 案例 I: Investigating Mobile Users' Ringer Mode Usage and Attentiveness and Responsiveness to Communication 工具:自行開發App 方法:Logging, Event-based Diary (Chang and Tang, 2015) 研究目的: 了解Mobile User如何使用 鈴聲系統, 以及鈴聲對觀看與回應訊 息的相關性
  78. 78. • Pre-Study • 2 –week Collection – Logging – Event-Based Diary • Post-Study – Label Acquisition – Survey – Interview • 38位Android手機使 用者(完成人數:28) • 16男12女,年紀大多 介於18-35 • 每日用手機傳收訊息 • 各種全職職業和研究 生 研究流程 受試者
  79. 79. 監控事件: • 所有語音通話,傳收訊息,發送E- mail動作 • 與通知有關的動作(移除,選取,觀 看) • 調整鈴聲模式 記錄資訊: • 位置,各種Sensor,網路,電量,聯 絡人(如果有) 監控, 偵測, 記錄
  80. 80. 82 追蹤受試者資料 • 要求受試者至少每兩天要將手機連一 次Wifi來上傳資料 • 發現兩天以上有異樣情況寫信給受試 者 – 沒有Data (Android各式各樣問題很多) – 沒有回Diary • 必要時要延長研究時間
  81. 81. 84 事件問題 偵測到事件 • Unread notifications • Missed calls • Changing ringer modes Event-Based Diary
  82. 82. 85 Post Study: 標記位置
  83. 83. “一個”事件的記錄: 1.37687E+12 OUTGOING_MSG 8/18/2013 18:46:17 OutNoResponse NA NA 46.56389999 -81.13970184 1.37687E+12 -685505735 NULL com.android.mms.ui.ComposeMessageActivity com.android.mms null Normal Mod 1 5 still61 gps1 1 -9999 -87 -9999 Wifi 1 1 0.867367983 0.15388 6.400090218 6.513850212 FaceUp 6 5 NA NA NA NA NA NA NA 3.51746E+14 46:24.3 null null 0.899999976 Not Charging 7 null Home MondayBaseballCaptain NULL NULL 記錄到434,525事件, 81.3%是收到手機通知
  84. 84. 87 Post Study: 回顧訪談
  85. 85. 一些記錄到的例子…
  86. 86. 0% 20% 40% 60% 80% 100% Inferred Ringer Mode Usage From Logs Ringer Silent Mode (%): Ringer Vibrate Mode (%): Ringer Normal Mode (%): 0% 20% 40% 60% 80% 100% Self-Reported Ringer Mode Usage (From Survey) Ringer Silent Mode (%): Ringer Vibrate Mode (%): Ringer Normal Mode (%): 對照Self-Report和Log
  87. 87. 1.期待有通知進來 2.怕被干擾 3.怕打擾環境 Diary : 三個主要調鈴聲的理由
  88. 88. a.Missed 51% b.Busy 12% c.Chose 20% d.Ignore 6% e.OtherDev 6% ??? 5% Diary: 沒看手機通知的理由 832 Valid Responses
  89. 89. 0 50 100 150 200 250 300 350 From Normal From Silent From Vibrate To Normal To Silent To Vibrate Log: 使用者比較常切換成 什麼鈴聲模式
  90. 90. 0 2 4 6 8 10 12 14 16 18 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Catchup Home NA Other Social Station Work Log: 使用者比較常幾點在 哪裡切換成靜音 調成Silent
  91. 91. 0% 20% 40% 60% 80% General Attentiveness 0% 20% 40% 60% 80% Attentiveness to Incoming SMS New 0% 20% 40% 60% 80% Attentiveness to Incoming SMS Chat Log: 不同鈴聲下看通知和 SMS訊息的速度和模式
  92. 92. Responsiveness to New Message Responsiveness to Chat Message 0.0% 20.0% 40.0% 60.0% 80.0% 100.0% Responsiveness to Attended SMS Overall Responsiveness Silent Vibrate Normal Overall 0.0% 20.0% 40.0% 60.0% 80.0% 100.0% Responsiveness to Attended SMS Overall Responsiveness Silent Vibrate Normal Overall Log: 不同鈴聲訊息回應率
  93. 93. 案例 II: What Do Smartphone Users Do when They Sense Phone Notifications? 工具:自行開發App 方法:Logging, Event-based Diary (Chang et al, 2017) 研究目的: 了解Mobile User在收到通 知後的感知與猜測如何影響他們看 通知行為
  94. 94. • Pre-Study • 2 –week Collection – Logging – Context-Triggered ESM • Post-Study – Prompted Recall Interview • 37位Android手機使 用者(完成人數:34) • 17男17女,年紀大多 介於20-36 • 使用不同種鈴聲與震 動模式App • 使用靜音模式不超過 8小時 • 16全職工作,18學生 研究流程 受試者
  95. 95. Context-Triggered ESM: • 當使用者剛開始使用手 機10秒鐘後跳出ESM問卷 • 詢問使用者1-3則三十分 鐘內收到的通知 • 每個ESM之間至少隔90分 鐘 Logging: • 通知,位置,各種 Sensor,網路,電量,手機 使用,能抓的都抓了。 ESM, 紀錄,訪談 為了之後訓 練預測模型 Post-Study Interview: • Prompted Recall Interview • 訪談前寄信給受訪者所 有他們回答的資料跟記 錄到的通知統計 • 訪談針對最新的事件詢 問。
  96. 96. ESM 問卷
  97. 97. 101 追蹤受試者資料 • 要求受試者每天要將手機連一次Wifi來 上傳資料 • 發現兩天以上有異樣情況寫信給受試 者 – 沒有Data (Android各式各樣問題很多) – 沒有ESM – 沒有isAlive的紀錄 • 必要時要延長研究時間
  98. 98. Category % amount Messenger 51.7 86989 System 17.1 28756 Utility 14.4 24188 Mail 5.2 8732 Social 4.7 7965 Research App 2.2 3765 Reader/News 2 3378 713,866 通知事件。168,262 有效個別通知。 Log: 手機通知
  99. 99. ESM 問卷通知 3833 問卷被產生 75% 問卷填答率 4412 通知得到回應
  100. 100. ESM: 猜測通知來源 53% 通知有感受到 71% 被感受到的通知有被猜測來源 49% 猜 40% 猜 4% 猜 app app 7% +
  101. 101. ESM:猜測通知準確率 猜 87% 都正確 猜 94% 正確 猜 55% 正確 app app+
  102. 102. ESM: 感覺到通知比例 0% 20% 40% 60% 80%
  103. 103. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% messenger social mail Accuracy on app Accuracy on person ESM: 猜測正確率 猜Email傳送者特別不準
  104. 104. ESM: 觀看通知比例 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Speculated about both Speculated about the sender Speculated about the app Was not able to tell Did not speculate
  105. 105. 最近手機上有動作 最近手機螢幕有亮 最近手機有通知 最近有用一樣的App 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 5 10 30 60 >60 Log + ESM: 猜測通知與最 近手機狀態相關性 最近有用該App更容易感知到該通知
  106. 106. 案例 III: A Field Study Comparing Approaches to Collecting Annotated Activity Data in Real-World Settings 工具:自行開發App (Minuku) 方法:Logging, ESM, Diary, Wearable Camera (Chang et al.,2015a) 研究目的: 比較並找出透過Mobile User收集行為資料的有效方式
  107. 107. 收集交通行為資料
  108. 108. 任務: 錄下並標記交通 行為(e.g. 開車) Logging Annotating 三種方式: • Participatory (PART) • In Situ (SITU) • Post Hoc (POST)
  109. 109. Participatory (PART) 受試者主動參與
  110. 110. In Situ (SITU) ESM:偵測交通模 式後通知註記
  111. 111. Post Hoc (POST) Prompted Recall:偵測 交通模式後記錄,晚 點註記
  112. 112. 要(請)求受 試者整天戴 穿戴式相機 為了保護隱私權,我們允許受試者 不舒服時拿掉相機,他們有七天可 檢視並刪除照片.但我們強調我們 很想要戶外照片 收集Ground Truth
  113. 113. 當時候選相機: SenesCam MeCam Narrative Clip Parashoot Looxcie GoPro 選擇穿戴式相機 選擇條件: • 容量跟電力可以撐 至少兩天 (受試者很 可能忘記上傳照片 或充電) • 拍攝速度越快越好 • 容易上傳照片 • 相機不顯眼
  114. 114. 最終選擇Narrative Clip
  115. 115. • Pre-Study • 12-Day Data Collection – Logging – Photos – Event-Based Diary – (ESM) • Post-Study – Interview • 37位Android手機使 用者(完成人數:29) • 16男13女,年紀大多 介於18-35 • 每天固定通勤的人 • 公車:10,車子:13, 走路:4,腳踏車:2 • 各種全職職業和研究 生 研究流程 受試者
  116. 116. 受試者提供的旅程Recording: • 三個方式都要使用, 一個方式四天, 順序隨機 • 每天至少錄兩段,多錄一段就給15台幣 被動記錄資料: • 位置,Google Activity Recognition,手機 電量 (手機靜止的時候不記錄Location) Logging
  117. 117. • 使用雲端硬碟上傳 照片 • 受試者每天將相機 連結電腦上傳照片 到雲端資料夾,並 順便充電
  118. 118. 122 錄了但沒標記 到的旅途 沒錄到的旅途 1. 詢問沒錄到或沒標記 到的旅途的原因 2. 每天遇到的挑戰困難 Event-Based Diary
  119. 119. 123 追蹤受試者資料 • 要求受試者至少每兩天要將手機連一 次Wifi來上傳資料 • 發現兩天以上有異樣情況寫信給受試 者 – 沒有Data (各種問題都可能出現) – 沒有回Diary • 必要時要延長該受試者參與時間
  120. 120. 124 追蹤受試者照片 • 要求受試者至少每兩天要將電腦連一次Wifi 來上傳照片 • 照片上傳不像Log這麼快 – 量多 – 受試者要花時間檢視和刪除照片,所以通常只能 追蹤數量而不看內容 • 需要檢查照片看鏡頭是否有被衣服遮 住
  121. 121. 125 Post Study: 回顧訪談 • 每個方式遇到挑戰困難 • 每個方式的標記策略 • 喜歡每個方式的什麼地 方 • 希望怎麼改進每個方式
  122. 122. • 有效照片117,000張 • 兩位Coder從照片找旅程 Start End Post Study: Code照片 (Chang et al.,2015b)
  123. 123. Post Study: Activity Logs AR 2014-08-14 15:06:08 -0400 1408043168971 still:69;;in_vehicle:31 42.2793599,-83.7473669 Screen_off AR 2014-08-14 15:06:08 -0400 1408043168974 still:69;;in_vehicle:31 Screen_off AR 2014-08-14 15:06:19 -0400 1408043179130tilting:100 42.2798329,-83.7473109 Screen_off AR 2014-08-14 15:06:19 -0400 1408043179133tilting:100 Screen_off AR 2014-08-14 15:06:28 -0400 1408043188915 in_vehicle:35;;unknown:31;;still:23;;on_bicycle:6;;on_foot:6;;unknown:6 42.2800935,- 83.7472938 Screen_off AR 2014-08-14 15:06:28 -0400 1408043188942 in_vehicle:35;;unknown:31;;still:23;;on_bicycle:6;;on_foot:6;;unknown:6 Screen_off AR 2014-08-14 15:06:49 -0400 1408043209704in_vehicle:75;;still:21;;on_bicycle:2;;unknown:2 42.2804274,-83.7466806 Screen_off AR 2014-08-14 15:06:49 -0400 1408043209707 in_vehicle:75;;still:21;;on_bicycle:2;;unknown:2 Screen_off PROBETR 2014-08-14 15:06:55 -0400 1408043215177 Cancel Suspection:state:Confirmed Screen_off AR 2014-08-14 15:06:55 -0400 1408043215440 in_vehicle:77;;on_foot:8;;still:8;;unknown:8;;unknown:8 42.280416,-83.746497 Screen_off AR 2014-08-14 15:06:55 -0400 1408043215443 in_vehicle:77;;on_foot:8;;still:8;;unknown:8;;unknown:8 • 搜尋交通模式關鍵字判斷時間 • 模糊地帶很多
  124. 124. Post Study: Location Logs • 用Google Earth播放Location
  125. 125. 結合照片與Log產生: 1,414 旅程 受試者產生: 3,070 Recordings 2,587 Valid Recordings 1919 Labeled Recordings 994 Noted Recordings
  126. 126. 一般研究步驟: Pre-Study Data Collection Post-Study
  127. 127. • 確認研究問題和目標群眾 • 想好Incentive • 準備收集資料的Mobile App • 測試 (lab & pilot testing) • 尋找研究受試者 • 研究的行前確認 Pre-Study Data Collection Post-Study
  128. 128. 確認研究問題 • 要研究的行為和情境是否適合透過手機 研究?抓得到嗎?多常發生?一次發生多久? • 如果用其他研究方法,得到的資料差異性 在哪裡? • 目標群眾是否有手機,是否可安裝你的 App,有多少手機使用經驗?
  129. 129. 準備收集資料的系統 • 要記錄什麼? 要偵測什麼? 要不要受 試者input? 受試者需不需要檢視記錄 的資料? • 資料收集頻率與方式?會不會用到他們 的手機網路? • 需不需要自己開發App?後端? • 存資料的格式: 效能 vs. 好處理 • 每款手機sensor設定皆可能不同
  130. 130. 找研究受試者 • 建議先用問卷過濾受試者 • 問卷包含以下問題: –手機使用經驗,常用App –使用手機廠商,型號,OS • 找接下來不會長期旅行的受試者 • 先說明會記錄跟追蹤的資料
  131. 131. 行前確認 • 敘述研究目的 • Informed Consent –說明如何收集, 處理, 和保存資料 –先提醒他們App Logging會耗電 • 同意參與後安裝App • App使用教學 –最好有文件可供參考
  132. 132. • 追蹤資料收集進度 • 與受試者互動 Pre-Study Data Collection Post-Study
  133. 133. 追蹤資料 • 記得將身份與手機做連結 • 1-2天追蹤一次並記錄 • 請工程師設計一套好追蹤資料 的機制 • 若為透過Wifi上傳資料,多給予 時間
  134. 134. 與受試者互動 • 儘量客氣跟保持善意 • 定期主動跟受試者聯絡(代表你有 在注意) • 不要討論細節,他們會認為你在持 續偷窺他們 (就算你真的是) • 鼓勵他們研究過程遇到問題或不順 心就與你聯絡,
  135. 135. • 問卷(如果需要) • 訪談(建議有) • 給獎勵 • 彙集和處理資料, 移除個人聯絡資訊 • 分析資料 Pre-Study Data Collection Post-Study
  136. 136. 研究後調查及訪談 • 有些資訊需要受試者給予有意義的 註解:地方,聯絡人關係 • 看是否需要了解General behavior • 看了資料後,決定是否需要更深入 調查特定行為及情境 • 若要訪談, 帶著(視覺化後)的資料幫 助他們回憶
  137. 137. 彙集處理資料 • 以研究目標為主軸來處理資料 –因為整理的方式太多種 • 注意Log裡的雜訊,例外,和模棱兩 可情況,魔鬼常藏在細節裡 • 質性與Log可分開分析, 但結合在 一起資料會更有意義 • 記得移除與身份聯結的資訊
  138. 138. 結論
  139. 139. 用行動裝置研究的挑戰 • 收集, 整理, 和處理不同形態的資料 –需要有技術人才 • 讓受試者長期合作 • 隱私, 信任, 和安全性問題 • Log只能用來”推測”行為 • 耗電問題
  140. 140. • 行動科技有其他方法無法取代的優 點, 但也有其缺點和限制 • 質化解釋量化,量化驗證質化 • 非適用所有研究,也非所有行為都 可以感測的到 • 回到原始點: 你想研究什麼? Takeaways
  141. 141. 免費Sensing & ESM APP MIT Funf PACOIntel Sensing SDK Emotion Sense ohmage AWARE
  142. 142. 需要ESM靈感?試試這些 https://www.onx.ms/ https://ifttt.com/
  143. 143. 相關書目
  144. 144. Stanley Chang: armuro@cs.nctu.edu.edu FB: armuro.chang 謝謝聆聽

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