PPT-Getting User Feedback

UserCentered Design Agenda Focus groups Inlab studies AB testing Card sorting Traffic analysis Focus Groups What are focus groups A somewhat informal method of gathering qualitative . Multimedia Databases. via Relevance Feedback . with History and Foresight Support. DBRank. 08, April 12. th. 2008, . Cancún. , Mexico. Marc Wichterich. , Christian Beecks, Thomas Seidl. Outline. Motivation. Miradi Share program-level management and reporting. Miradi Share geo-spatial module. Supporting . mobile data collection/viewing (mainstreaming Mira). . Improve user journey – . Miradi Desktop . C. Design. Lesson 4. Revise Market Size Calculation. Revise your market size calculations based on the feedback you got from your mentor and teacher. . Modules. 4.1. . Product . Description. 4.2. . User Interface and Product Design. Speech and Language Technology. For Dialog-based CALL. Gary Geunbae Lee, POSTECH. Outline. Introduction. 1. Spoken Dialog Systems. 2. 4. PESAA: . Postech. English Speaking Assessment and Assistant. 5. Buyers complained about . uSell. reviews. And rightfully so:. Low quantity of feedback. Only ~3.5% of orders get reviewed. Since ratings affect a buyer’s Best Match ranking, buyers want to get more ratings from customers. Questions. Yes. No. Does the game have different colour. land at each end?. Does. the game have 2 saucers and 1 puck added?. Do the saucers move up and down the screen. when the keyboard is pressed?. Alexander . Kotov. . and . ChengXiang. . Zhai. . University of Illinois at Urbana-Champaign. Roadmap. Query Ambiguity. Interactive Sense Feedback. Experiments. Upper-bound performance. User study. Prasun Dewan. Department of Computer Science. University of North Carolina at Chapel Hill. dewan@cs.unc.edu. COLLABORATIVE APPLICATION. U1. U2. U3. Application. User inputs “draw red circle”. Coupling. By Josephine M. Giaimo, MS. March 14, 2014. Designing the User Experience (UX). What We’ll Discuss Today. What does a systematic usability process look like?. Some usability methods to reduce risk and improve quality. Sampath Jayarathna. Center for the Study of Digital Libraries. Computer Science & Engineering. Texas A&M University . Motivation. 2. Personalized Information Delivery. Customize search results based on the . Relevance Feedback: . Example. Initial Results. Search Engine. 2. Relevance Feedback: . Example. Relevance Feedback. Search Engine. 3. Relevance Feedback: . Example. Revised Results. Search Engine. 4. Wang. CS@UVa. Explicit relevance feedback. 2. Updated. query. Feedback. Judgments:. d. 1 . . d. 2. -. d. 3 . …. d. k. -. .... Query. User . judgment. Retrieval. Engine. Document. collection. Results:. Inspirations, ideas . &. plans. Motivation. Ideal situation: general-purpose image annotation with unlimited vocabulary. Reality:. Classifiers with limited vocabulary and dependency on labeled training data. Topics. Project Updates and Reminders. Design Change Updates. Super User Feedback. Marketplace Updates. Training Updates. Blanket . Order . Solutions. Grant Approval Update. 2.  .  .  .  .  . P2P Super Users .