10. 6 0 1 2 3 4 5 64206420 14 12 10 8 Mouse Trackball Joystick Touchpad TroughputError rate T roughput (bps) Error r ate (%) Image by MIT OpenCourseWare.

12. © Mozilla. All rights reserved. © source unknown. All rights reserved.

17. © Microsoft. All rights reserved.

19. © The Eclipse Foundation. All rights reserved.

20. © Interface Hall of Shame. All rights reserved.

21. © Microsoft. All rights reserved.

30. 1 1 50 40 30 20 15 10 8 6 5 4 3 2 2 3 4 5 6 8 10 15 20 30 40 50 Predicted execution time (sec) Obser v ed e x ecution time (sec) Graphic Editors MARK UP DRAW SIL Executive Subsystems All Subsystems Text Editors POET SOS DISPED Image by MIT OpenCourseWare.

37. High-level models of human-computer behavior Developing Theories in HCI –must explain and predict human behaviour in the human-computer system –must work in a wide variety of task situations –must work within broad spectrum of system designs and implementations Some low-level theories can be used to predict human performance –Fitts’ law: time to select an item with a pointing device –Keystroke level model: sums up times for keystroking, pointing, homing, drawing, thinking and waiting General models that explain human behaviour with machines –Syntactic/semantic model (Shneiderman) –Stages of interaction (Norman) –all of psychology!

38. Syntactic/semantic model of user knowledge A high level model of interaction, developed by Ben Shneiderman ActionObjectActionObject Task Computer Semantic Syntactic

39. 1. Syntactic knowledge The rules or combinations of commands and signals – seen as device-dependent details of how to use system – examples: backspace key delete previous character tab move to next field in a form right mouse buttoncontext menu grep find word in a file control-shift-Ksearch messages (thunderbird)

40. Syntactic 1. Syntactic knowledge (continued) User problems with syntactic knowledge syntactic details differ between (and within!) systems –little consistency –> arbitrary hard to learn easily forgotten

41. Action Object Computer 2. Semantic knowledge: Computer concepts The meaning behind computer concepts People learn them by –meaningful learning –demonstrations –explanations of features –trial by error –models, analogies relatively stable in memory –high level concepts –logical structure –cognitive model produced usually transferable across computer systems –but not always But: prefer to concentrate on task, not computer knowledge

42. Action Object Task 3. Semantic knowledge: task concepts The meaning behind the task concepts – is independent of the computer – Similar mechanism to computer concepts Examples – how to write a business plan format concerns stylistic concerns paragraph structure, etc. – creating a budget

43. What Syntactic/Semantic Model reveals Mapping between the 3 items is key – Task semantics to computer semantics to computer syntax task semantics: write document computer semantics: open a file, use editor, save it to disk computer syntax: select menu items, key strokes for formatting,... – Bad mapping: using latex to write document aside from task semantics, must also know semantics/syntax of: – text editor – latex – Unix compiling and printing sequence (to typeset and print) – Good mapping: trashcan to throw away files must know mouse syntax of selecting and dragging computer semantics almost analogous to task semantics

44. Guideline from syntactic/semantic model Reduce burden to task-oriented user of learning separate computer semantics and syntax computer semantics – Use metaphors – hide unnecessary information computer syntax – A little learning should go a long way... – As simple as possible and uniformly applicable – Generic commands – Syntax consistent between systems

45. Four Stages of an Interaction (a simplified version of Norman’s 7 stages) 1. Form intention 2. Select an action 3. Execute the action 4. Evaluate the outcome

46. The four stages when performing a task Physical activity Execution Action Specification Intention Goals Evaluation Interpretation Perception Mental activity expectation

47. Example task: improve document’s formatting intention-1 intention-2 intention-3 action specification evaluate-1 evaluate-2 evaluate-3 interpretation intention-4 evaluate-4 look better block para.pp->.sp execution perception get formatted output action specification execution interpretation printer perception

48. Image by MIT OpenCourseWare.

49. Goals Physical System gulf of execution Gulf of Execution Do actions provided by system correspond to the intentions of the user? Gulf: – amount of effort exerted to transform intentions into selected and executed actions Good system: – direct mappings between Intention and selections

50. Goals Physical System gulf of evaluation Gulf of Evaluation Can feedback be interpreted in terms of intentions and expectations? Gulf: – amount of effort exerted to interpret feedback Good system: – feedback easily interpreted as task expectations

51. Bridging the Gulfs Goals Physical System execution bridge intentions action specifications interface mechanism evaluation bridge interpretations interface display evaluations