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CS 160 MIDTERM EXAM 2025/2026 BANK QUESTIONS AND CORRECT ANSWERS CURRENTLY TESTING COMPLETE QUESTIONS WITH DETAILED VERIFIED ANSWERS /ALREADY GRADED A+

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CS 160 MIDTERM EXAM 2025/2026 BANK QUESTIONS AND CORRECT ANSWERS CURRENTLY TESTING COMPLETE QUESTIONS WITH DETAILED VERIFIED ANSWERS /ALREADY GRADED A+

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CS 160 MIDTERM EXAM 2025/2026 BANK QUESTIONS AND CORRECT ANSWERS
CURRENTLY TESTING COMPLETE QUESTIONS WITH DETAILED VERIFIED ANSWERS
/ALREADY GRADED A+




the design cycle - (ANSWER)design --> prototype --> evaluate



Rapid prototyping

What are the benefits of low fidelity prototyping?

What are disadvantages of high fidelity prototyping? - (ANSWER)



Evaluation - (ANSWER)Evaluate analytically Test with target users

• How do we figure out who they are? Different methods of evaluation

• List all that we've covered so far.



Task analysis



what questions should one ask when analyzing tasks? - (ANSWER)1. Who is going to use system?

2. What tasks do they now perform?

3. What tasks are desired?

4. How are the tasks learned?

5. Where are the tasks performed?

6. What's the relationship between user & data?

7. What other tools does the user have?

8. How do users communicate with each other?

9. How often are the tasks performed?

10. What are the time constraints on the tasks?

11. What happens when things go wrong?



Contextual Inquiry - (ANSWER)Middle ground between pure observation and pure interview

Get inside the user's head and see their tasks the way they do

,CS 160 MIDTERM EXAM 2025/2026 BANK QUESTIONS AND CORRECT ANSWERS
CURRENTLY TESTING COMPLETE QUESTIONS WITH DETAILED VERIFIED ANSWERS
/ALREADY GRADED A+




Qualities: Context, Partnership, Interpretation, Focus

Contextual Inquiry involves a master/apprentice model



Why is it important to develop personas? - (ANSWER)



Conceptual models - (ANSWER)design model ----> system image <----> user's model

what if the two dont match up?



gulf of evaluation and execution - (ANSWER)see screenshot image too



what are the 3 processors of the human model processor? other 2 aspects?



Why is this model helpful? - (ANSWER)cognitive, motor, perceptual

LTM & working MEM



May not be biologically accurate

But ...

- Provides rough estimate of performance

- Can help us compare and evaluate interfaces

Interfaces should both aid and exploit human capabilities!!



What is a mode? - (ANSWER)



Don Norman's Design principles - (ANSWER)1.Make controls visible

2.Make mapping clear

3.Provide feedback

,CS 160 MIDTERM EXAM 2025/2026 BANK QUESTIONS AND CORRECT ANSWERS
CURRENTLY TESTING COMPLETE QUESTIONS WITH DETAILED VERIFIED ANSWERS
/ALREADY GRADED A+




What is direct manipulation and how does it pertain to conceptual models? - (ANSWER)



Power law of Practice - (ANSWER)T(sub n) =T(sub1)n^(−a) +c

Task on nth time follows a power law.

You get better and faster the more you do something!

a = .4 ?



Applies to skilled behavior (sensory & motor)

Does not apply to:

- Knowledge acquisition

- Improving quality



Why model human performance? - (ANSWER)To predict impact of new technology/interface

Apply model to predict effectiveness

We could build a simulator to evaluate user interface designs



Human info processor - (ANSWER)Processors:

- Perceptual

- Cognitive

- Motor

Memory:

- Working memory

- Long-term memory

Unified model:

- Probably inaccurate

, CS 160 MIDTERM EXAM 2025/2026 BANK QUESTIONS AND CORRECT ANSWERS
CURRENTLY TESTING COMPLETE QUESTIONS WITH DETAILED VERIFIED ANSWERS
/ALREADY GRADED A+




- Predicts performance well

- Very influential



Perceptual Processor

working MEM decay time?

what kind of info does it get?

what kind of features does it include? - (ANSWER)Physical store from our senses: sight, sound, touch, ...

- Code directly based on sense used

- Visual, audio, haptic, ... features

- Selective

- Spatial

Pre-attentive: color, direction...

Capacity of visual store

- Example: 17 letters

- Decay time for working memory: 200ms

Recoded for transfer to working memory

- Progressive: 10ms/letter



Fitt's Law - (ANSWER)T =a+b* log(base 2)(D/S+1)

a, b= constants (empirically derived)

D = distance

S = size

ID is Index of Difficulty = log(base 2) (D/S+1)

Models well-rehearsed selection task

T increases as the distance to the target increases

T decreases as the size of the target increases
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