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content/notes/11-continuous-integration-2.md

@@ -1,5 +1,8 @@
 ---
-title: "11-continuous-integration-2"
+title: "11-continuous-integration-2"]
+sr-due: 2022-04-07
+sr-interval: 3
+sr-ease: 250
 tags: 
 - cosc202 
 - lecture
@@ -20,11 +23,11 @@ CI usually tets abd buiolds your prokects
 
 runs on a repo server. Usuially persistent, internet accessible
 
-## 1 Gitlab overall architecture
+## 0.1 Gitlab overall architecture
 ![](https://i.imgur.com/whU7QoF.png) : not in exam
 - many different services used
 
-## 2 Gitlab runners
+## 0.2 Gitlab runners
 run CI scripts
 
 - gitlab.com is a cloud computing service
@@ -36,7 +39,7 @@ run CI scripts
 	- e.g., to use a particular GPU, or other hardware you have
 	- GItlab runner itself is a small program written in Go
 
-### 2.1 Runner architecture
+### 0.2.1 Runner architecture
 
 - runs jobs
 	- on isolated infrastructure
@@ -51,7 +54,7 @@ RHS shows GitLab.com's CI hosting: uses google cloud
 
 ![](https://i.imgur.com/RaeYc1I.png) : not in exam
 
-## 3 How CI chagned website hosting
+## 0.3 How CI chagned website hosting
 
 - need to share stifacts produced by CI jobs
 	- using the web to share artefacts is ideal
@@ -64,7 +67,7 @@ RHS shows GitLab.com's CI hosting: uses google cloud
 
 e.g., https://cosc202.cspages.otago.ac.nz
 
-## 4 Debugging CI scripts
+## 0.4 Debugging CI scripts
 
 - first ensure config files YAML is valid
 	- vuilt in gitlab editor checks YAML as you type
@@ -76,7 +79,7 @@ e.g., https://cosc202.cspages.otago.ac.nz
 		- e.g., `if command supposed to fail; then true; else true; fi`
 	- Complex scripting? Beste to put script in a file and run it from CI
 
-## 5 Secrets used by CI scripts
+## 0.5 Secrets used by CI scripts
 
 ![](https://i.imgur.com/XtCap0P.png)
 ![](https://i.imgur.com/W2xBi4d.png)

content/notes/evaluating-designs.md

@@ -1,7 +1,154 @@
 ---
 title: "evaluating-designs"
+sr-due: 2022-04-07
+sr-interval: 10
+sr-ease: 210
 tags: 
 - info203
 ---
 
+#unfinished 
 
+Why to evaluate using 'outside' people:
+- how do we know if a [[Prototyping|prototype]] is good
+- designer/developers are not 'fresh' -> they already have experience with the product
+- designer/developers don't know what real users will do
+
+## 0.1 Issues to consider
+- Reliability/precision
+	- how accurate is your study?
+	- Is is reproducible -> if it was repeated, would you get the same result
+- Generalizability
+	- Is your sample representative
+- Realism
+	- Would observed behaviour also occur in the wild
+- Comparison
+	- Shows how different options were recieved
+	- rather than a "people liked it" study
+- work involved/efficiency
+	- How cost efficient are your methods
+
+## 0.2 Factors to consider when choosing an evaluation method
+- Stage in the cycle at which the evaluation is carried out -> (design / implementation)
+- Style of evaluation -> (lab / field)
+- Level of subjectivity or objectivity
+- Type of measurement -> (qualitative / quantitative)
+- Information provided -> (high-level / low-level)
+- Immediacy of response -> (real-time / recollection of events)
+- Level of interference implied -> (intrusiveness)
+- Resources required -> (equipment, time, money, subjects, expertise, context) 
+
+## 0.3 Styles of evaluation
+##### 0.3.1.1.1 Laboratory Studies 
+- 1st step: Designer evaluates his/her UI
+- Specialised equipment for testing available
+- Undisturbed (can be a good or bad thing) 
+- Allows for well controlled experiments
+- Substitute for dangerous or remote real-world locations
+- Variations in manipulations possible / alternatives
+
+##### 0.3.1.1.2 Field Studies
+- Within the actual user’s working environment
+- Observe the system in action
+- Disturbance / interruptions (+/-)
+- Long-term studies possible
+- Bias: presence of observer and equipment
+- Needs support / disturbs real workflow
+
+## 0.4 Quantitative vs Qualitative methods
+##### 0.4.1.1.1 Quantitative Measures
+- Usually numeric
+- E.g. # of errors, time to complete a certain task, questionnaire with scales
+- Can be (easily) analysed using statistical techniques
+- Rather objective
+- Most useful in comparing alternative designs
+- Test hypotheses
+- Confirm designs
+
+##### 0.4.1.1.2 Qualitative Measures
+- Non-numeric
+- E.g. survey, interview, informal observation, heuristic evaluation
+- Difficult to analyse, demands interpretation
+- Rather subjective 
+- User’s overall reaction and understanding of design
+- Generate hypotheses
+- Find flaws
+
+## 0.5 Stage in cycle
+##### 0.5.1.1.1 Design Stage
+- Only concept (even if very detailed) exists
+- More experts, less users involved
+- Greatest pay-off: early error detection saves a lot of development money
+- Rather qualitative measures (exceptions: detail alternatives; fundamental questions, ...)
+
+##### 0.5.1.1.2 Implementation
+- Artefact exists, sth. concrete to be tested
+- More users, less experts involved
+- Assures quality of product before or after deployment; bug detection
+- Rather quantitative measures (exceptions: overall satisfaction, appeal, ...)
+
+## 0.6 Methods
+### 0.6.1 Usability studies
+- Bringing people in to test Product
+	- Usage setting is not ecologically valid - usage in real world can be different
+	- can have tester bias - testers are not the same as real users
+	- cant compare interfaces
+	- requires physical contact
+### 0.6.2 Surveys and focus groups
++ quicly get feedback from large number of responses
++ auto tally ressults
++ easy to compare different products
+- responder bias
+- Not accurate representation of real product
+* e.g., ![[Pasted image 20220316130318.png]]
+* Focus groups
+	* gathering groups of people to discuss an interface
+	* group setting can help or hinder
+	
+### 0.6.3 Feedback from experts
+- [[Peer critique]]
+- [[Dogfooding]]
+	- Using tools yourself
+- [[Heuristic Evaluation]]
+	- structured feedback
+
+### 0.6.4 Comparative experiments
+- in lab, field, online
+- short or long duration
+- which option is better?
+- what matters most?
+- can see real usage
+- more actionable
+
+### 0.6.5 Participant observation
+- observe what people do in the actual evironment
+- usually more long term
+	- find things not present in short term studies
+- [[Observation]]
+
+### 0.6.6 Simulation and formal models
+- more mathmatical quantitative 
+- useful if you have a theory to test
+- often used for input techniques
+- can test multiple alternatives quickly
+- typically simulation is used in conjugtion with [[monte carlo optimisation]]
+
+## 0.7 Query techniques
+- [[Interviews]]
+- questionnaires
+	- less flexible
+	- larger samples possible
+	- design of questionnaire is for expert only
+	- use of standard (proven) questionnaires recommended
+	- types of questions: 
+		- general (age, gender)
+		- open ended 
+		- scalar (e.g., likert-like scales)
+		- multiple choice
+		- ranking
+
+## 0.8 Users
+- users can come up with great ideas
+	- lead user -> need specific soluton that does not exist -> often make up their own solution
+	- extreme user -> use existing solution for it's intended purpose to an extreme degree 
+	- typical user ->