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HCI460: Week 10 LectureHCI460: Week 10 LectureNovember 11, 2009November 11, 2009
2
Project 3: Questions?
Recap: Step-by-step stats for A-B comparisons– For within-subjects design – For between-subjects design
Summative testing– Case study & exercise: Pick best-in-class mp3 player
Other usability evaluation methods– Longitudinal– Focus groups– Remote unmoderated
Evaluating expert user interfaces
Review of Project 2
Grades
Outline
3
Project 3
4
Deliverable: – Final Report only
Due date: – In-class students: Nov 13th, 11:59pm (Friday)– Distance learning: Nov 17th, 11:59pm (Tuesday)
Report for Project 3Project 3
5
Executive Summary– Study background: what, when, how etc.– Summary of findings
Introduction (incl. objectives)
Method– Participants– Materials (or Stimuli)– Procedure
Findings– Quantitative results (with statistics) + “story” (i.e., interpretation
of what the results mean)
Recommendations (if applicable)
Report SectionsProject 3
6
This is a good time to ask.
Questions about Project 3?Project 3
7
Recap: Step-by-Step Stats for A-B Comparisons
8
SummaryStep-by-Step Stats Recap
For time on task, ratings, and number of errors data:– If you have a between-subjects design, use an unpaired t-test
(aka “independent samples t-test”)• DF (degrees of freedom) = sum of the participants minus 2
– If you have a within-subjects design, use a paired t-test (aka “dependent samples t-test” or “repeated measures t-test”)
• DF (degrees of freedom) = sum of all participants minus 1
For task completion (success/fail) data:– Use Fisher’s Exact Test
9
Recap: Step-by-Step Stats for A-B ComparisonsWithin-Subjects Design
10
New vs. old package inserts
Search tasks, e.g.:– Question: How many drops
of control are used in the sample cup?
– Correct answer: 3 drops
Study design– Within-subjects (all
participants saw both versions)
Measures:– Task completion (success/fail)– Time on task (per task)– Ease of use ratings (per task)
Within-Subjects Design: Study BackgroundStep-by-Step Stats Recap
Old New
11
Within-Subjects Design: The DataStep-by-Step Stats Recap
These findings now need to make it to your report.
What are your next steps?
12
Within-Subjects Design: Next StepsStep-by-Step Stats Recap
1. ?
2. ?
3. ?
4. ?
5. ?
6. ?
7. ?
8. ?
9. ?
10. ?
Form groups of 2 – 4 people
Write down your steps (plan of action) – “What are you going to do with these findings before you hand them in to the stakeholders (in a report)?”
No need to compute anything at this point
You have 5 - 7 minutes
Form groups of 2 – 4 people
Write down your steps (plan of action) – “What are you going to do with these findings before you hand them in to the stakeholders (in a report)?”
No need to compute anything at this point
You have 5 - 7 minutes
13
Within-Subjects Design: Step 1Step-by-Step Stats Recap
1. Calculate the averages to get a sense for what the data is telling you.– But…
14
Within-Subjects Design: Step 1Step-by-Step Stats Recap
1. Calculate the averages to get a sense for what the data is telling you.– But…
15
Within-Subjects Design: Step 2Step-by-Step Stats Recap
2. Delete time on task and ease of use data for participants who failed.– If within-
subjects design, delete all data for participants who failed A and/or B (at least one).[If between-subjects design, keep all correct data]
16
Within-Subjects Design: Steps 3 and 4Step-by-Step Stats Recap
3. Open Stats Usability Pak spreadsheet (or another statistical package).
4. Decide on the alpha level (before running any statistics).– .01?– .05? most
common– .1?
17
Within-Subjects Design: Step 5Step-by-Step Stats Recap
5. Select an appropriate test for task completion (success rate).
18
Within-Subjects Design: Step 5Step-by-Step Stats Recap
5. Select an appropriate test for task completion (success rate).– Run Fisher’s Exact Test.
19
Within-Subjects Design: Step 5Step-by-Step Stats Recap
5. Select an appropriate test for task completion (success rate).– Run Fisher’s Exact Test.– Write down the important number(s).
20
Within-Subjects Design: Step 6Step-by-Step Stats Recap
6. Select an appropriate test for the time on task measure.
21
Within-Subjects Design: Step 6Step-by-Step Stats Recap
6. Select an appropriate test for the time on task measure.– Run a paired t-test.
22
Within-Subjects Design: Step 6Step-by-Step Stats Recap
6. Select an appropriate test for the time on task measure.– Run a paired t-test.– Write down the important number(s).
Why is the sample size only 17?
Why is the sample size only 17?
Why do we need this number?
Why do we need this number?
Degrees of freedom!
DF for within-subjects design = n – 1
DF = 17 – 1 = 16
Degrees of freedom!
DF for within-subjects design = n – 1
DF = 17 – 1 = 16
23
Within-Subjects Design: Step 7Step-by-Step Stats Recap
7. Select an appropriate test for the ease of use ratings.
24
Within-Subjects Design: Step 7Step-by-Step Stats Recap
7. Select an appropriate test for the ease of use ratings.– Run a paired t-test.
7. Select an appropriate test for the ease of use ratings.– Run a paired t-test.– Write down the important number(s).
25
Within-Subjects Design: Step 7Step-by-Step Stats Recap
DF = 16DF = 16
26
Within-Subjects Design: Step 8Step-by-Step Stats Recap
8. Describe your findings for task completion (success/fail).– We conducted Fisher’s Exact test to compare the task
completion rate with the old insert (69%) to the task completion rate with the new insert (86%) and found no statistically significant difference at alpha level .05.
But what if there was a difference?• We conducted Fisher’s Exact
test to compare the task completion rate with the old insert (69%) to the task completion rate with the new insert (93%). The completion rates were significantly different (p < .05), such that more participants successfully completed the task when using the new insert than when using the old insert.
27
Within-Subjects Design: Step 9Step-by-Step Stats Recap
9. Describe your findings for time on task.– We conducted a paired t-test to compare the time on task
between the two insert versions. We found a significant difference (t(16) = 2.19, p < .05), such that participants took longer to complete the task when using the old insert (M = 33.1 s, SD = 12.8 s) than when using the new insert (M = 23.6 s, SD = 12.5 s).
28
Within-Subjects Design: Step 10Step-by-Step Stats Recap
10. Describe your findings for ease of use ratings.– We also conducted a paired t-test to compare the ease of use
ratings participants assigned to the two insert versions. There was no significant difference between the ease of use ratings for the old insert (M = 5.6, SD = 1.2) and the new insert (M = 6.0, SD = 0.9) at alpha level .05.
29
Recap: Step-by-Step Stats for A-B ComparisonsBetween-Subjects Design
30
Between-Subjects Design: The DataStep-by-Step Stats Recap
Now let’s pretend that we had a between-subjects design:– One group of participants used the old insert and another group
used the new insert:
31
Between-Subjects Design: Step 1Step-by-Step Stats Recap
1. Calculate the averages to get a sense for what the data is telling you.– But…
32
Between-Subjects Design: Step 1Step-by-Step Stats Recap
1. Calculate the averages to get a sense for what the data is telling you.– But…
33
Between-Subjects Design: Step 2Step-by-Step Stats Recap
2. Delete time on task and ease of use data for participants who failed.
34
Between-Subjects Design: Steps 3 and 4Step-by-Step Stats Recap
3. Open Stats Usability Pak spreadsheet (or another statistical package).
4. Decide on the alpha level (before running any statistics).– .01?– .05? most
common– .1?
35
Between-Subjects Design: Step 5Step-by-Step Stats Recap
5. Select an appropriate test for task completion (success rate).
36
Between-Subjects Design: Step 5Step-by-Step Stats Recap
5. Select an appropriate test for task completion (success rate).– Run Fisher’s Exact Test.
37
Between-Subjects Design: Step 5Step-by-Step Stats Recap
5. Select an appropriate test for task completion (success rate).– Run Fisher’s Exact Test.– Write down the important number(s).
38
Between-Subjects Design: Step 6Step-by-Step Stats Recap
6. Select an appropriate test for the time on task measure.
39
Between-Subjects Design: Step 6Step-by-Step Stats Recap
6. Select an appropriate test for the time on task measure.– Run an independent samples t-test.
6. Select an appropriate test for the time on task measure.– Run an independent samples t-test.– Write down the important number(s).
40
Between-Subjects Design: Step 6Step-by-Step Stats Recap
DF = (20+25) – 2 =43
DF = (20+25) – 2 =43
41
Between-Subjects Design: Step 7Step-by-Step Stats Recap
7. Select an appropriate test for the ease of use ratings.
42
Between-Subjects Design: Step 7Step-by-Step Stats Recap
7. Select an appropriate test for the ease of use ratings.– Run an independent samples t-test.
7. Select an appropriate test for the ease of use ratings.– Run an independent samples t-test.– Write down the important number(s).
43
Between-Subjects Design: Step 7Step-by-Step Stats Recap
DF = (20+25) – 2 =43
DF = (20+25) – 2 =43
44
Between-Subjects Design: Step 8Step-by-Step Stats Recap
8. Describe your findings for task completion (success/fail).– We conducted Fisher’s Exact test to compare the task
completion rate with the old insert (69%) to the task completion rate with the new insert (86%) and found no statistically significant difference at alpha level .05.
But what if there was a difference?• We conducted Fisher’s Exact
test to compare the task completion rate with the old insert (69%) to the task completion rate with the new insert (93%). The completion rates were significantly different (p < .05), such that more participants successfully completed the task when using the new insert than when using the old insert.
45
Between-Subjects Design: Step 9Step-by-Step Stats Recap
9. Describe your findings for time on task.– We conducted an independent samples t-test to compare the
time on task between the two insert versions. We found a significant difference (t(43) = -2.50, p < .05), such that participants who used the old insert took longer to complete the task (M = 33.5 s, SD = 12.6 s) than participants who used the new insert (M = 24.3 s, SD = 11.7 s).
46
Between-Subjects Design: Step 10Step-by-Step Stats Recap
10. Describe your findings for ease of use ratings.– We also conducted an independent samples t-test to compare
the ease of use ratings participants assigned to the two insert versions. There was no significant difference between the ease of use ratings for the old insert (M = 5.6, SD = 1.1) and the new insert (M = 6.0, SD = 0.8) at alpha level .05.
47
Case Study: Pick Best-In-Class MP3 Player
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Pick A Device, Any DeviceCase Study: Pick Best-In-Class MP3 Player
49
Understand the user experience related to popular MP3 devices
Identify usability issues
Recommend possible solutions to improve the UI
Any concerns?
Seemed reasonable to us.
Research ObjectivesCase Study: Pick Best-In-Class MP3 Player
50
What should we do?
What are we measuring?
Who are the participants?
How long is each session?
How many participants?
What Makes Sense?Case Study: Pick Best-In-Class MP3 Player
51
What should we do?– Formative research, but on several competitive products– Iterative usability test a UI
What are we measuring?– Success/Fail, ease-of-use, usability issues, etc.
Who are the participants?– Depends on market demographics
How long is each session?– Depends on what we test
How many participants?– Probably small samples, because this is formative
Our Thoughts…Case Study: Pick Best-In-Class MP3 Player
52
On market at the time…
Five MP3 PlayersCase Study: Pick Best-In-Class MP3 Player
53
21 Tasks of Interest were selected– High Frequency of Use (“Play Song”)– Priority (“Create Playlist”)
5 User Interfaces – Four Competitors– One Client Design (Echo)
Stakeholder Had More Information…Case Study: Pick Best-In-Class MP3 Player
Alpha Bravo Charlie Delta Echo
54
Task x Device MatrixCase Study: Pick Best-In-Class MP3 Player
What do you make of this?
55
Ensure that the new design will be best-of-breed relative to the competition
Oh, we failed to mention:– This is extremely high profile– Data will drive strategy for entire organization and all products– Report will go directly to C-level executives…
And oh, did I mention that we’re gonna need the results to be statistically significant…?
During Kickoff Meeting, More Was Revealed…Case Study: Pick Best-In-Class MP3 Player
56
21 Tasks x 5 Designs = 105 Combinations to test
Can we run a within-subjects design methodology? – Why or why not?
• Learning• Fatigue
Can we run a between-subjects design methodology?
What Concerns You?Case Study: Pick Best-In-Class MP3 Player
57
21 Tasks x 5 Designs = 105 Combinations to test
Can we run a within-subjects design methodology? – Why or why not?
• Learning• Fatigue
Can we run a between-subjects design methodology?
What Concerns You?Case Study: Pick Best-In-Class MP3 Player
58
21 Tasks x 5 Designs = 105 Combinations to test
Can we run a within-subjects design methodology? – Why or why not?
• Learning• Fatigue
Can we run a between-subjects design methodology?
What Concerns You?Case Study: Pick Best-In-Class MP3 Player
59
21 Tasks x 5 Designs = 105 Combinations to test
Can we run a within-subjects design methodology? – Why or why not?
• Learning• Fatigue
Can we run a between-subjects design methodology?
What Concerns You?Case Study: Pick Best-In-Class MP3 Player
60
Found out that not all tasks were possible on each device!!!
When We Looked Deeper…Case Study: Pick Best-In-Class MP3 Player
61
They analyzed the task flows and wanted to add another variant UI
Goal Is Best of Breed, So One More ChangeCase Study: Pick Best-In-Class MP3 Player
62
What Else Can I Say?Case Study: Pick Best-In-Class MP3 Player
63
Our ApproachCase Study: Pick Best-In-Class MP3 Player
Now, we did convince the client that– UC would design the study such that it would be sensitive
enough to detect statistical significance if it did indeed exist
Thus, there would be NO a priori assurances of finding significant differences!
We were charged with:– Research activities– Methodology that would provide data to justify design direction
A device was going to be built…
64
Core Elements to TestCase Study: Pick Best-In-Class MP3 Player
Core elements to test– Access points (navigating to a feature is easy)– Feature task flows (completing task may be hard)– Design look and feel– Iconography– Verbiage
65
Research Program InvolvedCase Study: Pick Best-In-Class MP3 Player
Testing functional task flows
Testing verbiage (navigation and features)
Testing access
Testing iconography
Testing graphic design treatments
Re-test of updated user interface (all elements)
66
Focusing on Testing Functional Task FlowsCase Study: Pick Best-In-Class MP3 Player
Thoughts on considerations for 21 Tasks x 6 Designs?
Areas for bias?– Order
• Task• Device• Combination
– Device hardware
67
Minimize BiasCase Study: Pick Best-In-Class MP3 Player
Realistically, participants could– Only interact with 2-3 designs effectively (not 5-7) – Assumed each participant could complete ~ 6 tasks
Create prototypes on a computer– Level the “playing field” to core task flow elements
Usability testing / Quantitative Data Collection– Recruited target demographic (incl. high schools) – Needed simultaneous test teams– In the end, we really needed to know the “story”
68
Approach: Create Blocks Case Study: Pick Best-In-Class MP3 Player
Sheer size of all possible combinations required blocking into groups– Designs x Tasks combinations
Within each block… Order of task presentation
– Tasks were systematically counter-balanced to reduce learning and order effects
Order of device presentation– For each participant, devices were randomized within each task
to reduce learning and order effects
69
Use of BlocksCase Study: Pick Best-In-Class MP3 Player
Participants assigned to blocks– Individual participant device x task combinations were
• Randomized • Assigned to participants to reduce learning effects
– Constraint: Familiarity biases were avoided • In this sanitized example, iPod owners did not test their
iPods
Blocks of four to six tasks were formed to roughly control for total number of steps to complete
70
Experimental DesignCase Study: Pick Best-In-Class MP3 Player
Design– Each participant received five or six (of the 21) tasks by three (of
the six) devices• Six devices (A/B/C/D/E/F) taken three at a time yields a total
of 20 unique device combinations per task (e.g., ABC, ABD, ABE…DEF)
• A participant was matched to each unique three device combinations
• Hence, to complete a full block, 20 participants were needed
Each device by task cell was represented with at least 10 participants (N=80 to have four blocks of 20)
71
Example of Participants #1 and #2Case Study: Pick Best-In-Class MP3 Player
Julian Tracy O.
Participant #1 Participant #2
16-24 years 25-44 years
task="11" device=Bravo task="16" device=Bravo
task="11" device=Foxtrot task="16" device=Delta
task="16" device=Charlie
task="6" device=Alpha task="13" device=Charlie
task="6" device=Foxtrot task="13" device=Delta
task="6" device=Bravo task="13" device=Bravo
task="4" device=Bravo task="8" device=Delta
task="4" device=Foxtrot task="8" device=Bravo
task="4" device=Alpha task="8" device=Charlie
task="15" device=Foxtrot task="18" device=Bravo
task="15" device=Bravo task="18" device=Delta
task="17" device=Foxtrot task="9" device=Charlie
task="17" device=Bravo task="9" device=Bravo
task="17" device=Alpha task="9" device=Delta
72
MeasuresCase Study: Pick Best-In-Class MP3 Player
Time-on-Task
Efficiency (Deviation from Optimal Path)– Total screens viewed / Optimal path for the task
• More incorrect “steps” increases this metric
Success– % participants in each cell (device x task) who successfully
completed the task
Preference– Pair-wise device preferences for a particular task with a
magnitude judgment
73
Task-by-Task AnalysisCase Study: Pick Best-In-Class MP3 Player
74
Need More Than Simply QuantCase Study: Pick Best-In-Class MP3 Player
We anticipated that the results would not be so clear cut as to identify a single design and declare a winner
On a particular task, we might find that Device Foxtrot was #1– But, what made Device Echo #2?
We need to know the “why”– Partitioned participants (N=16) for qualitative UT– Other participants (N=80) were sufficient for quantitative
75
Qualitative data: User CommentsCase Study: Pick Best-In-Class MP3 Player
76
Interesting PointsCase Study: Pick Best-In-Class MP3 Player
Start with the high frequency / high priority tasks…– Why did the Foxtrot design win?– Why did the Alpha design lose?– Compare quantitative with qualitative
Complex tasks – The fastest time was not the best– More clicks, less error, high satisfaction
Sometimes winners would emerge for different reasons…– How do you weigh different UI conventions?
77
Lessons LearnedCase Study: Pick Best-In-Class MP3 Player
“Know the story”– Benefit of Qualitative Data– Absolute “must have”– Asked for N=100, and pushed some to qualitative
Learning– Counterbalancing was sufficient– But, these are not walk-up-and-use devices…
Avoid “Frankenstein” Design– Do not simply pick the winning task flow and implement– Consistency matters!
78
Other Usability Evaluation Methods
79
FGs are structured group interviews that quickly and inexpensively reveal a target audience’s desires, experiences, reactions, attitudes...– Mostly used in market
research.– Capture opinions rather
than behavior.
However, FGs can sometimes be used for usability evaluation.– But the procedure and
questions should be modified.
Focus Groups (FGs)Other Usability Evaluation Methods
80
If it is a new product:– Participants interact with a
product and then discuss their difficulties as a group.
• You can have a few simultaneous one-on-one sessions followed by a FG.
• You can also have activities during FGs.
Benefits:– Participants are not just watching the moderator use the product
(as they do in traditional FGs) but get to experience it first hand.– Group discussion can provide more feedback and richer
feedback in less time than several individual UTs.
Focus GroupsOther Usability Evaluation Methods
81
If it is an existing product:– Participants who have experience interacting with the product
are invited to discuss their difficulties in a FG.• E.g., Pharmacists come to discuss their issues with particular
types of blister packs. They also get to interact with the packs (and a few prototype packs) during the session.
Benefits:– Group discussion can provide more feedback and richer
feedback in less time than several individual UTs.
Focus GroupsOther Usability Evaluation Methods
82
Usability testing assesses walk-up-and-use usability, not actual use– Assess learning, usage and satisfaction over time
“Drop and Soak” studies– Provide device and let soak– Transition from initial use to familiar/experienced use
Repeat user testing studies– Bring users back multiple times to assess evolution– Multiple usability tests with same participants– Iterations and evolution
User group – Rinse, wash, repeat
Sidebar: Why longitudinal research is rarely seen
LongitudinalOther Usability Evaluation Methods
83
Gap Between Web Analytics and SurveysOther Usability Evaluation Methods
Web AnalyticsWhat customers do
SurveysWho Customers AreWhat Customers Say
Automated TestingCombines Analytics andSurvey
84
Traditional online surveys measure satisfaction.– Collect quantitative measures.– Obtain attitudinal scores.
But, there is no connection to the experience.
Interactive surveys (remote un-moderated testing)– Task-based research using online panels (Keynote, UserZoom
etc.)– True-intent research using actual site visitors (LEOtrace)
Remote Unmoderated TestingOther Usability Evaluation Methods
85
Task-based Web SurveyOther Usability Evaluation Methods
86
Task-based Web SurveyOther Usability Evaluation Methods
87
Heatmaps of ClicksOther Usability Evaluation Methods
88
ScorecardsOther Usability Evaluation Methods
Navigation menu through the modules defined
The homepage highlights the most important information
from each module
Different colors illustrate different results
Comparative analysis
89
Evaluating Expert User Interfaces
90
User of interface is used an expert– User has extensive experience on application– User uses the application a lot
• One could argue MS Word is an example
Exercise– Break into groups– Evaluate usability of this expert interface– Customer relationship management (CRM) system
Not Walk Up and UseEvaluating Expert User Interfaces
91
Customer care information– Details on a single customer for residential service
Exercise: Review This Interface…Evaluating Expert User Interfaces
92
As with any customer, knowing the “history” can help– User clicks Notes on left to open Interaction Notes
Example: HistoryEvaluating Expert User Interfaces
93
From Interactive Notes, individual notes can be reviewed
Example: NotesEvaluating Expert User Interfaces
94
Call centers– Approx. 100 calls per day– Typical environment involves cubicle farms– Stand and ask questions– Multi-tasking– Time pressure– Possible sales incentives in effect– Rapid consumption of screens
Does this change anything?!?!
Does the evaluation differ?
Example: ContextEvaluating Expert User Interfaces
95
Quite a unique user group
Demonstrate over-learned behaviors– High number of transactions, high volume of calls– Rote memorization of commands and actions
Emphasis is all about their workflow – They make transactions quickly across multiple systems – In most cases, they do not need to look at entire screen
• Consider adding a new service, the first few screens may be irrelevant as they are observed clicking for navigation and not for information
– Introduction of any new system can have grave impacts
Reality: – Traditional walk up and use methods may be insufficient for this
audience and this type of interface
Expert UsersEvaluating Expert User Interfaces
96
S:– s
Take Another 5 minutes…Evaluating Expert User Interfaces
97
Findings…?
Main ScreenEvaluating Expert User Interfaces
98
Findings…?
Interaction NotesEvaluating Expert User Interfaces
99
Emphasis on pretty and “easily-trainable” – But, this is not a walk-up-and-use application
Design issues– Fitts’ Law
Workflow– More effort to get the same information
What is the impact?– Will users even click?– Poor knowledge of user history
• Impact?
Core IssuesEvaluating Expert User Interfaces
100
Change can be hard– Especially if this represents a paradigm shift from accessing
features and sections to a single view of customer• Seems logical that a single point of view is better, but this
assumes an thorough understanding of the workflow across a day
• However, theoretical savings on a spreadsheet do not often equate to benefits in the real world
– Users multi-task
Adoption and usage are key drivers…– How should you test these interfaces?!?!
Introduction of a New ApplicationEvaluating Expert User Interfaces
101
Usability tests uncovered issues on a page by page and task level basis– Areas such as time, content comprehension, navigation, layout,
labeling, and functionality – UT is exceptional at targeting page level details
However, usage adoption requires more than just page level data– How this system fits into workflow or “I won’t use”– UT participants were so task-focused – No mention of high-level impacts to their job or and daily tasks
Focus groups for interactive applications leads to “spoon feeding”
Try two-person UTs– Workflow becomes an issue as one watches the other work…
Discussion of MethodsEvaluating Expert User Interfaces
102
Review of Project 2
103
Executive summary– Context + main themes / key findings– Don’t forget to mention the positives– Issues: Too much detail or too high-level
Objectives Methods Findings Recommendations– “Dangling objectives” (no findings address them)– “Dangling methods” (no corresponding findings or objectives)– “Dangling findings” (no corresponding objectives or method)
Describing findings– Description of a participant difficulty/error.– Description of the source of the difficulty.
• What in the interface caused the difficulty?
ThemesReview of Project 2
TASK/USER-ORIENTED
PRODUCT-ORIENTED
104
Within-subjects design?
“Participants” vs. “users”
“Formative study”
Visual references– Video clips
ThemesReview of Project 2
105
ScoresReview of Project 2
ABB-C+CDF
106
Grades
107
15% Project 1: Expert evaluation
25% Project 2: Formative usability study
20% Project 3: Quantitative comparison study
10% Take-home midterm quiz
20% Final exam
10% Individual contribution to projects
GradingGrades
108
Project 1Grades
ABB-C+CDF A-
109
Project 2Grades
ABB-C+CDF
110
A 10 and 9.5 A- 9 B+ 8.5 B 8 B- 7.5 C+ 7 C 6.5 C- 6 D+ 5.5 D 5
MidtermGrades