Changes for page Measuring Instruments

Last modified by Mathieu Jung-Muller on 2022/04/04 13:37

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edited by Haoran Wang
on 2022/03/16 22:22
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To version Icon 7.1 Icon
edited by Haoran Wang
on 2022/03/17 09:59
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3 3  Evaluation is an important part of product design and it can last from the beginning to the very end. In the Human-Computer Interaction field, product evaluation can help researchers to identify good and bad designs, determine how usable features are, discover new features that were neglected before, and compare design choices to assist us in making decisions.
4 4  
5 5  = Frameworks=
6 -== DECIDE Framework==
7 -==== Determine the goals ====
6 +=== DECIDE Framework===
7 +**D**etermine the goals
8 8  
9 +* What are the high-level goals of the evaluation?
10 +* Who wants it and why?
11 +* The goals influence the approach used for the study.
12 +In our evaluation, our goals are to check if the different stakeholders are able to use our prototype smoothly. Investigate how Pepper affects stakeholders' lives and try to use evaluation to improve our prototype.
9 9  
10 -==== Explore the questions ====
14 +**E**xplore the questions ====
15 + Define goals and research questions. Our research questions are:
11 11  
17 +* Are the different stakeholders able to use our prototype smoothly?
18 +* Does the prototype allow the PwD greater autonomy in their day-to-day life?
19 +* Does the prototype improve the emotional state of the PwD and their relatives?
12 12  
13 -==== Choose the evaluation approach and methods ====
21 +**C**hoose the evaluation approach and methods ====
22 + The evaluation approach influences the methods used, and in turn, how data is collected, analyzed, and presented.
14 14  
15 15  
16 -==== Identify the practical issues ====
25 +**I**dentify the practical issues ====
26 + In our case, the most important practical issue is to gather our classmates to do the evaluation. We do not have any real dented people to evaluate. Besides, we have to make a schedule about when to evaluate our prototype.
17 17  
28 +**D**ecide how to deal with ethical issues ====
29 + Ethical issues are the basis of the evaluation. We would inform all participants about practical issues and make sure to get their consent before starting the evaluation. Users have the right to know their tasks, know what will happen to the collected data, stop participation and leave when they wish.
18 18  
19 -==== Decide how to deal with ethical issues ====
31 +**E**valuate, analyze, interpret and present the data ====
32 + How data is evaluated, analyzed, interpreted, and presented. To make the results reliable and valid, we have to consider biases, reliability, validity, scope, and ecological validity.
20 20  
34 +=== IMPACT Framework===
35 +**I**ntention: Clarify objectives and hypotheses/claims
36 +**M**etrics & Measures: What, how and why
37 +**P**eople: Target group & participants
38 +**A**ctivities: Derive activities from use cases
39 +**C**ontext: Social, ethical, physical, etc. aspects
40 +**T**echnologies: Hardware and software
21 21  
22 -==== Evaluate, analyze, interpret and present the data ====
23 23  
43 +===Formative Evaluation===
44 +Focus on the various processes of the human-technology interaction
45 +Derive open questions from the design specification.
24 24  
25 -== IMPACT Framework==
26 -==== Intention ====
47 +===Summative Evaluation===
48 +Focuses on the overall effects of the human-technology interaction
49 +Specify research questions and hypotheses based on claims.
27 27  
51 +===Data===
52 +====Qualitative Data====
53 +Explore, discover, instruct
28 28  
29 -==== Metrics & Measures ====
55 +* Understand and interpret interactions
56 +* Gain insight into views and perspectives
57 +* Open-ended, like interviews and participant observations
58 +* Try to identify patterns, features, themes
59 +* Study groups tend to be smaller
30 30  
61 +====Quantitative Data====
62 +Describe, explain, predict
63 +* Measure outcomes, test hypotheses, and make predictions
64 +* Precise measurements
65 +* Identify statistical relationships
66 +* Larger number of participants
31 31  
32 -==== People ====
68 +===Experimental Design===
69 +====Within-subjects====
70 +Each participant, all conditions
71 +* Few subjects needed
72 +* Reduced variability
73 +* More statistical power
74 +* Practice/fatigue effects
33 33  
34 -
35 -==== Activities ====
36 -
37 -
38 -==== Context ====
39 -
40 -
41 -==== Technologies ====
42 -
43 -
76 +====Between-subjects====
77 +Each participant, one condition
78 +* Simplicity
79 +* Less chance of practice/fatigue effects
80 +* More time, effort and participants
81 +* Individual variability
82 +* Environment factors