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Explainable AI for non-expert users
1. Explainable AI for non-expert users
APEx-UI 2022 - 21 March 2022
Katrien Verbert
Augment/HCI - KU Leuven
@katrien_v
towards the next generation of interactive and adaptive
explanation methods
Towards the next generation of interactive and adaptive
explanation methods
2. Human-Computer Interaction group
Explainable AI - recommender systems – visualization – intelligent user interfaces
Learning analytics &
human resources
Media
consumption
Precision agriculture
Healthcare
Augment Katrien Verbert
ARIA Adalberto Simeone
Computer
Graphics
Phil Dutré
LIIR Sien Moens
E-media
Vero Vanden Abeele
Luc Geurts
Kathrin Gerling
3. Augment/HCI team
Robin De Croon
Postdoc researcher
Katrien Verbert
Associate Professor
Francisco Gutiérrez
Postdoc researcher
Tom Broos
PhD researcher
Nyi Nyi Htun
Postdoc researcher
Houda Lamqaddam
PhD researcher
Oscar Alvarado
Postdoc researcher
http://augment.cs.kuleuven.be/
Diego Rojo Carcia
PhD researcher
Maxwell Szymanski
PhD researcher
Arno Vanneste
PhD researcher
Jeroen Ooge
PhD researcher
Aditya Bhattacharya
PhD researcher
Ivania Donoso Guzmán
PhD researcher
4. Explaining model outcomes to increase user trust and acceptance
Enable users to interact with the explanation process to improve the model
Research objectives
Models
11. Explanations
11
Millecamp, M., Htun, N. N., Conati, C., & Verbert, K. (2019, March). To explain or not to explain: the
effects of personal characteristics when explaining music recommendations. In Proceedings of the 2019
Conference on Intelligent User Interface (pp. 397-407). ACM.
12. Personal characteristics
Need for cognition
• Measurement of the tendency for an individual to engage in, and enjoy, effortful cognitive activities
• Measured by test of Cacioppo et al. [1984]
Visualisation literacy
• Measurement of the ability to interpret and make meaning from information presented in the form of
images and graphs
• Measured by test of Boy et al. [2014]
Locus of control (LOC)
• Measurement of the extent to which people believe they have power over events in their lives
• Measured by test of Rotter et al. [1966]
Visual working memory
• Measurement of the ability to recall visual patterns [Tintarev and Mastoff, 2016]
• Measured by Corsi block-tapping test
Musical experience
• Measurement of the ability to engage with music in a flexible, effective and nuanced way
[Müllensiefen et al., 2014]
• Measured using the Goldsmiths Musical Sophistication Index (Gold-MSI)
Tech savviness
• Measured by confidence in trying out new technology
12
13. User study
Within-subjects design: 105 participants recruited with Amazon Mechanical Turk
Baseline version (without explanations) compared with explanation interface
Pre-study questionnaire for all personal characteristics
Task: Based on a chosen scenario for creating a play-list, explore songs and rate all
songs in the final playlist
Post-study questionnaire:
Recommender effectiveness
Trust
Good understanding
Use intentions
Novelty
Satisfaction
Confidence
15. Design implications
Explanations should be personalised for different groups of end-
users.
Users should be able to choose whether or not they want to see
explanations.
Explanation components should be flexible enough to present
varying levels of details depending on a user’s preference.
15
16. User control
Users tend to be more satisfied when they have control over
how recommender systems produce suggestions for them
Control recommendations
Douban FM
Control user profile
Spotify
Control algorithm parameters
TasteWeights
17. Controllability Cognitive load
Additional controls may increase cognitive load
(Andjelkovic et al. 2016)
Ivana Andjelkovic, Denis Parra, andJohn O’Donovan. 2016. Moodplay: Interactive mood-based music
discovery and recommendation. In Proc. of UMAP’16. ACM, 275–279.
18. Different levels of user control
18
Level
Recommender
components
Controls
low Recommendations (REC) Rating, removing, and sorting
medium User profile (PRO)
Select which user profile data
will be considered by the
recommender
high
Algorithm parameters
(PAR)
Modify the weight of different
parameters
Jin, Y., Tintarev, N., & Verbert, K. (2018, September). Effects of personal characteristics on music recommender
systems with different levels of controllability. In Proceedings of the 12th ACM Conference on Recommender
Systems (pp. 13-21). ACM.
19. User profile (PRO) Algorithm parameters (PAR) Recommendations (REC)
8 control settings
No control
REC
PAR
PRO
REC*PRO
REC*PAR
PRO*PAR
REC*PRO*PAR
20. Evaluation method
Between-subjects – 240 participants recruited with AMT
Independent variable: settings of user control
2x2x2 factorial design
Dependent variables:
Acceptance (ratings)
Cognitive load (NASA-TLX), Musical Sophistication, Visual Memory
Framework Knijnenburg et al. [2012]
21. Results
Main effects: from REC to PRO to PAR → higher cognitive load
Two-way interaction: does not necessarily result in higher
cognitive load. Adding an additional control component to
PAR increases the acceptance. PRO*PAR has less cognitive
load than PRO and PAR
High musical sophistication leads to higher quality, and thereby
result in higher acceptance
21
Jin, Y., Tintarev, N., & Verbert, K. (2018, September). Effects of personal characteristics on music
recommender systems with different levels of controllability. In Proceedings of the 12th ACM Conference on
Recommender Systems (pp. 13-21). ACM.
25. Explaining exercise recommendations
How to automatically adapt
the exercise recommending
on Wiski to the level of
students?
How do (placebo)
explanations affect initial trust
in Wiski for recommending
exercises?
Goals and research questions
Automatic adaptation Explanations & trust Young target audience
Middle and high school students
Ooge, J., Kato, S., Verbert, K. (2022) Explaining Recommendations in E-Learning: Effects on Adolescents'
Initial Trust. Proceedings of the 27th IUI conference on Intelligent User Interfaces
26. Results: Real explanations…
… did increase multidimensional initial trust
… did not increase one-dimensional initial trust
… led to accepting more recommended exercises
compared to both placebo and no explanations
27. Results: Placebo explanations…
… did not increase initial trust compared to no explanations
… may undermine perceived integrity
… are a useful baseline:
• how critical are students towards explanations?
• how much transparency do students need?
28. Results: No explanations
Can be acceptable in low-stakes situations (e.g., drilling
exercises):
indications of difficulty level might suffice
Personal level indication:
Easy, Medium and Hard
tags
30. 30
uncertainty
Gutiérrez Hernández F., Seipp K., Ochoa X., Chiluiza K., De Laet T., Verbert K. (2018). LADA: A learning
analytics dashboard for academic advising. Computers in Human Behavior, pp 1-13. doi:
10.1016/j.chb.2018.12.004
LADA: a learning analytics dashboard for
study advisors
32. Results
LADA was perceived as a valuable tool for more accurate and
efficient decision making.
LADA enables expert advisers to evaluate significantly more
scenarios.
More transparency in the prediction model is required in order
to increase trust.
32
Gutiérrez Hernández F., Seipp K., Ochoa X., Chiluiza K., De Laet T., Verbert K. (2018). LADA: A learning
analytics dashboard for academic advising. Computers in Human Behavior, pp 1-13. doi:
10.1016/j.chb.2018.12.004
35. AHMoSe
Rojo, D., Htun, N. N., Parra, D., De Croon, R., & Verbert, K. (2021). AHMoSe: A knowledge-based visual
support system for selecting regression machine learning models. Computers and Electronics in Agriculture,
187, 106183.
37. Case Study – Grape Quality Prediction
37
Grape Quality Prediction Scenario [Tag14]
Data
Years 2010, 2011 (train) 2012 (test)
48 cells (Central Greece)
Knowledge-based rules
[Tag14] Tagarakis, A., et al. "A fuzzy inference system to model
grape quality in vineyards." Precision Agriculture 15.5 (2014):
555-578.
Source: [Tag14]
38. Simulation Study
AHMoSe vs full AutoML approach to support model selection.
38
RMSE (AutoML) RMSE (AHMoSe) Difference %
Scenario A
Complete
Knowledge
0.430 0.403 ▼ 6.3%
Scenario B
Incomplete
Knowledge
0.458 0.385 ▼ 16.0%
39. Qualitative Evaluation
10 open ended questions
5 viticulture experts and 4 ML experts.
Thematic Analysis: potential use cases, trust, usability, and
understandability.
40. Qualitative Evaluation - Trust
40
Showing the dis/agreement of model outputs with expert’s
knowledge can promote trust.
“The thing that makes us trust the models is the fact that most of
the time, there is a good agreement between the values
predicted by the model and the ones obtained for the knowledge
of the experts.”
– Viticulture Expert
43. Predicting duration to find a job
43
Key Issues: Missing data, prediction trust issues, job seeker
motivation, lack of control.
44. Methods
A Customer Journey approach. (5 mediators).
Hands-on time with the original dashboard (22 mediators).
Observations of mediation sessions. (3 mediators, 6 job seekers).
Questionnaire regarding perception of the dashboard and prediction
model (15 Mediators).
44
Charleer S., Gutiérrez Hernández F., Verbert K. (2018). Supporting job mediator and job seeker through an
actionable dashboard. In: Proceedings of the 24th IUI conference on Intelligent User Interfaces Presented at
the ACM IUI 2019, Los Angeles, USA.
46. Take away messages
Key difference between actionable and non-actionable
parameters
Need for customization and contextualization.
The human expert plays a crucial role when interpreting and
relaying in the predicted or recommended output.
46
Charleer S., Gutiérrez Hernández F., Verbert K. (2019). Supporting job mediator and job seeker
through an actionable dashboard. In: Proceedings of the 24th IUI conference on Intelligent User
Interfaces Presented at the ACM IUI 2019, Los Angeles, USA. (Core: A)
53. Design and Evaluation
53
Gutiérrez F., Cardoso B., Verbert K. (2017). PHARA: a personal health augmented reality assistant to support
decision-making at grocery stores. In: Proceedings of the International Workshop on Health Recommender
Systems co-located with ACM RecSys 2017 (Paper No. 4) (10-13).
54. Design
54
Gutiérrez Hernández F., Htun NN., Charleer S., De Croon R., Verbert K. (2018). Designing augmented reality
applications for personal health decision-making. In: Proceedings of the 2019 52nd Hawaii International
Conference on System Sciences Presented at the HICSS, Hawaii, 07 Jan 2019-11 Jan 2019.
56. Evaluation method
Within Subjects
n = 28 (1F, 27M) Ages from 22 to 38 (M = 25.81, SD = 4.57)
Post-Questionnaires
TAM (Technology Acceptance)
NASA-TLX (Task Load Index)
56
Gutiérrez Hernández F., Htun NN., Charleer S., De Croon R., Verbert K. (2018). Designing augmented reality
applications for personal health decision-making. In: Proceedings of the 2019 52nd Hawaii International
Conference on System Sciences Presented at the HICSS, Hawaii, 07 Jan 2019-11 Jan 2019.
57. Results
PHARA allows users to make informed decisions, and resulted
in selecting healthier food products.
Stack layout performs better with HMD devices with a limited
field of view, like the HoloLens, at the cost of some
affordances.
The grid and pie layouts performed better in handheld devices,
allowing to explore with more confidence, enjoyability and less
effort.
57
Gutiérrez Hernández F., Htun NN., Charleer S., De Croon R., Verbert K. (2018). Designing augmented
reality applications for personal health decision-making. In: Proceedings of the 2019 52nd Hawaii
International Conference on System Sciences Presented at the HICSS, Hawaii, 07 Jan 2019-11 Jan
2019.
61. 61
Gutiérrez Hernández, F.S., Htun, N.N., Vanden Abeele, V., De Croon, R.,
Verbert, K. (2022). Explaining call recommendations in nursing homes: a user-
centered design approach for interacting with knowledge-based health decision
support systems. IUI 2022.
62. Evaluation
12 nurses used the app for three months
Data collection
Interaction logs
Resque questions
Semi-structured interviews
62
64. Results
Iterative design process identified several important features, such as the pending
list, overview and the feedback shortcut to encourage feedback.
Explanations seem to contribute well to better support the healthcare professionals.
Results indicate a better understanding of the call notifications by being able to see the
reasons of the calls.
More trust in the recommendations and increased perceptions of transparency and control
Interaction patterns indicate that users engaged well with the interface, although some
users did not use all features to interact with the system.
Need for further simplification and personalization.
64
67. 67
Explaining health recommendations
Word cloud Feature importance Feature importance+ %
Maxwell Szymanski,Vero Vanden Abeele and Katrien Verbert Explaininghealthrecommendationstolayusers: Thedosand
don’ts – Apex-IUI2022
68. Biofortification info
Plants to cultivate
PERNUG
Increased access to more nutritious plants
Improved iron and B12 intakes for vegan and vegetarian
subgroups
Consumer app with recipe recommendations Hydroponic system with
biofortified plants
https://www.eitfood.eu/projects/pernug
71. Take-away messages
Involvement of end-users has been key to come up with
interfaces tailored to the needs of non-expert users
Actionable vs non-actionable parameters
Domain expertise of users and need for cognition important
personal characteristics
Need for personalisation and simplification
71
We focus specifically on visual analytics for non-expert users.
Non-expert users are defined as users that have little knowledge of data processing and analysis.
We research two algorithmic foundations: predictions models like regression and clustering and recommender systems that suggest items to users.
The key objective is to communicate the uncertainty of these models to support decision-making and increase trust.
We do it through the use of visualization techniques that explain the models.
Amazon.com gebruikt een collaborative filtering techniek: zoekt gelijkenissen tussen gebruikers en gaat dan op basis van wat gelijkaardige gebruikers kopen aanbevelingen doen.
The procedure contains the following steps:
\begin{enumerate}
\item \textit{Tutorial of study} - Participants were invited to read the description of the user study and to choose a scenario for generating a play-list. Then, they were asked to watch a task tutorial. Only the features of the particular setting were shown in this video. The ``Start'' button of the study was only activated after finishing the tutorial. Users logged in with their Spotify accounts to our experimental system, so that our recommenders could leverage the Spotify API and user listening history to generate ``real'' recommendations.
\item \textit{Pre-study questionnaire} - This questionnaire collects user demographics and measures user's personal characteristics such as musical sophistication and visual memory capacity. %and their trust in recommender systems.
The visual memory capacity is measured by ``Corsi block-tapping test''. In the test, a number of tiles are highlighted one at a time, and participants are asked to select the tiles in the correct order afterward. The number of highlighted tiles increases until the user makes too many errors. In Experiments 1 and 3, we used a test with a more sophisticated implementation of the Corsi test~\footnote{\url{https://www.humanbenchmark.com/tests/memory}, accessed June 2018}, which allows us to better distinguish participants by the level of visual memory capacity. In Experiment 2, to control the workload of participants in the within-subjects design, we chose a simple version of the Corsi test~\footnote{\url{http://www.psytoolkit.org/experiment-library/corsi.html}, accessed June 2018} for measuring visual short-term memory.
\end{itemize}
\item \textit{Manipulating Recommender and rating songs} - To ensure that participants spent enough time to explore recommendations, the questionnaire link was only activated after 10 minutes. After tweaking the recommender, participants were asked to rate the top-20 recommended songs that resulted from their interactions.
\item \textit{Post-study questionnaire} - Participants were asked to evaluate the perceived quality, perceived accuracy, perceived diversity, satisfaction, effectiveness, and choice difficulty of the recommender system. After answering all the questions, participants were given opportunities to provide free-text comments of their opinions and suggestions about our recommender.
\end{enumerate}
Figure 2 shows that participants with low NFC are reporting
a higher confident in their playlist with the explanations in-
terface than in the baseline. Participants with a high NFC
reported the opposite. Hence, the participants with low NFC
have more confidence in the explanation interface than in the
baseline, in contrast to user with low NFC. An explanation
might be that low NFC participants benefited from the expla-
nations because they did not spontaneously engage in much
extra reasoning to justify the recommendations they received,
and when they received the rational from the explanation this
increased their confidence in their songs selection.
Figure 2 also indicates that as the NFC increased, the con-
fidence of participants in the playlist created in the baseline
also increased. This result indicated that participants with a
high NFC were more willing to understand their own musical
preference in relation to the attributes of the recommended
songs. This may have resulted in a higher confidence in their
playlist.
We did not see the same increase in trust as NFC increases
in the explanation interface. As Figure 2 shows, the NFC
scores in the third quartile were almost the same for both
interfaces. At the highest NFC level, participants had a higher
confidence in the baseline than in the explanation interface.
The reduced confidence within the explanation interface could
be an indication that users with a high NFC have less need for
explanations.
We employed a between-subjects study to investigate the effects of interactions among different user control on acceptance, perceived diversity, and cognitive load. We consider each of three user control components as a variable. By following the 2x2x2 factorial design we created eight experimental settings (Table~\ref{tab:table1}), which allows us to analyze three main effects, three two-way interactions, and one three-way interaction. We also investigate which specific \textit{personal characteristics} (musical sophistication, visual memory capacity) influence acceptance and perceived diversity. Each experimental setting is evaluated by a group of participants (N=30). Of note, to minimize the effects of UI layout, all settings have the same UI and disable the unsupported UI controls, e.g., graying out sliders.
As shown in section ~\ref{evaluation questions}, we employed Knijnenburg et al.'s framework~\citep{knijnenburg2012explaining} to measure the six subjective factors, perceived quality, perceived diversity, perceived accuracy, effectiveness, satisfaction, and choice difficulty~\citep{knijnenburg2012explaining}. In addition, we measured cognitive load by using a classic cognitive load testing questionnaire, the NASA-TLX~\footnote{https://humansystems.arc.nasa.gov/groups/tlx}. It assesses the cognitive load on six aspects: mental demand, physical demand, temporal demand, performance, effort, and frustration.
The procedure follows the design outlined in the general methodology (c.f., Section \ref{sec:general-procedure}). The \textit{experimental task} is to compose a play-list for the chosen scenario by interacting with the recommender system. Participants were presented with play-list style recommendations (Figure~\ref{fig:vis1}c). Conditions were altered on a between-subjects basis. Each participant was presented with only one setting of user control. For each setting, initial recommendations are generated based on the selected top three artists, top two tracks, and top one genre. According to the controls provided in a particular setting, participants were able to manipulate the recommendation process.
Main effects: REC has lowest cgload and highest acceptance
Two-way: All the settings that combine two control components do not lead to significantly higher cognitive load than using only one control component. combing multiple control components potentially increases acceptance without increasing cognitive load significantly.
visual memory is not a significant factor that affects the cognitive load of controlling recommender systems. In other words, controlling the more advanced recommendation components in this study does not seem to demand a high visual memory. In addition, we did not find an effect of visual memory on acceptance (or perceived accuracy and quality).
One possible explanation is that users with higher musical so- phistication are able to leverage different control components to explore songs, and this influences their perception of recommenda- tion quality, thereby accepting more songs.
Our results show that the settings of user control significantly influence cognitive load and recommendation acceptance. We discuss the results by the main effects and interaction effects in a 2x2x2 factorial design.
Moreover, we discuss how visual memory and musical sophistication affect cognitive load, perceived diversity, and recommendation acceptance.
\subsubsection{Main effects}
We discuss the main effects of three control components. Increased control level; from control of recommendations (REC) to user profile (PRO) to algorithm parameters (PAR); leads to higher cognitive load (see Figure \ref{fig:margin}c). The increased cognitive load, in turn, leads to lower interaction times. Compared to the control of algorithm parameters (PAR) or user profile (PRO), the control of recommendations (REC) introduces the least cognitive load and supports users in finding songs they like.
We observe that most existing music recommender systems only allow users to manipulate the recommendation results, e.g., users provide feedback to a recommender through acceptance. However, the control of recommendations is a limited operation that does not allow users to understand or control the deep mechanism of recommendations.
\subsubsection{Two-way interaction effects}
Adding multiple controls allows us to improve on existing systems w.r.t. control, and do not necessarily result in higher cognitive load. Adding an additional control component to algorithm parameters increases the acceptance of recommended songs significantly.
Interestingly, all the settings that combine two control components do \textit{not} lead to significantly higher cognitive load than using only one control component. We even find that users' cognitive load is significantly \textit{lower} for (PRO*PAR) than (PRO, PAR), which shows a benefit of combining user profile and algorithm parameters in user control. Moreover, combing multiple control components potentially increases acceptance without increasing cognitive load significantly. Arguably, it is beneficial to combine multiple control components in terms of acceptance and cognitive load.
\subsubsection{Three-way interaction effects}
The interaction of PRO*PAR*REC tends to increase acceptance (see Figure \ref{fig:margin}a), and it does not lead to higher cognitive load (see Figure \ref{fig:margin}c). Moreover, it also tends to increase interaction times and accuracy. Therefore, we may consider having three control components in a system.
Consequently, we answer the research question. \textbf{RQ1}: \textit{The UI setting (user control, visualization, or both) has a significant effect on recommendation acceptance?} It seems that combining PAR with a second control component or combing three control components increases acceptance significantly. %KV: this paragraph refers to different RQs: either rephrase or omit? -SOLVED
\subsubsection{Effects of personal characteristics}
Having observed the trends across all users, we survey the difference in cognitive load and item acceptance due to personal characteristics. We study two kinds of characteristics: visual working memory and musical sophistication.
\paragraph{Visual working memory}
The SEM model suggests that visual memory is not a significant factor that affects the cognitive load of controlling recommender systems. The cognitive load for the type of controls used may not be strongly affected by individual differences in visual working memory. In other words, controlling the more advanced recommendation components in this study does not seem to demand a high visual memory.
In addition, we did not find an effect of visual memory on acceptance (or perceived accuracy and quality). Finally, the question items for diversity did not converge in our model, so we are not able to make a conclusion about the influence of visual working memory on diversity.
\paragraph{Musical sophistication}
Our results imply that high musical sophistication allows users to perceive higher recommendation quality, and may thereby be more likely to accept recommended items. However, higher musical sophistication also increases choice difficulty, which may negatively influence acceptance.
One possible explanation is that users with higher musical sophistication are able to leverage different control components to explore songs, and this influences their perception of recommendation quality, thereby accepting more songs. Finally, the question items for diversity did not converge in our model, so we are not able to make a conclusion about the influence of musical sophistication on diversity.
As I have already explained, this is the LADA Dashboard that predicts the chance of success and presents a set of components that are intended to help the student adviser to give feedback to the student.
We evaluated this dashboard with both laymen and experts. They used LADA based on real data of students to plan a semester for a student in two Conditions:
Using the dashboard.
Using the traditional system.
Results indicate that the prediction models enables users to explore more possible scenarios,
but more transparency is required.
The quality indicator is insuffient to increase user trust
The first application domain is agriculture
Precision agriculture is an interesting domain to research the representation of data and uncertainty associated with both data and prediction models for non-expert users, such as farmers.
This domain faces some typical challenges of Visual Analytics, missing data and uncertainty of predictions.
In this work, we conducted a systematic review of visualisation techniques and the representation of uncertainty.
The transcribed data were coded and analysed following the thematic analysis approach (Braun and Clarke, 2006), which resulted in four main themes: potential use cases, trust, usability, and understandability.
Marimekko charts difficult
Showing the dis/agreement of model outputs with expert’s knowledge can promote understandability and trust.
ability to see dis/agreements between models' predictions and an expert's knowledge can help them inspect further and thus promote trust.
u
Job recommender systems have become a well researched area. In this dissertation, we designed and evaluated two interactive dashboards that can help explain the reasoning behind job recommendations and predictions.
A first dashboard has been elaborated that explain predictions of the chance to find a job in a particular job area.
The second dashboard explains job recommendations by showing competences and competence gaps instead of the typical matching score used by recommender systems.
This is the first dashboard:
We designed this dashboard on top of a prediction model to explain the inner workings to job mediators.
We make a distinction between actionable and non-actionable parameters.
Age is an example of a non-actionable parameter.
We used a user-centered design methodology consisting of the steps listed on this slide.
This is the first dashboard:
We designed this dashboard on top of a prediction model to explain the inner workings to job mediators.
We make a distinction between actionable and non-actionable parameters.
Age is an example of a non-actionable parameter.
Job mediators highlighted the importance of customising the dashboard to be able to control the message.
Five mediators used negative parameters to support their message
Two mediators removed negative parameters to avoid demotivation.
“age can be demotivating”
“too much information might be difficult to process” “would like to see an overview of everything” “depends on the job seeker”
Our explanatory tool helps mediators to control the message they wish to convey depending on the situation context.
Health is another interesting domain: here we try to provide relevant information to end-users that is trustworthy and has a positive impact on decision-making.
We researched the representation of uncertainty of a prediction model that predicts the impact of a food product on weight as well as different layouts to present this data together with recommendations in an AR setting.
We found that the stack visualisation performs better with HMD devices with a limited field of view, like the HoloLens, at the cost of some usability affordances (RQ4).
For handheld devices, the grid and the pie tended to score higher in terms of confidence in decision making, compared to the list and stack layouts (RQ1, RQ4).
The prediction model shows the impact of the food product on the weight of the participant. Opacity is used to represent the uncertainty of this prediction. (POINT to third card)
The prediction model shows the impact of the food product on the weight of the participant. Opacity is used to represent the uncertainty of this prediction. (POINT to third card)
We compared four different layouts to represent this information:
a stack layout,
a list layout,
a grid layout and
a pie layout.
We compared their use in two implementations:
one using the Microsoft HoloLens,
and a second one using an Smartphone
We evaluated these layouts in a user study with 28 participants in a lab setting.
and measured both subjective and objective data collected from the use of our application,
We found that the stack visualisation performs better with HMD devices with a limited field of view, like the HoloLens, at the cost of some usability affordances (RQ4).
For handheld devices, the grid and the pie tended to score higher in terms of confidence in decision making, compared to the list and stack layouts (RQ1, RQ4).
Q11: not mych effort
Q2, Q5
During the interviews, some participants also appreciated the explanations of the system, indicating in particular the detailed view of the call, where they can see the most frequent reasons for a call: \say{\textit{\textbf{P8}: I like that you see the most frequent reasons. Why they call most often. That is the most important to me. That you can see it again afterward. Maybe, when a person calls a lot, that you can reflect. they go a lot to the toilet, maybe they have a urinary infection. When many nurses visit a resident, maybe you don’t see this if you don’t look at the overview.}} Overview of resuldents
