Course talk:CPSC522/User-Adaptive Information Visualization

From UBC Wiki
Jump to: navigation, search

Contents

Thread titleRepliesLast modified
Critique - Prithu110:55, 14 March 2016
Comments110:54, 14 March 2016
Critique of User-Adaptive Information Visualization110:53, 14 March 2016
Suggestions109:20, 14 March 2016

Critique - Prithu

Great work Yaashaar. Your page gives nice examples and provides enough base to understand the content. I had very little prior knowledge on Info Viz, but still was able to follow most of the content. However the topic seems a bit off-track in terms of relevance to AI. Nonetheless, your write-up was a pleasant read. On a side note, few figures should be enlarged to improve their readability such as figure 2

03:58, 10 March 2016

Hi Prithu,
Thank you very much for reading my page and the kind words you used:

  1. In the first paragraph of Introduction I pointed out its association with AI. Machine learning (ML) models (classifiers) are the core elemets of adaptive systems, and ML is categorized under AI. The truth is the AI’s direct techniques and artifacts such as sequence planning models (HMM, Max Entropy Markov Model, etc.), all come very handy when we discuss the advanced type of problems in User Adaptive Information Visualization. However, as the purpose of this page was to introduce the field by two papers from two different research teams, I decided to select these two papers, because they present a nice introduction to the type of problems that are investigated in U-AIV, and without this foundation, discussing the advanced problems would be a bit pointless.
  2. I added a dedicated section (under introduction), in which, I elaborated this relationship.
  3. I enlarged the figures’ size a bit to enhance their readability. You can also click on them to view them in their original size.


Thanks again for your valuable remarks ;)
Yaashaar

09:09, 14 March 2016
 

Nice page. Maybe you can elaborate more on how this topic is related to general AI.

04:59, 11 March 2016

Hi YanZhao,
Thank you very much for reading my page and your kind suggestion:

  1. In the first paragraph of Introduction I pointed out its association with AI. Machine learning (ML) models (classifiers) are the core elements of adaptive systems, and ML is categorized under AI. Machine learning (ML) models (classifiers) are the main core of adaptive systems, and ML is categorized under AI. For example, in one study, they analyzed eye-gaze data to create user- and task model; they used a classifier to classify different affective states based on the eye-gaze features related to the area of interests in a visualization. In every U-AIV system, there is a user or task modeling phase, which provides the basis upon which they can decide when to present the adaptive visualizations or adaptive support in general. In the first study those highlighting interventions, and the second study, those feedbacks should be based upon a user model or task model and that's where we can see the most AI techniques and ML models are used. The other AI’s direct techniques and artifacts such as sequence planning models (HMM, Max Entropy Markov Model, etc.), all come very handy when we discuss the advanced type of problems in User Adaptive Information Visualization. However, as the purpose of this page was to introduce the field by two papers from two different research teams, I decided to select these two papers, because they present a nice introduction to the type of problems that are investigated in U-AIV, and without this foundation, discussing the advanced problems would be a bit pointless.
  2. I just added a dedicated section (under introduction), in which, I elaborated this relationship.


Thanks again for your valuable suggestion ;)
Yaashaar

10:17, 14 March 2016
 

Critique of User-Adaptive Information Visualization

Hi Yaashar, A well written and detailed page. I have the following observations; 1. In the second study, why the '5 affective states' where chosen, I couldnt understand whether there is any particular background behind this. 2. Also, I failed to understand what AI techniques in general has been used in providing adaptive feedback. In my understanding providing such feedback seems to be the AI application of the study. Hope this helps :)

07:51, 11 March 2016

Hi Abed,
Thank you very much for reading my page and your kind suggestions:

  1. I just added a few lines, explaining the source of their decision. However, if you mean, why they chose 5 and not 6 for instance, there was not any explicit reason presented in the paper, but I assume they simply wanted to keep the number of variables managable, and the reason they chose that particular set was because of their higher significance in the study of Pekrun which I referred to.
  2. Machine learning (ML) models (classifiers) are the core elements of adaptive systems, and ML is categorized under AI. For example, in one study, they analyzed eye-gaze data to create user- and task model; they used a classifier to classify different affective states based on the eye-gaze features related to the area of interests in a visualization. In every U-AIV system, there is a user or task modeling phase, which provides the basis upon which they can decide when to present the adaptive visualizations or adaptive support in general. As you mentioned presenting those feedbacks should be based upon a user model or task model and that's where we can see the most AI techniques and ML models are used. The other AI’s direct techniques and artifacts such as sequence planning models (HMM, Max Entropy Markov Model, etc.), all come very handy when we discuss the advanced type of problems in User Adaptive Information Visualization. However, as the purpose of this page was to introduce the field by two papers from two different research teams, I decided to select these two papers, because they present a nice introduction to the type of problems that are investigated in U-AIV, and without this foundation, discussing the advanced problems would be a bit pointless.
  3. I just added a dedicated section (under introduction), in which, I elaborated this relationship.


Thanks again for your valuable remarks, I really appreciate it ;)
Yaashaar

10:07, 14 March 2016
 

Suggestions

Hi Yaashaar,

A solid draft that I learned a lot of Information Visualization stuff. Here are some suggestions:

  1. It is better to add some motivating examples for the page to make it is easier to understand.
  2. In Highlight Study section, I think put the Research Questions before Research Procedure is easier to understand.

Other parts are great.

Bests,

YuYan

07:34, 10 March 2016

Hi YuYan,
Thank you very much for reading my page and your kind suggestions:

  1. In the first paragraph of the introduction section, I tried to elaborate the case where User Adaptive Systems are appreciated as some questions which establish the foundation of this field. As this assignment was about reviewing two papers, I tried to be loyal to the material of the papers and just evaluate them in conclusion sections. I didn't find presenting a separate motivational example, any useful as two concrete and complete cases (experiments) are reviewed.
  2. You're right, I just changed its position to be presented before the procedure section.


Thanks again for your valuable suggestions ;)
Yaashaar

09:19, 14 March 2016