Summary

Fraser et al.’s paper “DiscoverySpace: Suggesting Actions in Complex Software” explores how action suggestions can be incorporated into a complex system that can help beginners and inexperienced users navigate and gain confidence while interacting with the system. This research is motivated by the observation that there are several problems that novice users might face several problems when trying to use complex software: (1) new users may be unfamiliar with the vocabulary used in the application, making it difficult to explore the desired features; (2) online tutorials might be difficult to suit their situation and goals; (3) several shortcuts in the software to accomplish the same task are available but inexperienced users might get overwhelmed by the multiple approaches and have no idea how to locate the most efficient ones; (4) users only get exposure to a small number of software features which may limit their cognition of its potential power. To address the above issues, the researchers develop DiscoverySpace which is a prototype action suggestion software extension for Adobe Photoshop to help beginners get started without feeling overwhelmed.

Reflection

I think this paper conducted a very interesting study on how the proposed action recommendations can help novice users build confidence, accomplish tasks, and discover features. Probably many of us have similar experiences that when we are trying to get started with a complex software used by professionals in different domains, the learning curve is so deep that we feel frustrated and discouraged at the beginning. If there is no necessity to use the software or light-weighted alternatives available, we may give up using it. The proposed extension does a good job presenting some basic features of the complex software and develops “one-click” operations for easy tasks, which can make new users gain some sense of achievement while interacting with the system and may want to continue exploring other features.

As presented in the analysis and experimental results, the largest improvement of the proposed Adobe Photoshop extension is among the users who just get started using the software. The evaluation is mostly based on their self-reported confidence. However, another important aspect is ignored in the process – how much did the users learn in the process. That relates to what goal we want to achieve by using professional software. If the users just expect to leverage the power of Photoshop to do really simple tasks, there are a bunch of mobile applications available with only a few easy clicks. If the objective is just to beautify a selfie, the Instagram application has built-in operations and is very easy to use. As we know, Photoshop is used by professionals for image editing. If the users would like to learn how to use the software and build up their skills over time, the current version of the proposed extension does not seem to be helpful. The proposed approach is to encapsulate a sequence of operations into a single click. There is no denying the fact that it is very easy to use, but the light-weighted operations may not contribute to long-term learning. I am a Photoshop user and use it frequently as needed. The current version of the proposed extension may not be very useful to me, but I feel there is a lot of potentials to improve to make it more powerful. Firstly, it would be very useful to have a dialogue box to present the back-end steps conducted within a one-click function. Knowing the basic sequence to achieve a simple task can help the users build their knowledge and know what to do or at least what to search when they need to achieve a similar task. Secondly, it would be helpful to have some interactive modules that enable users to adjust a number of parameters, such as brightness, contrast, and so forth. These are fundamentals for users who want to enhance their skill levels and get experienced with Photoshop.

Discussion

I think the following questions are worthy of further discussion.

• In what user scenarios you think the proposed software extension would be most useful?
• Do you think it is helpful to incorporate a sequence of operations in one click or there is a need to present the operations step by step to users?
• Do you think that this approach with newly released extensions can assist experts in complex professional software?
• Can you think about some examples of other applications in the same or different domains that could benefit from the proposed approach?

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Summary

               Fraser et al.’s paper discusses how difficult some software programs are, especially for novices, and a possible solution to help ameliorate the steep learning curves they often possess. Their solution is the DiscoverySpace interface – a tool for the creation of macros to help novice users learn new capabilities of the software they want to use. They further test this with a prototype that is used to help participants learn how to use the popular image editing program Photoshop. After finding 115 different macros from various sources, they designed their workflow and interface and ran a study to evaluate it. They found, in a study with 28 participants, that found that participants were significantly less likely to be discouraged or say they couldn’t figure something out with the DiscoverySpace tool installed with Photoshop.

Personal Reflection

               This paper provides a fantastic workflow for easing novice users into using new and difficult programs. I liked it because it provides a slightly more customized experience than the youtube video walkthroughs and online tutorials that I’m accustomed to using. I would even like to actually use this interface for Photoshop, as its one program I attempted to break into a few times early on in my college career, always failing as there were too many features too obliquely described.

               I was surprised that the authors removed the suggestions that contained pauses and dialogue. I would have expected those situations to be better able to present the user with the appropriate background for the effects they wanted to do. However, when they explained their reasoning – that the explanations often were not enough and confused the users – it made a lot more sense to remove those altogether.

               I’m not sure how I feel about their comment that later on they are supporting “paid” actions, where the macros do something that can be considered of a higher quality and thereby require some form of compensation for the macro creator. I don’t think an academic paper is the place for that sort of suggestion, as it doesn’t really add to the software or approach the paper presents. All tools that academic papers present could be used in commercial software, so why would that be of particular note in this paper?

               Lastly, and this is more of a quibble, I was more off put than I thought I would be by the text-aligned images as seen in figures 3 and 4. The alignment is more difficult to read than it would be in a casual magazine-type environment, and should be reserved for that sort of publication.

Questions

1. Do you think the 115 presented actions are enough of a testbed for the prototype tool? I.E., should they have more to present a better amalgamation of possible uses? How would they generate more?
2. Beyond using image analysis to present some initial ideas to the users, what other ways might you improve their approach to make it more automated, or do you think there’s enough or too much automation already?
3. What other programs could use this approach, and how might they integrate it into their platforms?

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Summary

In this paper, the authors introduce Discovery Space, an extension to Adobe Photoshop that suggests high-level macro actions based on visual features. Complex platforms such as Photoshop are great tools to aid creativity. However, their features are rather complex for beginners making for a steep learning curve. DiscoverySpace utilizes one-click actions suggested in the online community and makes these macro actions available to new users thus softening their introduction to the software.

For the experiment, the authors maintain two independent control groups. One group has access to the DiscoverySpace panel in Photoshop and the other only has the basic tool. Experiments show that DiscoverySpace helps beginners by suggesting initial macro actions. Subjects in the no tool group were frustrated with the tool’s complex features producing worse results than the subjects in DiscoverySpace group. Also, the authors suggest that some steps in the process can be replaced by advances in AI algorithms in the future which will lead to faster processes.

Reflection

The paper is really interesting in its approach to reduce the system’s complexity by integrating macro action suggestions. The framework is very generalizable and can work in a multiple number of complex softwares such as Excel sheets (to help with common macro functions), Powerpoint presentations (to apply popular transitions or slide formats) and AI frameworks (pre-building popular networks).  Another important aspect is that such technologies are already being applied in several places. Voice assistants have specific suggestions to introduce users to common tasks such as setting up alarms, checking weather, etc.

However, the study group is relatively very small. I do not understand the reason for this. The tasks could be put into an MTurk type format and given to several users. Given the length of the task (~30 min), the authors could potentially use train and work platforms such as Upwork too. Hence, I believe the conclusions of the paper are very specific to the subjects. Also, the authors suggest the potential of integrating AI systems to their framework. I think it would help if more examples were given for such integrations. 

Also, utilizing DiscoverySpace-like mechanisms draws in more users. This provides a monetary-incentive to businesses to invest in more such ideas. One example can be the paper-clip assistant in initial versions of Windows that introduced users to the operating system.

Questions

1. I believe machine learning frameworks like tensorflow and pytorch have examples to introduce themselves to beginners. They could benefit from a DiscoverySpace-like action suggestion mechanism. Can you give some examples of softwares in your research area that could benefit from such frameworks?
2. I believe the limited number of subjects is a huge drawback to trust in the conclusions of the paper. Can you provide some suggestions on how the experiments could be scaled to utilize more workers at a limited cost?
3. The authors provide the example of using advances in image analysis to replace a part of DiscoverySpace. Can you think of some other frameworks that have replaceable parts? Should we develop more architectures based on this idea that they can be replaced by advances in AI?
4. Give some examples of systems that already utilize DiscoverySpace-like framework to draw in more users?

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Summary:
“DiscoverySpace: Suggesting Actions in Complex Software” by Fraser et. al. talks about complex software and ways novice users can navigate these complex systems. “DiscoverySpace is a prototype extension panel for Adobe Photoshop that suggests task-level action macros to apply to photographs based on visual features.” The authors find out that the actions suggested by DiscoverSpace help novices maintain confidence, accomplish tasks, and discover new features. The work highlights how user generated content can be leveraged by interface designers to help new and inexperienced navigate a complex system. There are several problems that a beginner might face when trying to access new complex systems: (i) the novice user might not be familiar with the technical jargon used by the software, (ii) the online tutorials might be difficult to follow and assume a certain amount of background knowledge, (iii) there can be several ways to accomplish the same task and the user might get overwhelmed and confused when getting to know about them. The paper presents DiscoverySpace which is a prototype action suggestion software to help beginners get started without feeling overwhelmed.

Reflections:
This is an interesting paper as it talks about the methodology that can be adopted by software designers to build a system that aids novice users, instead of overwhelming them. This reminds me of the popular ‘cold start’ problem in computational modeling. The term essentially refers to the problem when computers do not have enough information to model the desired human behavior. This is due to the lack of initial user interactions. The authors try to mitigate this problem in DiscoverySpace by conducting a survey to help identify and narrow down the kind of help novices need. It was an interesting find that participants who used the web to look for help achieved their results less often. I would have expected it to be the other way round. The authors suggest that the participants failed to find the best way to accomplish the task and Google does not always help find the best results. One of the limitations of the study was that the task was open-ended. If the task were more directed, the results would have led to better findings. Also, self-reporting expertise on a task might not be the most reliable way to assess the user as a novice or an expert. Another thing to note here is that all the participants had some kind of domain knowledge, either through the basic principles of photography or through simpler photo-editing software. It would be interesting to see how the results pan out for users from a different field. I was also wondering if the design goals presented by the authors are too generic. This can be a good thing as it allows other systems to take these goals into consideration but it might also prove harmful as it might limit the capability of DiscoverySpace by not taking into account the specific design goals that this particular system might benefit from.

Questions:
1. Did you agree with the methodology of the paper?
2. Which design goal do you think would apply to you?
3. Can you think of any other software that is complex enough to require design interventions?
5. How are you incorporating creativity into your project?

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Authors: C. Ailie Fraser, Mira Dontcheva, Holger Winnemöller, Sheryl Ehrlich, and Scott Klemmer.

Summary

This paper proposes DiscoverySpace, an extension panel for Adobe Photoshop, to help onboard new users execute tasks and explore the features of the software. DiscoverySpace suggests task-level action macros to apply to photographs based on visual features. These actions are retrieved from an online user community. The findings from user evaluation showed that it helped novices maintain confidence, accomplish tasks and discover features.

Reflection

This paper addresses an important problem that I often encounter while building tools/interfaces for novice users — how do we efficiently handle the onboarding process? While DiscoverySpace, in its current form, is far from being the ideal tool, it still opens the doors for how we can leverage the strengths of recommender engines, NLP and UI designs to build future onboarding tools for complex softwares.

Something we discussed in the last class – DiscoverySpace also demonstrates how need-finding exercises can be translated into design goals, and in doing so, it increases the likelihood of being useful. I wonder, how this process can be scaled up for a general purpose workflow of designing onboarding tools for any software (or maybe it is not necessary).

In one of my previous reflections, I mentioned about how in-app recommendations for using different features helped users explore more, which was a stark contrast to the notion of filter bubbles. This paper also demonstrated a similar finding, which leads me to believe that maybe in-app feature recommendations are useful for exploring the space when the users, by themselves, cannot explore or unaware of the unknown space. I am hoping to see a future study by the RecSys community, if there isn’t one already, to understand the correlation between tool feature recommendations and the user’s expertise level.

This paper certainly made me think a lot more about general purpose applicability i.e. how can we build a toolkit that can work for any software. I really liked the discussion section of the paper as it discussed the topics that would essentially form the pathway to such a toolkit. Building a corpus of actions is certainly not impossible, considering the number of users for a software. Most plugins and themes are user-generated, and that’s possible because of the low barrier to contribution. Similar pathways and incentives can be created for users to build a repository of actions, which can be easily imported into DiscoverySpace, or whatever the future version is called. The AI engine can also learn when to recommend what kind of actions with increasing data. Considering how big the Open Source Community is, that would be a good starting place to deploy such a toolkit.

Questions

1. Have you ever had trouble onboarding novice users for your software/tool/interface? What did you wish for, then?
2. Do you think a general purpose onboarding toolkit can be built basing off the concepts in DiscoverySpace?
3. The paper mentions about the issue of end-user control in DiscoverySpace. What are the challenges of designing DiscoverySpace if we think about extending the user base to expert users (obviously, the purpose will change)?

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Summary

As software develops over time the complexity increases with all the newly added features. The acquired complexity over time could be beneficial for experts yet it presents issues for beginner end users. When thinking of developing off the shelf software for end users, developers must consider the different technical backgrounds their end users have and try to have the interface accessible for all potential users. To resolve this difficulty in Adobe Photoshop, the authors present DiscoverySpace, a prototype extension panel that suggests task-level action macros to apply to photographs based on visual features. The extension is meant to help new Adobe Photoshop users by making suggestions once the user starts a task (opens a picture), uses simple human language in search, shows previews of what a suggestion does (before and after), offer faceted browsing to make searching a better experience, and show suggestions that are relevant to the users’ current task which also alerts him to new or unknown possibilities. The authors investigate the effectiveness of the extension by running a study and comparing two groups, one was using the extension, and the other did not. They find that action suggestions might help new users from losing confidence in their abilities, help them accomplish their tasks, and discover new features.

Reflection

As an on-and-off Adobe Photoshop user, I was interested in this paper and this extension. I thought it would be nice to have those suggestions as a reminder when I use the software after months of not using it. Since I am more focused on Adobe Lightroom when it comes to editing photos, it is easy for me to confuse the panels and actions available in both softwares. I was somewhat surprised that the users who had the extension were still answering that they couldn’t figure something out 50% of the time. Even though there was a drop of 30% from users who were not using the extension, it still raises the question: where is the problem? Was it the software? Extension lacks some details? Or was it just the fact that users need time to become familiar with the interface? 

I also was puzzled when I saw that the authors used random sampling when it comes to suggesting actions to the user. I feel like editing photos is a process and depending on the photo there are actions that should be taken before others. Maybe using that functionality would be specifically for learning about the interface or the result of each action. Else, I don’t think it was the best functionality to propose. 

I don’t know if I agree with the authors with how they measure the performance confidence in their survey. Using technology has always made us feel more confident. I trust a calculator more than doing simple math in my head real quick. I felt that this wasn’t a fair comparison measure.

Discussion

• Would you use this extension if you had Adobe Photoshop? Why? Or Why not?
• What would you change about this extension? Why?
• Can you think of other extensions that you use on a regular basis that are useful in terms of learning about some software or platform?

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Software tends to grow in complexity as time goes on, with more features being added as more needs arise. This can be problematic especially for consumer software intended to be used by a wide array of people, where they can’t figure out how they need to do the specific thing they need to do just once because of the vast number of options arrayed before them. DiscoverySpace is proffered as a solution to this problem specifically for Photoshop, by allowing users to browse crowd sourced pre-built actions to apply on their image. They create an additional panel and ask the user to enter the kind of image they have. DiscoverySpace does keyword matching in order to identify the most suitable collection of actions to suggest, returning a random sample mix of results to promote discoverability of new Photoshop features by the user. They conduct a couple of studies, first examining how novices interact with the vanilla software with experts guiding them and showing them what they can do. And another study where they compare the usage of Photoshop by novices with and without the presence of DiscoverySpace. They find that having DiscoverySpace as a feature greatly helps novices in performing their tasks and they don’t struggle as much, but also find that it is limiting because DiscoverySpace only applies effects on the whole image.

I feel like a really good expansion of this work can be to have an additional window pop up after an action has been done with various tweakable parameters specific to that action. This feels like it could really promote the Photoshop’s plugin ecosystem and make it more accessible for ordinary folks to contribute to plugins, not just companies that specialize in it. Another idea in this paper that I find interesting is the use of random sampling to find and suggest actions. I think this is genius because it promotes curiosity in the user about the different things that Photoshop can offer and slowly, over time, allow the user to become more familiar with all the features particularly if they are curious and look through the History panel to see what effects were applied when they clicked on a DiscoverySpace action. It can serve as a useful learning tool that will allow those who prefer to work with practical examples to learn how different things work and emergently get an idea of how Photoshop as a whole functions.

1. Would you find this a useful learning tool or do you only see it as something that is used to just immediately serve your purpose?
2. What other software would something like this be useful?
3. Is the random sampling they do to promote discoverability a good idea? Would their purpose be better served by optimizing suggestions solely for the specific task at hand?

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In this paper, the authors introduce an add-on prototype interface called DiscoverySpace that recommends actionable items to a user using photoshop. For this application, the authors focused on providing the followings:

– List possible actions at the start

– Use human language

– Show previews of before and after action

– Offer faceted browsing

– Provide relevant and possible new suggestions.

The authors conducted a between-users study to measure the performance of the software. One control group did not have access to DiscoverySpace and used plain Photoshop whereas the other group did. The authors report improved performance for users using DiscoverySpace.

In this interface, the authors require the users to provide information about an image at the start. They mentioned this could be improved in the future by automatic image analysis. I think this feature is desperately needed, at least in my case. When I decide to use a tool, I prefer the tool automatically configuring basic things for me. Also, surprisingly many users interact with interfaces in the wrong way even if it is a very simple one. I am certain some people will fail to configure DiscoverySpace. Object classification inside an image is pretty well-established today, so I think this feature will greatly improve the accessibility to users.

It was interesting to find that users still could not figure out some functionality 50% of the time while using DiscoverySpace. It is certainly better than 80% from the control group, but this percentage still looks too high. I think this says something about the complexity of the tool or the lack of information in the database that they used to provide the suggestions.

Overall, I felt that the study was done in a bit of a rush. In the result section, the study’s participants talk about the lack of functionalities such as dialing down the effect of a filter. I think the idea of the tool itself is pretty cool, but it could have benefitted from more time. I think there could have been a better result from refining the tool a bit more.

The followings are the questions that I had while reading the paper:

1. Do you think this tool has a benefit over simply using Internet search? The authors state that their tool suggests more efficient solutions. However, I think it’ll take a significantly shorter time to search on the Internet. What do you think? Would you use Discovery Space?

2. Did you also feel that the study was a bit rushed? What do you think could have changed given that the authors spent more time refining the tool? Would the participants have provided more positive feedback? What parts of the tool could be improved?

3. It seems that the participants’ survey ratings were used to measure creativity. What other metrics could have been used to measure creativity? I would especially like to hear about this since my project is about creative writing. Would it be possible to measure creativity without simple human input? Would the method be quantitative or qualitative?

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Summary

This paper introduced an interface, DiscoverySpace, which provides task-level action recommendations. The main objective of this interface is to help novices gain confidence when using sophisticated software. To achieve this goal, the author designed the DiscoverySpace prototype as a Photoshop extension panel for Adobe Photoshop. This prototype allows users to explore the software functionality by providing refinement or radical recommendations based on the current task classification or the natural language. The author conducted an experiment that compares the participants in the DiscoverySpace condition used Photoshop with DS panel and the participants in the control condition using Photoshop without it. The experiment results indicate that those beginner participants tend to gain confidence with the help of the DiscoverySpace condition while losing confidence in the Control condition. However, there is no significant difference between participants who already have Photoshop expertise. Nevertheless, most of the participants indicated that this interface is most useful for quick exploration of complicated software.

Reflection

Nowadays, when talking about editing the image, I believe the majority of people use a “one-click” retouching application on their phone, such as meitu, Ulike, VSCO, etc. These tools can provide a variety of retouching effects, provide users with powerful picture editing capabilities, and have no particular requirement for users. I think this is what the user expected in this paper regarding recommending an “item-based” collaborative filtering algorithm. From my perspective, what the user is trying to achieve in this prototype is to encapsulate the complicated process and present users the most straightforward effect based on the image user selected. 

I agree with the assumption the author put up with at the beginning, “complex software offers power for experts, yet overwhelms new users”—my experience of using Photoshop at first, just like what the author described. Thus, I think the author’s work would improve the user experience and confidence significantly. I wish I could have such extension penal when I was first using Photoshop; I might continue to use it now. 

 Besides, I think the target users of using Photoshop are people who have higher requirements of image editing instead of people who just want to publish their selfies to Instagram. Thus, I would expect the system aims to help the novice get familiar with the system faster and facilitate exploration. 

However, I do not think that automatic image analysis would help a lot. One benefit of letting the user select the feature of the image is to provide corresponding advice regarding the user-selected features. For example, the user would perceive an image with Sunset, seaside, back view of themselves as landscape pictures, while automatic image analysis would probably recognize it as a portrait. Therefore, letting the user enter the feature of their figure would facilitate the system to identify the portion that the user wants to emphasize.

Questions

1. Even though the author just takes Photoshop as an example, I am still curious to do people still use PS frequently except for people who have professional needs nowadays, as the emergence of multiple “one-click” retouching software. Do you agree with the assumption made at the beginning of the paper?
2. Do you think it is helpful that encapsulates a sequence of operations for users to apply in one click, meanwhile, demonstrate those operations to users?
3. When would you prefer to use DiscoverSpace instead of “one-click” retouching Apps? 
4. What kind of software(or what specific software) do you most like to implement the idea similar to DiscoverySpace? Here the idea refers to that system provides users aggregated action suggestions based on users’ current tasks.

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Summary

In this paper, the authors aim to help beginner users of complex software to execute complex tasks in a simple way to help them build confidence and not lose interest in the software. The approach used as an extension to photoshop and collects instructions available in the community to build macros that can execute multiple steps to achieve an action or a goal. Users might use information available online, but they might get lost with the overloaded information available or choose a solution that is not efficient.

The approach offers suggestions to the users in the context of the current action to help them with executing the next desired action.

The authors asked the users to participate in two surveys to measure their confidence level of using photoshop application. The participants level of experience in photo editing software varied from beginner to expert and first survey showed that beginner users might lose confidence when there is no  suggestion feature that can help on executing tasks. The second survey was about DiscoverySpace system which showed that beginner users gained confidence when they use a tool that can help them with executing complex task to achieve their goal.

Reflection

I found the approach used by the authors to be very interesting. Providing suggestions to new users to execute different tasks to achieve a goal can help beginner to be successful in their job or on the project that they are working on.

Using information and instruction available online to build the list of tasks that are required to achieve a certain action is a nice implementation and can be used in lots of domains.

The idea of using recommendations based on the context of the available action is more effective than the help options available on the application that might give too much information and links that make it difficult to find the solution especially for beginner users.

I think this approach can also be used for new hires or new students to offer templates for a certain actions. Often new hires and new students need to execute multiple steps in order to achieve a goal and failing on these tasks might cause multiple issues that prevent them from having a smooth onboarding experience.   I also think that this approach can be used in software development to help new programmers to use template to design new systems or to solve coplex problems. Stack overflow has solutions to thousands of programming issues and can help on building macros or templates to solve a specific issue.

Questions

• The approach used by the authors can help beginners on execute a set of tasks to accomplish a goal in photoshop, can we use a similar approach in a different application or in different domain?
• Can you use a similar approach in your project?
• Do you think that this approach can also help experts on using these systems especially when a new major feature is released?

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