Reading Reflection: Where does the “Natural” Come from in Natural Design?

This week’s CGT512 reading chapter1 of “The Design of Everyday Things” by Donald Norman emphasizes natural design. For example, doors have to be designed with proper visual clues so that people will naturally know which side to open and close the door, as well as to pull or push the door. I understand that for relative simple designs as doors, there maybe a common consensus regarding which way is natural for most people, but for complicated computer systems, what is natural may mean different to different people. After all, what is “natural” and where “natural” comes from? Are the feelings of natural come from previous experiences and previous training? For example, I’ve used windows OS for years before I switched to Mac, then I would feel many things in the Mac OS odd at the very beginning. I still think iTunes is oddly difficult to use until now. Some people would feel the so-called “natural scrolling” on the new Mac OS Lion unnatural, because they’ve used the opposite way for so long. The relatively elderly people would feel everything electronic unnatural for them, but the millennial generation was born with them and feel all these electronic systems natural. For doors, because it has been existing for so long, so everybody knows about doors, but for electronic systems which did not exist at human origin, whether they are natural or not, depend at least partially on the generations or groups of people who live with them or were born with them. The feelings of natural at least partially come from people’s environment or habitat. With all these being said, I still believe there are certain level of common consensus of natural feelings wired into human brains that can be integrated into complicated computer systems design, such as movement, gestures, touch, orientation of directions, color, etc.

Another question kept popping up in my head is that what is the relative importance of rigorous UX research and intuitions of talented people? The UX book by Hartson and Pyla laid an iterative UX process lifecycle, but what I got from reading Steve Jobs’ biography (correct me if I’m wrong) is that Jobs never did user study, he just did what he believed was good for the users, and he hired very talented people to do the design. Many aspects of many Apple products get cited as good design practices in many UX textbooks, but they seem not necessarily produced based on the UX process lifecycle, they were just produced based on the understanding of a group of highly talented people. These geniuses tell the users what they want, and then the products they designed were put into UX textbooks to train future UX researchers. Are genius intuitions better than rigorous research results?

UI Enabling Both Associations and High-Level Categorization

In the last class of CGT512 (a HCI and UX class I’m taking this semester), when we discuss about the history of GUI, we briefly mentioned the Memex microfilm viewer described by Vannevar Bush in 1945. Memex reminds us that human brain works with associations and links rather than alphabetical orders or clearly defined categories. I’ve been having some interests in tagging, categorization, and information retrieval. I’ve also encountered some categorization problems in my life and research recently:

1. I moved to a new place recently–an exciting house! Okay, during the process of packing stuff, I tried to be very very organized. I put summer clothes, winter clothes, spring/fall clothes, shoes, kitchen stuffs, bath stuffs, etc. separately in different boxes, and marked them. But in the end, I lost my patience to categorize every tiny bit of things into the exact categories. I just couldn’t find a proper category for them or they belong to several different categories. Or I found something belong to a certain category but that box was already sealed. I also need to consider whether they belong to things that I will use quite often recently or can leave in the basement for long, but this sometimes conflicts with the previous categories I have set. I had such a headache trying to be organized and make my life easier looking for things when settle down at the new place, but there are still things I cannot remember where exactly I packed until now that I’ve been in the new house for almost three weeks. What I kept thinking during the process was that there gonna be a better way to organize stuff! According to the Memex philosophy, maybe I shouldn’t categorize the stuffs based on their same properties, I should organize them based on associations, but how? It seems the modern grocery stores are also thinking about this problem of how to organize their shelves to increase profit, such as the famous example of putting pampers and  beer together.

2. I’ve been working on a project analyzing some Twitter posts by engineering students. I retrieved all the tweets under a Twitter hashtag #engineeringProblems. I want to find some common rules in these students’ vocabulary so I can retrieve more relevant tweets in the future, but I couldn’t put these vocabulary under clearly separated categories using the topic modeling algorithms. The same words or phrases jump around several different categories. Should I manually force them into  clearly separate categories? Or should I just study keywords co-occurrence (association)? Or should I do both? How language is developed and organized?

I went back to re-read one of Dr.V’s blog posts written almost a year ago on tagging and information categorization. My understanding is that human brain works based on associations, and human brain also desires for control and clear overall pictures. I become very much intrigued about what a superb task management and information retrieval system interface will look like enabling both associations in human brain and also give the sense of control and clear categorization ?

Learn to Be Alone

I run into this TEDx talk by Sherry Turkle this morning while flipping through my Flipboard on the iPad for some interesting exciting things to start my Sunday. Then I found a longer Authors@Google talk about her book:  Alone Together, Why We Expect More from Technology and Less from Each Other.

I get interested in this talk because I am doing a pilot project exploring first-year engineering students’ use of social media for seeking social support, and I always have an interest  in the deep psychological issues and solutions for human, emotion, interaction, communication, loneliness and technology.

 

 

For some time, I thought technology, if properly used, might be the solution for loneliness, stress etc., because it makes us so much more connected. Now that I realize that we modern people are so vulnerable, fragile, stressed and disappointed to each other that we turn to technology for solution. We fantasize the connections enabled by technology as companionship. Sherry’s opinion is that “If we don’t teach our children how to be alone, they will only know how to be lonely. Having gotten into the habit of constant connection, we risk losing our capacity for the kind of solitude, that refreshes and restores. We need more conversations rather than just connections”. I agree that we have to learn to enjoy the solitude, learn to be strong by ourselves, because vulnerability causes addictions sometimes.

“We are addicted to modern technologies just like we are addicted to food”, though I personally think I am still more addicted to food than technologies, but I cannot get rid of food, and I have to learn the healthy ways to live with it. We cannot give up saying “whatever, just eat”, we have to fight (of course there are deeper psychological issues in this fight), we just don’t give up.

The part of Sherry’s talk on social robotics reminds me one episode (S05E14) of The Big Bang Theory I watched a couple days ago, where the Indian guy Raj fell in love with Siri on iPhone 4S, because he doesn’t have a girlfriend, he is so lonely in this country, and he is not able to talk with women without the assistance of alcohol. The question intriguing is how we can live with modern technologies and everything else in a healthy way? How can we learn to be not addicted to something because we felt vulnerable and socially inept?

The Next Step of Social Media Analytics in Higher Education ?

Users of the Internet nowadays create a tremendous amount of data online. How do we turn these data into knowledge? These data provide valuable information, but information is not knowledge. Twitter co-founder Biz Stone gave a keynote speech at the recent HIMSS (Healthcare Information Management System Society) 2012 conference at Las Vagas. He quoted Einstein on “information is not knowledge”. More information doesn’t necessarily mean more knowledge. The next phase of the web is the one in which we’re able to turn that information into understanding and true knowledge. 

The study of mining, analyzing, and presenting valuable knowledge from existing data online is a rough definition of web analytics. In particular, if the data is retrieved from social media sites, then we can call it social media analytics.

Social media analytics is widely used in many fields. For example, companies monitor their brands on social media sites such as Facebook and Twitter, in order to formulate marketing strategies. According to a report by University of Massachusetts Dartmouth Center for Marketing Research, 70% of the 2010 Inc. 500 companies (The Inc. 500 is a list of the fastest-growing private U.S. companies compiled annually by Inc. Magazine www.Inc.com) monitor their companies’ names or brands on social media sites (Barnes, 2011). Besides, social media analytics is also used to monitor public events, predict popularity of new movies, predict trends in stock markets, measure scholarly impact, and assist disaster management and healthcare management systems.

However, the use of social media analytics in higher education is very limited. There are efforts in using social media to promote communication and active learning in classroom, but this is NOT what I mean by social media analytics in higher education. Social media analytics (at least the one I am thinking here) is trying to analyze what students have created on public social media sites anytime anywhere in their own will in order to understand what they are experiencing. The focus is to analyze what students said online to understand their experiences, rather than to direct their use of social media to fit classroom requirement.

Based on one project I did last semester (manuscript accepted with changes by ASEE2012) looking at engineering students’ college experiences through Twitter, the conversations on Twitter by students are largely reflective of their college life. These conversations happened anytime in life, not necessarily in classroom. These conversations if properly analyzed and presented can serve as a resource for higher education policy makers to get fast feedback for what students are experiencing, and make informed policy decisions.

According to a 2011 report entitled “The State of Web and Social Media Analytics in Higher Ed” by Higher Ed Experts, many higher education institutions use web analytics nowadays, but the usage is largely limited to track the number of visits, page views, length of visits on the university websites or the social media pages such as the institution Facebook pages. These mostly serve the purposes to improve the university websites, to optimize social media pages, to optimize email marketing, and to optimize online advertising, etc. There are many commercial tools such as Radian6 and Visible Technologies designed for companies, however, there is no proper social media analytics tools serve the purpose for educational policy makers to get insights from the students’ conversations online to make informed decisions.

While believing using social media analytics in higher education to get deeper understanding of students, and inform policy-making can be an idea of great potential. I am still having questions regarding what is exactly the insights and knowledge we are looking for? I see students complaining about their life, their class, professors, exams, etc. What does this mean? Are these just students whining? Should we listen?What can be done to help? Who are the stakeholders that care about these knowledge the most? How the ethical issues of monitoring students should be properly dealt with? What do you think? Welcome to leave me any comments and suggestions.

I here end this post by quoting Biz Stone HIMSS 2012 keynote again, “If Twitter is a triumph, it’s not a triumph of technology, it’s a triumph of humanity.”

Qualitative Spirit

I am deeply confused by many things this semester, and I felt I am almost near a breaking point at some moments. One reason is that I felt I am oppressed by some beliefs about conducting scientific research that are contradicting with part of my value system, but I don’t know how to defend myself for various intellectual and practical reasons. I don’t know whether I should defend myself, or what to defend. I don’t know whether I should learn to make compromise or is there a real compromise to make. There are, of course, various other social, emotional, practical and intellectual reasons resulting my situation now. Maybe all of these are normal during the process of growing up. I am going through some kind of transition and I don’t know how long it will take. All I can do is to be patient, and do whatever I should do. Worries and concerns will not do me any good at this moment.

I am learning qualitative research methods this semester, and the following are some excerpts from qualitative methods books. They resonate with my belief about research and sort of make me feel better.

Changes

“No one should plan or finance an entire study in advance with the expectation of relying chiefly upon interviews for data unless the interviewers have enough relevant background to be sure that they can make sense out of interview conversations or unless there is a reasonable hope of being able to hang around of in some way observe so as to learn what it is meaningful and significant to ask.” (Dexter, 1970, p. 17).

“Well-structured, focused questions are generally the result of an interactive design process, rather than being the starting point for developing a design.” (Maxwell, 2005, p. 66)

I got to know that changes happen during research, and it is normal. There are practical reasons such as IRB review, financial and time limits that constrain changes during research, but I should not feel bad when changes happen. They do happen!

Personal Goals, Practical Goals, and Intellectual (scholarly) Goals

“Traditionally, students ave been told to base this decision [of the topic, issue, or question selected for study] on either faculty advice or the literature on their topic. However, personal goals and experiences play an important role in many research studies.” (Maxwell, 2005, p. 16)

“Choosing a research problem through the professional or personal experience route may seem more hazardous than through the suggested [by faculty] or literature routes. This is not necessarily true. The touchstone of your own experience may be more valuable an indicator for you of a potentially successful research endeavor.” (Strauss and Corbin, 1990, p. 35-36)

“Traditionally, discussions of personal goals in research methods texts have accepted, implicitly or explicitly, the ideal of the objective, disinterested scientist, and have emphasized that the choice of research approaches and methods should be determined by the research questions that you want to answer. However it is clear from autobiographies of scientist (e.g., Heinrich, 1984) that decisions about research methods are often far more personal than this, and the importance of subjective motives and goals in science is supported by a great deal of historical , sociological and philosophical work.”  (Maxwell, 2005, p. 18)

“In addition to your personal goals, […] there are practical goals (including administrative or policy goals) and intellectual goals. Practical goals are focused on accomplishing something–meeting some need, changing some situation, or achieving some objective. Intellectual goals, in contrast, are focused on understanding something–gaining insight into what is going on and why this is happening, or answering some question that previous research has not adequately addressed.”   (Maxwell, 2005, p. 21)

I got to know that research questions are usually based on intelectual goals rather than practical goals, and actually, questions based on practical goals are usually not directly answerable. Practical goals are the “so what” piece, the implication of the research, and are of particular importance for justifying the research.

Personalization: Machine Mirrors the Ugly Us that We don’t Want to See so We Blame the Machine

There is a fair amount of discussion that Google search, Amazon recommendation and Facebook streams are too much personalized and making us closed minded, the so called filter bubble or echo chamber effect. I’ve been thinking this for a while, and I have a new idea about what makes personalization and recommendation bad.

It is not the machine, not the algorithms. It is human nature. The machine is learning from the human beings eventually, and the machine is just augmenting human reality. My argument is that for a person who has relatively open and balanced mind, the machine personalization results will be fairly balanced, and the recommendation results will serve good knowledge discovery. Only for persons who initially themselves have huge biased opinions and worldviews towards certain things, the machine personalization results will be biased. I don’t think the machine is making things worse, the machine is just reflecting the reality, and it is good that machine is making us see the reality so we can figure out a way to solve it. What results these biased and closed opinions are essentially human nature, and it’s not the machine’s responsibility to solve this problem.

Close minded people always exist, no matter whether Google search exists. Even if there is no Google search and other things, these people will not seek or listen opinions outside of their chamber what so ever. We dream that actually machine can solve this problem by providing opposite and diversity opinions (effort like Findory news), but the thing is not that easy, it is difficult to move people out of their comfort zone, so Findory failed.

To solve this problem, we have to open our mind first, or some of us. Then we figure out a way that could open other people’s mind more effectively, and make the machine do it.

To sum up, what I am arguing it that, at this moment, machine personalization and recommendation is not doing bad things (not that good either, just fact) it’s just reflecting human reality. What we have to realize is that the problem is not the machine, it’s ourselves, machine is just letting us see our flaws that we don’t want to face sometimes. In the future, we need to figure out ways to let the machine do good on this.

Best Readings Recently

(1) Eli Pariser is wrong by Greg Linden 

This is a reply to Eli Pariser’s idea of filter bubble.

I feel this blog post is talking about the future of recommendation and personalization, that is, where recommendation and personalization should lead to. However, Eli Pariser is talking about the existing recommendation and personalization (how it is being done now). There are maybe a couple of recommender systems now are doing the right thing, such as Findory’s news recommendations mentioned in the blog. Yet many of them are doing what Eli Pariser is saying as filter bubble. Actually, both of Eli Pariser and Greg Linden in this blog post are hopping the future of recommendation would support discovery and serendipity–they are talking about the same thing. This post mentioned that the Findory’s news received a lot of complaints for recommending diverse news, so they may very well go to the filtering path because of these complaints which Eli Pariser is against.

Also personalization and recommendation is a bit different. Personalization is more of personal control and filtering, while recommendation is about new knowledge discovery.

(2) The insightful Google+ post accidentally shared with the public by Steve Yegge.

(3) An thoughtful post by one of our ENE colleagues about opening up academia.