Knowledge Navigator Envision Video 24 Years Ago

This video by Apple 24 years ago is amazing and fun to watch. Think about our envision 25 years ago and what we have now. We have to acknowledge that how far our imagination can lead us into the future.

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RAA#2: A Semantic Content-Based Recommender System Integrating Folksonomies for Personalized Access

1. APA Citation: Lops, P., Gemmis, M., Semeraro, G., Musto, C., Narducci, F., & Bux, M. (2009). A Semantic Content-Based Recommender System Integrating Folksonomies for Personalized Access. In G. Castellano, L. C. Jain, & A. M. Fanelli (Eds.), Web Personalization in Intelligent Environments (Vol. 229, pp. 27-47). Berlin, Heidelberg: Springer Berlin Heidelberg. Retrieved from http://www.springerlink.com.login.ezproxy.lib.purdue.edu/content/e6603207661850m7/

2. Problem Statement: Traditional content-based personalization (recommender system) is usually driven by string (keywords) matching operations, i.e., to match attributes of user profiles with the attributes of content objects. This method is unable to capture the semantics of the user interests behind the keywords.

3. Purpose of the Research: This paper aims at improving recommender system by incorporating user ratings and tags, and also utilizing semantic analysis techniques derived from research in Information Filtering, Machine Learning, and Natural Language Processing.

4. Research Question: Does the integration of tags cause an increase of the prediction accuracy in the process of filtering relevant items for users?

5. Some Definitions: Static Content, SocialTags(I), PersonalTags(U,I)

Static Content: compared with the dynamic tags provided by the users, the title, author, original descriptions of the items are static content.

SocialTags and PersonalTags: Given an item I, the set of tags provided by all the users who rated I is denoted as SocialTags(I), while the set of tags provided by a specific user U on I is denoted by PersonalTags(U, I).

6. Methods:

(1) A system called FIRSt (Folksonomy-Based Item Recommender syStem) is designed integrating semantics analysis algorithms and User Generated Content (interest ratings and tags). FIRSt allows users to give an interest rating (1-5, 1=strongly dislike, 5=strongly like ) and free tags to museum paintings.

(2) A user experiment is conducted. 30 non-experts and 10 experts are recruited to rate and tag 45 paintings chosen from the collection of the Vatican picture-gallery. The participants are selected according to availability sampling strategy. The 30 non-experts are from young people having a master degree in Computer Science or Humanities, while experts are teachers in Arts and Humanities disciplines.  They did statistics evaluation using classification accuracy measures (precision and recall) to investigate whether using only the personal tags or the whole set of social tags are more accurate to recommendation, and whether expertise will influence the accuracy of recommendation.

7. Main findings:

(1) The highest overall accuracy is reached when user preference learned from both static content and personal tags (not social tags) are exploited in the recommendation process.
(2) The expertise of users contributing to the folksonomy does NOT actually affect the accuracy of recommendations.

8. Analysis: This paper is very related to our research team’s current and future work in building the personalization and collaboration system for engineering education community.

(1) The conclusion here that expertise doesn’t matter is interesting. This recommendation system is mainly for recommendation of artworks, movie, music and books. If the items being expanded to others such as academic papers, I am afraid that expertise will matter.

(2) My current work is designing the UI that allow the users to specify interest ratings, write tags and comments to papers. For one things, this will driven the personalization in My Library and My Gallery (the two personalized components in our system). For another, we also thinking about ask the users to specify quality ratings of the paper which could contribute to the whole community in making sense of academic papers in this field.

(3) I have a deep concern about the meaning of personalization and recommendation, that is, the filter bubble, or social segmentation, or the echo chamber. We only see things that’s recommended to us by the machine, and being isolated from the world outside of our world.

Credibility Issue for Online Education and Outsource Education

Blog Post: Online University, from “Embedded in Academia” by John Regehr

I read this thought-provoking blog post mentioned one student from that university requested credit for taking the Stanford AI class. This semester, Stanford put three courses online free to register for the general public, including Artificial Intelligence, Database and Machine Learning. They provide video, and quiz, discussion forum. Students need to submit homework, and will get a completion certificate and ranking among classmates at the end. I actually registered for the database class, and thought it would be nice if I can get credit. There is student requesting! From the author’s update in the comments section of the blog post, the student’s request is not granted up to that day. Usually, if we need credit, we have to pay. We are paying for the credit, not the real education experiences. We can get the education experiences from Stanford for free, but not the credit! Although we cannot get the credit from our home institution, but maybe if we put that on resume, companies will consider that when we go to find a job?

This blog post also brought up an interesting idea that in the future, universities can simply contract out classes like Calculus to big organizations and universities. In this case, all other universities can contract out Artificial Intelligence, Database and Machine Learning to Stanford and pay part of tuition to Stanford. This will totally change the structure of higher education! I believe something revolutionary has to happen, if not this one!

There are lots of discussion about online learning. For example,

Connecting Informal and Formal Learning Experiences in the Age of Participatory Media

Make it a Two-Way connection: A Response to “Connecting Informal and Formal Learning Experiences in the Age of Participatory Media”

Connecting Informal and Formal Learning Experiences in the Age of Participatory Media: Commentary on Bull et al. (2008)

These articles are talking about how to connect informal learning on social media with formal classroom learning. I think the above blog post has a more revolutionary thought about where the Internet will lead the education to, and how the structure will change. It also brought up the credibility issues of taking free online courses.

TimeDiff: Yet Another Site of Crowdsourcing

There is a new website started by a group of Ivy League graduates from China called TimeDifference (www.ShiCha.com or www.TimeDiff.cn). Basically, what they do is crowdsourcing Chinese students abroad who have experience in applying for schools outside of China to help students who are in China now and want to do further study abroad. Domestic students who want to go abroad (the clients) will have questions about how to prepare for GRE, TOEFL, ILSE tests; how to write CV and personal statement;  how to find the proper schools to apply and how to communicate with professors; and all other questions regarding the application processes. They can go to this website and put a task there, also offer an amount of payment. All students abroad (the consultants) can go there to compete for the task and earn the payment. Every consultant has a profile on the website. Along the way, the consultants can accumulate credits for providing high quality services to the clients. So if their credits are high, they will likely get more tasks in the future.  I’ve noticed that many major studying-abroad consulting companies have also put their profiles there.

I think this might be a good idea, but I have two deep doubts:

(1) This TimeDiff company has started to recruit consultants from abroad promising minimum $15/hour payment in the recruitment advertisement earlier this year. Out of curiosity, I applied and passed the interview. After they have recruited the consultants, they announced that there will be no payment from the company to the consultants, all payment will come from the clients, and the consultants have to compete for it. They also require 10 hours free service before the consultants can compete for payment. This was the time when we realized we were getting exploited and we got pissed off and organized a discussion group on QQ, and also sent letters to the company to complain. There was no result for the protest, and the company still insisted there will be no payment from the company. After that, many consultants simply left, some still hang in there wanting to earn some money. They might also get some other new consultants via other venues. Even if this website is a very good idea, the company doesn’t have a very ethical practice.

(2) There are many many other online discussion forums where students abroad offer information and share experience to domestic students for free. How well this website will be doing for charging a fee for this kind of information? It definitely offers more services (e.g., the consultant may help edit the CV and PS of the clients, while those free forums usually only offer guidelines and information) and personalized one-on-one interaction between consultant and client. However, does these really help that much?

RAA#1: Whither the Experts?

1.The paper analyzed here:

Welser, H. T., Gleave, E., Barash, V., Smith, M., & Meckes, J. (2009). Whither the experts? Social affordances and the cultivation of experts in community Q&A systems. 2009 International Conference on Computational Science and Engineering (pp. 450–455). PDF

2. Definitions:

Social affordances: refers to the properties of an object or environment that permit social actions. (Wikipedia)

Community-based online Q&A: Online Q&A services can be divided into digital reference services, expert services, and community Q&A sites (Harper, 2008). The focus of this paper is community-based systems like Yahoo! Answers, and Live QnA.

Expert: this paper employs a behavioral definition of social roles: experts are contributions who provide technical and factual answers for the majority of their contributions, typically 80% of their messages or more.

Technical answers: technical answers are explanations or descriptions of courses of action. Technical answers are instructive and will often define terms and connect to resources that aid in the solving of some problem or task.

Factual answers: a factual answer provides a statement of fact that can potentially be verified, in general, and is not dependent upon the identity of the author.

Opinion: opinion is one type of non-answer contributions. Opinion provides an assessment, evaluation, or judgement about something. The opinion provided in the response is not used to provided support guidance, or advice; it simply involves a stated opinion.

3. Purpose of the research: This article compare the Live QnA and USENET as examples of community-base Q&A systems to answers the two questions: (1) To what extent do these systems foster experts who provide technical and factual answers? (2) Which social affordances of these systems encourage or discourage the cultivation of expertise and the performance of the experts role?

4. Method: This paper takes a mixed-method approach. They did a content analysis of 5,972 messages from 288 contributors to Live QnA (10% of the contributors who contribute 95% of the messages); Compared Live QnA to USENET; Identified some issues on Live QnA. Then they take a qualitative approach: generate a series of expectations about how social affordances are likely to alter the role ecology of online systems based on previous literature.

5. Major findings:  On Live QnA, they found that none of the sampled contributors dedicate 80% or more of their contributions to technical and factual answers. According to the definition of “experts” in this article, there is no expert on Live QnA. Only 1.3% of the sampled contributors dedicate 60% or more of their contributions to technical and factual answers. 23% of the sampled contributors dedicate 60% or more of their contributions to opinions. On the contrary, in a sample from USENET, 52% of the contributors can be classified as experts, while only 3% of contributors dedicate 60% or more of their contributions to opinions. This means the social affordances on USENET cultivate more experts than Live QnA. The authors then describe connections within a general theoretical framework for modeling the behaviors of individuals, the setting they are in, and how different social outcomes are likely to emerge from the combination of the attributes of the social setting and the goals and actions of individuals:

A. Emergence of collective outcomes. The emergence of collective outcomes is due to a combination of social affordances, individual goals, and social actions. There is no direct link between social affordances and the outcome, so when we design a system, we can not simply assume certain kind of design will result in certain kind of outcome, we have to take into consideration of individual goals and social actions.

B. Reputation, reactions, and roles. One example here is that if reputation scores are based on quantity rather than quality of posts, the system is likely to tend towards trivial contributions, one possible shortfall of the Live QnA system.

C. Context, boundaries and tags. One possible interpretation of this finding is that the content of a newsgroup acts as a set of unwritten norms that, over time, encourage the posting of similar content and discourage the posting of very different content. Contextual boundaries seem to be fuzzier in Live QnA than in USENET.

D. Reinforcing norms of dedicated contributors. This raises the general issue of how implicit and explicit reputation systems alter how people behave and can result in different online role ecologies.

6. Analysis:

  • The overall structure of this paper is very clear, but many details are quite fuzzy, especially the qualitative section. The authors seems to repeat similar but different concepts here and there, and use many different vocabularies which I am not sure whether they refer to the same things or not. So I don’t how to summarize many of the conclusions.  Some sentences structures are quite odd. For example, in the data collection section, it says “We divided our sample equally between selections from the top ten percent and one percent activity levels as defined by the number of messages (questions, answers or comments) posted”. I don’t understand what “equally” mean here? how could that be “equally”? Also, the “as defined” part makes the sentence very long and difficult to read.
  • This paper talks about social affordances, but didn’t give clear definition of social affordances. In the qualitative section, it talks about connections between social affordances and role ecologies, but what exactly are the social affordances specified here is unclear to me. What is the distinction of social affordances and other characteristics of the system is unclear.
  • This article is helpful to me to understand the culture of online community. I learned the things I have to take into consideration when designing system to attract experts’ contribution.  This paper can connect to last week’s Tech621 readings about crowdsourcing and heavyweight community. From the surface, community based Q&A is very much like the first type of crowdsourcing (e.g. InnoCentive), however, one major difference is that InnoCentive-type of crowdsourcing (the two types of crowdsourcing need names, I will call them InnoCentive-type, and MTurk-type) is lightweight, while community based Q&A is more heavyweight (Haythornthwaite, 2009). Or, in some cases, in between.
  • One limitation of this paper is that Live QnA is a small size QnA, thus may not be a representative sample for most community QnAs.
  • This paper suggests future directions: (1) test the connections between social affordances and role ecologies proposed by this paper. (2) comparative study of systems vary primarily of such social affordances. (3) study of reputation system and contexual boundaries. (4) how temporal constraints (each thread on Live QnA has a 4-day life span) affect the quality of data repositories and the roles emergent in them.

Diversity in Higher Education: US vs. China

This sounds like a really big title, but I just want to make a small point inspired by one conversation with my roommate last night. By “diversity” here, I refer to the personalities of the students, rather than demographic diversity.

High school students have more freedom to choose their majors when they go to college in the U.S. They also have more freedom to change majors in college. The result is that they all self-selected into different groups and they tend to think other groups of people are weird and not their thing. For example, many of the geeky high school kids go to engineering, and become more geeky. Many of the “social animals” go to liberal arts, and tend to think “engineering is not my thing.”  I am speaking in relative terms. Of course, there are geeky students in liberal arts and not all students in engineering are geeky. My point is that there are very very different cultures in different majors. As an engineering education PhD, I am taking both engineering classes and social science classes this semester, and I feel the classroom atmospheres are so different in the two types of classes. Usually, when students feel they don’t belong to certain cultures, they change majors.

In China, it’s also true that different majors have different cultures, but in my opinion, less obvious. High school students in China don’t have that much distinct sub-groups of different personalities compared with those in U. S. Most of them share similar educational experience, and the same goal: college entrance exam. They look quite similar, but in my opinion, they still maintain very diverse personalities underneath the look which is trained by the education. When they go to college, they have some freedom to choose majors, but not as free as here. In my case, I didn’t get into the major I chose (psychology), because my college entrance exam score was not high enough, and they randomly put me into electrical engineering. Similar situations were very common among my cohorts then. Changing majors is very difficult. The policy was that you have to have very high GPA in the major you don’t like to change to another major. You also need to take tests and pass the interviews to change major. So many students stayed and survived in the major they don’t like, and may end up like it. I was thinking that this kind of policy in China is not very good, and there are also many efforts going on to change it. Now I feel one good thing is that, when they randomly put students into different majors, they maintain the diversity of different majors. Each major contains students have different personalities. (This may be only true in my undergraduate university, other universities maybe different.)

Diversity of personalities is as important as demographic diversity to maintain. In high school career service, it should be carefully considered whether a student is geeky and good at math, then it’s definitely right to suggest him/her to engineering.