Information Science

During the overwhelming first week, I have been enriched with many new conceptions or familiar conceptions that didn't make so much sense to me. So many that I don't know which to begin with. Maybe, the name of my department, IST, is a good start.

Founded in 1999, IST (Information Sciences and Technology) is a comparatively new college. It is about Information, Technology, and People. There used to be a debate about the third element: should it be People or Users, users of information and technology? Finally People wins, because proponents argue that we not only care about users who use our technology, but also those who don't. To analyze why they don't use our technology helps us keep moving too. I think that's a good idea, and the idea lays the foundation that IST is a discipline centering on humans.

So what is information science? And what is information?

These seem to be very broad problems, problems we generally never come down to think about. We keep on talking about "information", "information age", "information science" everyday, but we actually take them for granted and never give a second thought about they are "intentionally".

Personally I have been immersed in GIS for four years. Shamefully I now find my understanding of it far from enough. I have always been thinking of it as a system, or a science of geographic information. And more accurately, it's just about geographic data. Open an arbitrary GIS textbook, and you'll find all it deals with is data: data acquisition, data storage, data analysis, and data presentation, although some may touch a bit on the difference between data and information in the beginning of the book. Now I come to realize that GIS can also be viewed as a subset of "information science", in the geographic domain. This gives us broader views of GIS, and can be guided by some more mature theories in information science.

Of course, just like GIS, as a newly emerging discipline (emerged in the 1950s), there are still many disputions over concepts, definitions and theories in information science. The difference is, information science has been drawing wide attention both from government and society from the very beginning, and its wide application appeals to scholars in various disciplines, which guarantee its fast booming over the last few decades, both theoretically and pragmatically.

Information science is driven by problems. To solve a problem, we need information. We seek relative data akin to problem, and organize the data into information trying to answer what, when, where, and who. Finally we come to conclusion "why" this problem happens and "how" to solve the problem, which ends up in the so-called "knowledge". This is the basic data-information-knowledge-wisdom hierarchy proposed by Ackoff 1989.

Technology advances rapidly, so we are faced with ever increasingly large amounts of data and ever increasingly complex systems to automatically deal with these data. But no matter whether we are generating data or consuming data, one thing we have to keep in mind is that our single task is to solve problem. We are not developing awesome software, but designing systems that better assis people to solve a problem. In this sense, "people" should always be the center of research. We try to understand how people learn and use information.

There are three major branches in information science: information retrieval, information relevance, and information interaction. Information retrieval is the basic need in this information overload age. And information relevance is closely related to the effectiveness of retrieval. They are more concerned with algorithms, and user participance is limited, with only a query input. In contrast, information interaction is, in my opinion, more of upper level. It asks the system and the user to work together in an interactive way, to co-solve a problem. It is what I will be engaged in, I believe, the so-called human-computer interaction, or more specifically, human-GIS interaction.

The overwhelming first week

开学第一周，相当慌乱。

共选了五门课，其中三门大课，一门IST的关于怎么阅读文献的，一门IST的information management，还有一门Geography的Geovisual analytics Seminar。每门课都要做大量的文献阅读。

现在才知道原来课可以这么上：不需要你有太多的基础（prior knowledge），而是通过阅读来快速学习，然后通过课堂的分享和讨论来培养critical thinking。想起蔡老师跟我讲的，作为一个phd，你可以一开始不懂某个领域; 但一个星期以后，你不仅要了解，还要能说出个1234来。

这里的学生可能就是这种教育的成果。They keep talking。而这正是我缺乏的：怎么把输入整合之后输出并传播出去？

第一周遇到的很大的问题是阅读文献的速度。四篇文献约100页，我花了两整天才读完（当然也包括经常读不下去倒头睡觉的时间）。以后要注意阅读的方法。The Thinker's Guide to Analytic Thinking中提到的关于reasoning的几个要素，可以借鉴下：

另一个遇到的问题是，我完全听不懂学生的发言。学生的发言与老师的讲话不同，他们极其随意、含糊，而且语速很快、没有停顿。而如果听不懂他们的发言我就很难参与课堂讨论。

在Geovisual Analytics的课上终于见到了传说中的Alan MacEachren，看上去年纪很大了，可是却非常健硕，眼神非常犀利。据说他的课任务非常重，从他的syllabus可见一斑：课程的project希望能至少放会议上发表。Sigh...

不过第一周还是相当充实的，接触了很多新的概念，有一种迅速膨胀的感觉。接下来，加油！