How best to view the internet as a learning tool

I work on some projects that require knowledge of basic biology, such as cell structure, biochemistry, and laboratory technique. However, I was trained as a physicist and engineer, and, as a result, have had an extremely limited education in the biological sciences. For example, my last biology class was anatomy during my junior year in high school.

The internet has been essential in bringing me up to speed in these topics. I've put resources such as MIT's OpenCourseWare and the independent OpenWetWare to good use. Companies such as Invitrogen provide valuable tutorials and explanations on laboratory practices as well. The best part about these resources is that I can find exactly the information that I need to know when I need to know it.

I believe that a very few doubt the usefulness of the web as a learning tool, but how to use it as a tool is certainly a topic of debate. Based on my own experiences, I think that internet learning is best used as an independent collection of bits of knowledge that are accessed as needed.

Let's break this definition down into parts. By independent, I mean that the value of internet resources is determined by the individual who needs to know something. A catalog of optical parameters of semiconductor materials will likely serve little purpose to a field biologist. The downside to this is that the web must contain an exhaustive amount of knowledge to be useful to everyone. If there's a possibility that someone may wish to know something, then it must be contained already on the web [1].

"Bits of knowledge" makes intuitive sense, but a formal definition may not exist. If I wish to know how to stain a cell using immunofluorescence, is each step considered a bit of knowledge, or is the entirety of the process considered one "chunk?" I don't think that this detail is particularly relevant to my discussion, but it is interesting to think about how one may quantify knowledge [2].

Finally, the ability to access knowledge as needed makes it efficient. The human brain can only hold on to a limited amount of data. Some details are best stored on machines; otherwise numerous human specialists would be required to perform complex tasks, each one intimate with one small part of the task. In my graduate work, I can learn about cytoskeletal filaments as needed, or my advisor could hire on a cell biologist to consult me on a small number of issues. The first option is decidedly cheaper. In addition, ease of access is important, and spans topics such as mobile devices, bringing the internet to developing countries, and search algorithms.

So, in my opinion, internet learning is best utilized as a user-valued collection of information that is accessed accordingly. Communications through the internet, such as e-mail correspondence with teachers, is important, and is compatible with my definition since I do not put limits on how knowledge is delivered. Failure to properly use the internet as a learning tool usually comes from poor access (e.g. bad search engine algorithms) or a user improperly identifying what they need to know. In the last case, the success of internet learning cannot be determined by machines; like many things it boils down to the human element.

[1] I can't get the thought of the internet as a causal knowledge database out of my head right now, since it can only contain knowledge that has already been generated. It will never contain knowledge from the future, unless, perhaps, new knowledge can be generated from data it already holds, but that opens the question of the definition of knowledge.

[2] Information theory comes to mind here. The information content of a signal is quantified as a logarithm of the number of symbols in the signal.

When is a conclusion good enough?

I've been doing a lot of reading recently on modern probability theory, Bayesian analysis, and information theory. One of the central tenets of these theories is that any proposition has associated with it a degree of plausibility, i.e. a probability of being true, that reflects the amount of information available. The proposition, "the sky is blue" is extremely plausible since it is based off of daily self-observations and confirmed by others. As another example, I judge the proposition, "thirty million Americans have blue eyes" as plausible based on the knowledge of the current population of the United States and my own observations on the frequency of encountering blue-eyed people. However, this is not as likely to be true as the first statement.

A conclusion in a scientific paper is nothing more than a proposition and thus possesses its own degree of plausibility. The information available to the reader for determining the plausibility of the conclusion is the data presented in the paper and all previously published work on the same topic. Of course, other factors weigh in, such as prejudices for particular theories and affinities or dislikes for the authors on the paper. Temporarily placing these other factors aside, I wonder, "when does a conclusion possess a large enough degree of plausibility to be considered true?"

Of course, a definite answer doesn't exist. No scientific proposition can be true with 100% certainty and it is silly to think that we can even assign a threshold probability for evaluating a scientific paper's correctness. Just imagine a paper successfully passing through the peer-review process so long as it is evaluated to be 78.63% or more true by its reviewers. But the question remains relevant. Science is a culture and every culture has criteria by which it evaluates claims.

In a closely-related post I wrote about the fallacies that workers commit while evaluating other work. But I find it much more difficult to identify the criteria for establishing the truthfulness and quality* of research. Unfortunately, I don't think I'll be able to fulfill my full potential as a scientist until I am capable of doing so.

Note: The problem of defining quality has been approached at great length by American author Robert Pirsig in his popular novel "Zen and the Art of Motorcycle Maintenance." One of his primary arguments is that Quality is actually an undefined construct present at the seminal moment when an observation is made and processed by the brain. People may know if something possesses Quality, but it is inherently undefinable.

When is too much data necessary?

Ben Goldacre wrote an interesting article last week about research findings that never receive the attention or scrutiny of the academic arena or the public. His point was a bit blurred by the article's strange introduction, but I believe that he was trying to say that our prejudices selectively filter results that we find interesting. This in turn leads to "walled gardens" of knowledge that can negatively impact not just ourselves but society.

However, without any type of filtering I suspect that the amount of information that needs to be processed and weighed is simply too much to handle. Goldacre alludes to this in the concluding paragraph.

The most interesting questions aren’t around individual nuggets of data, but rather how we can corral it to create an information architecture which serves up the whole picture.

What is a good information architecture for society to adopt? Clearly a single individual can not process everything, so the task must be performed by a complex body of professionals and academics.