Much of what higher education is about has structure and method. Indeed, this overview is structured as a classic Five Paragraph Essay ( Writing Ninjas: How To Write A Five-Paragraph Essay youtube 5 minutes). Rather than teaching the individual statements that may or may not make up five paragraph essays that students are to write in the future, it would seem to make more sense to focus on teaching the general technique, and let the student apply it to what they are interested in. Indeed, to have impact, the importance of teaching such techniques in the context of the material of interest has been noted ( The Disciplinary Literacy Discussion Matrix: A Heuristic Tool for Initiating Collaboration in Higher Education).
In addition to writing, another general technique is reading. A classic presentation of the basic methods of reading is in How to Read a Book ( wikipedia entry). Indeed, there are many universities that have been strongly influenced by the importance of a core focus on reading ( The 25 Best Great Books Programs), ranging from The University of Chicago to The University of Texas at Austin to St. John's College. Issues like determining the vocabulary of an author and whether or not they are using words the same way as other writers is discussed. In our textbooks, often colloquial phrases take on technical meanings and the transition can be missed. How many of our textbook readers (students) are aware of this as a potential problem? In how many classes is it discussed?
In the STEM subjects, there is more that students need than reading and writing (although these are important aspects of STEM as well). A major focus is problem solving. Here too, there is a classic text that most students are unaware of, How to Solve It ( wikipedia entry) as well as Mathematics and Plausible Reasoning ( wikipedia entry). Here basic concepts like trying an example or looking for a simpler problem to solve first are discussed. But are our students trained in these? When they have problems, do they come to us asking if they have found a good example or if their problem simplication is correct?
The scientific method itself is a classical example of a method for approaching problems ( wikipedia entry). Structuring the investigation of empirical issues around Question, Hypothesis, Prediction, Experiment, and Analysis would seem to be a straight-forward specialization of general problem solving methods. It is frequently taught throughout the science curriculum. But, when students have a question, do they follow this process, or do they instead just ask what the answer is? There is a significant difference between training in methods where people use a method when they are told to versus training in methods where people use a method when a situation arises where it could be applied.
Academia, in particular, the STEM side of academia (where I was trained), is filled with methods to approach various issues. Indeed, one could say that it is the methods that are widely applicable that are more important than the problems or the problem's solutions that these methods are used to address. Some of these methods are so `mechanical' that they can be followed by computers (e.g., much calculus can be done by programs like Macsyma, Maple, and Mathematica), and so are probably not worth teaching students to do in their limited time at University. Particularly now that cell phones have given everyone access to such programs whenever they need them. Other methods are still beyond the capabilities of modern computers (such as figuring out when quantifying rate of change is useful in solving a problem) and worth spending some time on. It is this distinction among university material (and the related training methods) that I think is central to the future of higher education.
The methods for developing the formal structure of a text are known as knowledge representation ( wikipedia entry). This generally has two parts. One part is an analysis of the conceptual structure of the task similar to what is started with concept maps. This will develop into an `ontology' ( wikipedia entry). The other part is some sort of reasoning mechanism, which is what Prolog ( wikipedia entry) provides. An ad hoc approach based on Prolog should allow students to get the basic idea, and see how it can impact their study of a topic as well as providing a mechanism to enable a computer to partner in that study.
The area of knowledge representation is a large research area with many alternatives to plain Prolog. As an example of what such a system might look like if specialized to a particular problem area, consider BioNetGen
In Mathematics, much of the foundations were worked out pre-World War II. An important early work was Principia Mathematica by Betrand Russell and Alfred North Whitehead ( wikipedia entry). And so there the tools for using the computer to support domain thinking are best developed (see Coq, Mizar, HOL Light, below). Much of the argumentation in Science is grounded in logic, implying that it is also open to being processed by similar tools. Of the various tools for processing logical structures, Prolog seems to be the most accessible in that it takes a relatively limited view of logic that focusses on the goals that need to be achieved in order to achieve a larger goal, which is the basic structure of most problem solving methods. Since it places less concern on efficiency than traditional programming languages, it has proven suitable for teaching non-Computer Science students and thus can be used as a flexible tool for unearthing the logical structure of text in any subject area.
Being able to make use of the leveraging capabilities of computers in whatever task one is undertaking is going to be an important skill in the future. As noted in Terry Winograd's Bringing Design to Software ( Amazon entry; UWO card catalog), office software was built by people who didn't work in offices and so reflects the views of strangers about office work rather than how office workers view their work. If a student is to be able to influence the software environment that they work in, they need to know how to understand the level of detail necessary for the computer to do what they want (or realize that that level of detail is unknown and so one can't expect a computer to help). This involves developing an appreciation of the foundations of one's area of interest and skills in close reading of natural language material that are central to understanding what is going on in all fields of study. Such skills can most fruitfully be developed in the context of problem solving in the area of interest, as opposed to as an abstract study on the side.