Deep Processing – Conveying Structure with Lectures

I was happy to discover that “Four things lecture is good for” by Robert Talbert comes to the defense of lectures. I’ve observed in the past that traditional lectures are very often dogged on, almost to the point of being unfair. Perhaps I’ve just wanted to play Devil’s advocate, but maybe the traditional lecturing style was the best we could do with what we knew in the past. Many learning theories are relatively young disciplines, with learning sciences and engineering education being younger still. Further, lecturing had to have some capabilities or it wouldn’t have been practiced. In fact, there might be certain fields or topics for which it is very useful.

As Talbert points out, there are some purposes for which lectures are useful. Of the four purposes listed as being “well-suited” to lectures, the sharing of “cognitive structures” stuck out to me. The Cambridge Handbook of Engineering Education Research provides a set of principles for three learning theories, or what it calls “conceptual frameworks” of learning and knowing. In the cognitivist framework, one of the principles in designing instruction is to aim for “deep processing” of information:

The general idea of deep processing is that learners should understand the structure of information to be learned, such as the main ideas and how they relate to one another and to sub-ideas that might derive from them…

The more organized information is, the easier it is to remember and understand. In “Successful Lecturing: Presenting Information in Ways That Engage Effective Processing”, it is also presented that the provision of structure in lectures results in better learning. Providing students with outlines or knowledge maps (concept maps) of the lecture material results in better notes, better testing results, and better memorization (sidenote: if you’re interested in knowledge maps, you should check out The Book of Trees: Visualizing Branches of Knowledge). Even as sparse as a title can be beneficial. At the other end of the spectrum, if outlines become too full and veer towards content, their benefits become negated:

Outlines containing only headings and subheadings are maximally effective in that they encourage note taking, whereas outlines that provide too much detail inhibit note taking

Assessment as a Single Tool Among a Mixed Toolbox

I recall back to the TED talk from Ken Robinson (“How to Escape Education’s Death Valley“), how we do need some agreed upon notions of what is generally good or bad (cholesterol was the given example). This isn’t meant to exclude the need for individualization, but it does indicate the use for standardization at some level. Further, though a number for cholesterol may not be an accurate reflection of one’s overall health, we need to be able to represent and communicate status efficiently. Having a single number, letter, or symbol allows for broad but quick insights and high level comparisons. Having a cholesterol level also doesn’t exclude the ability for other tests or information to be considered.

My point with this is that assessment can have a scope of application, but it should be limited and taken into account with a myriad of other factors. It is simply one tool among a whole toolbox. It might be thought of as a hammer – useful with nails, but it plays a role aside saws, wood glue, levels, etc. If the focus of a worker is to always use a hammer, they’ll smash beams rather than saw them, nail rather than adhere, and further. Alfie Kohn in “The Case Against Grades” echoes the pitfalls of focusing on a single tool in terms of grading:

  • Students force grades rather than understanding out of learning
    • They become less interested in what the tools are actually making and instead center on just using the tools
  • Students do enough to get a grade rather than enough to learn
    • They finish the exterior of a house to make it appear done but leave the interior unfinished
  • Students don’t concentrate on or care what they’re meant to be learning
    • They forget the overall goal of what they’re meant to building

Assessment shouldn’t be thought of as the end goal. It isn’t the house being built, it is just one of the tools. Tests of various forms can be used to try to gauge a student, but it shouldn’t be considered a perfect representation of them. The results are a part of the holistic student, used with other facets to try to individualize education to best suit that student.

I’m reminded of how there are numerous learning theories, each which looks at the learner and their environment from a different perspective. Each one tries to capture and study a subset of all that a learner is or affects the learner. A behaviorist approach treats learners as black boxes with inputs and outputs. Social learning considers the societal and cultural contexts that gives meaning to what one does or come to understand. Different perspectives each have their use, and some can help explain situations that others cannot. Some or all of them can be used to help build a better and more complete learning experience.



Goals of an Engineering Research University

I was curious what one would need to focus on if they were going to create an engineering research program. With this aim, I compared the Mission Statement of two electrical and computer engineering departments: Virginia Tech (Blacksburg, VA), and MIT (Cambridge, MA). Both are research universities, though Virginia Tech (VT) is a public university while MIT is private. VT has a much larger undergraduate base, while MIT has a larger research portfolio.

Virginia Tech
Undergraduates: ~23,000
Graduates: ~6,500
Endowment: ~$800 million

Undergraduates: ~4,500
Graduates: ~6,500
Endowment: ~$13.5 billion

Overall, both universities have similar tenets in their mission statement. Not surprisingly, both discuss research as an important part of the program’s structure. The department should perform cutting edge research, and provide these opportunities to the student.

Important traits of graduating students should be:

  • foundational knowledge in math, science, and engineering
  • ability to problem solve
  • ability to communicate
  • understand ethical concerns
  • have an appreciation for life-long learning

These traits were shared by VT and MIT. Virginia Tech in addition stressed the ability to design, to work in teams, and to understand modern issues and tools. In contrast MIT discussed more the path of a student in their program, starting with “foundation subjects”, selecting “header subjects” to focus on, and then concentrating further on their chosen “intellectual themes”. Here, MIT expresses the ability of their students to study what interests them most.

To achieve these various goals, both universities place an importance on recruiting skilled faculty. Professors and researchers are able to create and run labs, which allow them to produce research while also opening the door for students. Similarly, through courses skilled professors are able to help endow and inspire the desired knowledge, skills, and attitudes in graduating students.