How long do you need to learn something new?

Or build on something old?

A recent article from the Inside Higher Ed highlighted how experimentation in the delivery of online courses are driving the discussion on what the proper length of a class should be.

The familiar 12 to 15-week blocks align with my experience, and it was only after starting my position at the University of Utah where I realized this was no longer the norm. In my department, two, 3-credit, semester-long courses, were broken up (long ago) into six, 1-credit, 5-week courses. For upper division and graduate level classes, other departments offer “first-half” and “second-half” courses during the traditional semester allowing students the opportunity to broaden their experiences as they can select from a broader range of topics than what might otherwise be available. What has been the shortest course length? A single 3-credit course over five days (8:00 am to 5:00 pm), with a caveat that readings and assignments are due before the first day of class (i.e., there is pre-work involved) and they should expect additional homework each evening.

Most academics consider the last example extreme; however, this model is typical for professional development in many industries. I was fortunate to work in a company that valued professional development and participated in two courses—each taught as full days over a single week—that were similar to a university course. While not graded in the traditional sense, managers have to approve the cost of the course and weigh the loss of immediate work against the promise of improved productivity in the future. Good luck getting additional professional development approved if you cannot demonstrate benefits from your previous development courses. One of the biggest challenges in professional development is getting people to focus on the course and set aside the distractions of work—easier said than done.

So, back to my original question: How long does it take to learn something new or to build upon a previous skill? Can this be done in a single week? Or does it take three-plus months? For a traditional course, the mantra is two to three hours of study per credit hour—for three hours in the classroom each week, the expectation is a minimum of six hours of work outside — a total of 9 hours per week or 135 hours over the 15-weeks of a traditional semester. Assuming that the class-to-study ratio is closer to 1:1, the total time is 90 hours; depending on the topic, 90 hours would be manageable, and this could be more viable with structured pre-work. Of course, one is not an expert at this point but has obtained a level of competency with the subject. As a “self-directed” learner, 45 to 90 hours is a good approximation of the time needed for learning as I’ve built up various skill sets.

Could this type of intense schedule work? Would it be possible to take a three-course calculus series over 15 weeks if that was the focus? Probably, but we also need to consider the instructors. University faculty members need time outside the class for non-teaching activities: research, service, administration, course preparation, and advising are the most visible out-of-class activities expected at the modern university, and this out-of-class engagement is needed. But there might be some appeal to faculty as completing a teaching assignment in five weeks may open up opportunities for focused work during the rest of the semester.

If universities can exploit technology to maximize the high-value activities of their faculty, the traditional classroom will change, and it may reflect the time-intensive learning environments used by industry for professional development. It is worth exploring as the need for life-long learning will force us to become more efficient in education.

Lifelong Learning

The concept of continual improvement is an established business practice in today’s economy. The idea was founded in the statistical process control methods developed by Walter A. Shewhart of Bell Labs and later generalized by W. Edwards Deming into the PDSA cycle:

  • Plan: What are the desired outputs? What can be changed to achieve the desired goal?
  • Do: Implement the plan and gather data.
  • Study: Review the outcomes based on the collected data (more commonly called the “Check” phase).
  • Act: If the outcomes were meet, act to make the plan the new standard.

Of course, when completed, we return to the planning phase and look for further improvement to continue the cycle. Are we communicating this idea to students and employees? Can we apply this principle to the concept of “lifelong learning?”

Is the sum of the whole greater than the parts?

Professionals don’t receive letter grades at work (at least, I’ve never received one). During our annual reviews, we might see statements such as “meets expectations,” or “exceeds expectations”—and hopefully not “needs improvement”; however, the summary statement is, or should be, part of a larger dialog on what went well over the past year, what didn’t, and what are the expectations for the next year.

Compare the way we evaluate work performance with education. For employees, we assess performance over a monthly, quarterly and yearly time frame. Students have a fixed time—days or weeks—to master a new set of skills and it is pretty much “all or nothing.” For employees, we have a simple metric, is the work getting done and meeting expectations! For students, homework is assigned, exams are given, and the answers are graded, and at the end of a class, we assume a transfer of knowledge.

At work, we are expected to improve and develop competencies continuously, and while most follow an annual review cycle, the tasks determine the schedule. The team requires a short time frame for some jobs, and others take months; often, the ability of the individual or group drives the plan. “How long is it going to take?” When the answer does not match the need, we look to adjust the scope or budget.

We have a fixed schedule in education. 15 chapters or five novels and reviews or three research papers—in 15 weeks regardless of ability or prior training. For mathematics and the sciences, knowledge is assumed based on the previous courses taken, but what if the student did not obtain competency? Can we expect that a C stands for competent?
 We assume that the sum of knowledge obtained in individual courses is greater than the parts.

As someone who has hired people to do work, the standard resume is limited in what it tells me about what the individual can do. Admittedly, some folks can craft two-pages that make a compelling case to pursue an interview, but we don’t know what someone can do unless we’re fortunate enough to see their work. This need makes hiring within your network compelling—you often have seen the applicants work, or at least have personal connections with those who have. Making the decision is expensive for the employer and fraught with risks—will this person be able to do the work, will they be able to integrate into the team, will they be able to contribute to future projects.

Educators have a responsibility to help students document how their skill sets address these concerns, and it’s not by issuing a diploma and a transcript. We need to help students document the competencies they acquire during the course of their education.

Don’t let my timeline limit your path forward

Applying the mantra of “what can I do now” is an effective way to move forward on long, complex projects that may be stuck or delayed. So, before becoming fixated on the finish, focus on how to keep moving so you can get started.

Following that . . . Academic calendars are not very flexible.
K-12 education has been on a fall/spring schedule for over 100 years and, surprising to me, an agricultural calendar was not the driving factor. (https://www.pbs.org/newshour/education/debunking-myth-summer-vacation)

For better-or-worse, higher education follows a similar calendar although most public colleges and universities now allow undergraduate students to start a program during fall or spring or summer (i.e., they have rolling enrollment) but this is not the case for most graduate and professional programs where a new cohort is formed each year. If someone wants to start a professional program and they miss the application deadline, they will look at the calendar and think there is nothing they can do until next year—don’t let the institution’s schedule prevent you from moving forward.

In the fall, I get emails asking if it’s too late to enroll—and at this point, it is for our program. However, for those wanting to start a graduate degree or certificate program, and it is past the official application deadline, I’ve recommended that they take the opportunity to identify possible gaps in their education or training that may come up during the application review when they do apply. Admission committee’s look at many factors (GPA, letters of recommendation, statement’s of purpose, etc.); however, most are trying to answer a simple question—will this person be successful in our program. Here a few items I’ve recommended to potential students as they navigated the application process.

Enroll in undergraduate courses (for credit) as a non-matriculated student to address gaps between your undergraduate degree and the graduate program. (A non-matriculated has permission to register but is not formally working toward a degree.) Earning a “B or better” can demonstrate readiness for more advanced work in the field and can effectively offset concerns that admission committees may have if a transcript shows underperformance as an undergraduate. If your undergraduate work was sufficient and you meet the pre-requisites then . . .

Ask for permission to enroll in a graduate course (for credit) as a non-matriculated student. If you are able to take a class associated with the program of study, it may be counted towards the graduate degree. But be warned, Colleges and Universities have strict rules for if this may be done and, if it is allowed, how many credits earned as a non-matriculated student can be applied to the degree or certificate.

My advice is pretty simple, even though you can’t start a program now, you can move forward and what looked like a one-year delay may become less than six months.

On being a professional problem solver

I spent one morning on a recent weekend with my oldest daughter at the Tracy Aviary here in Salt Lake City. I never considered myself a “bird” person; however, my daughter’s academic interest in animal behavior has sparked a before unregistered curiosity. Recently, a new space for Kea’s was opened giving the birds a large area and visitors a much better chance to observe their behavior. Kea’s are an olive-green parrot from New Zealand; I had read they were good problem solvers, but I had underestimated what this meant.

During our recent visit, the staff assembled close to a dozen puzzles built using everyday household items–PVC pipe, plastic containers, balls, etc.—each one designed to be solved with the reward being treats falling out when successfully disassembled. As the staff member worked, the Kea would immediately take to solving the puzzle. But here is the strange part—the Kea didn’t eat the treats. He went quickly to the next puzzle. The reward wasn’t the treat, but releasing of the items from captivity.

The reward was solving the problem.

When asked to describe “what I do,” I used to respond that I was a “professional problem solver.” Although my academic background is chemistry, it’s been quite a while since I worked in the field.

As a professional problem solver, it is easy to get caught up in the excitement of tackling the most current—often urgent—problem. Behaving just like the Kea.

It’s taken time, but I no longer jump into problem-solving mode at the drop of a hat; I have found looking for meaningful impact to be a more meaningful reward. I wish I had learned this early in my career.

While solving an isolated, single problem has immediate rewards, working on a problem that has impact takes time and requires multiple steps and is part of a comprehensive strategy (i.e., the solution is part of a BIGGER problem). After all, the first part of any good strategy is diagnosing the problem—we don’t design strategy around non-problems.

While we can be amazed at the problem-solving skills of the Kea, make a point not follow his example. We don’t want to be trapped solving problems for rewards that we don’t need—rewards that don’t have an impact.

We don’t want to get caught up in the high-tech version of busy-work.

My current professional role is much easier to define; however, I still consider myself a professional problem solver—a professional problem solver who seeks problems that when solved will have a meaningful impact on either my organization or the individuals I get to work with.

Building New Skills

There is no better gig than getting paid to learn.

In August of last year, I decided to take on the challenge of teaching an applied statistical techniques course—one small problem—I’m not a statistician. Like most scientist and engineers, I’ve used (and abused) statistics all my entire career, so I do have experience with the concepts. Also, my math skills are not too shabby as I have incorporated calculus, differential equations, and other advanced topics into my work over the years. (Yes, some people do use algebra.) The good news? I had about four months before the first day of class and enough control over my schedule where I could commit 10+ hours per week to develop the course content. The real challenge was to create the course using R, a language, and environment for statistical computing and graphics. Being open about my own abilities, I was not proficient with this language.

With this in mind, I set out to create the course—with emphasis on the “applied” and “techniques.” Starting in the fall, I developed a module that students would work through each week. I estimated my 10–12 hours of work each week would translate into the three hours of material for each class and this worked out surprisingly well.

The course is coming to an end this week, and the best part of the experience was how much I learned (or maybe relearned). But most surprising, it was how much I learned the SECOND time I worked through the materials during the weeks I taught the course.

I classify skill sets at three levels:

  • novice: apply basic principles to solve structured problems,
  • hack: gather external resources to solve moderately complex problems,
  • expert: apply advanced principles to solve complex problems with the minimal use of external resources.

I still consider myself a hack when it comes to performing statistical analysis using R, but having the opportunity to expand my own skill set and providing a framework for others to learn something new—that was a great gig.