Instructor Training

Introduction

Overview

Teaching: 30 min
Exercises: 0 min
Questions

  • How can we describe teaching and learning how to teach?

  • What common vocabulary do we need to talk about teaching and learning?

Objectives

  • Describe how learning to teach is like learning a performing art.

  • Identify where in a lesson the instructor is using content knowledge, general knowledge, and pedagogical content knowledge.

  • Use an example to illustrate the difference between top-down and bottom-up learning.

This section outlines the structure of this two-day course and provides some essential terms for discussion.

Practice and Feedback

To become accomplished at any complex skill (such as playing a sport or musical instrument), one must do the following:

Learning a new skill requires sustained and deliberate practice, and feedback from experts and fellow practitioners.

In this training we will:

The “secret sauce” of Software and Data Carpentry lies not in its lessons or workshops, or even the pedagogical skill of its instructors, but in the community of people who are dedicated to teaching computing and data skills as well as they possibly can. The goal of this training is thus not only to strengthen instructors’ teaching skills, but to connect instructors:

Key Readings

An excellent overview of research results in education and learning is Ambrose et al’s How Learning Works (which is also an excellent example of what secondary literature ought to look like). Green’s Building a Better Teacher is lighter but no less informative: it explores why educational reforms in the past 50 years have mostly missed the mark, and what we should be doing instead. The ultra-short summary “Deans for Impact report” contains useful, practical insights, and is required reading for this training.

Pieces focusing specifically on computer science education include Guzdial’s “Why Programming is Hard to Teach” and “Top 10 Myths About Teaching Computer Science”, and Porter et al’s “Success in Introductory Programming: What Works?”, all of which you should read before starting this class.

A Common Language: Terms

The following terms will support the discussions and activities in the rest of the training by giving us a common vocabulary to talk about teaching and learning to teach.

Educational Psychology

Educational psychology is the study of how people learn. It touches on everything from the neuropsychology of perception and the mechanisms of memory to the sociology of school systems and the philosophical question of what we actually mean by “learning” (which turns out to be pretty complicated once you start looking beyond the standardized Western classroom). Within the broad scope of educational psychology, two specific perspectives have primarily influenced Software and Data Carpentry’s lessons and teaching practices (and by extension, this instructor training).

One perspective is cognitivism, which treats learning as a problem in neuropsychology. Cognitivists focus their attention on things like pattern recognition, memory formation, and recall. It is good at answering low-level questions, but generally ignores larger issues like, “What do we mean by ‘learning’?” and, “Who gets to decide?”

Our other guiding perspective is situated learning, which focuses on the way that legitimate peripheral practice leads to people becoming members of a community of practice.

Unpacking those terms, the situated learning perspective focuses on the transition from being a newcomer to being accepted as a peer by those who already do the activity in question. Situated learning is directly relevant to our learners, many of whom ease into scientific computing by doing small tasks that experienced practitioners would regard as straightforward, but who learn how to take on bigger and more novel challenges both from what they do and from the feedback (and welcome) it elicits. It is equally relevant to our instructors (i.e., you), who are approaching evidence-based teaching in the same way.

Software and Data Carpentry aim to serve researchers who are exploring data management and programming on their own (legitimate peripheral practice) and make them aware of other people doing that work (simply by attending the workshop) and the best practices and ideas of that community of practice, thereby giving them a way to become members of that community. Situated learning thus describes why we teach, and recognizes that teaching and learning is necessarily rooted in a social context. We then depend on the cognitivist perspective to drive how we teach the specific content associated with the community of practice.

Other Perspectives

There are many other perspectives outside cognitivist theory — see this site for summaries. Besides cognitivism, those encountered most frequently include behaviorism (which treats education as stimulus/response conditioning), constructivism (which considers learning an active process during which learners construct knowledge for themselves), and connectivism (which emphasizes the social aspects of learning, particularly those made possible by the Internet).

Instructional Design

Educational psychology does not tell us how to teach on its own because it under-constrains the problem: in real life, several different teaching methods might be consistent with what we currently know about how learning works. We therefore have to try those methods in the class, with actual learners, in order to find out how well they balance the different forces in play. This is called instructional design (ID); if educational psychology is the science, ID is the engineering.

For example, most children today are taught to read using a bottom-up technique called phonics, which introduces them to the sounds of letters, then puts those letters and their sounds together to make simple words like “bug” and “run”, then assembles the words into sentences and stories. But another technique called whole language takes a different approach. Instead of starting with letters, its proponents teach children to recognize entire words so that they get the positive feedback of being able to read whole stories as early as possible. The basic idea is that once children find reading rewarding, they’ll be motivated to go back and figure out the letters.

These approaches are not equally consistent with what we know about how children learn to read. There is some evidence that proficient, adult readers identify whole word patterns. But humans are really good at associating sounds with meaning, and the scientific consensus among linguists, psychologists and neuroscientists is that the bridge phonics builds from letters to sounds makes learning to read easier.

So while whole-language instruction takes an optimistic and student-centered view on learning – encouraging enjoyment in learning as a key element in the process – it is not consistent with the scientific view of the way that learning to read actually takes place. Nevertheless, when these methods were studied in classrooms, the findings have been contradictory: while some classroom studies find that phonics works best, others show that whole language yields better results.

One explanation is that a third factor is at play. If children sense that their teachers are enthusiastic about something, they will respond to that enthusiasm and learn better. If they feel their teachers are just marking time, though, they will emulate that instead. Thus, when studies are done by people who are proponents of a new technique (as they often are), they are likely to produce positive results, but when those techniques are deployed more widely to teachers who have seen one bandwagon after another roll by, results will regress to the mean, or even beyond.

Quantitative and Qualitative Research

One of the major challenges in educational research is that human beings are very different from one another. In order to get meaningful quantitative results, studies must therefore include large numbers of subjects, which is expensive. Quantitative studies are also not able to answer many “why” questions. For those, researchers need to use qualitative methods, such as grounded theory. Physical scientists are sometimes skeptical of such studies and their findings, but done rigorously, they can provide actionable insights that quantitative methods cannot.

Pedagogical Content Knowledge

In the end, effective teaching often depends on what the teacher knows. The things teachers know can be divided into:

Pedagogical Content Knowledge

This training course focuses on general pedagogical knowledge through the two major categories (educational psychology and instructional design) described above. It assumes you know as much as you need to about basic programming (our content knowledge).

When it comes to PCK, we will see later some of the PCK of the Software and Data Carpentry communities at work. Within Software Carpentry, we are also trying to support the curation of PCK by including an instructor’s guide with each lesson that describes particular teaching techniques for that lesson’s content.

Finally, this training includes times for discussion, observation, and feedback, precisely so that participants are able to share their PCK with each other over the course of the next two days.

Examples of PCK

Pseudoscience

One [well-known scheme][wikipedia-learning-modes] characterizes learners as visual, auditory, or kinesthetic according to whether they like to see things, hear things, or do things. This scheme is easy to understand, but are [almost certainly false][amazon-myths]: what little evidence that does exist for it is very weak. Unfortunately, that hasn’t stopped a large number of companies from marketing products based on it to parents and school boards.

Three Kinds of Knowledge

Think of a memorable moment from a class you took or taught. Describe it, and explain how the instructor used domain knowledge, general pedagogical knowledge, and pedagogical content knowledge to create that moment.

Bottom Up or Top Down?

How would you describe the way you learned what you already know about using computers in research: bottom up, top down, or a mix of both? Is that how you prefer to learn?

Key Points