An Overview of 9 Modes
for thinking-and-action in
Integrated
Design Process
If you want a quick summary of the 9 modes,
you can read The Process of Design – Two Overviews in An Introduction to Design.
In early 2012, I began developing a new website with
many improvements (by revising, adding, cutting),
so I strongly recommend that you
read it instead of this page.
DEFINITION — in Modes 1A-1B
1A — CHOOSE AN OVERALL OBJECTIVE (for a DESIGN PROJECT)
Based on everything you know, you choose an overall objective by deciding what you want to design. This requires recognizing a problem (an opportunity to make things better) and deciding to pursue a solution because — after you have evaluated the potential benefits and probability of success, compared with alternatives (the other ways you could use your limited resources of time, money, and people, plus the available knowledge and technology) — you make a strategy-decision that “yes, this design project will be a wise investment of resources.”
1B — DEFINE GOALS (for desired PROPERTIES of a solution) to use as EVALUATION CRITERIA
Grounded in a knowledge of what
is (*) and inspired by thinking about what could be, you define specific goals for
the desired properties of a satisfactory problem-solution. These goals are the evaluation criteria used to evaluate options (that are candidates for a solution) in the Quality Checks of Mode 3A. / * Searching for information (with Preparation in Mode 2A) is often useful when Defining Goals, to help you generate ideas about the goal-properties you want, for a solution with higher quality than in the options now available.
For example, if the design-objective (from 1A) is a product, you can define the desired properties (for a satisfactory product) in terms of a product's composition
(what it is), functions (what it does), and performances (how well
it does the functions). For an activity, you can ask “what, who, when, where, and how” in the context of “why”. For a strategy, you ask “what kinds of results do I want a strategy (and the actions it requires) to produce?” The desired properties of a theory, or a system of theories, are described when we look at science during design. / In addition to these specifications for desired properties, usually we must consider practical constraints such as a reasonably low cost, ease of manufacturing, and (for the design process itself) meeting time-deadlines. The goal criteria that you define will include both specifications and constraints, because both are important aspects of the desired properties you want in a satisfactory solution.
The goal-properties you define (for specifications & constraints) are the focus of
action during your process of design, because these goals provide “aiming points” to guide the creative generation of ideas, and their critical evaluation with Quality Checks in which quality is defined by your goals.
Quality Control: For most design projects a solution must be actualized by converting an idea into reality, which occurs when you produce a product, do an activity, apply a strategy, or use a theory. Eventually, but maybe not until you have chosen a problem-solution, your goals should include criteria for Quality Control in which you evaluate the quality of actualization and make adjustments, if necessary, in an effort to control (to observe-and-improve) the quality of actualization.
Objectives and Goals
In this model of Design Process (1A-1B-2A-etc), what is the difference between
an objective and a goal? They are similar in some ways, but occur at different levels.* For example, in An Introduction to Design we first defined the overall objective as a minivan, rather than a pickup truck or sports car. Then we turned our attention to goals for the objective, for the minivan, by thinking about the kinds of properties (for its body, engine, performances, aesthetics, consumer appeal, costs,...) we want it to have. / * And people within a company can have different perspectives about levels.
Before continuing with the modes, moving from 1B (above) to 2A (below), here is a brief interlude to ask “what is a mode?”, explain two simplifications (Assuming... + Levels...), look at some pretty pictures, and compare mode-numbers with mode-words:
A Mode is not a Step — Flexibility and Overlaps
Although 1A is the
“first” mode of thinking-and-action in this model of Design Process, the process of design is
not a rigid sequence of steps that must be followed in a particular
order. Instead, it's a flexible framework that is useful for describing the logical goal-directed improvising that occurs during a process of design. This is why the actions are called MODES, not STEPS.
And there is overlapping of modes because action
in one mode often involves action in other modes, with many Interactive Relationships between Modes.
Assuming our objective is a Product: In the rest of this page, usually most modes (all except 3B where the focus is a theory) will be described for the design of a product, because this narrow focus improves clarity (so you can understand more easily) and simplicity (so you won't have to frequently read "product, activity, strategy, and/or theory"). This descriptive focus is generally acceptable because most thinking-and-actions (but not all) are analogous whether the objective of design is a product, activity, strategy, and/or theory.
Levels of Exploration: A teacher who uses this model of Integrative Design Process (1A, 1B, 2B, ...) can decide how to teach it with an appropriate level of detail for students. This level can begin with the Simple Diagram below (showing a two-step cycle of design (prepare → generate-evaluate-generate-evaluate-...) and move on to the Basic Diagram, and maybe (it's optional) the Detailed Diagram. You can see this progression in the 3 miniaturized diagrams below, and then read about it in Moving from Simplicity to Complexity when Teaching Design.
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Simple Diagram |
Basic Diagram |
Detailed Diagram |
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These three diagram-levels (simple, basic, detailed) will help you understand
the modes of thinking-and-action and
their relationships. If you click this link a page with full-sized diagrams (these 3 & more, including a table)
will open in a new window, and this page will remain open in this window. If you carefully arrange
the windows you can see both pages at the same time, to allow visual-and-verbal learning
when you look at the visual representations in the diagrams-page
and read the verbal descriptions in this page.
Mode-Numbers and Mode-Words
When you're learning the modes, you can supplement the mode-numbers (1A,...) with mode-words (Choose Project, Project Choice,...), as in this table:
| using VERBS | using VERBS | using NOUNS | |
| 1A | Choose Project | Choosing a Project | Project Choice |
| 1B | Define Goals | Defining Goals | Goal Definitions |
| 2A | Find (Prepare) | Preparing (Finding) | Preparation |
| 2B | Invent | Inventing | Invention |
| 2C | Predict | Predicting | Prediction |
| 2D | Observe | Observing | Observation |
| 3A | Check Quality | Checking Quality | Quality Checks |
| 3B | Check Theory(s) | Checking Theory(s) | Reality Checks |
| 4A | Coordinate | Coordinating | Coordination |
For your own learning, one option is to use a “memory scaffold” by opening this page in a new window by clicking this link and then looking at the table whenever I refer to a mode-number.* Or you can just ignore the mode-numbers — which will be in aqua font during the rest of this description of modes (until we begin looking at the Interactive Relationships between Modes) — and focus on the words that are used to describe the mode. {* Or instead of using this numbers-and-words table, you can use the combination of “mode-numbers and mode-words” while you're reading. } / For me, thinking about modes by using either label (numbers or words) has become second nature, and probably this also will happen for you, when you practice “thinking with Design Process” and your understanding of design develops.
GENERATION — in Modes 2A-2B-2C-2D
2A — FIND - SEARCH for old information (about options, observations & predictions, theories)
Preparation: In an effort to understand the current situation more accurately and thoroughly, you SEARCH for useful old information that is already known, is not newly generated. In this context, old does not necessarily mean obsolete; you should critically evaluate all information, both old and new, to decide whether it's relevant and useful.
Two Kinds of Memory: During a search for information you can try to REMEMBER it from
your own memory, or LOCATE it in our collective memory, in what is remembered by other people and is recorded (culturally remembered?) in web-pages, books, audio & video recordings,...
Options & Properties: You can look for old options that are candidates for a problem-solution. Then, for each old product-option (if your objective is a product) — especially for options that seem promising because they are similar to your desired goal-product in some ways, so you SELECT them as — you can find (by remembering and/or locating) additional old observations and old predictions about its properties, so each option can be evaluated for its quality by using Quality Checks in Mode 3A.
Experimental Techniques & Systems: How were the old observations-about-properties generated? Maybe these experimental techniques & systems will be useful for generating new observations in Mode 2D.
Conceptual Knowledge: You also can search for old theories (descriptive or descriptive-and-explanatory) about relationships between products, properties, observations, and (in Mode 2C) predictions. This conceptual knowledge can be expressed in forms that are verbal, visual, and/or mathematical.
Procedural Knowledge: A wide variety of skills-knowledge about “how to do things” has been developed by designers (by engineers, scientists, and others) and is available for use by you. Useful procedures have been developed for a wide variety of contexts, including "textbook" problems and "real world" problems.
Preparation as a Foundation for Problem-Solving Strategies: To solve a typical textbook problem, a useful first step is to search for the procedural & conceptual knowledge that you think is relevant because it will help you solve the problem. A solution strategy for real-world design problems is similar, but usually is more complex, involving options-and-properties, experimental systems, theories & procedures.
The Value of Knowledge, and Interactions between Modes: A solid foundation of knowledge (starting with Preparation in Mode 2A and continuing with knowledge you learn during your thinking-and-actions in other modes) is useful throughout the process of design, from beginning to end. The value of knowledge becomes apparent very early in the design process, when information you already know (from 2A) is useful for choosing an objective in 1A, and defining goal-properties in 1B. And it continues when you generate new information with predictions & observations (in Modes 2C & 2D), and generate new options in Mode 2B.
2B — INVENT - by generating ideas for new options
Invention by Revision: To INVENT, you can REVISE old options (found in 2A) to IMPROVE these options. How? Guided by evaluations of quality (in Quality Checks, 3A) so you know the ways that a particular option's properties don't completely match the desired properties you have defined as your goals, you creatively invent modifications by imagining the changes that are possible, and predicting how each change
would affect the properties of this option, with the whole process being guided by retroductive logic when you ask “how can I modify the option to get a closer match with my goals?” You may find it useful to analyze the old option into different aspects, and think about ways to revise each aspect to get a closer match with your goals, as described in Tools for Analysis. { The creative-and-critical process that we typically use to retroductively generate ideas is examined later, briefly and in detail. }
Or you might INVENT a new product-option that is extremely different, that is not just a revision of an existing product-option, that isn't an improved version of an old idea. Although it's difficult to invent a new option that is totally different and is also useful, in the spectrum of innovation — ranging from no change through minor revision and major revision to very innovative — some new options are especially innovative, and thus are “more new” than others. When you're aiming for innovation, but you seem to be stuck in a rigid mental rut, if you ask “am I making any unwarranted assumptions that are hindering the flexibility of my thinking?” this may help you improve your creativity, so you'll have more ideas that are more productive.
Theory Invention: You also can invent a new option for a theory (not just for a product, activity, or strategy) in a process that is analogous to the creative retroductive logic described in the two paragraphs above, except that Reality Checks (which “check the quality of theories” in 3B) are more important than Quality Checks (in 3A), although Quality Checks can also be considered, as explained later.
2C — PREDICT - do mental experiments to generate new prediction-information
To make a Prediction you do a Mental Experiment — choose an Experimental System, and use if-then logic by reasoning that “IF the system behaves as expected, THEN my prediction (my logical expectation) is that ___ will be observed.” You can PREDICT using if-then logic in one or more ways, by using deduction (with deductive logic that assumes theories about the experimental system) or simulation (as in running a computerized model of the system*) or inductive extrapolation based on experience (by assuming that “what happened before, in similar situations, will happen again”), or in other ways.
* Analytical Strategies for Making Predictions: During your experimental design using analytical strategies when you "think about the variables in an Experimental System, with each combination of variables producing a different variation of the system," you may also want to construct a model of the system (for what it is and what it does) based on your theories about all parts of the system and how they interact. Then you can use this model by “running a simulation” (or with deduction and extrapolation) to make predictions about the system. If you change the system, and compare your predictions before & after the change, asking “were the changes in predicted results what I expected?” will mentally test your model of the system, your application of the model to make predictions, and your understanding of the model. And you can scientifically test your model in a Reality Check (3B) by comparing your predictions about a system with observations of the system. / Basic principles for predicting are summarized in my Introduction to Scientific Method and, with extra details about Supplementary Theories, in four paragraphs of my Detailed Overview of Scientific Method.
Quality Control for Predictions: The quality of predictions depends on the quality of theory-application when a theory is used to make predictions. The process of Quality Control for Predictions is described in the second paragraph (following an explanation of Quality Control for Applications of a strategy, product, or activity) when you click this link.
You can generate useful information by finding an old prediction (in 2A) or making a new prediction (in 2C). You can use predictions (old or new) in both science and conventional design.
In science, when the main objective is improving our knowledge by designing theories & experiments, predicted observations (from 2C) are compared with actual observations (from 2D) in a Reality Check (Theory Check) during 3B.
In conventional design, when the main objective is a product or activity or strategy, a solution-Option is part of an Experimental System that lets you predict some property of the Option, and this predicted property is compared with your desired property (defined as a goal in 1B) in a Mental Quality Check during 3A.
In both conventional design and science, we use predictions in evaluative comparisons (Reality Checks or Mental Quality Checks) that let us pursue our objective — to design a better theory or experiment, product, activity, or strategy — in a creative-and-critical process of retroductive generation.
2C and 2D — Experimental Design
for Mental Experiments and Physical Experiments
When you design a Mental Experiment (2C) to generate Predictions, or a Physical Experiment (2D) to generate Observations, an experiment is broadly defined as “any way
to gather information” whether the Experimental System is controlled to uncontrolled, or whether it occurs inside or outside a laboratory.
Mental versus Physical: Experimental Design begins with Mental Experiments, which allow quick-and-cheap exploration of options for a variety of Experimental Systems. This lets designers imagine the Observations that could be produced with each experiment, in a divergent search for information that might be interesting and useful. Then they can decide which Systems to use for Physical Experiments that typically require much larger investments of time and money.
Imagining the Impractical or Impossible: Mental Experiments can be done for systems that are difficult or impossible to test physically. Einstein asked “what would I observe if I could ride a photon?” even though he knew he could never do this. An engineer can ask “what would happen to this building in an earthquake?” and — using a knowledge-based model that includes the architecture & materials of a proposed building, the forces experienced by buildings in various types of earthquakes, and theories (with mathematical relationships) about relevant physical interactions — can run simulations for different building-options, to predict what will happen. Although the models-and-simulations will be based on previous “field experiment” Observations of buildings and earthquakes, producing Predictions with computer simulations is more practical than doing physical experiments with buildings and earthquakes.
Strategies for Generating Information: When designing a Mental Experiment, you can plan to make predictions by using one or more types of if-then reasoning: theories-and-deduction, models-and-simulation (run with or without a computer), or experience-and-extrapolation. You can design a Physical Experiment in which observations are made by using senses (sight, sound, smell, touch, feel) and/or instruments. With all experiments you'll want to estimate the precision-quality and accuracy-quality of the information (the predictions or observations) that will be generated, and seek ways to improve both qualities, in the precision and accuracy of information.
Analytical Strategies for Designing Experiments: One useful strategy for experimental design is to think about the variables in an Experimental System, with each combination of variables producing a different variation of the System. You choose a System-Option that has a particular combination of values, and make Predictions in a Mental Experiment, or Observations in a Physical Experiment. To design another Option you can change a variable quantitatively (by giving it a different numerical value) or qualitatively (for example, by using a different solution-Option as part of the system). In a series of experiments you can decide to control some variables by keeping them constant, while you let others vary. You can try to generate new information about one solution-option, or several options; strategies for analytical invention-by-revision, by adjusting variables in an effort to generate options with properties that more closely match your goal-properties, are introduced in Mode 2B and are examined more deeply in Tools for Productive Thinking.
Innovative Strategies for Designing Experiments: Or, if you want to aim for a more extreme revision, instead of settling for analytical “variations on a basic theme” you can change to a different type of experimental system. As with all idea-generation, an idea for a different system can be old (found in 2A and selected) or new (invented in 2B). And it can be slightly different or very different.
For all designers, especially scientists, a valuable scientific skill is generating ideas (in a creatively divergent process) for experimental systems, and critically evaluating system-options so you can choose a system. This creative-and-critical process is The Generation-and-Evaluation (the Design) of Experiments. Then you use the experimental system to make predictions (in 2C) and/or observations (in 2D), which you can use for comparative evaluations in Quality Checks (in 3A) and/or Reality Checks (Theory Checks) in 3B.
In other pages you'll find useful ideas about Generating Observations by using Internal Senses and External Instruments (in a section that ends with a link to "more ideas about the creative generation and critical evaluation of ‘opportunities for observation’ during the process of experimental design") and Goal-Directed Designing of Experiments and A Condensed Overview of Experimental Design.
2D — EXPERIMENT
- do physical experiments to generate new observation-information
After a process of Experimental Design ...
• In science, you choose an Experimental System and do a Physical Experiment to produce Observations that provide information about the System. You compare these actual observations (from 2D) with your predicted observations (from 2C) for the same System, in a Reality Check (Theory Check) in 3B.
• In conventional design, you choose a solution-Option, either old (selected in 2A) or new (invented in 2B), acquire this Option (if possible) or construct it (if necessary) and construct an Experimental System involving the Option, so you can do a Physical Experiment that produces new Observations about
properties of this Option. You compare these observed properties with your desired properties (defined in 1B) in a Quality Check in 3A.
RETRODUCTION = creative GENERATION guided by critical EVALUATION
Using Creative-and-Critical Retroductive Logic to guide your Generation of Information
What are the interactive relationships between the creative GENERATIVE MODES (above) and critical EVALUATIVE MODES (below)?
Design Process defines generation broadly: the overall objective of a design process is to design (to select, invent, or improve) a solution for a problem, so we can generate options for a solution by selecting, inventing, or improving.
The process of generating information & options (in 2A-2B-2C-2D) should be guided by retroductive logic (based on evaluative Quality Checks or Reality Checks in 3A-3B) that is used when you are motivated to retroductively generate options — either old options (that you find-and-select in 2A) or new options (that you invent-or-improve in 2B) — for the wide variety of things highlighted in bold:
products with properties that, using either of the Quality Checks in 3A (Mental or Physical, both shown in the diagram below) match the desired properties you have defined as goals in 1B; similar logic is used for a retroductive generation of option-ideas for an activity or strategy or (using non-empirical criteria) a theory;
theories producing predictions that, when using a Reality Check in 3B (also shown in the diagram), match known observations from 2D;
experimental systems that are useful because they let you make predictions (in 2C) and/or observations (in 2D) that will be useful for comparative evaluations using Quality Checks (in 3A) and/or Reality Checks (in 3B).
In addition, retroductively asking “what kinds of problems might be solved by the current options?” can lead to finding new goals (in 1B) and new objectives for design projects (in 1A).
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| Retroduction is similar (except for the "Predict" & "Compare" columns) in the two contexts-of-design above: Retroductive Generation involves 2A/2B-2C-3A/1B in conventional design, in the top row. Retroduction Generation involves 2A/2B-2C-3B/2D in science, in the bottom row. |
Retroductive Logic and Analytical Thinking are useful partners during a creative-and-critical process of generating options, as described earlier — in 2B (invention by revision), 2C (making predictions), and 2C/2D (designing experiments) — and later, A Strategy to Stimulate Productive Design-Thinking that is the conclusion of a detailed examination of retroduction.
3A — EVALUATE OPTIONS by using QUALITY CHECKS (with quality defined by your GOALS)
Each option — each potential problem-solution, whether it's old (found and selected in 2A) or new (invented in 2B) — can be evaluated by comparing your goals (for the desired properties you defined in 1B) with predictions (of predicted properties for the option, from 2A or 2C) or with observations (of actual observed properties for the option, from 2A or 2D). Each of these comparisons (of goals with predictions, or goals with observations) is a Quality Check (a Mental Quality Check or Physical Quality Check, as shown in the diagram above) that shows you how closely an option's properties match the desired properties you want.
And for some projects you'll also want to do Quality Checks that are Quality Controls to check the quality of actualizing, of either applying a strategy or producing a product or activity, or using a theory.
During a comparative evaluation of competing options, you do Quality Checks for each option:
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Option 1 |
Option 2 |
Option 3 |
Option 4 |
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3B — EVALUATE THEORIES by using REALITY
CHECKS (with reality defined by REALITY)
To evaluate your theories — which are assumed when you make if-then predictions by using deduction, simulation, and/or experience — you compare predictions with observations in a Reality Check that is an empirical Theory Check which lets you test the predictive quality of a theory (about one aspect of the world) by letting you see how closely “the way you think the world is (according to your theory)” matches “the way the world really
is.” You can do comparative evaluations of competing theories in multiple Reality Checks in 3B, analogous to multiple Quality Checks in 3A.
Quality Checks (in 3A) are the central focus of Design.
Reality Checks (in 3B) are the central focus of Science.
As explained later, Quality Checks and Reality Checks (in 3A and 3B) play key roles in the Process of Design & Process of Science, respectively. And each can be used for Creative-and-Critical Retroductive Thinking.
4A — EVALUATE THE PROCESS and MAKE ACTION-DECISIONS
In this mode you observe the process of design and think about it by considering both urgency and importance, so you can choose an immediate action (by asking “what is the best use of my time right now?”) which occasionally is a planning of longer-term actions.
You try to understand the current state of the problem-solving process — by aware observation of the current situation (your Now-State) and knowing where you want to go (the Goal-State) — so you can generate action-options (for what you can do in all modes, in 1-4), evaluate action-options, and make action-decisions about the actions (and associated thinking) you will do because you think these actions will help you make progress in solving the design problem, by moving from NOW toward your GOAL.
You may find it useful to imagine the problem-solving process as a journey in which you travel across various types of modes-terrain (by defining, generating, evaluating, and coordinating) to reach your goal. If your progress is temporarily slowed by an obstacle in the path of problem solving, you search for a way to avoid or overcome the obstacle so you can continue making progress.
When you are coordinating actions in 4A, a valuable tool is conditional knowledge (knowing the conditions in which an idea or skill will be useful) that helps you find a match between “WHAT things need to be done” and “HOW things can get done.” You decide “WHAT needs doing” by comparing your now-state and goal-state. You'll know “HOW to do it” by developing, for each mode, conditional knowledge about its functional capabilities (the things it can help you accomplish) and its conditions-of-application. When you use your conditional knowledge to find a match between a recognized need (for things to do) and a capability (for how to do things), you can make an effective action-decision.
Here are some practical tips for developing and using conditional knowledge: For each of the many skills you learn, know how to use it, and also when to use it, and why. Ask “what can it help me accomplish?” and “what are its conditions of application? in what contexts (indicated by what kinds of contextual clues) is it likely to be useful?” Creatively imagine if-then scenarios for potential applications in the future — by thinking “if the situation is ___ , then I can use this skill” — and learn the contextual clues for recognizing how to match various situations-and-skills.
How can you increase retention-and-transfer of procedural knowledge in the future? First, intentionally learn by asking “what can I learn now, including conditional knowledge, that will help me in the future?” Then intentionally remember by asking “what have I learned in the past that can help me now?” and trying to search for a situation-and-skills between your current situation (what needs to be done) and skills (with capability to do these things).
Timings for Planning: Strategies for problem-solving actions begin in the initial planning for a design project. Later,
during the process of design when more information becomes available, these preliminary plans are modified
and supplemented by improvised planning, with process-evaluations and action-decisions (in 4A) being mixed with action in other modes, occurring during these modes or between them.
Quality Control: The purpose of coordinating actions in all modes (in 1A through 3B, plus 4A itself) is to improve your quality of applying design-strategies, to achieve the goals of using your time more efficiently and finding a better solution.
More: This exploration of the design process (in 4A) continues later: You have many options when using Quality Checks "stimulates action in other modes, for various purposes." And in the third part of this page we'll look at relationships between similar strategies that are used in the differing contexts of a design project (when you “think about process” to help you achieve your objective for an external design project) and metacognitive self-education (when you “think about thinking” to help you improve the quality of your thinking-and-learning for an internal self-education project) — in a major section about Metacognition which includes and Combining Cognition with Metacognition in the Process of Coordinating Design and a Strategy for Learning and Strategy for Teaching (or for Supervising a team of designers and Coordinating their design-actions).
Part 2 :
Teaching Design Process and Exploring Interactive Relationships
(in a progression from simplicity to complexity)
The first part of this page describes the 9 modes of thinking-and-action. This second part begins with a way to teach Design Process by using a progression that begins with simplicity and gradually increases the level of detail. Quoting from An Introduction to Design:
After students finish a design project, ask “what did you do” and help them see how their main actions follow a two-step cycle of creatively Generating Ideas and critically Evaluating Ideas, and (maybe at a later time) how this two-step cycle uses 3 key elements of design — Predictions & Observations, and Goals — in all of the 3 possible two-way comparisons, in two Quality Checks and one Reality Check. At this point, students already have begun to learn about the modes of thinking-and-action used in design. Then you can decide how deeply to explore these modes and their interactive relationships, for your own learning and for teaching students. [ And in separate questions, you can decide how deeply to explore ways to promote an effective combining of Creativity with Critical Thinking and Cognition with Metacognition for your own learning, and for teaching students. ]Below you can see this progression in three verbal-and-visual diagrams — Simple & Basic & Detailed — that show the Two-Step Cycle & Three Comparisons & Interactive Relationships between Modes, respectively. If you haven't yet opened the diagrams-page in its own window, you can do this now to see these diagrams (and others) in their full-size undistorted clarity, instead of the shrunken images below.
| A Two-Step Cycle — The Simplicity of Design The following description of design is simple yet fairly accurate: After some preparation-and-decisions (by finding information, choosing a design-objective, defining solution-goals), designers use a cycle of generating options and evaluating options: prepare → generate-evaluate-generate-evaluate-... An optional second step in teaching with a Two-Step Cycle is to use an expanded diagram — two versions of it are shown below the basic Simple Diagram in the diagrams-page — that distinguishes between Mental Quality Checks (used to Plan a Strategy) and Physical Quality Checks (used to Monitor the Strategy and its Application). This diagram, and the Plan-and-Monitor process of design that it shows, is explained later in the page, for a Learning Strategy. Three Comparisons — The Strategies of Design |
Simple Diagram |
⇒ |  Basic Diagram |
| More? After helping students learn the Two-Step Cycle and Three Comparisons, a teacher can decide how much to explore & explain the modes of thinking-and-action in design. Interactive Relationships between Modes — The Details of Design This Detailed Diagram of Design Process verbally summarizes the thinking-and-action in each mode, and visually organizes the interactive relationships between modes. The diagram's logical organization and mode-labels help you mentally visualize the interactions between modes, and understand the integrated relationships within this model of Integrated Design Process. The Basic Diagram shows the same relationships and most of the modes, but in less detail and without the “1A,...4A” labels for modes. These functionally integrated relationships are analyzed below in two additional ways, in a Colorized Verbal Summary and a spatially organized Verbal-and-Visual Table. |
Mode-Words (Choose Project,...) and Mode-Numbers (1A,...)
As described earlier, for your own learning you can use Mode-Words and also Mode-Numbers, but for teaching you may want to use only Mode-Words.
When you're teaching Design Process, if you use only mode-words your students won't have to memorize mode-numbers (1A,...) and translate these numbers into concepts (... Preparing, Inventing,...). This simplicity — with the mode-word describing the mode-concept, what the mode is — will make it easier to
include design-modes
in your discussions of design-process.
But mode-numbers do offer some advantages. The most important benefit is providing a structure that makes it easier to think about groups of different-yet-related actions (defining in 1A-1B, generating in 2A-2B-2C-2D, evaluating in
3A-3B, coordinating in 4A) and the many kinds of relationships within groups and between groups.
Even if you don't regularly use mode-numbers for teaching, you can explain these groupings (1, 2, 3, 4) to your students, and give them links to this page and An Introduction to Design in case they get excited about design and they want to explore it more deeply.
And here is another benefit: mode-numbers allow representations that are more compact verbally and visually, as in the Detailed Diagram above, and below in the summaries of relationships.
You can find additional tips for teaching with Design Process throughout Problem Solving & Metacognition in Education. Reading (or skimming) the introductory Overview-Summary is a quick way to discover ideas you may find interesting and useful.
A Colorized Verbal Summary of Interactive Relationships between Modes
During your reading about the 9 modes of thinking-and-action used in design you've noticed that "action
in one mode often involves [or depends on, or is related to] action in other modes," as described in Modes - Flexibility & Overlaps and illustrated in these interactive relationships:
INFORMATION about Options (for a product, activity, or strategy) and Predictions, Observations, or Theories— that is GENERATED in 2A-2B-2C-2D by remembering-or-locating old information (in 2A) and by inventing-or-producing new information (in 2B-2C-2D) — is used for EVALUATION in 3A-3B: Predictions (for actual Option-Properties) and Observations (of actual Option-Properties) can be used in two ways, in 3A by comparing either with Goals (for desired Option-Properties) in Quality Checks used for Design, or in 3B by comparing them with each other in Reality Checks used for Science;
the quality-check EVALUATIONS in 3A are used in retroductive logic that guides a GENERATION of old Options (by finding-and-selecting in 2A) and new Options (by inventing in 2B); the reality-check EVALUATIONS in 3B are used in retroductive logic that guides a GENERATION of old Theories (by finding-and-selecting in 2A) and new Theories (by inventing in 2B);
in similar ways, EVALUATIONS from both 3A and 3B are used in retroductive logic that guides a GENERATION of ideas (old found in 2A, or newly invented) for experimental systems that let you GENERATE useful Predictions and Observations by finding them (in 2A) or by producing them with Mental Experiments (in 2C) or Physical Experiments (in 2D).
Two Verbal-and-Visual Summaries of Interactive Relationships between Modes
The interactions described above are summarized and spatially organized in a Detailed Diagram and this table:
GOALS are used as Evaluation Criteria
( you define Goals for the Desired Properties of a satisfactory Problem-Solution, in 1B ) |
|||
USE - Compare PREDICTIONS vs GOALS (Actual Properties vs Desired Properties) this is a Quality Check in 3A - for Design |
USE - Compare OBSERVATIONS vs GOALS (Actual Properties vs Desired Properties) this is a Quality Check in 3A - for Design |
||
Generate & Use OPTIONS that are possible Problem-Solutions |
Generate & Use PREDICTIONS for Predicted Properties of OPTION |
Generate & Use OBSERVATIONS for Observed Properties of OPTION |
|
Find (Remember or Locate) to GENERATE Old Options in 2A |
OLD |
Find (Remember or Locate) to GENERATE Old Predictions in 2A |
Find (Remember or Locate) to GENERATE Old Observations in 2A |
Revise/Invent retroductively to GENERATE New Options in 2B |
NEW |
you do a Mental Experiment to GENERATE New Predictions in 2C |
you do a Physical Experiment to GENERATE New Observations in 2D |
PREDICTIONS for Experimental System (for Option-Properties & more) |
OBSERVATIONS of Experimental System (of Option-Properties & more) |
||
USE - Compare PREDICTIONS & OBSERVATIONS (this is a Reality Check in 3B - for Science),
retroductively search for a match - by selecting an Old Theory, or inventing a New Theory |
|||
Now we'll look at four modes in more depth: 1A (choosing an overall objective for a design project) plus 1A-and-1B (Objectives, Goals, and Perspectives), 3A (evaluative Quality Checks in Design), 3B (evaluative Reality Checks in Science), and 3A-3B (Combining Creativity with Critical Thinking in Design).
Mode 1A — Initial Interactions in the Process of Design
Action in this project-initiating mode requires action in other modes: Building a solid foundation of knowledge by finding information (in 2A) is an important part of the preparation that lets you make wise decisions in 1A, and also 1B. Before you conclude (in 1A) that a problem exists and a problem-solution is worth pursuing in a design project, you must recognize that when you compare (in 3A) the properties of known product-options (found in 2A) with the desired properties you want (defined in 1B), none of the currently known options is a totally satisfactory match, and you can imagine (for goals in 1B, and new possibilities in 2B) that a better match might be possible.
Modes 1A & 1B — Objectives, Goals, and Perspectives
Although objectives and goals are similar in some ways, they occur at different levels of the design process. But levels can be defined in different ways, so achieving one of several desired goals (defined in 1B) could be viewed as achieving one sub-objective within the overall objective of a design project (defined in 1A). For example, if one goal of designing a minivan is to have a certain type of body, this goal can be viewed as a sub-objective when making plans for the overall design project.
In a large company the distinctively different types of goals for a minivan (for the desired properties of its body, engine, marketing appeal,...) will be pursued in different areas of the company. Pursuing a satisfactory solution for each major type of goal (and the goal-properties within it) can be considered a design project in itself, semi-independent from efforts to find solutions for the other goals, yet coordinated within the company's overall design project.
As you can see, in some situations the distinction between 1A and 1B depends on perspective, on how parts-of-the-process are viewed. An activity could be viewed as pursuing one design goal (in 1B of an overall project) by the company's CEO, but as a design project (in 1A) by the people who are responsible for this part of the company's overall project. {more about Design by Groups}
Mode 3A — Quality Checks in the Process of Design
Action in Mode 3A — when you are using Quality Checks to evaluate options for a product (or activity, strategy, theory) — can stimulate action in other modes, for various purposes:
2B – Invention of Product-Options: Your evaluations of product-options will reveal mismatches between option-properties and your desired goal-properties. This may inspire you to revise some options, especially those that show potential for becoming a satisfactory solution, to convert them into new options. This continuing research-and-development uses a process of Creative-and-Critical Retroduction. Of course, you can also revise options for an activity, strategy, or theory.
2B – Invention of Theory-Options: While you're doing Quality Checks — comparing goals with both predictions and observations — you may notice that, for the same experimental system, predictions and observations don't match. This mis-match, which is a “failed” Reality Check, can motivate you to think about your theories and ask whether one (or more) of them should be revised. Or, in a direct use of Quality Checks for a theory, you can check whether a theory matches your goals for conceptual quality and cultural-personal quality.
2A,2C-2D – Preparation with Improved Knowledge: Your evaluation of multiple options using Quality Checks in 3A (or Reality Checks in 3B) may reveal a knowledge-gap where you need more information (old found in 2A, or new produced in 2C-2D) about some properties for some options. This awareness can produce cycles of evaluation-and-generation, with evaluation followed by generation of more information that you then evaluate & interpret, and so on. During each cycle your knowledge will increase. Over a period of time you will generate more and more information (old & new) about the properties of options, so your evaluation-conclusions about options may change with time.
1B – Weighting of Goal-Criteria: A comparative evaluation where each of two options "has its own strengths" (as in Competing Partial Solutions) may motivate you to think more deeply about the relative importance of different goals-based evaluation criteria. This could lead you to change their weighting-emphasis, and your strategies for achieving an optimal balance between
criteria.
1B – Changing of Goal-Criteria: Multiple evaluations may persuade you that revising a goal will be beneficial, or even is necessary. Your original standards may now seem
impractical and too difficult to achieve, so you lower the standards and are willing to settle for less. But you might raise the standards if you recognize new
possibilities
for a product with properties that are better (or just different)
than what you could imagine earlier in the process of design.
1A – Solving the Problem: Eventually
you may decide that one (or more) of your options is sufficiently satisfactory, so you accept it as a solution for the problem. I say "sufficiently" because even if an option is not totally satisfactory, you may decide that it's “good enough to accept” due to urgency (if a partial solution now is preferable to a better solution later, due to the disadvantages of delay) or because you want to end this project so you can invest more of your time in other projects.
1A – Not Solving the Problem: Or you may want to delay work on this project for awhile, just to take a break from it, or because later you can take advantage of information or technology currently being developed by others. Or you may decide that the project is
not worth continuing, so you abandon it.
1A – Finding New Problems: You can revise your overall objectives, based on what you have learned
during the process of design. And you might be inspired to generate new projects
that are “spinoffs” related to the current project, or are “new” and not closely related.
1A – Finishing a Project: When you have decided to accept one option as a problem-solution, the project might seem finished. But it's not yet finished if you then begin manufacturing, marketing, and selling the product. Each of these (manufacturing,...) was an important factor to be considered when you made a decision about a problem solution, so you've already been thinking about them; but now each could be considered a new design project (or sub-projects within the continuing overall project, depending on perspective) with its own set of design-decisions, including...
1B – Quality Control for Actualizations: For most design projects, a solution must be actualized — converted from an idea into reality, by producing a product, doing an activity, or applying a strategy — and you can use Quality Control to control (to observe-and-improve) the quality of actualization. To illustrate the process of Quality Control, my Analysis of a Cognitive-and-Metacognitive Strategy for Learning carefully examines a design project that has two objectives – to improve the quality for a strategy and also your application of the strategy. You can pursue both goals with an observing-and-improving process of design, using evaluative Quality Checks (for a strategy) and Quality Controls (for a strategy-application). The same logic is used to evaluate the quality of either a strategy or its application; you compare the properties (predicted or observed, for a strategy or strategy-application) with desired properties (defined by your goals for an ideal strategy or strategy-application) in a Quality Check that I'm calling a Quality Control when it's used to evaluate an actualization, such as the application of a strategy. The process-of-design used for a Learning Strategy (when you are trying to improve the quality of your learning, thinking, and performance) can also be used, with minor modifications, for the actualization of any strategy, product, or activity.*
For example, a manufactured product may work well (in principle) if it's well made, but (in reality) perhaps too often the manufacturing process produces a defective product with flaws that make it unsatisfactory, so you think about changing the product or the method of producing it. Or, if you're a basketball coach with a strategy for a game, if your team loses (or wins) it might be due to either the strategy, or your players' performance in executing the strategy; or maybe the other team just had better players (or worse players), or there were other contributing causes:
Actualized Strategy (=
Strategy + Strategy-Application) → Observed Results.
2C – Quality Control for Predictions: If a theory is “actualized” when it's used to make predictions, or if we consider predicting to be a strategy, we can use Quality Control to observe-and-improve the quality of making predictions. A summary of strategies for predicting is in Mode 2C.
Modes 3A & 3B in Science & Conventional Design
One part of An Introduction to Design examines relationships between science & conventional design and explains why I claim that science is a special type of design in which the main objectives are to design accurate theories and useful experiments, rather than a product, activity, or strategy. Because science is design, both use similar methods of problem solving, which are coherently organized in my models of Integrative Design Process and the closely related Integrative Science Process (aka Scientific Method).
Mode 3B - Reality Checks in the Process of Science (and Design)
Action in Mode 3B — when scientists use Reality Checks by comparing theory-based predictions with observations — is the essential foundation of modern science.
But science is more than just Reality Checks and theories. Why?
First, when scientists select a theory (in 2A) or invent a theory (in 2B) they use a Reality Check (in 3B) to test this theory's empirical quality, but they also want a theory with conceptual quality and cultural-personal quality, and these non-empirical properties are evaluated in Quality Checks (3A). These three evaluation factors (empirical*, conceptual, and cultural-personal), and their use in scientific methods of thinking, are explained in Sections 1-4 of An Overview of Scientific Method which for the empirical factor (*) describes Degree of Agreement (this is directly tested in Reality Checks) and also Degree of Predictive Contrast (determined by comparing a theory's predictions with the predictions of other theories). / Later, I say "Competing Partial Solutions are possible... for a theory." When this occurs — usually because two theories make similar predictions, so they have a low predictive contrast — non-empirical factors (conceptual & cultural-personal) play an increased role in theory evaluation.
Second, "another major activity for scientists is designing experiments, because observations are essential for Reality Checks; improving our knowledge about nature (with observations) is essential for improving our understanding of nature (in theories). ... In their everyday work, the usual objectives of scientists are to design experiments so they can make observations, and to select-or-invent theories that explain the observations, so we can understand what is happening and why."
Both aspects of science, improving our explanatory theories and our experimental information, are important when scientists are generating knowledge. And both of these ways to generate knowledge can be useful during conventional design.
Part 3a :
Combining Creativity and Critical Thinking in Design
An Introduction to Design says, about creativity and critical thinking, "these mutually supportive skills are intimately integrated in the problem-solving methods used in a wide range of ‘design’ fields... where the objective is to design a product, activity, strategy, or theory." During the process of design an important use of creative-and-critical thinking is for a retroductive generation of ideas:
In contrast with deductive logic that asks, when making a prediction, “If this is the theory, then what will be the observations?”, retroductive logic asks a reversed question in the past tense: “These were the observations, so what is the theory?” The essence of evaluation-based retroductive inference is doing mental experiments, over and over, each time “trying out” a different theory-option that is being proposed (by selection [in 2A] or invention [in 2B]) with the goal of producing predictions (i.e. theory-based deductions) that match the known observations. Basically, the goal is to find a theory that, if true, will explain what has been observed. { quoted with minor revision from Section 5, about Theory Generation, in A Detailed Overview of Integrated Scientific Method }This is a creative use of critical-thinking Reality Checks (in 3B) for a theory. In a similar way, you creatively use critical-thinking Quality Checks (in 3A) for a product (or activity, strategy, theory) when you use evaluation-based retroductive inference by doing mental experiments, over and over, each time “trying out” a different product-option that is being proposed (by selection in 2A, or invention in 2B) with the goal of seeing [in your predictions or observations] option-properties that match your desired-properties.
Competing Partial Solutions
getting the best of both: Imagine that your best current options are a2 and b2, but they are very different and each has its own strengths. Instead of limiting yourself to viewing this only as a competition of a2-versus-b2, so it's a2-OR-b2, if you think “a2-AND-b2” this may motivate you to seek a creative solution that combines the best of both. You could ask “how can I revise a2 so it will gain some of the unique benefits of b2, or revise b2 so it also has what a2 offers?”, or you can try to invent an innovative new “ab” option that is a hybrid of a-and-b, combining the best of both while minimizing their weaknesses.
deciding what is most important: But if neither of these actions (revising or combining) seems possible, so you're forced to choose between the differing benefits of a2 and b2, you'll have to think carefully about your goals to decide which goal-properties are more important, based on your values and priorities.
Retroductive Generation can be Convergent or Divergent
In science when you retroductively search for the best theory, guided by Reality Checks (in 3B), Competing Partial Solutions are possible. But competing solutions are much more likely in conventional design when you retroductively search for the best product, activity, or strategy, guided by Quality Checks (in 3A), because you are more likely to see a tough competition where "each has its own strengths" with each option being better for matching some goal-properties but not others.
Why is there a difference in location on the spectrum of convergence-to-divergence when you are retroductively inventing a theory instead of another objective? Here is an explanation for why the potential amount of divergence depends on objectives, in a comparison of retroductive generation for experiments (which are a special type of activity) and theories: "In the process of experimental design [which is the generation-and-evaluation of options for science experiments] the divergent objectives — looking for experimental outcomes that might be interesting or useful — are less clearly defined than during theory-retroduction where, despite a divergent search for theories [by testing many theories in multiple Reality Checks], the convergent objective is to find a theory whose predictions match the known observations."
Despite some claims of radical postmodernism — which takes a rational idea and “runs with it” to silly extremes —
nature operates in a particular way, and if some scientists describe it in another way their science is wrong. Sometimes different theories offer different perspectives
on the same truth (with truth defined by “the way it is” or “the way it happened”), or different theories inspire different useful ways of thinking about nature and doing science. But there is a limit to the possible divergence in scientific theories that are true and/or useful. Reality puts constraints on our search for truth, and thus on possibilities for divergence if want to find truth. We see these constraints in Reality Checks. {for more about silliness and rationality, truth & truth-claims, The Solar System Question, two types of reality (humanly-constructed & human-independent) and more, check Reality 101 and Should Scientific Method be EKS-Rated?}
A Divergent Search for New Objectives
Even when retroductive searching strategies are divergent, usually the focus is on finding the best solution for a particular problem with its specified objectives. In a different type of divergence, we can ask “what kinds of problems might the current options solve?” The purpose of this retroductive search is to find potential new applications for a known option (as-is or modified to make it more effective for a different application), to find new objectives for new design projects.
The Many Possible Results of Retroduction
In a section symbolically located between the modes we use for generation (2A-2B-2C-2D) and for evaluation (3A-3B), Generation-and-Evaluation briefly describes the many ways that retroductive inference can be used, for a generation (by selection or invention/improvement) of problem-solving options (for a product, activity, strategy, or theory) or for an experimental system that lets you produce useful predictions or observations.
Other Perspectives on Design Thinking
In this page the focus is on my model of Integrated Design Process. But other educators & designers also have contributed useful ideas about the process of design.
My model for Integrated Scientific Method was constructed in the context of current scholarship. This model of science was built on a solid foundation of knowledge, after I did extensive research to learn what other scholars (scientists, philosophers, historians, educators, psychologists, sociologists,...) have written about the methods of thinking that are used by scientists, as individuals and in communities.
By contrast, my construction of a model for Integrative Design Process was mainly independent from current scholarship. This model of design was built on a solid foundation of logic. I simply thought about what designers think-and-do during a process of design, analyzed their thinking-and-actions to find the functional relationships, and organized all of this into an integrative framework.
Recently, however, in November 2011 I've begun to look more closely at what others are saying about Design Process. Now I'm learning from them, and so can you. Here are three examples:
I.O.U. — soon, probably by Tuesday afternoon, Nov 29, I'll finish writing the end of this subsection, with examples from the new K-12 Science Standards, Science Buddies (comparing Design Process & Scientific Method), and the Stanford Design Program. / Here are some rough ideas you can ignore until the revision/completion:
• The new Framework for K-12 Science Education emphasizes the importance of Scientific and Engineering Practices by including this combination as one of the 3 "dimensions" outlined on page 4 of its Executive Summary. /
• Science Buddies compares Scientific Method and Engineering Design Process. / Scientific Method [ok] & Engineering Design Process [ok] and comparing them [? parallels not correct?] &
• Leigh Abts -- Scientifific Reasoning and the Design Process -- “Edison's genius lay in his ability to conceive a fully developed marketplace, not simply a discrete device. He was able to envision how people would want to use what he made, and he engineered toward that insight.” -- similar to "empathy" step by Stanford BB
• Among its many activities, the Stanford Design Program has a "bootcamp" and their "Bootcamp Bootleg" document describes specific ways to do thinking-and-action in some modes, including 1B (using "empathy" to define goals that will meet the felt needs of those you want to provide a service for) and 2A (to inspire creative ways to generate ideas) and more, that I'll discover when I explore their file more thoroughly. [they do this without claiming “a model for a method”, they just show useful ways to stimulate productive design thinking, and useful ways to view the process of design.]
Stimulating Productive Creative-and-Critical Thinking
Many aspects of design process encourage a synergistic blending of creative generation and critical evaluation, with each supporting the other to make the thinking more productive. When ideas are flowing smoothly during high-quality design thinking, you quickly shift back and forth between aspects of thinking (creative, critical,...) and modes of thinking, in ways that are intuitive and spontaneous.
Productive Retroduction: During creative-and-critical retroduction you "quickly shift back and forth" to generate options (old in 2A or new in 2B) guided by predictions (2C) and evaluations (3A or 3B) in an effort to find an option (for a product, activity, or strategy) that matches the desired properties you have defined (in 1B) or to find a theory whose predictions match the observations you know (from 2D). This process of Retroductive Reasoning is explained above and in An Introductory Overview of Retroduction.
Productive Strategies: Sometimes productive creative-and-critical thinking, during retroduction and in other ways, can be stimulated by a conscious use of strategies. For example,...
Tools for Analysis: The introduction to retroductive invention suggests "analyzing the old option into different aspects." In the left-side table below, for a particular product-option (with Aspect A,...) you can look at the column for Goal 1 and ask “which aspect is causing a matching or mismatching of the option's properties with desired goal-properties, and how can I revise this aspect to improve the partial matches and reduce any mismatches?” and then ask these questions for Goal 2,...
Two Perspectives when Using a Table: As just described, you can focus on the column for a goal and ask “which aspects could be revised (in what ways) to achieve a better match with this goal?” Or you can focus on the row for an aspect and ask “how can I revise this aspect to achieve a more optimal matching of all goals?” Or you can do both, when you have mis-matches for multiple goals, and (in an effort to achieve better matches) you want to consider all possible options for revising aspects.
If you find a conflict between achieving different goals — for example, if revising Aspect A to achieve a better match for Goal 1 makes it less satisfactory for Goal 2, ask “how can I modify Aspect A to produce the best overall result with the least amounts of goal-mismatching?”, and when defining " best overall result" you can weight the goals by asking “which evaluation criteria are more important, those for Goal 1 or for Goal 2?” Or you may find that the best overall result is produced by a combination of revisions; for example, if a particular revision of Aspect A makes the product-option worse for Goal 2, the amount of this goal-mismatch might be reduced by one of the possible revisions for Aspect B, or C, D,...
|
 |
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Or instead of making a table of options-and-goals, you could make it with options-and-properties, and include an extra row that lists the goal-properties of your ideal solution. These will be similar because you define goals for properties, but you may find one perspective more useful than the other. With either perspective, the table can help you notice knowledge gaps. If any table-cells are empty (with no information for predictions and/or observations) or have inadequate information (if it's imprecise or you suspect it might be inaccurate) you can decide whether it's worthwhile to gather additional information (old in 2A, or new in 2C or 2D) with predictions or observations, or both.
Productive Thinking — by combining Creative Thinking with Critical Thinking
Thinking that is productive is useful in all types of design, including science. You'll find strategies for improving the productivity of thinking, by effectively combining creativity with critical thinking, in my 9-part Detailed Overview of Integrated Scientific Method: Theory Generation, in Part 5, explains invention by Retroductive Generalizing, by Revision Using Analysis, and more; Generation-and-Evaluation of Experiments, in Part 6, includes the quotation about divergence (for experiments) and convergence (for theories) in retroduction; and Part 9 examines Productive Thinking that combines creative thinking and critical thinking, uses memory (which "is not sufficient for productive thinking, but is necessary"), is inspired by motivation, and is featured in a separate page that includes this advice for creativity:
The process of inventing useful ideas requires both aspects of thinking (creative and critical) but being overly critical, especially in the early stages of invention, can stifle creativity. We shouldn't hinder the motion of a car by driving with the brakes on, and we shouldn't hinder the flow of creativity by thinking with restrictive criticism. But a car needs brakes, and a creative person needs critical thinking. One strategy for creativity is to “play games” with the aspects by shifting the balance in favor of creativity for awhile, by experimenting with different balances between the aspects during different stages in the overall process of productive thinking. For example, [design teams can try to] enhance creative thinking by using a technique of brainstorm-and-edit. ...
Strategies for Creative Thinking: My page for Productive Thinking continues (beginning with "brainstorm-and-edit...") by summarizing a few strategies for stimulating creativity. These strategies are explored more deeply in my links-page for Creative Thinking in Education & Life.
Strategies for Critical Thinking: Another links-page is for Critical Thinking in Education & Life. My page about Productive Thinking doesn't say much about critical thinking, just a brief paragraph with two sentences: "... A simple two-part strategy for skillful critical thinking is knowing how to do it (as explained in Parts 1-4 [of my model for Scientific Methods of Thinking-and-Action]) and deciding that you will do it, which requires motivation (wanting to do it) and discipline (so you'll do it consistently)." Another principle is summarized above; if a situation has Competing Partial Solutions and you cannot generate a hybrid that combines the best of both, and a decision is necessary, you must acknowledge the need for compromise, weigh the pros & cons, and "decide which goal-properties are more important, based on your values and priorities." Doing this well requires a careful consideration of your values and priorities.
Allow Creative-and-Critical Productive Thinking: As explained in Part 3b below, sometimes "thinking about thinking" is a good way to stimulate and guide productive thinking, but when ideas are flowing smoothly you can just "go with the flow... and allow productive thinking."
Part 3b :
Combining Cognition and Metacognition in Design
In early 2012, I began developing a new website with
many improvements (by revising, adding, cutting),
so I strongly recommend that you
read it instead of this page.
When you think about thinking for the purpose of improving the quality of your thinking, learning, and performance, this is metacognition because it's cognition about cognition. { principles & applications of metacognition }
Combining Cognition with Metacognition in the Process of Coordinating Design
During a Coordinating of Design-Actions — when you evaluate your process of design and make decisions about using design-actions (defining, generating, and evaluating, plus coordinating) in the 9 modes of thinking-and-action — you are using cognition and (when you think about the thinking you use to make decisions about actions, and also to perform actions) also metacognition. Ideally, your cognition-and-metacognition will be smoothly integrated into an effective combination.
This view of a “combination for effective thinking” is consistent with the views of other educators & psychologists, including Jennifer Livingston who explains how cognition and metacognition "are closely intertwined and dependent upon each other... [and] may overlap...", but there are differences "in how the information is used... [and for metacognition you] actively utilize this information to oversee learning." Even though cognition and metacognition are intertwined and may overlap, it can be useful to think about effective ways of using either cognition or metacognition, or both together, so that's what you'll see in the following ideas.
• Avoiding Metacognition: Sometimes your best strategy is to go with the flow and just “let it happen” when it's going well and "ideas are flowing smoothly... in ways that are intuitive and spontaneous." When a process of problem solving (or reading, writing, listening,...) is going well, probably you should continue focusing on what you're doing. Avoid “thinking about thinking” and just think. By doing this, you allow productive thinking.
• Using Metacognition: But in some situations you'll want to stimulate productive thinking by making plans (asking “what is the best use of my time right now?” or thinking carefully about longer-term plans) or by finding strategies to improve the quality of your thinking.
• Regulating Metacognition: How can you know when to avoid metacognition, and when to use it? If you develop an accurate knowledge of yourself in the contexts of different situations, this will help you decide when you should focus on whatever you're doing, and when you can pause to think about planning and strategies.
• Within Modes & Between Modes: When you regulate cognition-and-metacognition, your dual goals are to get maximum effectiveness in two ways – within each mode when you're thinking & performing in that mode, and between modes with interactions that are mutually supportive and productive, because action in the modes is skillfully coordinated.
• Coordinating Actions: When you're not deeply engaged in productive thinking-and-action, this interlude might be a good time to ask “what should I do next?” to coordinate your actions, or stimulate your thinking. Or maybe you're making fast progress, but you wonder if you're moving in a productive direction, so you slow down to examine what you're doing. The result of this reflection may be a quick return to your work with renewed vigor. Or when you ask “is this the best use of my time” you may shift to another part of the project, or even a different project, if you think it has higher priority because it's more urgent and/or important, or is a better way to make progress, so it's a better way to invest your valuable time.
• Using Intuition Wisely: When you reach a point (like after a Quality Check) with many possible next-step actions, which action do you find most emotionally appealing? If you do this action, will you be more motivated to do it well, in a way that makes your work more productive? Is your feeling an indication that at an intuitive level (which may or may not match what you would decide with a purely logical evaluation) you think this action will be the best use of your time? Or are you taking “an easy way out” by avoiding other actions that you should do, that might be more productive although less fun?
• Opportunities and Timing: If you're procrastinating to avoid a high-priority project (or part of a project) even though you think it is urgent and/or important, you should ask “why?” Do you think the work will be unpleasant? does the project seem overwhelming, impossible for you to finish or do well? are you having any doubts about whether you should do it? After “thinking about your thinking” for the purpose of discovering what is causing your delay, you can plan a strategy for action (to end the procrastination) so you'll have good timing that "lets you take advantage of opportunities while they're still available."
• Preparation and Production: If you are writing a paper, what is your best strategy? Maybe you need to learn more about the topic by Preparing (in Mode 2A) to build a stronger foundation of knowledge that will help you produce ideas worth sharing in your paper. Or maybe you already know enough (from 2A) and the best use of your time is to begin the process of producing the paper by Imagining (2C) and Writing (2D).
• Stimulating Creativity: Maybe you are not satisfied with your Invention of New Options (in 2B) so you try to increase the quantity and quality of your inventions by asking questions, using principles & techniques for Stimulating Productive Creative-and-Critical Thinking: Is my creativity constrained by unwarranted assumptions that limit the flexibility of my thinking? should I try to escape my rigid mental ruts with a free-flowing session of minimally restricted brainstorming? Or should I try to stimulate my creativity in a disciplined context of analysis by using an aspects-and-goals table to help me systematically search for ways to reduce mismatches between an option's properties and my goal-properties, or by using an options-and-properties table that might help me see things in a new way so I can more easily imagine new possibilities?
Optimal Performance
Regarding mental focus and peak performance, my page about Test Anxiety and Exam Performance says, "However you feel, it's
OK, because whether
you feel excited
or relaxed, it's what you do that counts, so just concentrate on the here-and-now
action of answering the exam questions." And I summarize ideas from Tim Gallwey (who wrote The Inner Game
of Tennis and
similar books for other areas of life), including a formula for performance:
POTENTIAL - INTERFERENCE = PERFORMANCE,
where POTENTIAL Performance depends on your ABILITIES-plus-PREPARATION,
and INTERFERENCE is minimized by ignoring “extras” that would
distract you, which
ALLOWS YOU to do Productive Actions that will maximize your Actual PERFORMANCE.
You can increase the quality of your PERFORMANCE by increasing your POTENTIAL with better PREPARATION or (in a program of intentional self-improvement that views ability as a combination of what you were born with plus how you develop it during life) by increasing your ABILITIES, or by decreasing INTERFERENCES.
After you have identified sources of interference, how can you minimize them? Should you just tell yourself to “stop doing that,” or will other approaches work better for you? Some useful strategies for reducing interference that is related to anxiety, in the context of exams, speaking, music, and athletics, are in a section on Optimal Performance covering many types of skills (mental, physical, social,...), extending beyond cognition and metacognition (the focus in this section) to include a wide range of abilities — linguistic, logical-mathematical, musical, spatial, bodily kinesthetic, intrapersonal, interpersonal — in our multiple Intelligences which include our designing-and-actualizing of mental and physical strategies. These intelligences can be improved by a skillful use of Learning Strategies.
During any activity your focus can be short-term or long-term, with a main objective of optimal performance (by doing in the present) or optimal education (by learning for the future) because you can Learn from Experience by using past learning for the present, or present learning for the future, or some combination of both.
Off-and-On Metacognition: In the beginning of this section you'll find ideas about avoiding, using, and regulating metacognition: "Sometimes your best strategy is to go with the flow and... allow productive thinking. ... But in some situations you'll want to stimulate productive thinking by making plans... or finding strategies." If you "develop an accurate knowledge of yourself and your situations" this will help you regulate metacognition by turning it on and off, to maximize its actual positive effects (in helping you improve your thinking, learning, and performance) and minimize its potential negative effects (in being a distracting INTERFERENCE that will reduce performance) in an effort to achieve optimal benefits.
In situations where metacognition is unproductive — if there is too much introspection of the wrong kind with the wrong timing — the difficulty is not metacognition, it's unskillful metacognition, due to a deficiency in skillfully using metacognition. A few useful principles for combining cognition with metacognition are described above, and you'll discover more from other educators and from your own experience. One principle is the importance of developing the valuable metacognitive skill of knowing yourself (and your situations) well enough to know when to use metacognition, and how to use it, so it will be optimally beneficial.
Using Cognition-and-Metacognition — in Design and for Education
Performing and Learning
My friend became an expert welder by learning how to learn. He continually improved by following the wise advice of his teacher, “every time you do a job, do it better than the time before.” Here are some principles for performance and learning:
when you're doing an important job, so you're on-task with a performance objective, you should ALWAYS concentrate on quality of thinking-and-action in the present, which SOMETIMES involves metacognitively asking “how can I do it better” and “what have I learned in the past that will help me now?”; and OCCASIONALLY you'll ask “what can I learn now that will help me in the future?”,
but at other times in life you'll be on-task with a personal education objective, when asking “what can I learn now?” is the top priority.
Below, we'll look at five strategies that include both objectives, for performance and personal education.
Five Strategies (used in the Process of Design, and for Education by Learners & Teachers)
The logical foundation for this section is a section about "Five Types of Strategies" in another page. If you want, you can read it before continuing in this page: first read paragraph #4 (it's part of the summary for An Introduction to Design); after clicking its link for "five strategies" you can read a summary of Coordinating Actions (in Mode 4A) and The Five Strategies. Then your back-button will return you to here for the five strategies:
pC/pMC, which is used for Coordinating Actions in Mode 4A, is the strategic process-of-design, that combines Cognition (in pC) with MetaCognition (in pMC), where the design-objective is any strategy (for learning, teaching, or anything else) or is a product, activity, or theory.
eL is using design-for-education when, as a Learner, your objective is to improve the quality of your thinking-and-learning; eT is educational design when, as a Teacher, your dual objectives are to help your students improve the quality of their thinking-and-learning and to improve the quality of your teaching. These two types of design projects, in which the design-objective is an educational strategy (viewed from the perspective of a Learner in eL, and a Teacher in eT), use a process-of-design (pC/pMC, described in the previous section) with the objectives of improving your uses of Cognition-and-Metacognition for Learning and Cognition-and-Metacognition for Teaching.
ne is a design project where the objective is any other kind of strategy, so it's a non-educational strategy.
Process and Result: pC/pMC is a process-of-design strategy (used for Coordinating Design-Actions) that occurs inside the process-of-design, but three strategies (eL, eT, ne) are an overall result-of-design, and are used outside the process-of-design.
4 of the 5 strategies are examined above & below, in 3 sections: as shown in the Venn Diagram, the process-of-design strategy pC/pMC (Combining Cognition with Metacognition in the Process of Design) can be used for any design project, including eL (A Learning Strategy using a Quality Control process-of-design that can be used for the actualizing of any product, activity, or strategy, including ne) and eT (Teaching and Coordinating for a Design Team).
In early 2012, I began developing a new website with
many improvements (by revising, adding, cutting),
so I strongly recommend that you
read it instead of this page.
Learning Strategies (developing Strategies and improving their Application)
One valuable educational application for design is a Cognitive-and-Metacognitive Strategy for Learning that uses an observing-and-improving process of design to improve the quality of learning, thinking, and performance. Below, you'll see three analytical descriptions of this process:
First, A Simple Overview describes the basic process of design that is used for a Learning Strategy
Second, A Basic Analysis explains the ideas in a less condensed way (so the ideas may be easier to understand) and with more details of the design process.
Third, A Cycle-of-Design Analysis (including Quality Control) is similar to the Simple Overview, but it uses additional terms from Design Process (Quality Checks & Quality Controls) and describes more details.
Quality Control is used to observe-and-improve the quality of your actions when you apply a strategy for learning. In this section the objective is to improve a learning strategy, but a similar design process can be used (with appropriate modifications) for other design projects where you use Quality Controls to observe-and-improve the quality of actualizing a solution by applying a teaching strategy or non-educational strategy, or producing a product, producing an activity, or using a theory.
A Simple Overview
To begin the process of design for a Learning Strategy, shown in the diagram below, you Choose an Objective (we'll imagine that you want to improve your learning in psychology lectures) and Define Goals (you want to understand more accurately-and-thoroughly, remember what you have learned,...). You PREPARE by searching for strategies about “how to learn more in lecture” from other people (what do they recommend, and why?) and by remembering what you've done in the past and how well it worked.
MAKE A PLAN: During this PREPARATION and afterward, you GENERATE OPTIONS for Lecture-Strategies (from others or from your own experience, maybe with revisions) and you Evaluate each Option by imagining (Predicting) the Results (how well it would work to help you learn) so you can CHOOSE a Strategy-Option (which might combine several sub-strategies) to use in the first lecture.
OBSERVE-and-EVALUATE: During the first lecture you USE the chosen Strategy and you OBSERVE what is happening; you observe the situation (is the lecture what you expected?), your Actions (are you applying the strategy the way you wanted?) and the Results (are you learning effectively?). During the lecture and afterward, you Evaluate the Strategy (and your Strategy-Application) by comparing your Observations of the Results (and your Actions) with what you wanted to happen (in your Goals for your Actions in applying the Strategy, and the Results of your Learning when you Use the Strategy) so you can EVALUATE your Strategy-Application & the Strategy.
During the "EVALUATION" part of the cycle, what are you evaluating? Have you figured out the color-symbolism and its meaning? It's analogous to color mixing with pigments (blue + red → purple), showing that your Learning Result depends on two factors, the Strategy you choose and the quality of your Strategy-Application, which can be summarized as (Strategy + Strategy-Application → Results). Therefore, you cannot evaluate an isolated Strategy. You can only evaluate a Strategy in the context of an Application of this Strategy. And you can try to predict how well the Strategy could work (what Results do you predict?) if you improve the quality of your Strategy-Application. And you can think about ways to improve your Strategy-Application, and predict how much it could improve, and how likely this is.
MAKE A NEW PLAN: For the second lecture you re-PLAN in the first phase (Generate Options, Evaluate Options, Choose an Option) based on your OBSERVATIONS and EVALUATIONS, by mentally evaluating the Strategy and your Strategy-Application (in a continuation of the EVALUATION phase) to decide whether you want to maintain the Strategy as-is, or make adjustments to generate a revised Strategy or a change in the way you will apply it. You repeat the cyclic process — PLAN, OBSERVE, EVALUATE, PLAN,... — for each lecture (or part of a lecture) so your Learning Strategies and Learning Skills will continually improve.
A Basic Analysis
This second analysis includes more detail than the Simple Overview above. While you're reading it you may want to occasionally look at the Basic Diagram of Design Process, which opens in a new window with this link. { some comments about colors: The symbolic colors from above (blue + red → purple) are not used in this analysis. Instead, purple is used for all of the main design-actions. To describe the 9 modes of Design Process I'll use word-labels that are easy to understand, plus number-labels (2A, 2B,...) but if you want to ignore the numbers it will be easy because they're in aqua-font. }
This analysis highlights an important benefit of using Design Process as a framework for Learning Strategies: "This application of Design Process uses a 3-Step Cycle (Plan, Observe, Evaluate, Plan,...) that is similar to the learning cycles recommended by experts in Self-Regulated Learning [and in programs for improving Study Skills]. But using Design Process offers an additional benefit by promoting a transfer of thinking skills because the process of design-thinking that students use for their Learning Strategies also can be used in other areas, for almost everything they do in life." {more about design-based Learning Strategies and Self-Regulated Learning}
A Learning Strategy is a special type of design project, where the objective is a strategy that will help you learn (and think & perform) more effectively. During this analysis I'll use a framework described by Jennifer Livingston, who skillfully summarizes ideas from other scholars in her Overview of Metacognition; along with most other educators & psychologists, she views a metacognitive strategy for learning as a person's use of metacognitive knowledge for the purpose of metacognitive regulation.
• KNOWLEDGE: As in any process of design, for a Learning Strategy you can Prepare in Mode 2A by building a strong foundation of metacognitive knowledge about person variables, task variables, and strategy variables, which are described by Livingston. And you can personally customize this general knowledge based on “knowing yourself” from past observations of yourself (as the person) using different strategies in the contexts of various tasks.
An example of the metacognitive knowledge you can include for Preparation is a subsection for Active Listening — that is part of a section about Active Learning in my page for Effective Learning Skills — which includes:
a brief description of the similarities and differences between actively reading and actively listening;
an in-depth examination of a strategy for learning from lectures in 3 phases (by what you do before, during, and after lectures);
the differences between pre-lecture preparation by using lecture notes (definitely do this) & textbook reading (maybe do this);
during lecture, coping with the complexities of time-sharing and time-shifting (for activities of listening & seeing, thinking and writing, plus metacognitive observing), using these in ways that can help you learn-and-remember more effectively;
how to minimize distractions that begin externally or internally.
• REGULATION occurs through a combination of PLANNING and MONITORING:
• PLANNING: You can apply your metacognitive knowledge to make a plan for metacognitive regulation in a specific learning situation. As in the first analysis, we'll imagine that you want to learn more effectively during lectures for a psychology course. Therefore, you will focus your Knowledge Preparation in 2A by searching for old information about strategies for this type of lecture situation: what do experts recommend based on their observations of people, and what have you learned from your experiences of observing yourself? During this search for knowledge, you may be inspired to Invent New Strategy-Options (in 2B) that usually are modifications of old strategies. You choose some strategy-options, either old (selected in 2A) or new (invented in 2B), and for each option you do a Mental Experiment (2C) by imagining what will happen if you use this option, thus making Predictions for the option. In an evaluation that is a Mental Quality Check (3A), for each option you compare your Predictions (about what will happen) with your Goals (for what you want to happen). After you have evaluated some strategy-options, you Choose to Use one of these options (or a combination of several options) for a lecture.
• MONITORING: You use your chosen strategy(s) during the first lecture, and you OBSERVE what happens. You can make three main types of Observations by asking “what is the situation?” (the lecture's characteristics), “what are my actions?” (in applying the strategy), and “what were the results?” (in quantity-and-quality of learning). Answers for the first two questions define the Experimental System for a Physical Experiment (2D) in which you apply the strategy, and your Observations for this experiment answer the third question. Then you can EVALUATE — using the new information generated by your question-stimulated observations — by comparing what actually happened (your Observations in 2D) with what you wanted to happen (your Goals defined in 1B) in two Physical Quality Checks (for Strategy-Application & Strategy*) that help you decide how well you are using the strategy, and how effective the strategy is for helping you learn. / * Strategy-Application: You evaluate the quality of your actions by comparing your observed actions (how you actually applied the strategy) with your desired actions (for the kind of strategy-application you want) in a Physical Quality Check that I'm calling a Quality Control. Strategy: You evaluate the quality of this strategy by comparing the observed results (for actual learning) with the desired results (for what you wanted to learn); but the observed results also depend on your strategy-application (Strategy + Strategy-Application = Actualized Strategy) and (Actualized Strategy → Observed Results), so when you interpret this Physical Quality Check you can evaluate the Actualized Strategy as-it-is now, and also — by doing Mental Experiments to predict how much your application will improve (and how likely this is) and how this improved application would change the observed result — the Actualized Strategy as-it-could-be in the future.
Metacognitive Monitoring: Did you notice the metacognition in the second & third questions when you observe-and-evaluate your own thinking for your actions & learning? When you do this observation-and-evaluation for monitoring, ideally (as discussed in On-and-Off Metacognition to Allow Productive Thinking) this secondary objective will not interfere with your primary objective, which is learning from the lecture. But achieving your secondary objective (by improving your skill in learning from lectures) is important in the long run, because in the future it will help you achieve your immediate primary objectives (of learning more from each lecture).
• REGULATION by continued PLANNING-and-MONITORING: In a Learning Strategy, as with other types of design, "action in a Quality Check in Mode 3A ... can stimulate action in other modes" which could take you in many different directions, but here is what usually happens:
When you interpret the evaluative Quality Checks (3A) — to decide if there was a satisfactory match with your goals, and if not whether any mismatches were due to the Strategy or your Strategy-Application, or both — this Evaluation (of the past) overlaps with PLANNING (for the future). In a planning-decision you may decide that in the future you want to continue using the strategy as-is; or you can make adjustments in the Strategy {or your actions in Applying the Strategy} if a Quality Check for the Strategy {or Quality Control check for your actions} shows you that a revision of the Strategy {or your Strategy-Application} would be useful. You make adjustments in a process of Selection (2A) or Invention (2B) using retroduction in Mental Experiments (2C) guided by evaluation (3A). Or if your Observations (in 2D) show that the lecture has unexpected characteristics (because you made incorrect assumptions when defining this part of the Experimental System so you could make Predictions in 2C) you revise your assumptions about the lecture, and maybe revise your Predictions and your Choice of a Strategy (or combination of strategies) for Learning.
Then you repeat the process of Observation-and-Evaluation for the second lecture. Each new lecture — or each part of a lecture, since you can observe, evaluate, and make regulative adjustments many times during a lecture — is another Physical Experiment in which you MONITOR by making Observations (in 2D) that you can use for Evaluation (in 3A) and Interpretation (to connect Evaluation with PLANNING), which may lead to a revising of Strategies or Strategy-Application actions, and this revision is a re-PLANNING.
Each lecture (or part of a lecture) is an opportunity to learn about cognition-and-metacognition, so you can add to your knowledge in 2A; and if your monitoring-observations during lecture (2D) are surprising, maybe you'll want to revise (in 2A) some of your theory-knowledge about metacognition and about yourself.
As with other types of design, you can Coordinate your Design-Actions for your Learning Strategy.
A Cycle-of-Design Analysis that includes Quality Control
This is a third description of the same Learning Strategy. It's similar to the Simple Overview Description but with more depth, using terms from Design Process (Quality Checks & Quality Controls) plus terms from models of metacognition (Plan & Monitor), and the more detailed diagram you see below. These terms and details will help you understand the Learning Strategy at a deeper level, and may give you ideas to use for teaching.
As in the Simple Overview, this analytical description uses the Two-Step Cycle of Design but now instead of GENERATE-and-EVALUATE the two steps are broadened to PLAN (which includes GENERATE plus EVALUATE & CHOOSE) and MONITOR (it includes OBSERVE followed by EVALUATE). As in the analogous diagram for the Simple Overview, the cycle-diagram below has been expanded (compared with the simplest diagram of the two-step cycle) to show details, including colors and a third step (with MONITORING split into OBSERVE & EVALUATE), because these details make it more useful for describing this particular use of design where the objective is to improve your skills in thinking, learning, and performing.
While writing this, I'm assuming you have read the Simple Overview and Main Analysis which explain terms (situation, actions, results,..., planning & monitoring) and the key principle of evaluating a strategy and also your actions when applying the strategy.
To begin the process of design, you Define an Objective for better learning, and you Define Learning-Goals for the desired results you want to achieve. Again, we'll imagine that you want to learn more accurately-and-thoroughly in your psychology lectures, and also remember.
The next step in the process of design is to PREPARE by finding information, by searching for “how to learn more from lecture” strategies (what do other people recommend?) and by remembering what you've done in the past and how well it worked.
Then you begin a Cycle of Design. As indicated by the arrows below, this cycle moves clockwise, following a sequence: first you PLAN with Mental Quality Checks (to GENERATE-and-EVALUATE-and-CHOOSE a strategy to use), then you MONITOR the strategy and your strategy-application in a Physical Experiment that lets you OBSERVE so you can EVALUATE with a Physical Quality Check. Then you continue doing cycles of PLANNING-and-MONITORING so you can REGULATE your learning.
At the bottom of this diagram, notice the color-coding for the two types of Physical Quality Checks you do with two comparisons (• observed actions... and • observed results...) in two parallel processes of comparative evaluation.
This distinction between evaluations of a Strategy (in a Physical Quality Check based on results) and your Strategy-Application (in another Physical Quality Check, based on actions, that I'm calling a Quality Control) is useful because your observed actions (your actual application of the strategy during a lecture, in this example) may not match your desired actions for an ideal application of the strategy. Therefore, if you are disappointed because your observed results (for actual learning) don't match your desired results (for the learning you wanted), this might be caused by an ineffective strategy and/or your ineffective use of the strategy; this two-factor causation is symbolized by colors (blue + red → purple, with pigments),
Or there might be some other reason, including an actual observed situation that wasn't the predicted situation for which you made plans. Distinguishing between all of the potential causes is important when you think about a strategy (as-it-is now, and as-it-could-be in the future if your strategy-application improves) and how to apply it more effectively, when you decide your PLANS for the future.Actualized Strategy ( = Strategy + Strategy-Application ) → Observed Results
Here, in more detail that includes additional color coding, is the cycle-of-design in which you PLAN (GENERATE & CHOOSE) and
MONITOR (OBSERVE & EVALUATE):
PLAN — You GENERATE OPTIONS for Strategies that are old (by remembering lecture-strategies from others or from your own experience) or new (usually by revising old strategies) and you evaluate each option with Mental Quality Checks by imagining (by Predicting in Mental Experiments) how well it would work and how this compares with your Goals (for desired application-actions and desired learning-results) so you can CHOOSE a Strategy (which might combine several sub-strategies) to apply in the first lecture.
MONITOR — During the first lecture {and after it} you OBSERVE {and remember} what happened — was the lecture what you expected? (situation) did you apply the strategy the way you wanted? (actions) did you learn effectively? (results) — and you EVALUATE this Strategy-Option (and your actions in applying it) by using a Physical Quality Check in which you compare these observations (of situation, actions, results) with what you expected (for the lecture situation) and what you wanted to happen (in your Goals for your actions in applying the strategy, and the results of your learning).
PLAN & MONITOR,... — You continue to REGULATE your learning with the PLAN-and-MONITOR cycle. When you PLAN for the second lecture, your options will include the first-lecture Strategy (and your application of it) as-is or revised, and maybe other strategies. You evaluate all options using Mental Quality Checks to imagine/predict what will happen with each option, so you can decide whether to maintain the Strategy (and your application of it) as-is, or make adjustments (*) to invent a revised Strategy or Strategy-Application, or use another strategy. Then you MONITOR (observe & evaluate) during your second lecture, to help you PLAN for the third lecture, and so on. You continue using this cyclic process of Planning-and-Monitoring (which is Regulation) so your learning strategies and learning skills will continually improve. * When you "GENERATE OPTIONS for Strategies", you "select old strategies or invent new strategies" by using retroductive logic.
Experimental Systems: Above, I say that "answers for the first two questions [“what is the external situation?” and “what are my personal actions?”] define the Experimental System for a Physical Experiment [that lets you make Observations]," which can be summarized: Actualized Strategy (= Strategy + Strategy-Application) → Observed Results.
This Experimental System also can be used for a Mental Experiment in which (by using "experimental system + a system of theories") you make Predictions. The process of predicting (and observing) is shown in the left-side diagram for a strategy-option actualized by your application. The right-side diagram shows the analogous process of making predictions for a product-option with "an experimental system involving this option [in a situation-context]." For either type of design project, whether the objective is a personal strategy-for-learning or a product (or an activity, strategy, or theory), the Experimental System can be used in a Physical Experiment (to allow Observations) or, combined with A System of Theories, in a Mental Experiment (to make Predictions).
In early 2012, I began developing a new website with
many improvements (by revising, adding, cutting),
so I strongly recommend that you
read it instead of this page.
(but currently there is no updated version of this section)
Teaching and Coordinating for a Design Team
The two educational strategies — viewed from the perspectives of a Learner and Teacher, so they're a Learning Strategy and Teaching Strategy — are closely related in two ways:
#1 — What Learners do in a Learning Strategy for themselves, Teachers do for others. When you’re thinking as a teacher, you look at everything that occurs in a Cognitive-and-Metacognitive Learning Strategy (when a learner uses Quality Checks for their strategies, plus Quality Controls for their strategy-applications) and you ask “how can I motivate my students so they will want to do this, and how can I help them do it more effectively?”
#2 — Teachers can try to improve the quality of their own teaching in a variety of ways — including their support for the Learning Strategies of students in #1 — by developing improved Strategies for Teaching (and improved applications of these strategies) in a process-of-design that is analogous to Strategies for Learning.
Teachers, Coaches, and Supervisors
As stated in its title, this section combines ideas about Teaching (in any context) and Coordinating for a Design Team. While writing this I’m thinking about teachers (with their students) and supervisors (with their workers) and also coaches (with their athletes), because all perform services that have many similarities despite some differences.
Teaching: All three want to teach other people in #1, by helping them learn & perform more effectively, and motivating them so they will want improve their learning & performing. And for #2 they all want to improve the quality of their own teaching (or supervising or coaching), which includes producing a group atmosphere (in the classroom, office, or field) with cooperative teamwork.
Metacognition: For #1, all use “external metacognition” to observe the thinking-actions of others (students, workers, or athletes) and interpret these observations, so they can provide formative feedback to the others for the purpose of helping them improve the quality of their thinking, learning, and performance. And for #2 they all use a Learning/Teaching Strategy that includes self-observing metacognition.
Coordination of Design-Actions (in Mode 4A) by a Supervisor
Always this involves a Coordinating of Project Actions (in Modes 1A-4A), and principles for doing this well are in Coordinating Design-Actions and Combining Cognition-and-Metacognition in the Process of Coordinating Design.
Sometimes it also involves a Coordinating of Project People in a design team if you are a supervisor, or a team member who is serving as an unofficial leader. A basic coordinating of design-actions is similar for your own actions or a team's actions, except for a team you ask "what is the best use of [everyone's] time right now [and later]?" Coordinating the design-actions of your team members can be done by direct decisions (if you decide what they should do and when) and indirect delegation (if you give them responsbility for some of their own action-decisions), in whatever balance you think will be most effective, when all things are considered. As described above, you use the two parts of a Teaching Strategy, appropriately adapted for the context of your project and people, by encouraging your team members to develop & use their own Strategies for Learning-and-Performance (in #1, for delegated responsibilities) and (in #2) by developing & using your own Supervising Strategies (analogous to Teaching Strategies) to improve your learning-and-performance.
Producing Teamwork: When a group works on a design project, everyone* — especially the leaders, official and unofficial — should consider the social aspects of the process. They should design strategies for optimizing their use of resources (people, time, money, knowledge,...) in a way that helps individuals enjoy their work and gain satisfaction from it, while building an “us” feeling in the group with good attitudes toward each other, as co-workers and as people. Doing this well requires skillful cognition plus aware “external metacognition” in the social context of their working environment. / * Those being supervised also play valuable roles by doing their jobs with skill, and being good team members.
Overcoming Challenges: A group may have to cope with the pressures of a difficult project when their work is constrained by the limitations of time deadlines and resource budgets. There might be interpersonal tensions between some people, or institutional structures that hinder teamwork. Any of these factors, and others, can put a strain on individuals, their relationships, and the teamwork; in addition to the harmful personal effects for the people involved, the practical effects for a business can be a decrease in the effectiveness of a design process and the quality of a resulting solution. Supervisors and other leaders, as part of their official or unofficial responsibilities, can try to develop strategies for achieving the best possible process-of-design and results-of-design, in ways that are also personally beneficial for the people on their team.
If you want to learn more about these ideas, you can explore the final section — Using Cognition-and-Metacognition for Learning & Teaching — in my page about using Design Process to improve Problem Solving and Metacognition in Education.