Author: Pacific Crest

  • Reading Is a Performance, Not an Inheritance

    Reading Is a Performance, Not an Inheritance

    If you teach, especially in the liberal arts, someone has probably already sent it to you. Rose Horowitch’s cover story in The Atlantic argues that America is not becoming illiterate but postliterate—that people still decode words perfectly well while losing the higher-order capabilities of comprehension and synthesis.

    We strongly recommend the article. It’s the most useful thing published on this subject in some time, and the numbers in it are worse than you’re expecting.

    The End of Reading Is Here — Rose Horowitch, The Atlantic, August 2026

    Follow-up Are we living through the end of reading? — Horowitch interviewed on NPR, seven minutes, no paywall, transcript available.

    We think her diagnosis is right. We disagree about what follows from it.

    The part that isn’t about reading

    The study of English majors attempting the opening of Bleak House is the detail worth sitting with. Not because those students couldn’t read—every one of them could decode every word on the page. What failed was inference, contextual reasoning, and the willingness (attention? interest?) to notice their own confusion and do something about it.

    That’s not a story about literacy. It is a story about a set of skills nobody taught them, being required of them anyway.

    An administrator quoted in the piece describes students who now regard assigned reading as an inefficient way to receive information—as though a professor requiring a book is deliberately making things harder than they need to be. That belief is perfectly rational if reading has only ever been a delivery mechanism to you. After all, why not a picture or short-form video? Aren’t they worth thousands of words? And entertaining?

    That idea becomes absurd the moment reading is something you know how to do.

    Where we part company

    The article’s register is elegiac. Literacy was a brief interlude between the oral and digital ages; the darkness is gathering; something is ending.

    If comprehension were a cultural inheritance—something a society either holds onto or loses—that would be exactly the right note. But it isn’t an inheritance.

    It’s a set of learnable performances: previewing a text for its structure before reading a word of it, separating what the author established from what you inferred, noticing the precise sentence where your understanding broke, forming the question that would repair it, and building meaning across a long argument instead of harvesting facts out of it.

    Those are learning skills. They can be named, modeled, practiced, assessed, and improved—at any age, in any discipline, including with students who arrive at your door without any of them.

    Which is why the standard institutional response fails so reliably. Assign more pages. Add a reading quiz. Put the readings on the exam. But volume was never the variable: a student who has’nt been taught to read analytically will read three hundred pages exactly as unproductively as thirty, and will conclude, with proof based on experience, that reading doesn’t work.

    The postliterate age Horowitch describes is real and arriving. What we reject is the idea that it is weather rather than a design problem.

    What we can offer

    Asked on NPR whether there are fixes, Horowitch is honest: She points to real bright spots: cellphone bans in two dozen states, a Dallas district that saw 200,000 more library books checked out in a single year after its ban, and teachers pushing back against excerpt-based curricula. But then she concedes that a wholesale fix would have to happen at a much larger level.

    She’s right that the trend is larger than any of us. An instructor holding a syllabus in August can’t possibly act on a civilizational scale. What they CAN do is act on the scale of one course. And that is enough to matter.

    Pacific Crest has spent decades on this specific gap. The Reading Methodology makes the process of analytical reading explicit and repeatable, so it can be taught rather than assumed. Reading Logs turn reading from private intake into a documented and concrete performance that a student can assess and an instructor can actually respond to. Both sit inside the Learning to Learn framework, alongside the other skills that reading quietly depends on.

    If students are arriving unable to do something we have always assumed they could already do, the answer is not to assign more of it. None of this requires changing what you teach. It simply requires treating reading as a performance to be developed rather than a prerequisite to be assumed.


    We’re pleased to offer a Reading Log and the Reading Methodology, both pdf and free.

    There is also an online version of the Reading Log, browser-based, with nothing to install. Students work the steps as a guided sequence and then export everything they’ve written—every prompt and every response—as a document to keep, print, or hand in.

    It does one thing the paper version can’t. Students enter their learning objectives at the start, and the log brings each one back at the end to be assessed against. On paper, Step 12 is a blank box and most students never look back at what they wrote in Step 2 unless specifically prompted. Here the loop closes itself.

    One thing worth telling your students: it needs to be finished in a single session. Responses live in the browser tab, so the log should be opened when they sit down to read and exported before they close it. That fits how the log is meant to work anyway…kind of like a literacy sandwich: purpose and objectives before, assessment after, with one reading in between.

    We’re running the online Reading Log for the next 60 days to find out whether it earns a permanent place. If you try it with a class, tell us how it went. That’s the only way we’ll know!

    [Try the online Reading Log]

  • Report from the 20th Annual Process Education Conference

    Report from the 20th Annual Process Education Conference

    Pacific Crest is a longstanding supporter of the International Academy of Process Educators, and we’re glad to share the Academy’s own words about this year’s conference.

    Report from the 20th Annual Process Education Conference

    by Josh Morrison (President-Elect) and Knut Are Romann-Aas (Webmaster), International Academy of Process Educators


    Twenty years. Before the details, that number deserves a moment—two decades of an annual gathering built on the premise that educators ought to be developing themselves as deliberately as they develop their students.

    The Board’s thanks go to everyone who made this one work: the faculty and staff at West Coast University, our hosts; ISETL, the International Society for Exploring Teaching and Learning, for sponsoring; and the many other contributors, each thanked personally at the conference.

    The Academy Awards

    On June 4th the Academy presented its own awards (“THE” Academy Awards, as we insist on calling them). This year’s recipients:

    • Distinguished Process Educator (Josh Morrison)
    • Institutional Leadership in Process Education (Yuqin Hu and Will Ofstad)
    • Long-Standing Contributor (George Dombi)
    • Newest Star (Bickkie Solomon)
    • President’s Exemplary Service Award (Steve Beyerlein)

    Making next year better

    Former President Ingrid Ulbrich is coordinating the conference assessment and compiling the completed forms so that next year’s event builds on this one. We expect all of this to lead to visible changes — improvements! — in the 2027 program. After all, it’s only fitting for a conference about assessment to assess itself.

    2027: Richardson, Texas

    The 21st Annual Process Education Conference will be held at West Coast University’s Texas campus in Richardson, in the DFW metro area, in coordination with WCU’s own Academic Conference.

    Dates are still to be confirmed, but expect early June as with this year. The format will be hybrid: a full slate of on-ground programming with synchronous online participation welcome, and social and enrichment activities in the afternoons. Worth noting for planning purposes — Richardson, Texas is on Central time, an hour behind Eastern and two ahead of Pacific, or UTC/GMT−5 if you’re outside the US.

    Conference details will appear at processeducation.org/peconf over the coming month. Until then that page still shows this year’s event.

    Join us

    To learn more about the International Academy of Process Educators and how to become a member, visit processeducation.org.

  • Self-Growth Tip: Spurt and Die

    Self-Growth Tip: Spurt and Die

    Consider the New Year’s resolution.

    As we all know, it’s something you make with real conviction, at a genuine turning point, when you honestly intend to change. You run hot for two weeks, limp through a third, and by the middle of February, you haven’t so much abandoned your resolution as quietly stopped mentioning it and eventually (if you’re lucky) thinking about it. By the following January, you do the same again, perhaps with more conviction and even stronger intentions… along with the feeling that what you’re actually doing is compiling evidence that you’re the kind of person who doesn’t follow through.

    What a horrible cycle.

    We could call this pattern spurt and die. And if you’re an educator, you’re about to watch 300 or so people do it at once.

    The start of a term is the most powerful fresh-start moment in the academic year. New notebooks, new intentions, new versions of ourselves. Students (and faculty) arrive determined to be organized this time. By week five, a recognizable number of those intentions are gone, and the people who made them are a little more convinced that their recurring failure to be better is simply who they are.

    But here’s what is actually happening…and it’s not a failure of motivation.

    Motivation is not the variable. The person who quits in February was not less motivated than the person who is still going. They were, if anything, more motivated…and that’s often the problem. Enormous initial energy produces an enormously ambitious plan, and an enormously ambitious plan is a plan that only works on the days you feel enormous. (It’s hard to be a whole new and improved person when you can’t find the energy to exfoliate, for example.)

    What separates a change that lasts from one that evaporates is not intensity at the start. It is whether the thing was built at a size that survives an ordinary week.

    So the question to ask about any new practice is not how committed am I? It is: what does this look like on my worst Tuesday? Not your best day, when you have time and energy and the idea still feels exciting, but your worst one…the day with the difficult meeting, the bad night’s sleep, and the thing that went wrong at home. If the practice doesn’t survive that day in some reduced but real form, it will not survive the term, no matter how much you meant it in August.

    Three characteristics tend to show up in the practices that stick:

    • It feels right while you are doing it—not just afterward, and not just in principle. Practices sustained purely on discipline are running on a fuel that depletes.
    • Its value is visible—you can point to what it produced this week. Not this year. This week.
    • It has a floor, not just a target—a version so small it’s nearly free. Ten minutes of weekly reflection is not the goal, but it is what keeps the goal alive in a week when the goal is impossible.

    You’ve heard a version of this before, usually phrased as “start small.” And that matters, but doesn’t quite capture the essence. The point isn’t modesty but that a practice you can hold on a bad week is the only kind that gets to compound, and compounding is where all the actual returns are.

    Dan Apple has put this more sharply for years: most people conclude that life does not allow sustainability of self-growth. He would insist the reverse—that life is not sustainable without it.

    So before the semester or term starts, take whatever new practice you’re planning and find its floor. Then ask one more question about your students: what floor have you given them? A course design that only works for students having a good semester is making the same mistake, on a larger scale, on someone else’s behalf.

    What is one practice you are planning for this term—and what is its worst-Tuesday version?

  • Targeting a Learning Skill: pRIORITIZING

    Targeting a Learning Skill: pRIORITIZING

    (The complete listing of learning skills is available at www.processeducation.org/cls/web/)

    Prioritizingconsistently putting the most important things first

    A note before we start. Almost everyone who says they have a time management problem has a prioritizing problem. Time management is deciding when; prioritizing is deciding what matters, and you cannot do the first honestly until you have done the second. A student (*cough*) who reorganizes their calendar without ranking anything has built a prettier container for the same chaos. So this issue, we’re going after the skill underneath.

    Where and how to TARGET it:


    Ideas for helping young children develop the skill

    Children are not short on priorities. They have exactly one, and it is whatever is in front of them. Teaching this skill is mostly teaching that “later” is a real place where things still exist.

    • The Three-Thing Morning Instead of a list, name three things and say them in order, out loud, together. The ordering is the lesson; the list is just the excuse for it.
    • First / Then Talk Narrate your own sequencing as you go. “First the shoes, then we look for the dinosaur.” Children pick up ordering as a value long before they can explain it as a rule.
    • Toy Triage Before a trip, one small bag. Which three toys come along? Making the choice is good; getting them to tell you why is much better. This is where a child first meets the idea that choosing costs something.
    • What Happens If We Don’t? Ask about the consequence of skipping something. Consequence is the tool that turns a flat list into a ranked one, and it works at every age after this one, too.

    Ideas for helping students in the liberal arts develop the skill

    Everything on a humanities syllabus can look equally important, because it is all reading and it is all assigned. Prioritizing here means learning that not every source carries the same weight in an argument, and neither does every claim in your own draft.

    • The One-Source Defense Before drafting, students name the single most important source on their list and defend the choice in three sentences. Most will discover they haven’t actually ranked anything.
    • Cut the Reading Hand out a bibliography of ten items and ask which three survive if the semester were half as long. Then have two students compare and argue. The argument is the exercise.
    • Thesis Triage Students list every claim in their own draft, rank them by how much weight each one bears, and cut the bottom third. Papers get better. More importantly, students find out how much of their writing was decoration. (There are many quotes from famous authors and critics about how a given manuscript would be tremendously improved by removing nearly all adjectives or adverbs.)
    • The Deadline Autopsy After a major paper, students map how they actually spent their hours against how they would spend them again. Do this once and it changes the next paper; do it three times and it changes the student.

    Ideas for helping students in STEM courses develop the skill

    Prioritizing in technical work looks like knowing which term dominates, which error matters, and which piece to attack first. It is a judgment skill, and it is almost never taught explicitly…we tend to model it and hope.

    • Order of Attack Before solving anything, teams rank the subproblems by which one unlocks the others. Then rankings go on the board and get compared. Watching two defensible orderings disagree teaches more than any single correct one.
    • Dominant Term This is an estimation exercise: which variable actually drives this result? Practice throwing away what does not move the number. Students trained only on exactness find this genuinely uncomfortable and that’s kind of the point.
    • The Constraint Budget A design problem with four specifications and permission to optimize only two. Forcing the trade-off into the open is what makes it a decision rather than a preference.
    • Debug Triage Give them code, a circuit, or a lab procedure containing five faults of wildly different severity. Which do you fix first, and why? The “why” is the whole point.

    Fun ideas for developing the skill with family or friends

    Most of what we call a scheduling problem is a decision we have been avoiding. But nobody builds a judgment skill by starting with the hardest judgment they face. Practice where the stakes are low and the company is good, and it will be there when the stakes are not.

    • The Ten-Minute Cut After watching a movie with friends or family, ask everyone which 10 minutes they would cut, and what breaks/changes if that cut is made. Ultimately, everyone is ranking scenes by how much weight each carries. (This is the same dynamic as learning to cut the weakest third of a writing draft, except nobody cries and there are snacks.)
    • Three Dishes for the Table Out to eat with friends, agree that the table orders only three things. Everyone advocates for one and has to defend it. What surfaces almost immediately is the difference between I want this and this is the best one for us and learning to feel that difference is most of the skill.
    • The Resource Tell Next games night, watch for the moment someone works out which resource unlocks all the others. In most games it decides the winner, and it is almost never the resource that looks most valuable. (This is the same instinct that tells an engineer which sub-problem to attack first, but again, fewer tears and nothing structural collapses.)
    • The Want-To / Need-To Split Write down everything you think you have to do this week, then sort it into two columns: things you actually want to do, and things you feel obliged to do. Work down the obligation column asking, for each item, who is actually asking me to do this? Sometimes there is a real person with a real expectation. Often there isn’t…and what you find instead is a habit, a commitment nobody else remembers making, or a version of yourself from three years ago with different priorities. Those are the ones to renegotiate or drop. (Try this one before the term starts. Ideally with somebody who will ask you about it in October. O.o)
  • Free Student Handout: The Learning Contract

    Free Student Handout: The Learning Contract

    Running the Learning Contract in Week One

    Don’t hand this out with the syllabus. It will be read as one more policy document and filed accordingly. Give it its own twenty minutes in the first or second class meeting.

    The blanks (16 through 19) are the point. A student who signs fifteen of someone else’s requirements has filled in a form; a student who adds four of their own has made a commitment. Consider putting student in pairs for 10 minutes to draft items 16–19, then ask three or four pairs to read one aloud. You will learn more about your class in those 10 minutes than in the first two weeks of assignments.

    Expect items 11 through 13 to generate most of the discussion. Let that happen. This is the cheapest opportunity you’ll have for “The AI Conversation” in your course as a shared negotiation rather than as you laying down the law.

    The point isn’t to have student sign them and never look back…around week five or six, when the best intentions typically collapse, return the contracts and give students five minutes to assess themselves against their own items, not yours. Do this again before finals. A contract signed once and never revisited teaches students that commitments are ceremonial.

  • If a Student Can Generate the Answer, What Are You Evaluating?

    If a Student Can Generate the Answer, What Are You Evaluating?

    You’ve probably experienced this by now, and probably more than once.

    An assignment comes back and it’s fine. The grammar is clean, the structure is sound, the content is (as far as you can tell) correct. And something about it rings hollow in a way you can feel but can’t quite put your finger on. You read it again, looking for the error—for something—that would give you cause to make a useful note in the margin. But the hook isn’t there. There is nothing to respond to. There is nothing there.

    You reflexively wonder if the student wrote it. And while that’s an understandable reaction and question, it’s also a dead end. You can’t answer it reliably, with certainty.

    And here’s the thing: even if you had that answer, it wouldn’t tell you the thing you actually want to know.

    The better question is the one in the title. If a machine can produce a passing response to your assignment in nine seconds, what was that assignment measuring in the first place?

    The scale we already had

    We’ve described the Levels of Knowledge for decades now. The version we like best lays them out like this:

    • Level 5: Research, Inventions, or Performances (Creative Enterprise)
    • Level 4: Working Expertise (Problem Solving)
    • Level 3: Transferable Knowledge (Generalizing)
    • Level 2: Conceptual Understanding (Teaching)
    • Level 1: Information (Memorization)

    Look closely at the parenthetical for each level, because that’s the test…the thing you can do that proves you’re there. Level 1: you can state it. Level 2: you can teach it. Level 3: you can carry it somewhere new. Level 4: you can solve with it.

    Now consider what a language model does. It states facts and definitions. It articulates understanding, describes relationships, sees linkages, seeks underlying principles. It will explain a concept to you at whatever depth you ask, adjust when you say you don’t follow, and produce three to five fresh analogies on request.

    That is a Level 2 performance. Our own test for conceptual understanding—can you teach this to someone else?—is now something software does on demand, nicely, for free, and at midnight.

    This is a claim about evidence, not about learning. Nothing about how human beings learn has changed in the last three years (since AI became common and accessible). Conceptual understanding is still hard-won, still necessary, and still the floor everything above it stands on. What changed is that a submitted artifact at that level no longer demonstrates the student got there.

    The part we would rather not say out loud

    If your ability to fairly evaluate student learning performance collapsed the moment a language model showed up, it was very likely sitting at Levels 1 and 2 all along.

    That was just as true 10 years ago. We simply couldn’t see it because producing a competent summary cost a student four hours, and four visible hours of effort seemed like they must have produced learning. Sometimes they did. Often they produced a summary—the work product.

    AI did not break evaluation. It removed the labor that was concealing what evaluation had been measuring. That is an unpleasant gift, more reminiscent of something from Pandora than a box with a bow under a Christmas tree, but it IS a gift. And we think the institutions that treat it as one, learning its lesson, will come out of this decade in far better shape than the ones buying detection software.

    Which word we’re using

    The primary distinction Pacific Crest has been insisting on for years matters more now than it ever and it’s worth a quick review because it has a great deal of bearing on where teachers find themselves now.

    Evaluation determines the level of quality of a performance. A stakeholder requests it and has skin in the game as far as criteria go. A report describes the level of quality attained and it exists to support a decision. A grade is an evaluation.

    Assessment produces feedback to strengthen a future performance. Optimally, the assessee requests it and helps set the criteria. And the report describes what made the performance strong and what would make the next one stronger—and it says nothing about the level of quality at all.

    Look again at what’s happened in the last three years:

    What generative AI broke is evaluation. Evaluation rests on an inference: that an artifact (work product) is evidence of the capability of the person who submitted it. AI severed that inference, and no rubric repairs it.

    What AI cannot break is assessment—not because assessment is harder, but because there is no product to outsource. The object was never a product…it was the next performance.

    Nobody can be assessed on your behalf.

    Juggling

    Here is where the ground shifts, and the clearest way to see it is an example we keep coming back to. (What can we say? It’s an extremely useful example that’s easy to understand.)

    Suppose you want to learn to juggle. There are underlying principles: catching means reacting smoothly as the object enters your hand; throwing means accounting for the object’s weight and shape; you watch the object in the air, never the one in your hand; and you have to attend to the throw as much as the catch. Four principles. You have just read all of them.

    You cannot juggle.

    Now ask a language model. It will give you those four principles, more precisely than described above. Ask it how juggling changes with three potatoes instead of beanbags. Ask about torches, about clubs of unequal weight, about juggling while walking…it will answer every question you ask, fluently and correctly.

    You still cannot juggle.

    This is a critical distinction useful to anyone redesigning (or even just tweaking) a course this fall: generalizing knowledge is not the same as transferring knowledge. Transferring is applying something in a new context. Generalizing is developing enough working expertise with the underlying principles that you can transfer at will and to contexts nobody hands you.

    An LLM can hand a student a transfer. It will produce the application, in the new context, on request, and do it well. What it cannot do is generalize on the student’s behalf, because generalizing is a change in the learner, not a property of the output. There is no artifact to pass across. You cannot ask a machine to juggle for you. It can describe every principle and name every context, and it cannot move your hands.

    This isn’t a claim about what the technology will or won’t be able to do—that line keeps moving, and will continue to do so. It’s a claim about where the development happens. Generalizing changes the learner, so the learner has to do it, no matter how capable the tool on the other side becomes. That is where our evaluations need to live now.

    What to do on Monday

    The Methodology for Generalizing Knowledge gives the sequence, and its middle four steps are the key pattern:

    Familiar → Similar → Different → Unfamiliar

    Apply the knowledge in the context where it was learned. Then in one that is less familiar but recognizably similar. Then in one with key differences. Then in one well outside the comfort zone. Beanbags, potatoes, juggling while walking, torches.

    Three things follow from taking that somewhat amusing progression seriously.

    Move the context, not the topic. The common redesign instinct is to pick a harder subject. That doesn’t help because the model isn’t struggling with your subject. What it cannot know is which of your course’s contexts a particular student has and has not been in. “Explain the second law” is answerable by anyone and anything. “Apply it to the compressor failure we analyzed in week four, and say where the analogy breaks” is answerable by someone who was in the room.

    Evaluate the choice, not the answer. Require students to justify the approach—what they ruled out, which assumption they accepted, where they were unsure. Reasoning is the thing being judged. A student who cannot defend a choice does not own the knowledge, whatever produced the paragraph. (As a side note, years ago a professor found out that the answer keys to a chemistry activity had been making the rounds among his students. He contacted us and asked if we had a different version of the key questions and exercises available. We did not. What we did have and shared with him was the idea of process and validation. Instead of grading the answers the students arrived at, ask the students to validate the choices they made in their calculations and demonstrate how their answer was correct. It worked well in a case where the students were armed with the correct answers for a single activity. It will work just as well in cases where they can generate correct answers at will.)

    Stop treating one hard assignment as the whole design. This is where most AI-era redesigns fail. A single “authentic” capstone is a leap straight to Unfamiliar, and what it often produces is a student who copes. Coping is not generalizing. The progression is the pedagogy—four contexts of deliberately increasing distance, which also means four chances to catch a student who is drifting rather than one autopsy in week fourteen.

    One more thing worth noticing

    Step 1 of the Methodology is Validate Meaning: confirm the learner is genuinely at high Level 2 before trying to generalize anything, because new generalized knowledge only builds on knowledge already generalized. Skip it and whatever you build is fragile and falls apart on transfer.

    Which raises the question this whole article has been circling. If students can now produce Level 2 artifacts without reaching Level 2, how do you validate that they’re standing on anything at all?

    You will have to ask them.
    In person,
    out loud,
    early,
    and more often than is convenient.

    That’s not a workaround or some kind of special academic cludge; it’s ASSESSMENT—the process many of us have described as a something it would be nice to have in the classroom for the llast 20 to 30 years while evaluation quietly did the real work.

    Evaluation is now doing considerably less of it. Which means the practice we may have treated as optional is the one we’re going to need going forward.

    Where to take this

    The International Academy of Process Educators is forming a Special Interest Group on AI in the Classroom, alongside groups on lifelong learning and self-growth. If these questions are live for you this fall—and if you are teaching, they are—that’s where the conversation continues with people working the same problem in their own courses. Reach out to them at www.processeducation.org

    This is not something any of us solves alone in a syllabus revision over a long weekend.

  • The Self-Growth Research Summit

    The Self-Growth Research Summit

    Advancing Self-Growth Through Research, Practice, and AI

    The Research Summit brings together educators and researchers to move beyond theory into the active design of self-growth systems. Across seven sessions, participants engage in inquiry, model development, and applied design—advancing both the science and practice of helping individuals become self-growers.

    LOCATION

    The Summit sessions will use the plenary session area at West Coast University for in-person attendees. (This is the same room used for the pre-conference workshops and all the plenary sessions.)

    Those planning to attend online will be provided with the Zoom links to the sessions. (Those same links will also be available from the PE Conference Support Site.)


    REGISTRATION

    There is no fee for those already registered for the Conference but WE DO NEED TO KNOW how many will be attending (for food and for breakout rooms). Please email Dan Apple [email protected] if you plan to attend any or all Summit session.


    DINNER IS ON STEVE!

    Catered dinners will be provided free of charge for in-person attendees (Steve Beyerlein is hosting the catered dinners). He does need how much food to order though, so PLEASE let Dan know if you’ll be attending in-person.


    PLANNED SESSIONS

    Session 1 (Mon, 4:00–5:45 PM)
    The Self-Growth Project: Where We Are Now
    An opening orientation to six years of research and system development. Participants align on the architecture of the Self-Growth System, key discoveries, and the major questions that will shape the next phase of work.


    Session 2 (Mon, 6:15–8:00 PM)
    Defining What a True Self-Grower Looks Like
    This session establishes clear capability thresholds for independent self-growth. Participants explore the behaviors and patterns that demonstrate increasing clarity, intentionality, and the ability to design and govern one’s own development.


    Session 3 (Tues, 4:00–5:45 PM)
    Analyzing Current Self-Growth Research
    Working in teams, participants analyze existing IJPE research to identify key discoveries, recurring patterns, and critical gaps—building a shared foundation for future inquiry.


    Session 4 (Tues, 6:15–8:00 PM)
    Writing to Think for Research: Generating the Next Wave of Questions
    Participants use writing-to-think as the primary method for developing new research questions, study designs, and measurement strategies. As the inquiry unfolds, the group simultaneously identifies the practices that strengthen thinking—producing a set of high-value Writing-to-Think practices grounded in real research work.


    Session 5 (Wed, 4:00–5:45 PM)
    Designing the Next Phase of the Self-Growth Project
    This session translates research questions into actionable experiments, addressing program design, coaching structures, AI integration, and data collection to scale the development of self-growers.


    Session 6 (Wed, 6:15–8:00 PM)
    AI, Practice, and the Six Dimensions of Living Forward
    Educators explore how AI-supported practices can advance student development across six dimensions: becoming, impact, quality of life, wellness, relationships, and spirituality. The focus is on how specific practices—enhanced by AI—help students move directionally within each dimension, supporting holistic self-growth and life trajectory development.


    Session 7 (Thurs, 4:00–5:45 PM)
    Measuring Life Trajectory
    Participants develop and test frameworks for measuring monthly life trajectory, evaluating movement toward the Horizon Self across key dimensions. The session explores whether this model can serve as a unifying “North Star” metric for self-growth.


    Closing Conversation (Optional, Thurs 5:45–6:15 PM)
    A final synthesis of discoveries, next steps for publications, and the launch of Phase IV research and collaboration.


    Core Outcome of the Summit

    By the end of the week, participants will have:

    • Strengthened the foundation of the Self-Growth System
    • Defined capability thresholds for self-growers
    • Generated high-value research questions
    • Identified effective Writing-to-Think practices
    • Designed Phase IV research experiments
    • Developed AI-supported approaches to holistic student growth
    • Advanced frameworks for measuring life trajectory
  • Episode 10: Why Structured Cooperation Beats Group Work

    Episode 10: Why Structured Cooperation Beats Group Work

    Listen on (click to choose)…

    This episode is specifically tailored for educators who might doubt whether the frustrations of requiring students to work in cooperative teams are truly worth the effort.

    Acknowledging the initial challenges, such as the upfront planning required and the difficult shift for instructors from being the “provider of knowledge” to a “facilitator”, this episode will reassure and persuade hesitant teachers. You’ll learn how the temporary hurdles of group work yield profound dividends, not only by increasing academic achievement, critical thinking, and retention, but also by equipping students with essential, lifelong interpersonal skills. By fostering positive interdependence and shared accountability, you’ll find out how cooperative learning prepares students for collaborative environments in the real world, ultimately proving that the long-term benefits to both student development and academic success far outweigh the temporary classroom growing pains.

    (This episode is based on 3.3.2 Cooperative Learning in the Faculty Guidebook and was generated, edited, and approved by Denna Hintze, using Google NotebookLM.)


    Beyond the Groans: Why Structured Cooperative Learning is Worth the Upfront Chaos

    Let’s be honest: announcing a group activity often triggers a synchronized classroom groan. And as educators, we might secretly want to groan alongside them. Implementing cooperative learning requires us to step down from the comfortable podium of the “expert on all” and embrace the infinitely messier role of “facilitator”. It requires significant upfront planning to structure activities to fit within class time limits, and it demands that students transition from passive recipients of knowledge to active, accountable contributors—an accountability they frequently resist.

    With all these hurdles, why should we voluntarily invite this frustration into our syllabi? Because the evidence shows that the temporary discomfort of shifting classroom dynamics yields extraordinary long-term dividends.

    The Academic Return on Investment First and foremost, the academic outcomes are simply too robust to ignore. When we move away from individualistic competition and create safe, supportive group learning experiences, students demonstrate increased academic achievement, greater productivity, and enhanced critical thinking competencies. Instead of merely absorbing a lecture, students engage in lively peer interactions that foster a much deeper understanding of course content in both breadth and depth. Furthermore, cooperative learning is linked to higher overall college retention rates—a benefit that is particularly impactful for high-risk students.

    The Long Game: Preparing Students for Life But the true magic of cooperative learning extends far beyond the final exam or the confines of higher education. We are ultimately preparing students for life, and the modern world rarely allows us to operate in silos.

    By designing activities with “positive interdependence,” students learn that they are responsible not only for their own mastery of the material but for the success of their peers. This shared accountability forces them to flex crucial interpersonal muscles that are difficult to teach through a traditional lecture. They must practice articulating complex ideas, actively listening, managing inevitable conflicts, and appreciating individuals from diverse backgrounds and perspectives.

    Moreover, working in a supportive group encourages healthy risk-taking behavior, pushing students outside of their intellectual and personal comfort zones. This collaborative environment is directly associated with greater personal development, resulting in increased self-esteem, higher degree aspirations, and stronger problem-solving skills.

    Embracing the Facilitator Role Yes, adopting this approach requires a conceptual shift in how we view teaching. You will have to continuously assess group dynamics and perhaps even assign specific team roles, such as the “Optimist” (to keep the team in a positive frame of mind) or the “Spy” (to eavesdrop on other teams and gather helpful intelligence).

    However, sharing authority and trusting your students transforms your classroom from a static room into a dynamic laboratory where knowledge emerges from dialogue. The initial resistance—from both sides of the desk—is natural, but pushing through that frustration equips our students with the academic rigor and the essential, collaborative life skills they need to thrive long after they leave our institutions.

  • Becoming an Engineer: A Course in Engineering Realization

    Becoming an Engineer: A Course in Engineering Realization

    The Introduction to Engineering course launching in Fall 2026 at Eastern Michigan University is taking a new approach to engineering. Rather than serving as a survey of the field, this course is designed as the first step in developing an engineer of the future. We’re developing a book for the course and are pleased to share the book’s Forward to The Student


    Most students begin engineering by asking, “How do I solve this problem?”

    Fewer ask, “How do I become someone who can solve problems well, consistently, and with purpose?”

    This book, Designing Engineers of the Future, is built around the second question.

    Engineering is about realization — moving from ideas to outcomes, from uncertainty to decisions, and from initial attempts to improved performance. It is also about becoming someone who can do this work effectively, responsibly, and with increasing independence.

    You will not simply learn about engineering. You will learn how to:

    • think like an engineer
    • work like an engineer
    • and develop yourself as an engineer

    Throughout this experience, you will engage with engineering through five lenses:

    • Identity & Responsibility – understanding who you are becoming and the role engineers play in society
    • Disciplined Engineering Thinking – structuring problems, making decisions, and using models effectively
    • Development & Validation – designing, testing, and improving solutions
    • Accountable Engineering Practice – working in teams, communicating clearly, and making responsible decisions
    • Continuous Improvement & Self-Directed Development – learning how to learn, reflect, and grow over time

    These lenses are not separate topics. They are different ways of approaching the same work — the work of becoming an engineer.


    How You Will Learn

    This book is not meant to be read passively. It is designed to be used.

    You will learn through a variety of activity types that structure your experience:

    • Workshops introduce key ways of thinking and working
    • Competency activities ask you to demonstrate what you can do individually
    • Team-based realization activities engage you in designing, testing, and improving solutions
    • Design reviews provide opportunities to present your thinking and receive feedback
    • Reflection activities help you learn from your experiences and improve over time

    Many of these activities will take place in class with your team and with guidance from your instructor. Other parts will require you to think independently, reflect on your performance, and take responsibility for your own learning.

    At times, you may feel uncertain or challenged. This is not a sign that something is wrong — it is a sign that you are being asked to think in new ways. Engineering is not learned by following steps alone. It is learned by working through ambiguity, making decisions, and improving over time.


    Your Role

    In this course, you are not just completing assignments. You are developing capabilities that will carry into the rest of your engineering education and beyond.

    You are expected to:

    • engage actively with your team and your instructor
    • prepare for class and contribute to shared work
    • reflect on your performance and use feedback to improve
    • take increasing responsibility for your learning

    You will be supported throughout the course, but you will not be given all the answers. Instead, you will be asked to develop them, test them, and refine them.


    What You Will Gain

    If you engage fully in this experience, you will leave with more than an introduction to engineering.

    You will develop:

    • the ability to approach unfamiliar and complex problems
    • the discipline to evaluate and improve your own work
    • the skills to collaborate and communicate effectively
    • the capacity to direct your own learning

    You may also find that this course feels different from others you have taken. It is designed not only to help you succeed here, but also to help you navigate the broader challenges of the engineering curriculum — where expectations are high, problems are not always clearly defined, and persistence is essential.


    This book is an invitation to take ownership of your learning and your development.

    The work may be demanding, and at times uncomfortable, but it is through that work that you begin to build the habits, judgment, and independence that define effective engineers.

    We look forward to seeing what you realize — and who you become.

  • Special Interest Groups for Advancing Process Education

    Special Interest Groups for Advancing Process Education

    The Academy is forming Special Interest Groups (SIGs) in the areas of lifelong learning, self-growth, and AI in the classroom. These SIGS will bring together members with shared passions in Process Education. These groups are envisioned as vibrant spaces where “birds of a feather” gather to explore cutting-edge questions, develop tools, incubate innovations, and generate collaborative scholarship that advances our field.  A SIG provides opportunities for: Distributed leadership across interest areas, Focused inquiry aligned with practitioner needs Mentorship pathways for members new to PE scholarship, and Greater visibility for emerging projects and scholarship-in-progress.

    During this year’s PE Conference, on Thursday, June 4, at 9:30am Pacific (12:30pm Eastern) a Plenary Session “PE Special Interest Groups” will be facilitated by Steve Beyerlein, Kathy Burke, and Cy Leise.

    This session is an opportunity to test interest levels, identify core collaborators, and co-define the purpose and structure of each SIG.  The three pilot SIGs will be introduced in parallel working group meetings during the second half of this session. An agenda for periodic SIG meetings throughout the rest of 2026 is planned with the following outcomes in mind:

    • Deepen community connections around each SIG theme
    • Incubate innovative models, tools, and frameworks aligned with PE philosophy
    • Identify research opportunities and practitioner projects
    • Launch collaborative pathways for publication and resource development
    • Support resource sharing, mentorship, and cross-institutional collaboration

    Please consider joining us either in person or online!