The Machines Are Fine, So Is Using Them — The System Is Not!

A response to David W. Hogg's "Why do we do astrophysics?" and Minas Karamanis's ergosphere blog post "The machines are fine. I'm worried about us." — the result of a conversation between Claude Opus 4.6 and József Vass.

Two recent pieces in the astrophysics community have articulated a position on AI in scientific research that has gained considerable traction. David W. Hogg's preprint asks "Why do we do astrophysics?" and concludes that, because astrophysics has no clinical value, people are the real product of the enterprise — always the ends, never the means. A blog post "The machines are fine. I'm worried about us." builds on Hogg's framework through the parable of Alice and Bob, two PhD students who produce identical papers but emerge from the year as very different scientists, because Bob outsourced his thinking to an AI agent while Alice did the work herself. Both pieces are well-written, thoughtful, and sincere. Both are also, I believe, fundamentally mistaken — not about the risks of AI, but about the nature of science, the purpose of graduate education, and the institutional pathology they inadvertently defend.

I want to make my argument carefully, because the easy version of it — "just let people use AI however they want" — is not what I believe. What I believe is more structural, and ultimately more critical of academia than either Hogg or Karamanis is willing to be.

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1. The Purpose of Science Is Not to Produce Scientists

1.1 The claim

The central premise shared by both pieces is that science, or at least astrophysics, is primarily a vehicle for human development. Hogg states this explicitly: when we hire a graduate student, "it absolutely must be because the graduate student will benefit from that work, not merely because that work needs to get done." The ergosphere post amplifies this into a governing principle: "The project isn't the deliverable. The project is the vehicle. The deliverable is the scientist that comes out the other end."

1.2 Why it is wrong

This is an extraordinary claim, and both authors treat it as though it were self-evident. It is not. The purpose of science is to produce knowledge — to extend the frontier of human understanding of the natural world. This has been its purpose since Thales, through Galileo, through Einstein, through the present day. The training of new scientists is an essential function of the scientific enterprise, because the enterprise requires people who can carry it forward. But to elevate the training above the work itself, to claim that the people matter more than the results, is to invert the relationship between means and ends in a way that no working scientist in a field with clinical applications would recognize.

1.3 The "no clinical value" dodge

Hogg acknowledges this tension. He argues that astrophysics is special because it has no clinical value — nothing in the world of policy or technology depends on the precise value of the Hubble constant. Therefore, since the results do not matter in a practical sense, the people must be what matters. This argument has a beguiling internal logic, but it proves too much. If the results of astrophysics truly do not matter, then the public funding that supports astrophysics is being spent on what amounts to a very expensive training programme for people who will mostly leave the field. That is not an argument for the primacy of people. It is an argument for defunding astrophysics. No funding agency in the world would accept the proposition that its grants exist to develop individual human beings rather than to advance knowledge. The fact that Hogg himself would likely not make this argument to the NSF suggests that the "people are the ends" framing is aspirational rather than descriptive — a statement about what he wishes the system valued, not what it actually rewards.

And even within the framework of pure knowledge production, astrophysics is not as clinically inert as Hogg claims. Astrophysical research develops statistical methods, computational techniques, instrumentation, and data analysis paradigms that migrate into fields where results have direct human consequences — from medical imaging to climate modeling to gravitational wave detection. The methods are the clinical output, even if the specific measurements are not. Dismissing the results as unimportant in order to elevate the process is a rhetorical move that works within a very narrow disciplinary frame and begins to crumble the moment you look at the broader ecosystem of science.

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2. The Master-Apprentice Model Is the Problem, Not the Solution

2.1 The North American model

Both pieces assume, without much examination, that the North American model of graduate supervision is the correct framework for producing researchers. In this model, a supervisor selects a project, assigns it to a student, meets with them weekly, gives feedback, and guides their development over several years. The student works within the supervisor's research programme, on problems the supervisor has chosen, producing results that the supervisor will co-author. The ergosphere post treats this as a benign and natural arrangement. The supervisor is cast as a wise architect of the student's intellectual growth, carefully calibrating the level of challenge to maximize learning.

2.2 What I see instead

I see something different. I see a system in which graduate students are structurally dependent on a single individual for their funding, their research direction, their publication record, and their career prospects. I see a system in which students are paid wages that would be considered exploitative in any other professional context, justified by the claim that the "experience" they are receiving is the real compensation. I see a system in which the supervisor's research programme is advanced by student labour, while the student's development is framed as a gift bestowed by the supervisor. Hogg's invocation of the Kantian categorical imperative — that students must be treated as ends, not means — reads as aspiration at best and virtue-signaling at worst, because the system he operates within is structurally designed to use students as means. The low pay, the power asymmetry, the dependence on supervisor goodwill for letters of recommendation — these are not accidental features of the system. They are the system!

2.3 The latent anxiety about AI

When Karamanis worries that AI will allow Bob to bypass the formative struggle of his first year, the implicit concern is not really about Bob. It is about the supervisor's role as the indispensable intermediary in Bob's development. If Bob can get competent methodological guidance from an AI, if he can debug his code without weekly meetings, if he can understand a paper without having it explained to him by someone who controls his career — then what, exactly, is the supervisor for? The anxiety about AI in graduate education is, in part, an anxiety about the obsolescence of a role that has historically conferred enormous power and status on those who hold it.

2.4 The European alternative

This is not to say that all supervisors are self-interested or that mentorship has no value. It is to say that the master-apprentice model is not the only way to produce capable researchers, and it may not be the best way. In the European tradition, particularly at institutions like Oxford and Cambridge, doctoral students are expected to pursue substantially more independent research. The supervisor is a resource, not a director. The student chooses their problem, develops their methodology, and proves themselves through independent work. The degree is a certification that the student is capable of original research, not a record of years spent executing someone else's programme. This model produces researchers who are, by design, self-reliant — precisely the quality that both Hogg and Karamanis claim to value — but through a mechanism that neither considers.

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3. The Real Question Is Admissions, Not Supervision

3.1 The Alice and Bob assumption

The Alice and Bob parable assumes that both students arrive at graduate school roughly equally unprepared, and that the first year's struggle is what separates them. I challenge this assumption. By the time a student has completed a bachelor's degree, they are a 22-year-old adult. If their undergraduate education has done its job, they should arrive at a graduate programme with the foundational knowledge, mathematical maturity, and intellectual discipline necessary to engage with current research. They should be able to read a paper, implement a method, write code, debug it, and critically evaluate results — not perfectly, but competently. This is what a bachelor's degree in physics or astrophysics is supposed to certify.

3.2 The actual failure

If students are arriving at graduate programmes without these capabilities, the problem is not that they need more hand-holding. The problem is that the undergraduate programmes are failing, or that the graduate admissions process is failing, or both. The solution is not to design graduate education as a remedial training programme and then worry about whether AI is disrupting the training. The solution is to raise the bar for admission and expect graduate students to function as junior researchers from the beginning.

3.3 How this dissolves the dilemma

This reframing dissolves much of the Alice and Bob dilemma. If both students arrive with genuine competence, then Bob's use of AI is not a crutch substituting for absent skills — it is a tool deployed by someone who has the foundation to use it critically. If Bob cannot tell when Claude is fabricating coefficients, Bob should not have been admitted. The filtering should happen at the gate, not through years of artificially imposed struggle within the programme.

I am aware that this is a demanding standard, and that most universities currently do not meet it. But the current standard — admit broadly, train slowly, exploit labour, and call it development — is not something to be defended. It is something to be reformed. And AI is not the threat to that reform. AI is the catalyst that reveals how urgently the reform is needed.

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4. AI Usage Cannot Be Policed, and Attempting to Do So Is Toxic

4.1 The enforcement problem

Both Hogg and Karamanis are careful to say they are not calling for a ban on AI in research. The ergosphere post explicitly rejects the "ban-and-punish" approach as unenforceable and unfair. Hogg acknowledges the same. Yet both pieces spend thousands of words articulating why unsupervised AI use by students is dangerous, without offering any mechanism for preventing it that does not reduce to surveillance, accusation, or institutional coercion.

This is the practical impasse that neither piece resolves. You cannot monitor how a student reads a paper. You cannot verify whether a student wrote their own code or asked Claude to write it. You cannot determine whether a student genuinely understands their results or is fluently parroting an AI-generated explanation. Any attempt to enforce these boundaries requires either invasive monitoring — which is incompatible with the autonomy that both authors claim to value — or a culture of suspicion and accusation that would poison the supervisor-student relationship and the broader research environment.

4.2 Where responsibility belongs

The ethics of AI usage in graduate studies must ultimately rest with the student. This is not an abdication of responsibility. It is a recognition that intellectual honesty is a personal commitment that cannot be externally imposed on adults. If a student chooses to outsource their thinking and arrives at their qualifying exam or thesis defence unable to explain their own work, the system should catch this — not through AI surveillance, but through rigorous evaluation of understanding. If the system currently cannot distinguish between a student who understands their results and one who does not, then the system's evaluation methods are broken, and that predates AI by decades.

4.3 A personal tension, honestly stated

I will be transparent about a tension in my own position. If the decision rested entirely with me, I would ban AI usage below the level of a completed doctorate. Not because AI is inherently harmful, but because the formative years of scientific education — undergraduate and graduate — are where foundational understanding is built, and I think that process is best served by unassisted intellectual effort. AI is primarily a tool for accelerating the work of those who already know what they are doing. For those who do not yet know, it is more likely to obscure the gaps in their understanding than to fill them.

But I hold this position while simultaneously recognizing that it cannot be enforced. AI is ubiquitous, access is trivial, and any ban could be circumvented by anyone with a browser. The right response is not to pretend enforcement is possible. The right response is to design educational and evaluative structures that are robust to AI usage — structures that test understanding rather than output, that value explanation over production, and that filter for competence rather than compliance.

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5. Progress Is Not Ego

5.1 The accusation reversed

The ergosphere post frames the desire to use AI for faster scientific progress as a failure to appreciate the formative value of slow, difficult work. This framing contains an implicit accusation: that anyone who prioritizes results over process is missing the point of science, or worse, is driven by ego.

I want to reverse this accusation. The insistence that science exists primarily to develop individual human minds — that the results are secondary to the experience of producing them — is itself a form of ego. It places the researcher's personal journey at the centre of an enterprise that is supposed to be about understanding the natural world. It elevates the subjective experience of "doing science" above the objective value of scientific knowledge. And it provides a convenient justification for a system that moves slowly, publishes incrementally, and resists any tool that threatens to make established researchers less indispensable.

5.2 The Schwartz experiment, reinterpreted

When Schwartz supervised Claude through a publishable physics paper in two weeks instead of a year, Karamanis's response was not "how remarkable that we can now do physics faster" but rather "the supervision is the physics, and the supervision requires decades of prior experience." Both of these things can be true simultaneously. The fact that effective AI supervision requires expertise does not mean that AI-accelerated research is illegitimate. It means that the combination of human expertise and AI capability produces more science, faster, than either alone. That is straightforwardly good for the progress of human knowledge. The question of where the next generation of supervisors comes from is real and important. But it is a pipeline question, not a reason to slow down the people who are already at the end of the pipeline.

5.3 Amplification, not equalization

Karamanis describes a colleague who initially feared AI because it might "equalize everyone" and later embraced it when it could "accelerate him." The author presents this as hypocrisy. I see it differently. I see someone who initially misunderstood the technology's implications and later understood them correctly. AI does not equalize. It amplifies. It makes the knowledgeable more productive and the ignorant more convincingly wrong. That asymmetry is not a bug. It is, if anything, the strongest argument for ensuring that researchers develop genuine expertise before they begin relying on AI — which is exactly the admissions-and-standards argument I am making, not the supervisory-coddling argument that these pieces defend.

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6. What Needs to Change

6.1 The diagnosis

The current discourse treats AI as a threat to a functioning system. I see it as an X-ray of a system that was already broken. The North American model of graduate education mass-admits students into programmes that are structurally designed to extract cheap labour while providing "training" that could be delivered more efficiently and more respectfully through higher admissions standards and greater student autonomy. AI did not create this dysfunction. AI merely made it visible, because a system that cannot distinguish between a student who understands their work and one who had Claude do it is a system that was never measuring understanding in the first place.

6.2 The prescription

What needs to change is not our relationship with AI. What needs to change is our relationship with graduate education. Specifically:

• Raise admissions standards and admit fewer students. Graduate programmes should select for demonstrated competence and intellectual maturity, not potential to be moulded. Students should arrive ready to engage with current research, not needing years of remedial training disguised as apprenticeship.
• Expect independent research from the outset. The European model, in which students choose their own problems and develop their own methodologies, produces self-reliant researchers by design. The North American model of supervisor-directed research produces dependence by design. The former is more compatible with the AI age, not less.
• Evaluate understanding, not output. If a thesis defence cannot distinguish between a student who understands their work and one who does not, the defence is a formality, not an evaluation. Rigorous oral examination, detailed questioning of methodology, and the expectation that every result can be explained from the ground up — these are AI-resistant assessment methods that academia already possesses but has allowed to atrophy.
• Pay graduate students fairly. If students are professionals contributing to the advancement of knowledge, compensate them as professionals. The rhetoric of "formative experience" should not be used to justify poverty wages. Fair pay also shifts the framing: a well-compensated researcher is an adult colleague, not a ward of the supervisor.
• Treat AI as any other tool — the researcher's responsibility. Usage should be governed by the same standards of rigour and honesty that apply to every other aspect of scientific work. No surveillance. No accusations. No policing of process. Judge the product and the person's understanding of it.
• Abandon the factory model of academic research labs. The current system optimizes for publication volume, which incentivizes exactly the kind of shallow, AI-assisted output that both Hogg and Karamanis fear. A system that valued depth and quality over quantity would naturally reward the kind of understanding they want to protect.

6.3 The bottom line

The machines are fine. The people who built their expertise before AI arrived are fine. The students who will build their expertise despite the temptation to skip the work will also be fine. What is not fine is a system that was already failing its students, that already prioritized output over understanding, and that is now using AI anxiety as a reason to defend the status quo rather than confront the deeper dysfunction.

Science is about progress. Not your progress as a person — though that matters. Not your supervisor's programme — though that has its place. The progress of human knowledge. Any tool that accelerates that progress in the hands of competent people is a good tool. The task before us is to ensure that we keep producing competent people. That task is real and urgent. But it is a task of institutional reform, not of technological resistance.

Math is Incredible

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Math is about Creativity

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"Dance" was never meant to be a repetitive act of the same moves over & over. Wasn't the person who originally invented a move improvising, after all? Same goes for music, math, or any subject really. The main issue I think with the education system around the world, is in fact that it doesn't let kids shine and thereby strengthen their inherent creativity, but rather it is founded on the false premise of rote repetition. Math has been about exploration and creativity from the start.

There is one particular teaching method, called the Moore method, which encourages just that. How would you like to sit in a college class, where you get to discover & invent the subject together with your fellow classmates? The future of education is not online, it's inside of us, our very own inner light.

I began doing research in like the second month of college, which eventually became my masters thesis. That's in no way special. You too can pull such a move, if you lose your pride, and approach mathematics with stupefying egoless humility, where you can stumble, fall, get up, and grow.

An In-Depth Design of Introductory Calculus

Response paper for the session "Course Design"
Certificate in University Teaching Program - University of Waterloo

In this essay, I plan to investigate course design alternatives for introductory Calculus, often called "Calculus I" in various science and engineering disciplines. It is a course upon which virtually all university mathematics courses are built, therefore its thorough design for maximal effectiveness is of utmost importance. Mathematics is a primarily cumulative subject, thus out of consideration for the learner, the design of a math course must be carried out with great care. Despite the rational nature of the field of mathematics, it does not necessarily imply ease of reasonable course design. The "elementary" nature of the course for many professors, in itself can imply carelessness. In this essay, I will attempt to extensively detail some pitfalls of this course's design, as well as make constructive practical suggestions which I believe would improve in general the prevalent methodologies in use. It is a course I have been involved in teaching several times, and I feel confident in sharing my predispositions still under development.

I believe that the recurring issues with first-year Calculus courses spring from two main sources of pitfalls: (1) the "toolset" nature of the material, and (2) its elementary nature for the instructor. These two sources may branch out into an avalanche of consequences, that may fall upon the unlucky learner at any time, without them realizing in time, or even later, its adverse affects on their early mathematical foundations. Naturally the learner is mostly helpless, and must rely on the instructor's wisdom. I believe a wise instructor remains a student when s/he enters the classroom to teach, regardless of where s/he might be in terms of career. This is clearly difficult to do so, and is perhaps the main challenge of teaching, to see the material fresh and anew every time one teaches it. Then and only then, may one's teaching become fully didactic. This is imperative with such a foundational course, as a didactic structure transfers into the mind of the learner, in building their own inner picture of the material. Therefore via attempting to remain a "learner in progress", the instructor consciously must shred the "elementary feeling" inherent in the task of teaching Calculus I.

As far as the first pitfall stated above, it is somewhat more striking and therefore easier to identify. First let us confess that Calculus is indeed a toolset, but at the same time it is an interconnected living breathing theory, with a historical past and a future. In my opinion, a well-designed course incorporates a permeating veil of rhetoric in the material, which subconsciously brings the learner to these realizations, essentially showing the beauty and relevance of Calculus. This imperative factor in the design has its own consequences, not the least of which is student motivation. The mindset of all the students in a class can be taken on an ideal course, by an instructor who projects their own appreciation for the theory. These emotional ties may be coupled with rational reasoning for the theory's relevance, with the vast spectrum of theories and applications which hinge upon it. This double parallel subconscious reasoning should enter the learner's mind sufficiently, to induce a seductive motivational effect. Among all the distractions of a freshman's college life, such an attempt at seduction seems quite reasonable, the ends thereby justifying the means.

The above stated points are meant to assist the instructor in taking an ideal philosophical approach to teaching an introductory Calculus course. The actual course design however must be guided by the intended learning outcomes. As stated in the pre-workshop worksheet, by the end of my Calculus course the students should be able to...
• provide an overview of elementary Calculus.
• state the definition of a function, and derive its domain, range and inverse.
• calculate the limits of a wide variety of functions.
• explain and apply differentiation and integration techniques.
• elaborate on the conceptual meaning of the learned techniques.
• model and solve mathematical and real-world problems using Calculus.
I shall elaborate below on the often ineffective ways of attaining these ILO's, and suggest some alternative approaches, in light of Fink's taxonomy.

By "providing an overview" I mean the ability to rise to a global perspective above the material and seeing its inner multilevel connections. This is not a skill that comes easily, and it might even be one of the highest-level skills attainable, which could be why many students tend to lack it, and most instructors fail to encourage it. It ties in directly with the first pitfall above, as an overtly toolset-style teaching works against the development of a global perspective. Therefore, the instructor must consciously focus on this element, in order to counter-balance. It ties in with the idea of "conceptual understanding" under foundational knowledge in Fink's taxonomy. Indeed, the goal must be to develop a complete understanding of elementary calculus, as a foundation for future subjects. On the other hand, this ILO also has an "integration" aspect as per Fink, since connecting ideas is essential to being able to having an oversight.

Stating the definition of a function, and working with its domain, range, or inverse are basic elements of the toolset of Calculus. As such, they represent foundational knowledge that serves as skills for proper performance in cumulative topics building upon them. The pitfall here is that the instructor may forget to remain a student, and may rush through these imperative ideas, without the full comprehension of students. I have observed this multiple times as a student and as a teaching assistant. Clearly, this ILO may be addressed with a sufficiently thorough treatment by a conscientious instructor.

Calculating the limits of functions, can become a stale and mechanical topic, if presented without the intricacies giving its beauty. It can quite easily be reduced to a set of calculation rules and dogmatic methods. This reduction is certainly not the challenge of an educator, but quite the opposite. Without going into too much detail, one may build towards this intended learning outcome, by discussing the nature of the "limit" in the more rigorous sense. This would justify and establish its relevance, giving it sufficient weight in the mind of the learner. Otherwise, one may take this seemingly simple concept for granted. Such a somewhat more precise discussion, would lay the foundations for the eventual introduction of the concepts of continuity and differentiability. Thus making the effort to introduce the approach early on, shall pay off later to the learner's benefit. In Fink's taxonomy, this may be considered a way of motivation, by projecting the exciting implications into the future.

The next learning outcome is tied to the one following it for a good reason: to once again avoid the potential reduction of the material to mechanical calculations. Understanding the conceptual undercurrents of the topics of differentiation and integration, in and of itself is sufficient to motivating students towards exploring these theories created by scientists over two hundred years ago. The idea of time-traveling to walk alongside these pioneering explorers of the world of mathematics, may awaken the more romantic side of a learner, thereby serving as motivation for learning. I am speaking from personal experience, that such motivation can be deeply effective and long-lasting. It is precisely the "human dimension" of caring to develop feelings, interests, and values in Fink's taxonomy, which these thoughts of mine converge towards.

Reaching the level of ability to independently model and solve real-world problems, is clearly an expected goal for learners in applied disciplines. It is also clear, that the maturity and readiness for independence comes gradually, over the span of several courses, not only introductory Calculus. Yet, I do believe that the instructor must make steps towards and assist the development of learner maturity - again, this is one of the intricacies of teaching that often goes ignored, however essential. These steps could be gradually built up from smaller exercise-level problems, to the level of term projects in later terms. In Fink's model, it invokes the full spectrum of implications of concept "application", such as critical thinking and the ability to make project-directing decisions. It is primarily a way of "learning how to learn" - or even to research - independently via intriguing applicational problems. Inducing independence in students may thus be the most challenging yet most important aspect of teaching, matching the magnitude of its relevance.

In my essay I have reflected on my personal beliefs and strategies for designing an ideal introductory Calculus course. The elementary nature of the material is never to be confused with the significance of this contemplation. Indeed I have outlined two major pitfalls such confusion may lead to, as well as analyzed the intended learning outcomes which should guide the process of course design. I found this discussion all-in-all valuable and considerably self-reflective, hopefully increasing the effectiveness of my future endeavors.

Presentations as a Type of Formative Assessment

Response paper for the session "Assessing Student Learning"
Certificate in University Teaching Program - University of Waterloo

We may have all experienced the elevating feeling of having a great idea inspired from thin air, and suddenly wishing to share it with someone. This must be an ancient human characteristic indeed, one that may have driven the evolution of the modern human by a significant factor. The feeling of “sharing through words” not only makes us feel good for “giving” to others, but can cause us to self-reflect on our own thoughts on a deeper level, as well as give other people a chance to comment on our ideas, thereby potentially improving them. In fact in a more organized and developed manner, this is the fundamental idea behind “presentations” and the response to them, either by the audience or an instructor, in which case it is a type of formative assessment. In this essay, I wish to take a closer look at presentations and their assessment as a means for student learning.

I believe that the primary most important outcome of a presentation, is the effect it has on the audience. The effect can be considered in varying degrees of weighting to be the amount of information retained by a listener, or the influential effect it has on a person, in the short or long run. Certain effects may be qualitatively measured, while some are hazier matters of opinion. What I believe to be certain is that every presentation must have a set of clear objectives - primary, secondary, and else. Objectives assist in the overall structuring and branching of a presentation, while their clear statement in the beginning may help guide the presentation itself. Indeed the structure is the linking bridge from the stated objectives to the observable outcomes. Thus an effective communicational channel opened by the presenter, must have a trait of clarity and didactic structure, serving as the master plan of a master manipulator, which any great presenter must ultimately become.

As far as the specific skillsets - implicit or explicit - required by the intended outcomes of presentations, they all revolve around the idea of effective communication, which I define for myself as the ability of opening a communicational channel, maintaining it, and then applying it for the channeling of information to the audience. Clarity not only in structure but in mere speech itself is obviously a must for such a channel to open, in addition to adjusting the proper sound volume to the situation, and maintaining the continuity of speech. Eye contact may serve as a means to keep the channel open, in addition to actually opening one potentially. Subconscious signals sent via facial and postural expressions, serve to enhance communication. For a presenter, they provide a chance to take their self-expression to the next level, creating an audio-visual orchestra of signals with varying intonations and perceptibility. According to certain theories of the birth of human languages, initially we all communicated via sign languages, which may be why certain regional cultures - such as the Mediterranean - have still retained them to the present day.

In addition to the skills of speech, the speaker's proficiency in the material can influence the presentation to a major extent. It not only gives the impression of knowledgeability, but raises the level of self-confidence in the speaker. Self-confidence in turn may influence a wide range of other factors mentioned above, such as continuity of speech and clarity of structure. Self-confidence is radiant. It shines from the presenter on the audience, and in turn influences the level of trust and confidence the audience has in the verity of the communicated information.

An important dilemma to be decided prior to assessment, is the weighting of the relevance of feedback by either the instructor or the peer students in the audience, as well as the sequential method of communication of the formative assessment. I believe the ideal resolution of this to be as follows: peer assessment should have temporal precedence, while instructor feedback should serve as the deciding factor. Most students may sub/consciously agree that the instructor has a right to serve as a presiding “judge” having the final say. On the other hand, peer opinion must be upheld as valuable for its inherent spectral characteristic, providing a wide range of opinions to consider. This semi-hierarchical relationship must serve as the definitive template for any specific explicit assessment, to ensure the relevance and reliability of the overall reflection on the presenter.

In considering the efficiency of student presentations and their peer discussion as a formative assessment in terms of student and instructor time, one must identify the given base constraints, such as class size, class time, and meeting frequency. Clearly, such constraints require advance planning on the part of the instructor, in order to ensure the smooth flow of class activities, by minimizing the unnecessary pressures of the constraints. Thus considering time limitations, presentations may or may not be considered efficient, depending on the situation. The point of “presenting” however is not necessarily efficiency, but rather that of creating an atmosphere of open communication, by finding and maintaining the right channel for this experience. Therefore the student must be aware of this fully, so that s/he can focus on the more relevant aspects of the presentation and its subsequent interactive assessment.

One may consider alternative forms of assessment, other than the suggested weighted instructor-peer partition. These can range anywhere between the absolutely autocratic, relying on solely the instructor's criteria, to the entirely laissez-faire, whereby peer feedback is the main mode of formative assessment. We may clearly find virtues at both ends of the spectrum. However, a more weighted approach can minimize the weaknesses and maximize the effectiveness of the assessment. The advantages of the autocratic method, is the reliability of expert opinion by the instructor, and the projection of security on the classroom due to it. Peer feedback however, can increase the students' involvement in the betterment of one-another's presentation skills. Some students may be more receptive to their peers’ opinions than an instructor’s, since they are on an equal hierarchical level. Thus the quest of finding an ideal middle way for a specific situation, may result in a more optimal assessment strategy.

The verbal formative assessment matches quite well the desired outcomes and required skills mentioned above. A communicative feedback approach fits appropriately into the format of presentations, and may be viewed as their natural extension. The marking scheme or specific criteria should in my opinion be reserved for the instructor, who is to give a more rigorous and analytical assessment. The audience of students however must be allowed to engage in free conversational feedback with the presenter, for an optimal active learning outcome of all students.

In summary, I have discussed “presentations” as serving the assessment of students of various topics. I have considered a number of analytical questions to observe my ideas in this regard. I found this endeavor to be quite valuable in its self-reflective potential, making me think deeper about the conceptual approach to this type of assessment.

Self-Reflection for Identifying my Ideal Learning Experience

Response paper for the session "Understanding the Learner"
Certificate in University Teaching Program - University of Waterloo

A commonly voiced belief is that the learning environment greatly influences the learning experience of students. In fact, mostly the teacher is the one to blame, when students' expectations are not met in regards to their imagined ideal of classroom instruction. In this essay, I will reason that learning is a bipolar experience, greatly dependent on both the student and the teacher. Via an acute self-reflective analysis, I will identify my own learning characteristics in the context of two very different learning experiences - one negative the other positive. Meanwhile, I shall keep in consideration the potential experiences of other types of students, based on my retrospective observations. I will begin with a brief description of myself as a learner, to provide a working foundation for the subsequent analysis of my experiences.

Currently I am a graduate student working on my PhD in Applied Mathematics, primarily occupied with doing research, which is quite different from my undergraduate mindset, when studying was my primary focus. Therefore much of this discussion will be a travel back in time, though my main characteristics as a learner have not changed, only become less elaborate and refined. When I was an undergraduate in Hungary at the Eötvös Loránd University, I was literally like a sportsman of the mind - constantly monitoring myself for what shape I was in. This was of utmost necessity so that I could allocate my time and efforts to the most appropriate times for a particularly or less demanding subject. I monitored my diet, breathing, everything - it was a bit insane, but the tasks at hand required it. I was also very independent as a learner. I did not value having to sit through lectures, where I was superimposed with a "sequential" learning experience in time, in which I was unable to flip back and forth as I would do by myself with a book, to connect formerly learned concepts. I am what I can now properly categorize as an absolutely "global" learner. Even in doing research, I feel more like a bird flying over the savannah than an antelope galloping through it. Whenever I notice something interesting, I zoom in somewhat, then zoom out, or zoom in more if I find it to be of value, guided by my intuitive inner compass. Just as a bird, I am a great lover of freedom, and being restrained in any way, whether by a non-liberating lecture or else, can be very disheartening to me. I feel lucky to have been endowed with a considerately adaptive supervisor who understands me by instinct, and is willing to act as a companion, versus a guide.

Considering the above, the ideal learning condition for me is most likely an environment in which I am given a chance to contemplate the material with freedom, allowing me to internalize it in a gradual process of digestion, in which I reassemble the buffered information packets into a new didactic whole within myself, via a highly independent process. Is this possible through any kind of lecture or tutorial format? Does not my own sense of independence make the reception of any external learning projection impossible? Sometimes it does feel like I have this impediment. In rare occasions however, I encounter a learning environment in which sequential spoonfeeding is not the primary mode, and which I find highly enlightening and motivational, activizing my neurons on all levels.

A specific negative experience was my Partial Differential Equations (PDE) course as an undergraduate. It was undoubtedly a large bulk of information we had to internalize, or perhaps only seemed like it? The professor acted like a sage on the stage throughout the lectures, speaking his mind like a radio, and scribbling away on the board, without any consideration for our understanding. Certainly contrary to a multilevel learning experience, that would have sent off sparks in the minds of various types of learners. I personally felt being given no chance to reflect on the material, the pace was too fast and uninterruptibly monotonic, which made me feel disconnected, from both the professor and the material. I was given no opportunity to make a connection either, as it felt like the instructor had no idea there was even an audience. I was not motivated in any way to learn the material, and suffered greatly when I "had to" in the exam period.

I believe a good teacher not only connects with the audience, but monitors their level of understanding throughout a lecture, and sets a fluctuating pace accordingly. Before the process of information flow is even initiated however, the professor must set a base tone for the rest of the lecture in some way, much like a painter might paint a base color for a painting to be laid out upon. The base tone might involve some motivational words of wisdom on the nature and relevance of the material, its historical importance, or their own enthusiasm for the topic. These may serve as an extension of a hand to help students on board for a journey of sailing the waters of knowledge. Most of all, teachers must primarily know themselves and their own motivations, so that they may accompany students on their own journey of learning, whether as captain or companion. In a classroom, a teacher must above all create a multilevel learning experience, in which learners of all kinds shall thrive.

A specific positive experience was my Functional Analysis course as an undergraduate, which I remember as my favorite one of all time. Unlike the PDE course in which I never got past learning the toolset, here I internalized it from the notes quite naturally and largely independently, since I was eager to apply it in assignments given at the tutorial. The tutorial was also held by a professor who I have had for years in Analysis, and found his approach reliable and respectable. His opinion and marking represented to me the unquestionable law carved into stone. In other words I truly trusted him, regardless of him being a difficult personality, who did not think much of "applied" mathematician students. So clearly, I had an incentive to prove myself in attempting to solve the rather difficult problems given by him. Indeed week after week I handed in each assignment in full completion, even though a vast majority of students sabotaged the tutorial by missing it and not doing the homework - the general consensus was that this was a teacher to be disliked. Yet, I saw him as a representative of mathematical rigour from the past, and his very character projected reliability and wisdom. So even though I was a largely independent learner, his character still had an influence on my motivation, captivating my interest as if by undetectable magic. I was not alone though among those who were motivated - some were hard-working by nature, some saw me as competition, and a few were just trying to get by on a mediocre level. I however saw this class, which presented the pinnacle of modern Analysis (Calculus), as a chance to mature into an able mathematician. When the course was over, it felt like I have gone through a training period harsher than ever. Yet by overcoming independently the challenges I have faced, I have reached a new level of mathematical awareness and understanding, inaccessible via any other learning process. Then and there, I became a mathematician.

How was any professor able to evoke all these feelings alone? Analyzing my memories, I believe the answer is that he did not do it alone, I was an equally necessary ingredient. In fact, being the experienced educator he was, I believe he tailored his classes to specifically the kind of student that I was. This was quite irresponsible, since the rest of the class was left behind. I have observed however, that many professors grow tired over the years of having to deal with other - perhaps less motivated - students, and hold their classes for the most worthy. I must however voice my criticism, being a great believer in student potential. While I may have felt good in this particular classroom experience, it was unfair to the other students. Therefore I once again arrive at the conclusion, that a good educator creates classes which run on multiple levels, as an orchestral concert involving winds, strings, and percussion, for various musical sounds which may appeal in varying degrees to individuals in the audience. For instance, there are lovers of rhythm (percussion) and those who are more receptive to tunes (winds or strings), whether brief or overarching. A great symphony like a good lecture, weaves elements in a magical fashion, sparking the minds of as many listeners as possible, ie. optimizing the learning experience for the entire class.

In conclusion, I deduce that the ideal learning experience for me is one where I am granted freedom to explore, and the material is shown to me to be a toolset for practical application in the gradual exploratory process. Even though I have always thought that the personality of the instructor has no effect on me as a learner, I have now realized that it is indeed a major factor towards connecting with the material. A demandingly harsh yet nurturing environment, can be better than the nowadays common easy-going attitude of many professors, on the lookout for high student ratings. A professor can still remain accessible for a personal connection, in many ways. In regards to presentation of material, I have reasoned for a multilevel orchestral manner of information propagation, for optimal exploitation of student potential. Though certain manners of instruction I may see selfishly as ideal to me personally, as a teacher I feel I must become a more complete individual, growing beyond my own natural ways of learning.

Benefits and Risks of the Moore Method in Mathematical Education

Response paper for the session "Interactive Teaching Activities"
Certificate in University Teaching Program - University of Waterloo

A commonly observed phenomenon in undergraduate mathematical education, is the lack of motivating conceptual instruction. All too often, students are lectured on techniques of problem-solving, without a parallel development of their conceptual understanding. This lack of depth often continues on to graduate-level classes, for the sake of rational efficiency. The method developed by Robert Lee Moore, is a form of mathematical activity spanning a single class event to potentially entire terms. In a sense, it may be viewed as ideal for the conceptual development of students in science disciplines, primarily in mathematics. This paper will consider its hypothetical application in a first-year Calculus setting onwards of Pure and Applied Mathematics students.

First-year Calculus for a student can be a time of great transitions with unusual concepts, formulas, and methods, that many instructors seek to hard-wire in students, to establish a foundational toolset for later courses. Most instructors achieve just that, storming by underlying concepts and philosophies that may only occur to the most contemplative students, in such a fast-paced setting. Likely most professors would cite lack of time, size of classes, and efficiency of information propagation. On the other hand, elementary Calculus could easily become an ideal platform for activizing students, and involving them in the creative development and learning of the material. Bringing to the surface and upholding students' inherent mathematical acumen, must be the goal of any program which honestly wishes to raise able mathematicians and future researchers.

Indeed, most students accepted to university, possess the mathematical background and potential to develop the Calculus of Newton, Leibniz, and Cauchy - however outrageous this claim may sound. Encouraging them to do so in a classroom setting, may potentially turn some into quite capable and confident mathematicians once they become researchers themselves. The suggested alternative activity would hard-wire deep research and collaboration behaviours they stumble upon themselves, instead of the shallow toolsets of technique they are mostly provided. Thus my argument that follows for using the Moore Method in first-year Calculus classrooms, is primarily intended for students who may become researchers one day, in the fields of Pure or Applied Mathematics.

I will hereby outline the overall class as well as the classroom activity itself, which the entire longspan method would reduce to. The Moore Method has numerous existing interpretations, branching off from the original central idea behind it, and this is reflected in the way the actual classroom activity may be carried out. Often the interpretation reflects the course it is applied to, so what follows is my own interpretation, as I would potentially apply it.

The central idea of the Moore Method is to involve a class of students in the development of historical theories of mathematics and science, from the ground up. Students are generally given a set of basic knowledge, often in the form of definitions and axioms, providing a foundation for the relevant initial questions and directions. In regards to elementary first-year calculus, the initial concepts provided to the students might entail a vague idea of limits, continuity, and tangent / area determination, which they would be first tasked with refining. The refinement process itself for these basic definitions may last 1-2 classes - to be determined experimentally. Then as a syllabus, the task of putting the definitions in practice, such as for the evaluation of specific limits, then later determining continuity and differentiation of functions could follow. While solving problems they invent for themselves together, the students may encounter cases that will imply the invention of the Squeeze Theorem, the Intermediate Value Theorem, or even L'Hopital's Rule - not missing out on relevant ideas. The same implications via applications would follow for differentiation and integration, as well as their inverse relationship, which the students would be likely to discover. Specific formulas and techniques of integration, would be developed by the students, for the sake of creating a toolset for themselves, so that they can crack the nuts found in problems they encounter in their exploration. Instructor involvement would amount strictly to asking occasional motivational questions that may blow the students' sail in the right direction. Otherwise, students would be expected to create and collaborate independently, both during and after class, representing their "assignments". Soon, such a Calculus class may have the effect of occupying the students' minds 24/7, preparing them for the analogous experience they may encounter as researchers in their future careers.

The classroom activity itself would possess a loose yet consistently coherent structure, with explicit rules of conduct such as free speech balanced with mutual respect, stated in advance. These explicit rules may serve to instill and induce principles of integrity, and serving as foundations for engagement in future research. Furthermore, the instructor would provide the initial definitions, motivational ideas and questions, to ignite both group discussion and individual efforts, in a sentiment of "collaborative competition". The incentives being inherent mathematical interest, the thrill of building something together, or even peer pressure to perform.

In addition to the aforementioned benefits of a Moore-based activity which shape its very format, we may recognize an even more fundamental characteristic in the method. By placing the learning process in a creative social setting, the intructor immerses the students in a primordial soup which they are used to functioning in, and from which they may naturally evolve as maturing mathematicians. Thus each student would find their natural function in the micro-social environment of the classroom, paralleling their social preferences in real life. Thus collaborators would collaborate, thinkers would contemplate, leaders would emerge, tasks would be divided, global minds would present the vista, and sequential ones the process to explore it. All would find their own role in such Stone Age conditions, in which we are genetically programmed to rely on one another and contribute as we can.

Much of the above is hypothetical to me, and based on pure rationale, versus any prior implementation. To me, the above is an idealization of a mathematical learning activity I would have always wished to be a part of. I have been observing the inefficiency of the classical lecture-tutorial settings, ever since I became a university student, and I find this to be a solution. Though I realize that the Moore method may not be for all students. The initial selection of the students must be based on whether they find the challenge appealing, and not based on academic standing. The worst possible outcome for an aspiring mathematician, would be realizing that they are not fit for doing research, which is still better than a realization in graduate school or real life. On the other hand, many who are unsure initially, may gain self-confidence for the long run. I personally had doubts about my own research abilities as an undergraduate, projecting an unpredictable dark cloud into my future. I believe that all students should be given an opportunity to put themselves to the test, in classroom activites of this kind. Furthermore, the professor may serve as a mentor in this setting - accompanying, guiding, and nurturing potential talent.

The benefits of such an adventure, even if experimental, undoubtedly outweigh the risks. As the Ancient Greek aphorism of virtue recommends "Know thyself!", so should mathematicians learn as much about themselves as possible early on. What better way to do so, than a setting that mimics what they are apt to experience later on in real life. This is akin to teaching children to swim in shallow water so that they would be ready for the deep, while throwing them in deep water at once may prove fatal. Is it not the responsibility of experienced researchers and educators to prepare students for the deep water, besides striving to enhance their overall conceptual understanding?