How to Make a Good Teacher School Student

How to Make a Good ~~Teacher School~~ Student (2016)

(1) A good school has:

(A) hi tech desks.

(B) small classrooms

(D) good teachers

(E) good students

(2) A good teacher is:

(A) well paid

(B) strictly controlled pedagogue

(D) trained by good teachers

(E) Finnish

This is an existential question. Whether we are managers looking to hire the current generation or parents preparing the future generation, it starts in the schools. The long term assumption is that the good talent comes from good schools. The current assumptions lean towards defining good schools (Question 1) as (D) having good teachers. There is no current consensus on what makes a good teacher (Question 2). A leading British-based publication and commentary review of practice votes for (D) effective teacher training with effective feedback communications and scoring (Economist, June, 2016) while acknowledging the mystery of what precisely makes a good teacher. Besides stating oddly that Finnish teachers are the best.

Yet in virtually all schools, quantitative scoring of a teacher's goodness consists solely on the performance outcome of the students. This technically means it takes good students to make good teachers. It takes good students to make good schools. (e.g. MCAS; No Child Left Behind Act.) Therefore, this paper takes a contrarian, yet oddly more direct logical approach by stating a good school by definition has good students irrespective of the teachers. While a good school - which again can only claim such by having high performing good students - may claim they are such due to having good teachers, they in fact can with equal validity claim they are good by having hi tech desks, or small classrooms, or large classrooms, or deep pockets. Hence the lack of consensus on what exactly makes a good teacher or school. In this light, it is only logical to focus on the sole consensus point of view: what makes a good student? For at the end of the day, does it matter how or why as long as we can scale up the current and future generations of hirees?

All the theoretical pedagogy in the world on making a good teacher is pointless without asking what makes a good student. And all the intuitive musings on how to make a good student is pointless without a grounding on what is a student and how a student learns or develops. Here follows a brief review of the five basic cognitive development models for students, defined as humans qualitatively in a more preparation and growing phase rather than in a serving or economic value producing stage.

Piaget/ Neo Piaget.

Represented by a sampling of work by (Halford, Wilson, & Phillips, 1998; Fisher & Farrar, 1988; Demetriou, Christou, Spanoudis, & Platsidou, 2002; Case & Okamoto, 1996). The basic premise is that development occurs in apparent qualitative stages effected through enhanced working memory capacity. To summarize memory: long term memories are stable sights, sounds, scents, or other data that remain permanently available for recall. Short term memories are volatile sights, sounds, scents, or other data that are available only temporarily for recall and become irretrievably lost after a short (minutes to hours) period. Working memory in contrast refers to the workspace whereby several memories - be it a sight and a sound or two sounds over time, etc - can combine to contribute to contextual situational awareness to enhance decision making. As students grow chronologically, working memory capacity gets bigger or it gets more efficient to combine more memories. Shortcomings on this branch of models include missing explanations at the low level neurological wet works on exactly how working memory becomes more efficient and how to measure memory requirements for a particular task. Piagetian and Neo-Piagetian models essentially rely on students being innately little scientists whose development is automatically tied to individual physical growth. A subscriber may thus point to good nutrition to make a good growing student. A good school has healthy, nutritious meals.

Psychometric.

Represented by Stanford-Binet Intelligence Quotient (IQ) exams and derivatives. (Andersen, 1992; Ceci, 1990; Gardner, 1993; Sternberg, 1999.) The basic premise is that students have 3 separate forms of intelligence: Analytic, Creative, and Practical. Traditional IQ exams focus on analytical intelligence focusing on storage, recall, and manipulation. Creative intelligence refers to novel combinations of materials to create new knowledge. Practical intelligence refers to the ability to apply theoretical facts to real world applications. Different students have different preferences among the 3 forms. Shortcomings in this branch of models include being unclear on the low level neurological ties and mechanisms on intelligence, especially with respect to the models' key aspect of generalizing and transferring between intelligences. A subscriber may point towards having like students develop together as a means of making good students. i.e. Analytical students should group with fellow analytical students to specialize on analytics and score extremely well on analytics exams. A good school has in-depth testing and segregation into small classes by type and innate abilities.

Production System.

Borrows the vocabulary and concepts of a computer without actually stating that students learn like computers. (Klahr & MacWhinnney, 1998; Jones, Ritter, & Wood, 2000.) It is an abstraction to provide a common baseline for discussion. The main premise is that behavior is dictated by a flowchart-like, if..then...else set of rules. These determine what a student does in a given condition, almost as an artificial intelligence script does for a machine. The more complete this production system set of rules, the more complex the behavior. Shortcomings in this branch of modeling revolves around the controversy of tying student learning with that of a computational system. However, these models are explicit on what low level computational analog must have changed during development. Subscribers may point towards "programming" the students sufficiently with highly skilled "programmer" teachers to handle all contingencies. A good school has good teachers.

Connectionist.

Based on artificial computational neural networks and deep learning. (Plunkett, 1996; McClelland, 1995; Shultz, 2003.) These models rely on implicit development in fully connected networks of parallel regressor summaries. Given a set of sights, sounds, and other stimuli, several different regressors - each essentially observing random subsets - provide various different summaries focusing on different stimuli. A final, central regressor aligns these subset regressors toward a desired target goal. Shortcomings of this class of models include being too abstract, missing the role of known biological hormones, and being very slow to learn since this form of learning system requires thousands of consistent repetitions to gradually align the various regressors into a consistent, unified whole. A subscriber to this approach would require students to repeat lessons over and over to learn properly and consistently. This necessarily implies the lessons must be pre-designed to be consistent, pure, balanced, and non-contradictory. A good school is strictly controlled and repetitive.

Evolutionary.

Represented by the works of (Changeux & Dehaene, 1989; Edelman, 1987; Geary & Bjorklund, 2000; Johnson & Gilmore, 1996; Siegler, 2000), these models loosely rely on evolution concepts. Namely, there are many more internal strategies than are needed or possible to express, they compete for expression, the more advanced strategies usually win out, and winners get more use. Model behaviors tend toward more analog behavioral development. The models allow flexible and dynamic behavior, including novel strategy discovery. Shortcomings of this form of modeling include being unclear on non-discrete or poorly defined tasks. Subscribers to these models would point to a diverse and dynamic environment with lots of room for errors to best drive the evolution within students. A good school has large, diverse classrooms for enhanced exposure and competition to enhance fitness.

Best vote: Evolutionary.

The most promising modeling approach is the cognitive evolutionary one. It is dynamic. It is self-directed. It is based on the strongest biological model on evolution and shares much in providing a provable baseline on a vastly, wildly expansive and complicated phenomenon. It is bottom up and explicitly does away with the need for a leviathan overseer. It implies the brain is not a unitary static memory storage object, but rather contains a roster of separate behaviors. It also naturally explains how creativity and the creation of novel behaviors may arise.

There are still glaringly missing key low level aspects on what exactly drives the competition. Biological evolution requires genetic death. Cognitive evolution has no clear analog. Biological evolution requires stochastic randomness. This may be an apt use of the large scale Law of Large Numbers. Applying random stochastics to an individual self implies insanity.

Nevertheless, it is on strong foundations and implies good students become so by evolving the self towards the environment. It makes no assumption and has no need for innate, elitist, self-perpetuating cycles like (A) good food from providers who were previous generations of good students; or (B) innate, genetic skills or talents; or (C) good teachers in good regions – we all know what that does to property values and who can afford to move there; of (D) good regions – again on the property values. Instead, a good student practices adapting by constantly trying new approaches and favoring those that succeed. In colloquial terms, it takes 10,000 hours to make a good expert. Nothing more, nothing less. A good right-handed baseball pitcher is good because she pitched right-handed for 10,000 hours in the sun, in the rain, in the snow, at home, while away, and under various different conditions. When pitching in the rain or with jeering crowds, the expert pitcher performs. She is accustomed or evolved to handle it. Still think innate talent is primarily responsible? Pitch left-handed.

It matters little from where the student came. Whatever the student studies, as long as he does it for 10,000 hours, he will be good.

This is a good student that is accustomed to change, competition, novel solutions, and selecting winners. This is a student who knows what needs to be done, does it, and adjusts to unforeseen opportunities as they arise. From a hiring manager's or a parent's perspective, can there be any better student?