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EXAPTER ENDAPTER / House of Applied Intelligence / Praxis

Praxis

Knowledge, theory, or method put into practice. Methods and intelligence translated into action.

Current practice

Work in public.

YouTube Videos

Lessons and demonstrations prepared for public audiences.

First public lesson forthcoming

One intentional feature will open this archive. Additional lessons will form a restrained index as they are published.

EXAPTER ENDAPTER / YouTube

Teaching Systems Explained

What each Working Artifact activity teaches, the examples it uses, and where it fits in practice.

Build the Entity

Build the Entity moves from basic modeling decisions to a complete connection path.

What it teaches
Entities, attributes, attribute quality, primary keys, shared identifiers, and connection paths.
How it works
Four rounds ask learners to sort attributes, diagnose or question attribute quality, choose primary keys, and build connection paths through shared identifiers. Drag-and-drop and click/tap controls support the activity.
Examples
online sneaker purchase, student course registration, medical appointment, and employee project assignment.
Use
Introduce or reinforce foundational relational-modeling decisions before moving into cardinality and relationship strength.
Launch activity Technical Lab Catalog record

Pick the Cardinality

Pick the Cardinality turns verbal business rules into minimum and maximum participation decisions and then into Crow’s Foot notation.

What it teaches
Business rules, minimum and maximum cardinality, and Crow’s Foot notation.
How it works
Seven questions are organized into three rounds covering unary, binary, and bridge-entity relationships. Learners make endpoint-by-endpoint cardinality choices and receive feedback.
Examples
employee mentorship, marriage, course prerequisites, customer orders, device warranty, club membership, and project assignment.
Use
Practice interpreting both ends of a relationship from business rules rather than memorizing notation in isolation.
Launch activity Technical Lab Catalog record

How Strong is the Relationship?

The activity separates relationship strength from ordinary association by asking whether the parent’s primary key becomes part of the child’s primary key.

What it teaches
Identifying and non-identifying relationships, parent primary-key migration, and child primary-key/foreign-key (PK/FK) structure.
How it works
Eight relationship scenarios ask learners to classify strong or weak relationships from the displayed keys, with immediate classification feedback. The evidence is whether the parent’s primary key becomes part of the child’s primary key, rather than business-language intuition alone.
Examples
Customer → Order, Order → Order Item, Department → Employee, two Course → Class variants, Employee → Dependent, Maintenance → Maintenance Line, and the EMP_EDUC bridge.
Use
Compare structurally similar-looking relationships to see why different key arrangements produce different classifications, even when the business language sounds similar.
Launch activity Technical Lab Catalog record

How Strong is the Entity?

The activity asks a different question from relationship strength: can the target entity be identified independently, or does its identity require an identifying owner?

What it teaches
Strong versus weak entity identity, independent identifiers, owner-dependent identity, composite identifiers, and PK/FK evidence.
How it works
Twelve entity scenarios ask learners to classify strong or weak entities. Identifier evidence is highlighted after checking; first-check scoring and a completion summary support review.
Examples
recording artists and track takes, project cameras and video projects, construction equipment, edit versions, VR scene objects, workshop instructors and meetings, scheduled flights, event venues, software build attempts, and fabrication job components.
Use
Distinguish identity from importance, relationship presence, or the mere existence of a foreign key. Keyboard choices include 1 for strong and 2 for weak; reduced-motion support is available.
Launch activity Technical Lab Catalog record

How Normalizing.

How Normalizing. follows one project–employee–job dataset through three successive repairs so the reason for normalization remains visible across the whole exercise.

What it teaches
Functional, partial, and transitive dependencies; composite primary keys; foreign keys; and normalized table design through first, second, and third normal form (1NF, 2NF, and 3NF).
How it works
The same project–employee–job dataset progresses through three staged repairs. Learners split records, choose keys, drag or place attributes, inspect dependencies, and then run assignment-lookup and rate-update tasks. Keeping one dataset across the stages makes the reason for each repair visible, rather than presenting the normal forms as unrelated definitions.
Examples
1NF splits repeating project–employee groups and identifies the composite assignment key; 2NF separates project, employee, and assignment facts; 3NF moves the hourly rate to JOB. Follow-up tasks locate Alex Rivera’s six hours on Rolling Tide and update the Designer rate from $90 to $95 once in JOB rather than in multiple records.
Use
Make dependencies and the practical effects of a cleaner model concrete. The activity runs locally in the browser with no account, tracking, or external dependencies.
Launch activity Technical Lab Catalog record

Professional Record

Curriculum Vitae

Josh Ayala

Education

University of Kentucky

Gatton College of Business and Economics

M.S., Finance

GPA: 3.8

University of Kentucky

College of Fine Arts

B.A., Arts Administration

Minor: Theatre · Dean’s List

Academic practice

Teaching

Adjunct Instructor

University of Kentucky — Gatton College of Business and Economics

Lexington, Kentucky

Aug. 2023 — Present

Teach undergraduate courses in business data management, finance, and spreadsheet applications.

  • Translate technical material into applied business decisions and clear communication.
  • Design data-driven coursework through business examples and case work.
  • Use analytical writing, software demonstrations, and results-oriented presentations.
  • Integrate professional experience and current business context into instruction.

Academic practice

Courses

CourseTitleTerm
AN/MKT 324Business Data ManagementFall 2026 · Sections 001/002
Fall 2025 · Sections 001/002
Spring 2024
FIN 405Capital Investment & Financing DecisionsSpring 2024
AN 105Business Applications of SpreadsheetsFall 2023

Selected evidence

Teaching Effectiveness

AN/MKT 324 · Fall 2025

Overall instructor quality
4.8 / 5
Overall course quality
4.8 / 5
Instructor preparation
4.9 / 5
Treating students with respect
4.9 / 5

FIN 405 · Spring 2024

Overall instructor quality
4.8 / 5
Overall course quality
4.9 / 5
Responding to questions in a way that aided understanding
5.0 / 5
Treating students with respect
5.0 / 5

Full University of Kentucky Teacher Course Evaluation documentation available upon request.

Finance & operations

Professional Experience

Collateral Risk Analyst

JPMorgan Chase & Co. · Columbus, Ohio

Collateral and margin-call operations; established collateral arrangements in credit systems and analyzed noncompliant scenarios. Coordinated across Credit, Bankers, Brokers, Lenders, Financial Advisors, and Client Service teams, while supporting operational metrics and recurring line-of-business reporting.

Teller / Associate Banker

JPMorgan Chase & Co. · Nicholasville and Lexington, Kentucky

Consumer banking operations spanning digital-platform adoption, deposit-account servicing, cash handling, and client issue resolution. Served as Culture Champion and ATM Custodian, later working across the Lexington market as a float Associate Banker.

Continuing study

Professional Development

AI for Business Specialization

University of Pennsylvania / Coursera

Power BI Essential Training

LinkedIn Learning

Introduction to OpenAI Codex

Scrimba / Coursera

Machine Learning Specialization

DeepLearning.AI / Stanford Online / Coursera

Generative AI with Large Language Models

DeepLearning.AI / AWS / Coursera

Working knowledge

Competencies

Data / BI

Power BI · SQL · Relational databases · Data modeling · Tableau · Python · pandas · NumPy · Alteryx · Data visualization · Dashboards · Data preparation

AI / Applied AI

Generative AI · Large language model workflows · OpenAI Codex · Prompt engineering · AI-assisted coding · Machine learning · Predictive modeling · Model evaluation

Finance / Operations

Financial analysis · Collateral analysis · Margin analysis · Risk management · Financial modeling · Fixed income · Equities · Portfolio analysis · Cash management

Business / Instructional Tools

Microsoft Excel · PowerPoint · Bloomberg Terminal · Google Workspace · Learning management systems · Presentation and instructional design

Languages

Languages

English · Spanish · French