Qualifying paper and dissertation support site

Tertiary Algorithmicity

A working resource for turning Beyond Secondary Orality into a dissertation architecture: core claims, chapter movement, reading clusters, committee questions, and writing routines for a project on Ong's media ecology, K-12 learning, tertiary algorithmicity, and pedagogical friction.

Browse Resources Open Chapter Map Dissertation Sites

Choose a task

Use this hub differently depending on whether you are preparing for review, drafting chapters, finding sources, or bridging into methods.

Committee prep

Start with the bridge, open the chapter map, then use the prompt lab to rehearse likely committee questions.

Companion lenses Media ecology Machine intelligence Literature review
Use this first

Four entry points.

The original interactive overview had strong conceptual energy. This version keeps that center, but makes the site more useful as a live companion for drafting, committee preparation, and resource organization.

01 / Bridge

Paper to dissertation

Clarify what the qualifying paper proves and what the dissertation must still build.

Map the expansion
02 / Architecture

Chapter movement

Track the project as a sequence of claims, cases, methods, and pedagogical implications.

View chapters
03 / Library

Resource clusters

Filter theoretical, methodological, pedagogical, and writing resources by project stage.

Open library
04 / Practice

Writing routines

Use repeatable protocols for synthesis, chapter planning, revision, and committee response.

Start workflow
Conceptual bridge

What changes after the qualifying paper?

The qualifying paper can establish the conceptual problem. The dissertation needs to show what the problem does across histories, interfaces, classrooms, assessment systems, and writing practices.

Qualifying paper

  • Extends Ong's developmental media ecology through algorithmic secondary orality and tertiary algorithmicity.
  • Shows why fluency, speed, and output cannot be treated as simple educational gains.
  • Frames pedagogical friction as a condition for durable learning rather than a defect to remove.
  • Builds the theoretical vocabulary for studying generative AI in K-12 learning environments.

Dissertation

  • Tests how educators navigate pedagogical friction under conditions of tertiary algorithmicity.
  • Distinguishes useful automation from unproductive success and from exclusionary barriers.
  • Turns friction into a design principle for curriculum, assessment, policy, and professional judgment.
  • Contributes a framework school leaders and teachers can use without reducing AI to cheating, compliance, or innovation rhetoric.

Working claim

Generative AI systems do not merely add new tools to old assignments. They reorganize the symbolic environment in which students think, compose, interpret, and demonstrate learning, making human authorship and cognitive struggle optional in ways schools must name directly.

Dissertation architecture

Chapter map.

Use the tabs to test whether each chapter has a distinct job. The sequence below is provisional by design: it keeps the dissertation from becoming one large theory chapter wearing several hats.

Resource library

Find the right material for the current task.

This is a lightweight research dashboard. Search by keyword, filter by project stage, or jump through clusters when you need a specific kind of support.

No resources match that combination yet. Clear a filter or add another resource card to the data list.
Writing workflow

Repeatable routines.

These routines keep the project moving without pretending that all writing tasks are the same. Use them as checkpoints before advisor meetings, committee drafts, and chapter revisions.

Weekly synthesis

  1. Name one claim that became clearer this week.
  2. Identify one source that changed the claim rather than merely supporting it.
  3. Write a five-sentence bridge from reading to chapter draft.
  4. Archive one paragraph that is interesting but not currently load-bearing.

Chapter stress test

  1. State the chapter's job in one sentence.
  2. List the evidence it uniquely contributes.
  3. Mark any section doing work that belongs elsewhere.
  4. Draft the transition that explains why the next chapter must follow.

Committee prep

  1. Write the strongest objection to the central claim.
  2. Prepare a version of the answer for theorists, teachers, and administrators.
  3. Separate negotiable structure from non-negotiable contribution.
  4. Bring two concrete decisions that the committee can help resolve.
Prompt and question lab

Generate a useful next move.

Select a mode and the site will produce a reusable prompt or committee question. The goal is not to outsource the thinking, but to stage the next encounter with the material.

Choose a mode

Each mode is tuned to a different research action: synthesis, revision, advising, teaching, or methodological design.


          

        

      

    


    

      

        

          

            
Open decisions

            
Questions worth keeping visible.

          

          
The project will improve if these decisions stay explicit. They are not problems to hide; they are the dissertation's design surface.

        

        

          

            Scope
            
How much AI history belongs here?

            
The dissertation needs enough genealogy to make the present legible without becoming a broad history of computation.

          

          

            Evidence
            
What counts as a case?

            
Interface analysis, assignment design, student-facing policies, and institutional assessment can each serve as cases if the method is named clearly.

          

          

            Contribution
            
Who needs the framework?

            
The argument should speak to K-12 educators, district leaders, curriculum designers, AI governance teams, and researchers studying human-AI mediation in learning environments.