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Learner-as-analyst: mechanistic transparency reframes the learner from spectator to analyst of a recorded process

Design · All designs
Evidence · 1 claim (1 for) · 1 study (1 design), q1 · 0 of 1 report an effect size · 1 claim rests on one study

Description

The article's organizing stance holds that every quantity in the Bellman update should be inspectable at every step, with the record of those steps belonging to the learner. Existing tools 'treat the learner as a spectator of a converging process, whereas Q-Learning Lab treats the learner as an analyst of a recorded process', a stance grounded in constructionism and experiential learning theory.

Design Implications

Context

Requirements

  • Requires a tool that renders the update equation with live numeric substitution at every step and logs the full pre-decision Q-row with greedy/random provenance, so the trace is a complete record of the agent's decision process.

Constraints

  • Instantiated here only for tabular Q-learning on a small deterministic, single-agent gridworld.

Target Learners

  • undergraduates encountering the Bellman update for the first time

Learning Goals

  • understanding the Q-learning update mechanism
  • developing diagnostic practices for RL agent behavior

Claims

Examples

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Key Sources

  • Jueng, Ekkachai. (2026). Q-Learning Lab: Teaching Reinforcement Learning Through Learner-Generated Trace Analysis. https://arxiv.org/abs/2607.10802. doi:10.48550/arxiv.2607.10802
Page metadata
Field Value
type design
id learner-as-analyst-mechanistic-transparency
title Learner-as-analyst: mechanistic transparency reframes the learner from spectator to analyst of a recorded process
description The article's organizing stance holds that every quantity in the Bellman update should be inspectable at every step, with the record of those steps belonging to the learner.
status review
generated by process:settle-candidates on 2026-10-11
trust unverified — no one has confirmed this page's content
sources 1 — jueng-2026

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