Background. Introductory programming courses enrol students whose prior experience differs widely, yet most practice sequences present the same tasks to everyone. Adaptive systems can individualise difficulty, but many rely on opaque models that teachers neither understand nor control. Purpose. This study designs and verifies an explainable adaptation mechanism for developing first-year students’ algorithmic competence. Methods. Following a design-and-development research approach, we built AlgoAdapt, a web-based system with a two-loop (co-adaptive) architecture: an inner loop in which a five-rule regulator (Q1–Q5) sets task difficulty and the amount of help after every attempt, and an outer loop in which teachers act on a process-based learner model of four competence components (cognitive, practical, metacognitive, reflective). The regulator was verified in a Monte Carlo simulation with 2,000 synthetic learners per condition and compared with an ablated version without hysteresis, a 1-up/1-down staircase and a fixed linear sequence. Results. After a short convergence period (median five tasks), the regulator kept 92.2% of tasks within the target difficulty corridor, compared with 68.2%, 61.6% and 19.3% for the three comparison policies, and produced on average 0.62 direction reversals per 60 tasks versus 34.67 for the staircase. The advantage held when ability grew during practice and across three values of the response-model slope. Conclusions. A small set of transparent, hysteresis-protected rules that first increases help and only then lowers difficulty can track learners stably. Classroom evaluation of learning outcomes is the necessary next step.
A Rule-Based Co-Adaptive Regulator for Keeping Novice Programmers in An Optimal Difficulty Corridor: Design of The Algoadapt System and A Simulation Study
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