Christina Maria Mayr | Searcharxiv

arXiv2026

Knowledge Markers: An AI-Agnostic Concept for the Design of Programming Courses

Generative AI enables students to produce plausible code quickly. Producing working code is therefore no longer a reliable indicator of understanding. This is particularly problematic in non-computer-science programmes, where time constraints make it hard to balance conceptual foundations with sufficient application practice. Empirical studies of AI tutors, educational chatbots, and code-assistance systems report useful but often case-specific findings, while learning theory remains too abstract to directly guide course design. As a result, instructors lack a simple, reusable way to make learning intent explicit and translate it into concrete teaching structures and student learning behaviour. This paper contributes knowledge markers as a lightweight, AI-agnostic, course-level operationalisation for course design. The markers label learning units by their primary emphasis: (A) Application knowledge (implementation), (S) Structure knowledge (concepts and mental models), or (P) Procedure knowledge (systematic methods, decision making, and verification). We show how the labels can be embedded at fine granularity in open teaching artifacts (interactive website, PDF script, and notebooks), paired with communication elements and optional AI-usage guidance. We demonstrate the approach by analysing, redesigning, and descriptively evaluating an introductory programming course using marker distributions derived from the table of contents. The paper is design- and artifact-oriented and does not claim measured learning gains; empirical evaluation is future work.

cs.CY↗

arXiv2022

Social distancing with the Optimal Steps Model

With the Covid-19 pandemic an urgent need to simulate social distancing arises. The Optimal Steps Model (OSM) is a pedestrian locomotion model that operationalizes an individual's need for personal space. We present new parameter values for personal space in the Optimal Steps Model to simulate social distancing in the pedestrian dynamics simulator Vadere. Our approach is pragmatic. We consider two use cases: in the first we demand that a set social distance must never be violated. In the second the social distance must be kept only on average. For each use case we conduct simulation studies in a typical bottleneck scenario and measure contact times, that is, violations of the social distance rule. We derive rules of thumb for suitable parameter choices in dependency of the desired social distance. We test the rules of thumb for the social distances 1.5m and 2.0m and observe that the new parameter values indeed lead to the desired social distancing. Thus, the rules of thumb will quickly enable Vadere users to conduct their own studies without understanding the intricacies of the OSM implementation and without extensive parameter adjustment.

cs.MA↗