Designing for Self-Regulation in Informal Programming Learning: Insights from a Storytelling-Centric Approach

TL;DR

This paper presents a storytelling-centric system designed to support self-regulation in informal programming learners by integrating social media practices, AI feedback, and reflective activities to enhance autonomous learning experiences.

Contribution

It introduces a novel storytelling-based design approach that connects resource curation, reflection, and social practices to aid self-regulation in online programming education.

Findings

Learners valued in-situ reflection and AI-generated story feedback.

The system showed promise as a self-regulation support tool.

Participants provided insights on design improvements and friction points.

Abstract

Many people learn programming independently from online resources and often report struggles in achieving their personal learning goals. Learners frequently describe their experiences as isolating and frustrating, challenged by abundant uncertainties, information overload, and distraction, compounded by limited guidance. At the same time, social media serves as a personal space where many engage in diverse self-regulation practices, including help-seeking, using external memory aids (e.g., self-notes), self-reflection, emotion regulation, and self-motivation. For instance, learners often mark achievements and set milestones through their posts. In response, we developed a system consisting of a web platform and browser extensions to support self-regulation online. The design aims to add learner-defined structure to otherwise unstructured experiences and bring meaning to curation and reflection activities by translating them into learning stories with AI-generated feedback. We position storytelling as an integrative approach to design that connects resource curation, reflective and sensemaking practice, and narrative practices learners already use across social platforms. We recruited 15 informal programming learners who are regular social media users to engage with the system in a self-paced manner; participation concluded upon submitting a learning story and survey. We used three quantitative scales and a qualitative survey to examine users' characteristics and perceptions of the system's support for their self-regulation. User feedback suggests the system's viability as a self-regulation aid. Learners particularly valued in-situ reflection, automated story feedback, and video annotation, while other features received mixed views. We highlight perceived benefits, friction points, and design opportunities for future AI-augmented self-regulation tools.