Sue Sentance

Robert Whyte, Manni Cheung, Katharine Childs et al.

Data literacy skills are fundamental in computer science education. However, understanding how data-driven systems work represents a paradigm shift from traditional rule-based programming. We conducted a systematic literature review of 84 studies to understand K-12 learners' engagement with data across disciplines and contexts. We propose the data paradigms framework that categorises learning activities along two dimensions: (i) logic (knowledge-based or data-driven systems), and (ii) explainability (transparent or opaque models). We further apply the notion of learning trajectories to visualize the pathways learners follow across these distinct paradigms. We detail four distinct trajectories as a provocation for researchers and educators to reflect on how the notion of data literacy varies depending on the learning context. We suggest these trajectories could be useful to those concerned with the design of data literacy learning environments within and beyond CS education.

Laurie Gale, Sue Sentance

Background and context: Debugging is a significant and often frustrating challenge for beginner programmers. Understanding students' debugging behaviours and strategies can help to identify common difficulties and inform approaches for alleviating these. Currently, there are limited studies of school students' debugging behaviour in a text-based programming language, a medium through which millions are learning to program. Objectives: In this paper, we investigate the debugging behaviour of 12-14-year-old students learning Python through a lesson-long classroom study. Method: We collected program snapshots from 73 students' attempts at a set of Python debugging exercises in an online code editor. Through qualitative content analysis of these snapshots, we developed a granular categorisation of the changes students made when debugging. Findings: While most students were able to resolve some errors, most also frequently exhibited ineffective debugging behaviours. Many students added errors through small-scale changes, reverted corrective changes, and repeatedly ran identical programs in quick succession. From the results, we identify four barriers to successful and reliable debugging for students learning a text-based programming language: fragile knowledge, a lack of systematicity and reflection, the syntax barrier, and dynamics of emotions and attitudes. Implications: This paper highlights some of the difficulties that secondary school students have when debugging in Python. We recommend that school teachers explicitly teach a systematic approach to debugging and discourage the use of ineffective debugging behaviours, and that programming environments should contain features that facilitate successful debugging.

Laurie Gale, Sue Sentance

Debugging is a vital but challenging skill for beginner programmers to learn. It is also a difficult skill to teach. For secondary school teachers, who may lack time or programming experience, honing students' understanding of debugging can be a daunting task. Despite this, little research has explored their perspectives of debugging. To this end, we investigated secondary teachers' experiences of debugging in the classroom, with a focus on text-based programming. Through thematic analysis of nine semi-structured interviews, we identified a common reliance on the teacher for debugging support, embodied by many raised hands. We call this phenomenon the "hands-up problem". While more experienced and confident teachers discussed strategies they use to counteract this, less confident teachers discussed the negative consequences of this problem. We recommend further research into debugging-specific pedagogical content knowledge and professional development to help less confident teachers develop approaches for supporting their students with debugging.