Search arXivSearch

arXiv · 2512.22407

Learning to Program != "One-Size-Fits-All": Exploring Variations of Parsons Problems as Scaffolding

Abstract

Lowering the barriers to computer programming requires understanding how to scaffold learning. Parsons problems, which require learners to drag-and-drop blocks of code into the correct order and indentation, are proving to be beneficial for scaffolding learning how to write code from scratch. But little is known about the ability of other problem types to do so. This study explores learners' perceptions of a new programming environment called Codespec, which was developed to make computer programming more accessible and equitable by offering multiple means of engagement. Retrospective think-aloud interviews were conducted with nine programmers who were given the choice between Faded Parsons and Pseudocode Parsons problems as optional scaffolding toward solving write-code problems. The results showed that offering Faded and Pseudocode Parsons problems as optional scaffolds supported comprehension monitoring, strategy formation, and refinement of prior knowledge. Learners selectively used Faded Parsons problems for syntax/structure and Pseudocode Parsons problems for high-level reasoning. The costs noted included the time it takes to drag-and-drop the blocks and the confusion experienced when a solution diverges from a learners' mental model. Faded Parsons problems were also perceived as a desirable challenge. This study contributes to the field of computing education and human-computer interaction by extending the functionality of problem spaces that support Parsons problems and by providing empirical evidence of the effectiveness of using other problem types as scaffolding techniques.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Carl Christopher Haynes-Magyar. 2025-12-26. Learning to Program != "One-Size-Fits-All": Exploring Variations of Parsons Problems as Scaffolding. https://arxiv.org/abs/2512.22407

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials

Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. Using a real-world dataset, we demonstrated the effectiveness of TrialCompass in providing insights into designing eligibility criteria for septic shock and sepsis-associated acute kidney injury. We also discussed the research prospects of applying visual analytics to clinical trials.

cs.HC

Mirror Skin: In Situ Visualization of Robot Touch Intent on Robotic Skin

Effective communication of robot touch intent is essential for safe and predictable physical human-robot interaction. While intent communication has been widely studied, existing approaches lack the spatial specificity and semantic depth necessary to efficiently convey robot touch intent. We present Mirror Skin, a cephalopod inspired concept that mirrors in-situ visual representations of a human's body parts onto the corresponding robot's touch region to communicate who shall initiate touch, where it will occur, and when it is imminent. We informed the design of Mirror Skin through a structured design exploration with experts and demonstrate the real-world feasibility of Mirror Skin with a proof-of-concept prototype. User studies in VR and with the physical prototype showed that Mirror Skin significantly improves accuracy and response times for interpreting touch intent and improves the user experience during physical human-robot interactions.

cs.HC

VisCanvas: A Node-Based Interface for Exploratory Visualization Authoring with LLMs

Visual data analysis involves both open-ended exploration and targeted question answering. Visualization authoring tools support this process by enabling users to create visualizations for these tasks. With the rise of large language models (LLMs), substantial effort has been devoted to developing visualization authoring tools that use natural language instructions. However, existing systems are typically based on a linear chat interface, which is not well suited to exploratory visual analysis workflows. In this paper, we introduce VisCanvas, a node-based interface for exploratory visualization authoring with LLMs. VisCanvas allows users to create, revise, branch, and merge visualizations in a non-linear way, enabling more efficient exploration of multiple analytical directions. We conducted a user study with 20 participants to evaluate the effectiveness of VisCanvas compared to a baseline chat-based interface. The results show that VisCanvas facilitates more diverse data interaction while maintaining performance levels (i.e., cognitive load and usability) that are indistinguishable from current prevailing methods. We then distill design principles for future AI-assisted visualization authoring environments. All supplemental materials required to reproduce the study are available at https://osf.io/gsxhn.

cs.HC