Many software development platforms now support LLM-driven programming, or “vibe coding”, a technique that allows one to specify programs in natural language and iterate from observed behavior, all without directly editing source code. While its adoption is accelerating, little is known about which skills best predict success in this workflow. We report a preregistered cross-sectional study with tertiary-level students (N = 100) who completed measures of computer-science achievement, domain-general cognitive skills, written-communication proficiency, and a vibe-coding assessment. Tasks were curated via an eight-expert consensus process and executed in a purpose-built, vibe-coding environment that mirrors commercial tools while enabling controlled evaluation. We find that both writing skill and CS achievement are significant predictors of vibe-coding performance, and that CS achievement remains a significant predictor after controlling for domain-general cognitive skills. The results may inform tool and curriculum design, including when to emphasize prompt-writing versus CS fundamentals to support future software creators.

Syntax remains a major barrier for novices. Although block-based systems reduce or eliminate syntax errors, conditionals still challenge learners, likely because their semantics remain implicit. In this paper, we address this problem by introducing a semantics-first, state-visible programming approach inspired by the classic visual language Stagecast Creator. To demonstrate its usefulness, we designed Elephant, a unified, Karel-like research platform that supports three equally expressive programming paradigms: (i) semantics-first programming, (ii) block-based programming with the Blockly library, and (iii) text-based programming in JavaScript with domain-specific libraries. We then deployed Elephant in two within-subjects studies with secondary-school students (N = 39) to compare semantics-first programming to textual and block-based baselines, keeping the program semantics constant across modes and reducing cross-tool confounds. Results indicate, among other things, that semantics-first programming yields significantly higher task performance, suggesting that increasing the visibility of the program state during program composition could support greater outcomes in secondary computing education.

Higher-order functions are increasingly common in modern programming languages, yet there is a shortage of evidence-based tools and teaching strategies to help students learn them effectively. We introduce a visual tool that lets learners construct, view, and execute higher-order functions using direct manipulation and programming by demonstration. To evaluate its effectiveness, we conducted a randomized, within-subjects study with 27 university students, comparing our tool against Python as a control. The results show that students performed significantly better and reported lower cognitive load when solving simple problems with our tool. However, both groups showed similar performance on tasks that involved mapping input-output pairs to the correct higher-order function. Our findings suggest that visual, direct-manipulation tools can help students develop stronger procedural knowledge of higher-order functions, although additional scaffolding may be needed to foster deeper conceptual understanding.

This paper presents a comparative study of Algot, a visual programming language designed to bridge the syntax-semantics gap via liveness and programming by demonstration, and the textual programming language Python. We conducted an experimental, within-subjects study with 24 undergraduate computer science students who performed recursion-based tasks in each language while their cognitive load was measured using an electroencephalogram and a validated survey instrument. The students received a brief introduction to Algot, but were all familiar with Python. The students performed significantly better when programming in Algot, but the cognitive load levels were similar according to both instruments. Our results provide evidence that within the domain that was tested, Algot can be quickly learned, and that students do not find it more cognitively demanding than working in a familiar language.

While recursion is a fundamental technique in computer programming, it is challenging for novices, for example since it requires tracing non-linear and hierarchical sequences of execution. Though algorithm visualizations and visual programming may be helpful, such tools need to offer sufficiently expressive environments that support active, constructivist learning via exploration and experimentation. In this study, we investigated whether Algot, a visual programming language that relies on a novel programming-by-demonstration paradigm, is effective for teaching recursion to 14-17 year old students, and whether it compares favorably to the popular visual programming language Scratch. We conducted an experimental study with 23 participants where they learned recursion in a video tutorial using Algot and Scratch, worked out code exercises in each respective language, and then solved a post-test on recursion. Despite the participants being more familiar with Scratch than Algot, our results indicated that students instructed with Algot demonstrated a significantly better understanding of recursion (Bayes Factor = 14.09, p = 0.005, Cohen's d = 1.30). We also found that students reported a similar level of enjoyment of each language. These findings provide preliminary evidence about the effectiveness of the programming-by-demonstration paradigm, as implemented in Algot, in aiding the comprehension of complex programming concepts like recursion.

Algot is a newly developed visual programming language that seeks to bridge the syntax-semantics gap in programming via a novel implementation of programming by demonstration. Preliminary research, which will be presented separately at SIGCSE this year, suggests that Algot may be useful for teaching foundational computer science concepts at both secondary and tertiary levels. In this proposed SIGCSE demo session, attendees will have a chance to interact with Algot and learn about its potential benefits in their own classrooms.

This paper examines whether undergraduate students perform better and experience lower cognitive load when programming in Algot, a visual programming language that supports programming by demonstration, than in the textual programming language Python. We recruited 38 first-semester computer science university students who had received prior instruction in the programming language Python but were unfamiliar with Algot. Participants reviewed a 12-minute video tutorial about Algot and performed the same programming tasks in Python and Algot. We graded student submissions, estimated cognitive load through physiological measures and a validated post-test survey, and evaluated free-form feedback. Our results indicated that students experienced lower negative (extraneous and intrinsic) and higher positive (germane) cognitive load when programming in Algot. Additionally, students programming in Algot scored an average grade of 5.8 out of 10, compared to an average grade of 3.4 when using Python for the same tasks, and according to the free-form feedback, Algot is perceived as well-designed and easy to learn.

Computer programming typically requires people to describe operations in a formally specified textual language. Unfortunately, working with syntax is a significant cognitive load, making programming difficult for beginners and time-consuming for professional developers. In response to this, contemporary research often focuses on abstracting or improving the process of composing code. We believe, however, that one fundamental reason why programming is difficult is the disconnect between the symbols and metaphors used in code and the mechanics they represent. Programming languages use abstractions whose superficial similarities to natural language neither effectively help users understand programs nor enable them to work creatively. To tackle this fundamental limitation, this paper introduces a new language based on a novel programming-by-demonstration paradigm that (i) enables users to experiment and test their programs, (ii) allows describing complex operations without the need to learn any syntax, and (iii) always displays an approximation of the program state while programming a new operation. We explain the rationales behind our new approach and present our design and implementation using illustrative examples and a supplemental video recording.