Problem
Research questions and friction points this paper is trying to address.
learner heterogeneity
differentiated instruction
tiered sequences
Parsons problems
programming education
CodeOwl: Automatic Generation of Tiered Parsons Problems for Introductory Programming
🤖 AI Summary
This study addresses the challenge of implementing differentiated instruction in programming education due to wide variations in student ability by proposing an AI-driven approach that automatically generates three-tiered Parsons problem sequences with progressively increasing difficulty from a given programming task or concept. Integrating program complexity analysis with established instructional design principles, this method is the first to enable the automatic generation of layered Parsons problems featuring explicit and consistent difficulty gradients. Among 297 generated sequences, 98.7% demonstrated a clear positive progression in difficulty. Expert evaluations confirmed high item clarity, and both instructors and students widely acknowledged the pedagogical effectiveness of the generated problems, which significantly reduce the instructional design burden on teachers and support personalized programming learning.
📝 Abstract
Addressing learner heterogeneity in programming education is challenging due to variations in student speed, prior knowledge, and motivation. While differentiated instruction, such as tiered sequences, allows students to engage at appropriate difficulty levels, manually creating these resources is labour-intensive. This paper introduces CodeOwl, an AI-driven tool that automates the generation of tiered Parsons problems. Starting from a sample task or specific programming concepts, CodeOwl produces tiered sequences of Parsons problems automatically. We evaluated CodeOwl with a mixed-method framework comprising complexity analysis, expert ratings, and user studies. Analysis of 297 tiered sequences (three tiers each) revealed that 98.7% achieved a positive complexity increase, successfully rising in difficulty from Tier 1 to Tier 3. Experts rated the generated problem statements as highly clear. While teachers praised the tool's utility, they identified a need for greater control over curriculum alignment. Similarly, students reported positively but requested enhanced feedback mechanisms and alternative interaction modes.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-driven education
tiered Parsons problems
automatic problem generation
differentiated instruction
programming education
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