# Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes

arXiv: https://arxiv.org/abs/2303.16359 · HTML: https://mlsanity.com/paper/2303.16359/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis](https://mlsanity.com/paper/1805.04276/) (2018)
- [Synthesizing Tasks for Block-based Programming](https://mlsanity.com/paper/2006.16913/) (2020)
- [Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference](https://mlsanity.com/paper/1809.01357/) (2018)
- [A Comparison of Inquiry-Based Conceptual Feedback vs. Traditional Detailed Feedback Mechanisms in Software Testing Education: An Empirical Investigation](https://mlsanity.com/paper/2011.13004/) (2020)
- [Automating Program Structure Classification](https://mlsanity.com/paper/2101.10087/) (2021)
- [The Impact of Looking Further Ahead: A Comparison of Two Data-driven Unsolicited Hint Types on Performance in an Intelligent Data-driven Logic Tutor](https://mlsanity.com/paper/2102.05741/) (2021)

## Built on by (most cited first, 10 of 10)

- [Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors](https://mlsanity.com/paper/2306.17156/) (2023)
- [Neural Task Synthesis for Visual Programming](https://mlsanity.com/paper/2305.18342/) (2023)
- [Evaluating ChatGPT and GPT-4 for Visual Programming](https://mlsanity.com/paper/2308.02522/) (2023)
- [Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming](https://mlsanity.com/paper/2310.10690/) (2023)
- [BugSpotter: Automated Generation of Code Debugging Exercises](https://mlsanity.com/paper/2411.14303/) (2024)
- [Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment](https://mlsanity.com/paper/2406.11334/) (2024)
- [Benchmarking Generative Models on Computational Thinking Tests in Elementary Visual Programming](https://mlsanity.com/paper/2406.09891/) (2024)
- [Exploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creation with Generative Models](https://mlsanity.com/paper/2512.18388/) (2025)
- [Synthesizing a Progression of Subtasks for Block-Based Visual Programming Tasks](https://mlsanity.com/paper/2305.17518/) (2023)
- [Learning Embeddings for Sequential Tasks Using Population of Agents](https://mlsanity.com/paper/2306.03311/) (2023)

## Abstract (arXiv)

Block-based programming environments are increasingly used to introduce computing concepts to beginners. However, novice students often struggle in these environments, given the conceptual and open-ended nature of programming tasks. To effectively support a student struggling to solve a given task, it is important to provide adaptive scaffolding that guides the student towards a solution. We introduce a scaffolding framework based on pop quizzes presented as multi-choice programming tasks. To automatically generate these pop quizzes, we propose a novel algorithm, PQuizSyn. More formally, given a reference task with a solution code and the student's current attempt, PQuizSyn synthesizes new tasks for pop quizzes with the following features: (a) Adaptive (i.e., individualized to the student's current attempt), (b) Comprehensible (i.e., easy to comprehend and solve), and (c) Concealing (i.e., do not reveal the solution code). Our algorithm synthesizes these tasks using techniques based on symbolic reasoning and graph-based code representations. We show that our algorithm can generate hundreds of pop quizzes for different student attempts on reference tasks from Hour of Code: Maze Challenge and Karel. We assess the quality of these pop quizzes through expert ratings using an evaluation rubric. Further, we have built an online platform for practicing block-based programming tasks empowered via pop quiz based feedback, and report results from an initial user study.