Human-AI Co-Creation of Worked Examples for Programming Classes

Worked examples (solutions to typical programming problems presented as a source code in a certain language and are used to explain the topics from a programming class) are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide line-by-line explanations for a large number of examples typically used in a programming class. In this paper, we explore and assess a human-AI collaboration approach to authoring worked examples for Java programming. We introduce an authoring system for creating Java worked examples that generates a starting version of code explanations and presents it to the instructor to edit if necessary.We also present a study that assesses the quality of explanations created with this approach

Authoring Worked Examples for Java Programming with Human-AI Collaboration

Worked examples (solutions to typical programming problems presented as a source code in a certain language and are used to explain the topics from a programming class) are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide line-by-line explanations for a large number of examples typically used in a programming class. In this paper, we explore and assess a human-AI collaboration approach to authoring worked examples for Java programming. We introduce an authoring system for creating Java worked examples that generates a starting version of code explanations and presents it to the instructor to edit if necessary. We also present a study that assesses the quality of explanations created with this approach.

Enhancing Programming eTextbooks with ChatGPT Generated Counterfactual-Thinking-Inspired Questions

Digital textbooks have become an integral part of everyday learning tasks. In this work, we consider the use of digital textbooks for programming classes. Generally, students struggle with utilizing textbooks on programming to the maximum, with a possible reason being that the example programs provided as illustration of concepts in these textbooks don't offer sufficient interactivity for students, and thereby not sufficiently motivating to explore or understand these programming examples better. In our work, we explore the idea of enhancing the navigability of intelligent textbooks with the use of ``counterfactual'' questions, to make students think critically about these programs and enhance possible program comprehension. Inspired from previous works on nudging students on counter factual thinking, we present the possibility to enhance digital textbooks with questions generated using GPT.

GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children

When caregivers ask open--ended questions to motivate dialogue with children, it facilitates the child's reading comprehension skills.Although there is scope for use of technological tools, referred here as "intelligent tutoring systems", to scaffold this process, it is currently unclear whether existing intelligent systems that generate human--language like questions is beneficial. Additionally, training data used in the development of these automated question generation systems is typically sourced without attention to demographics, but people with different cultural backgrounds may ask different questions. As a part of a broader project to design an intelligent reading support app for Latinx children, we crowdsourced questions from Latinx caregivers and noncaregivers as well as caregivers and noncaregivers from other demographics. We examine variations in question--asking within this dataset mediated by individual, cultural, and contextual factors. We then design a system that automatically extracts templates from this data to generate open--ended questions that are representative of those asked by Latinx caregivers.