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1. A Study of Worked Examples for SQL Programming

   Akhuseyinoglu, Kamil; Hardt, Ryan; Barria-Pineda, Jordan; Brusilovsky, Peter; Pollari-Malmi, Kerttu; Sirkiä, Teemu; Malmi, Lauri (July 2022, Proceedings of the 27th ACM Conference on Innovation and Technology in Computer Science Education)

   The paper focuses on a new type of interactive learning content for SQL programming - worked examples of SQL code. While worked examples are popular in learning programming, their application for learning SQL is limited. Using a novel tool for presenting interactive worked examples, Database Query Analyzer (DBQA), we performed a large-scale randomized controlled study assessing the value of worked examples as a new type of practice content in a database course. 

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2. Improving Engagement in Program Construction Examples for Learning Python Programming

   https://doi.org/10.1007/s40593-020-00197-0.  

   Hosseini, Roya & (January 2020, International journal of artificial intelligence in education)

   This research is focused on how to support students’ acquisition of program construction skills through worked examples. Although examples have been consistently proven to be valuable for student’s learning, the learning technology for computer science education lacks program construction examples with interactive elements that could engage students. The goal of this work is to investigate the value of the “engaging” features in programming examples. We introduce PCEX, an online tool developed to present program construction examples in an engaging fashion. We also present the results of a controlled study with a between-subject design that was conducted in a large introductory Python programming class to compare PCEX with non-interactive worked examples focused on program construction. The results of our study show the positive impact of interactive program construction examples on student’s engagement, problem-solving performance, and learning. 

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3. Authoring Worked Examples for JAVA Programming with Human AI Collaboration

   https://doi.org/10.1145/3605098.3636160  

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   Worked examples are among the most popular types of learning content in programming classes. 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 generate 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. 

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

   Hassany, Mohammad; Brusilovsky, Peter; Ke, Jiaze; Akhuseyinoglu, Kamil; Lekshmi_Narayanan, Arun_Balajiee (March 2024, Proceedings of 5th Workshop on Human-AI Co-Creation with Generative Models (HA-GEN 20224) at IUI 2024)

   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. 

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5. Engaging an LLM to Explain Worked Examples for Java Programming: Prompt Engineering and a Feasibility Study

   Hassany, Mohammad; Brusilovsky, Peter; Ke, Jiaze; Akhuseyinoglu, Kamil; Lekshmi-Narayanan, Arun-Balajiee (July 2024, CEUR Workshop Proceedings)

   Worked code examples 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 of a large number of examples typically used in a programming class. This paper explores the opportunity to facilitate the development of worked examples for Java programming through a human-AI collaborative authoring approach. The idea of collaborative authoring is to generate a starting version of code explanations using LLM and present it to the instructor to edit if necessary. The critical step towards implementing this idea is to ensure that LLM can produce code explanations that look meaningful and acceptable to instructors and students. To achieve this goal, we performed an extensive prompt engineering study and evaluated the explanation produced by the selected prompt in a user study with students and authors. 

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