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1. Self-explanation of worked examples integrated in an Intelligent Tutoring System enhances problem solving and efficiency in algebra

   Vest, N. A.; Silla, E. M.; Bartel, A. N.; Nagashima, T.; Aleven, V.; Alibali, M. W. (January 2022, Proceedings of the 44th Annual Meeting of the Cognitive Science Society)

   Culbertson, J.; Perfors, A.; Rabagliati, H.; Ramenzoni, V. (Ed.)

   One pedagogical technique that promotes conceptual understanding in mathematics learners is self-explanation integrated with worked examples (e.g., Rittle-Johnson et al., 2017). In this work, we implemented self-explanations with worked examples (correct and erroneous) in a software-based Intelligent Tutoring System (ITS) for learning algebra. We developed an approach to eliciting self-explanations in which the ITS guided students to select explanations that were conceptually rich in nature. Students who used the ITS with self-explanations scored higher on a posttest that included items tapping both conceptual and procedural knowledge than did students who used a version of the ITS that included only traditional problem-solving practice. This study replicates previous findings that self-explanation and worked examples in an ITS can foster algebra learning (Booth et al., 2013). Further, this study extends prior work to show that guiding students towards conceptual explanations is beneficial. 

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2. Exploring The Effectiveness of Reading vs. Tutoring For Enhancing Code Comprehension For Novices

   https://doi.org/10.1145/3605098.3636007  

   Oli, Priti; Banjade, Rabin; Lekshmi_Narayanan, Arun Balajiee; Brusilovsky, Peter; Rus, Vasile (April 2024, ACM Symposium on Applied Computing, SAC 2024)

   This paper presents a comparison of two instructional strategies meant to help learners better comprehend code and learn programming concepts: reading code examples annotated with expert explanation (worked-out examples) versus scaffolded self-explanation of code examples using an automated system (Intelligent Tutoring System). A randomized controlled trial study was conducted with 90 university students who were assigned to either the control group (reading worked-out examples, a passive strategy) or the experimental group where participants were asked to self-explain and received help, if needed, in the form of questions from the tutoring system( scaffolded self-explanation, an interactive strategy). We found that students with low prior knowledge in the experimental condition had significantly higher learning gains than students with high prior knowledge. However, in the control condition, this distinction in learning outcomes based on prior knowledge was not observed. We also analyzed the effect of self-efficacy on learning gains and the nature of self-explanation. Low self-efficacy students learn almost twice as much in the interactive condition versus the passive condition although the difference was not significant probably because of low sample size. We also found that high self-efficacy students tend to provide more relational explanations whereas low self-efficacy students provide more multi-structural or line-by-line explanations. 

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3. Enhancing conceptual knowledge in early algebra through scaffolding diagrammatic self-explanation

   Nagashima, T. (January 2020, 14th International Conference of the Learning Sciences (ICLS) 2020)

   Many studies have shown that visual representations can enhance student understanding of STEM concepts. However, prior research suggests that visual representations alone are not necessarily effective across a broad range of students. To address this problem, we created a novel, scaffolded form of diagrammatic self-explanation in which students explain their problem-solving steps in the form of diagrams. We used contrasting cases to support students’ sense-making between algebraic equations and diagrams in the self-explanation activity. We conducted a classroom experiment with 41 students in grades 5 and 6 to test the effectiveness of this strategy when embedded in an Intelligent Tutoring System for algebra. We found that scaffolded diagrammatic self-explanation enhanced conceptual knowledge for students who did not have prior knowledge of formal equation-solving strategies. The study is the first experimental study showing that visual representations can enhance conceptual knowledge in early algebra. 

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4. Enhancing Conceptual Knowledge in Early Algebra Through Scaffolding Diagrammatic Self-explanation

   Nagashima, T; Bartel, A.; Silla, E.; Vest, N.; Alibali, M.; Aleven, V. (January 2020, The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020)

   Gresalfi, M.; Horn, I. S. (Ed.)

   Many studies have shown that visual representations can enhance student understanding of STEM concepts. However, prior research suggests that visual representations alone are not necessarily effective across a broad range of students. To address this problem, we created a novel, scaffolded form of diagrammatic self-explanation in which students explain their problem-solving steps in the form of diagrams. We used contrasting cases to support students’ sense-making between algebraic equations and diagrams in the self-explanation activity. We conducted a classroom experiment with 41 students in grades 5 and 6 to test the effectiveness of this strategy when embedded in an Intelligent Tutoring System for algebra. We found that scaffolded diagrammatic self-explanation enhanced conceptual knowledge for students who did not have prior knowledge of formal equation-solving strategies. The study is the first experimental study showing that visual representations can enhance conceptual knowledge in early algebra. 

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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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