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1. Automata Tutor v3

   https://doi.org/10.1007/978-3-030-53291-8_1  

   D’Antoni, Loris ; Helfrich, Martin ; Kretinsky, Jan ; Ramneantu, Emanuel ; Weininger, Maximilian ( January 2020 , Computer Aided Verification)

   null (Ed.)

   Computer science class enrollments have rapidly risen in the past decade. With current class sizes, standard approaches to grading and providing personalized feedback are no longer possible and new techniques become both feasible and necessary. In this paper, we present the third version of Automata Tutor, a tool for helping teachers and students in large courses on automata and formal languages. The second version of Automata Tutor supported automatic grading and feedback for finite-automata constructions and has already been used by thousands of users in dozens of countries. This new version of Automata Tutor supports automated grading and feedback generation for a greatly extended variety of new problems, including problems that ask students to create regular expressions, context-free grammars, pushdown automata and Turing machines corresponding to a given description, and problems about converting between equivalent models - e.g., from regular expressions to nondeterministic finite automata. Moreover, for several problems, this new version also enables teachers and students to automatically generate new problem instances. We also present the results of a survey run on a class of 950 students, which shows very positive results about the usability and usefulness of the tool. 

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2. A Socratic Tutor for Source Code Comprehension

   https://doi.org/10.1007/978-3-030-52240-7_3  

   Alshaikh, Z. Tamang ; and Rus, V. ( July 2020 , Proceedings of the 21stInternational Conference on Artificial Intelligence in Education)

   Reported here are the findings of a comparative study on the effects of using a Socratic Intelligent Tutoring System for source code comprehension and learning computer programming. The result shows there are significant differences between the two groups where students who used Socratic Tutor ITS improved their knowledge by 45% in term of learning gain, developed a better understanding of concepts such as nested if-else and for loop, and improved their confidence level by 13%. Furthermore, the result of the Pearson product-moment correlation coefficient shows a positive correlation (r = 0.68) between feedback from the ITS and learning gain. 

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3. An Epistemic Model-Based Tutor for Imperative Programming

   https://doi.org/10.1007/978-3-030-78270-2_38  

   Kumar, Amruth N. ( June 2021 , Artificial Intelligence in Educaiton)

   null (Ed.)

   We developed a tutor for imperative programming in C++. It covers algorithm formulation, program design and coding – all three stages involved in writing a program to solve a problem. The design of the tutor is epistemic, i.e., true to real-life programming practice. The student works through all the three stages of programming in interleaved fashion, and within the context of a single code canvas. The student has the sole agency to compose the program and write the code. The tutor uses goals and plans as prompts to scaffold the student through the programming process designed by an expert. It provides drill-down immediate feed-back at the abstract, concrete and bottom-out levels at each step. So, by the end of the session, the student is guaranteed to write the complete and correct program for a given problem. We used model-based architecture to implement the tutor be-cause of the ease with which it facilitates adding problems to the tutor. In a preliminary study, we found that practicing with the tutor helped students solve problems with fewer erroneous actions and less time. 

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4. Improving Student-Tutor Interaction Through Data-driven Explanation of Hierarchical Reinforcement Induced Pedagogical Policies.

   Zhou, G ( January 2020 , In proceedings of the 28th Conference on User Modeling, Adaptation and Personalization.)
5. Learning problem decomposition-recomposition with data-driven chunky parsons problem within an intelligent logic tutor.

   Shabrina, P ; Mostafavi, B ; Tithi, D ; Chi, M ; Barnes, T ( July 2023 , Springer)

   Problem decomposition into sub-problems or subgoals and recomposition of the solutions to the subgoals into one complete solution is a common strategy to reduce difficulties in structured problem solving. In this study, we use a datadriven graph-mining-based method to decompose historical student solutions of logic-proof problems into Chunks. We design a new problem type where we present these chunks in a Parsons Problem fashion and asked students to reconstruct the complete solution from the chunks. We incorporated these problems within an intelligent logic tutor and called them Chunky Parsons Problems (CPP). These problems demonstrate the process of problem decomposition to students and require them to pay attention to the decomposed solution while they reconstruct the complete solution. The aim of introducing CPP was to improve students’ problem-solving skills and performance by improving their decomposition-recomposition skills without significantly increasing training difficulty. Our analysis showed that CPPs could be as easy as Worked Examples (WE). And, students who received CPP with simple explanations attached to the chunks had marginally higher scores than those who received CPPs without explanation or did not receive them. Also, the normalized learning gain of these students shifted more towards the positive side than other students. Finally, as we looked into their proof-construction traces in posttest problems, we observed them to form identifiable chunks aligned with those found in historical solutions with higher efficiency. 

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