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A scenario is a description of a series of interactions between a player and a virtual character for one-to-one communication skills training, where at each step the player is faced with a choice between statements. In this paper, we analyse the characteristics of scenarios and provide a classification to represent such scenarios. The analysis is performed through a literature review and by comparing virtual learning environments for scenario based training. Using this analysis we specify requirements for describing communication scenarios related to their: structure (linear, branching, interleaving), properties (static information stored per scenario like situation, background, which virtual character to show), and parameters (characteristics of a scenario that can be modified per statement like a score on a learning goal and an emotional effect in a virtual character). We define a schema for representing such communication scenarios and present an authoring tool to create a scenario.

Ask-Elle is a tutor for learning the higher-order, strongly-typed functional programming language Haskell. It supports the stepwise development of Haskell programs by verifying the correctness of incomplete programs, and by providing hints. Programming exercises are added to Ask-Elle by providing a task description for the exercise, one or more model solutions, and properties that a solution should satisfy. The properties and model solutions can be annotated with feedback messages, and the amount of flexibility that is allowed in student solutions can be adjusted. The main contribution of our work is the design of a tutor that combines (1) the incremental development of different solutions in various forms to a programming exercise with (2) automated feedback and (3) teacher-specified programming exercises, solutions, and properties. The main functionality is obtained by means of strategy-based model tracing and property-based testing. We have tested the feasibility of our approach in several experiments, in which we analyse both intermediate and final student solutions to programming exercises, amongst others.

This paper describes logax , an interactive tutoring tool that gives hints and feedback to a student who stepwise constructs a Hilbert-style axiomatic proof in propositional logic. logax generates proofs to calculate hints and feedback. We compare these generated proofs with expert proofs and student solutions, and conclude that the quality of the generated proofs is comparable to that of expert proofs. logax recognizes most steps that students take when constructing a proof. Even if a student diverges from the generated solution, logax still provides hints, including next steps or reachable subgoals, and feedback. With a few improvements in the design of the set of buggy rules, logax will cover about 80% of the mistakes made by students by buggy rules. The hints help students to complete the exercises.

There are many aspects of code quality, some of which are difficult to capture or to measure. Despite the importance of software quality, there is a lack of commonly accepted measures or indicators for code quality that can be linked to quality attributes. We investigate software developers’ perceptions of source code quality and the practices they recommend to achieve these qualities. We analyze data from semi-structured interviews with 34 professional software developers, programming teachers and students from Europe and the U.S. For the interviews, participants were asked to bring code examples to exemplify what they consider good and bad code, respectively. Readability and structure were used most commonly as defining properties for quality code. Together with documentation, they were also suggested as the most common target properties for quality improvement. When discussing actual code, developers focused on structure, comprehensibility and readability as quality properties. When analyzing relationships between properties, the most commonly talked about target property was comprehensibility. Documentation, structure and readability were named most frequently as source properties to achieve good comprehensibility. Some of the most important source code properties contributing to code quality as perceived by developers lack clear definitions and are difficult to capture. More research is therefore necessary to measure the structure, comprehensibility and readability of code in ways that matter for developers and to relate these measures of code structure, comprehensibility and readability to common software quality attributes.

Hypothesis testing involves a complex stepwise procedure that is challenging for many students in introductory university statistics courses. In this paper we assess how feedback from an Intelligent Tutoring System can address the logic of hypothesis testing and whether such feedback contributes to first-year social sciences students’ proficiency in carrying out hypothesis tests. Feedback design combined elements of the model-tracing and constraint-based modeling paradigms, to address both the individual steps as well as the relations between steps. To evaluate the feedback, students in an experimental group ( N  = 163) received the designed intelligent feedback in six hypothesis-testing construction tasks, while students in a control group ( N  = 151) only received stepwise verification feedback in these tasks. Results showed that students receiving intelligent feedback spent more time on the tasks, solved more tasks and made fewer errors than students receiving only verification feedback. These positive results did not transfer to follow-up tasks, which might be a consequence of the isolated nature of these tasks. We conclude that the designed feedback may support students in learning to solve hypothesis-testing construction tasks independently and that it facilitates the creation of more hypothesis-testing construction tasks.

Computational thinking (CT) is regarded as a fundamental skill set everyone should learn. Identifying when and how CT skills are used is challenging but important to inform interventions supporting their development. Previous research has examined how students and experts apply CT skills when solving introductory computational problems. However, the extent to which higher education students in introductory programming courses do so in depth is underexplored. We address this gap by examining how those students apply CT skills when solving computational problems, the difficulties they encounter, and the strategies they employ. We collected plans and solutions to Bebras tasks (short problems introducing CS concepts and considered effective for eliciting CT skills) in an introductory programming course for non-CS majors. We gathered 241 submissions from 58 students across five tasks, along with post-task comments and reflections on strategies. We analyzed the data using descriptive statistics, applied an existing coding scheme to identify CT skills, and conducted thematic analysis to identify difficulties and strategies. Submissions varied in structure and level of detail. The most prevalent CT skills were algorithmic thinking, abstraction, and decomposition, while evaluation and generalization appeared much less frequently. CT skill presence was positively associated with correct answers. Students faced challenges in four areas, including understanding the tasks and making a plan, and reported various problem-solving strategies. Consolidating and extending prior research on CT skills and problem solving, our findings show that students in introductory programming apply CT skills but can struggle to solve problems systematically and explain their reasoning. Furthermore, Bebras tasks create opportunities for this population to engage CT skills and could be used in future research.

Parametricity states that polymorphic functions behave the same regardless of how they are instantiated. When developing polymorphic programs, Wadler's free theorems can serve as free specifications, which can turn otherwise partial specifications into total ones, and can make otherwise realizable specifications unrealizable. This is of particular interest to the field of program synthesis, where the unrealizability of a specification can be used to prune the search space. In this paper, we focus on the interaction between parametricity, input-output examples, and sketches. Unfortunately, free theorems introduce universally quantified functions that make automated reasoning difficult. Container morphisms provide an alternative representation for polymorphic functions that captures parametricity in a more manageable way. By using a translation to the container setting, we show how reasoning about the realizability of polymorphic programs with input-output examples can be automated.

Evaluating large language models (LLMs) in long-form, knowledge-grounded role-play dialogues remains challenging. This study compares LLM-generated and human-authored responses in multi-turn professional training simulations through human evaluation (\\(N=38\\)) and automated LLM-as-a-judge assessment. Human evaluation revealed significant degradation in LLM-generated response quality across turns, particularly in naturalness, context maintenance and overall quality, while human-authored responses progressively improved. In line with this finding, participants also indicated a consistent preference for human-authored dialogue. These human judgements were validated by our automated LLM-as-a-judge evaluation, where Gemini 2.0 Flash achieved strong alignment with human evaluators on both zero-shot pairwise preference and stochastic 6-shot construct ratings, confirming the widening quality gap between LLM and human responses over time. Our work contributes a multi-turn benchmark exposing LLM degradation in knowledge-grounded role-play dialogues and provides a validated hybrid evaluation framework to guide the reliable integration of LLMs in training simulations.

This volume contains the proceedings of the Fourth and Fifth International Workshops on Trends in Functional Programming in Education, TFPIE 2015 and TFPIE 2016, which were held on June 2, 2015 in Sophia-Antipolis, France, and on June 7, 2016 at the University of Maryland College Park in the USA, respectively.

Structural induction is a proof technique that is widely used to prove statements about discrete structures. Students find it hard to construct inductive proofs, and when learning to construct such proofs, receiving feedback is important. In this paper we discuss the design of a tutoring system, LogInd, that helps students with constructing stepwise inductive proofs by providing hints, next steps and feedback. As far as we know, this is the first tutoring system for structural induction with this functionality. We explain how we use a strategy to construct proofs for a restricted class of problems. This strategy can also be used to complete partial student solutions, and hence to provide hints or next steps. We use constraints to provide feedback. A pilot evaluation with a small group of students shows that LogInd indeed can give hints and next steps in almost all cases.