A Programming Tutor for Haskell

Jeuring, Johan; Gerdes, Alex; Heeren, Bastiaan

doi:10.1007/978-3-642-32096-5_1

Cited by 13 publications

(15 citation statements)

References 32 publications

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“…We have developed Ask-Elle [Jeuring et al 2011;Gerdes et al 2012], an interactive tutor that supports the stepwise development of simple functional programs in the lazy, pure, higher-order functional programming language Haskell [Peyton Jones et al 2003]: see Figure 1 for a screenshot. Using this tutor, students learning functional programming develop their programs incrementally, receive feedback about whether or not they are on the right track, can ask for a hint when they are stuck, and get suggestions about how to refactor their program.…”

Section: Introductionmentioning

confidence: 99%

Model solutions and properties for diagnosing student programs in Ask-Elle

Jeuring

Binsbergen

Gerdes

et al. 2014

Proceedings of the Computer Science Education Research Conference

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Ask-Elle is an interactive tutor that supports the stepwise development of simple functional programs. Using Ask-Elle students receive feedback about whether or not they are on the right track, they can ask for a hint when they are stuck, and get suggestions about how to refactor their program. Our tutor generates this feedback from model solutions and properties that a solution should satisfy. This paper studies the feasibility of using model solutions together with the desired properties of solutions to analyse the work of a student. It describes an experiment in which we analyse almost 3500 log entries from students using Ask-Elle to solve functional programming exercises, to determine how many of these programs are diagnosed correctly based on model solutions and the desired properties of solutions. Ask-Elle manages to correctly diagnose 82.9% of the student programs. A further analysis of the student programs and the diagnoses shows that adding some reasonable model solutions, properties of model solutions, and general program transformations would increase this percentage to 92.9%.

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Section: Introductionmentioning

confidence: 99%

Model solutions and properties for diagnosing student programs in Ask-Elle

Jeuring

Binsbergen

Gerdes

et al. 2014

Proceedings of the Computer Science Education Research Conference

Self Cite

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show abstract

“…Modern Intelligent Programming Tutoring Systems (IPTS) provide a wide range of features for learning-process support in different subject domains, such as programming tasks with feedback, quizzes, execution traces, pseudo-code algorithms, reference material, worked solutions, adaptive features, and many others [18]. Some ITPSs allow generating tasks using templates and subject-domain models [19,20], but most of them use only predefined tasks [11,[21][22][23][24][25]. They are aimed at different levels of Bloom's Taxonomy objectives-comprehension [19], application [11,20,21], analysis and synthesis [10,[22][23][24][25][26]; some of them include limited support for underlying levels.…”

Section: Related Workmentioning

confidence: 99%

Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them

et al. 2021

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Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system.

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“…The 27 publications from 2009-2019 describe nine different tools that generate "next step"-hints. Some tools use a set of authored model-goal-solutions to automatically generate a set of valid step-by-step refinements (also called strategies) [9]- [16]. When a student asks for a hint or deviates from the path, a hint can be shown to direct the student back on track.…”

Section: Challenges In Automated Tutoringmentioning

confidence: 99%

“…Hinting systems generally restrict the solutions created by the students to those provided by the tutor even if some variation is allowed [9]- [16]. We argue that tools supporting more variation is a must when working with larger programming problems such that are introduced in university courses beyond introductory programming.…”

Section: Challenges In Automated Tutoringmentioning

confidence: 99%