Explainable Recommendations in a Personalized Programming Practice System

Barria-Pineda, Jordan; Akhüseyinoğlu, Kamil; Želem-Ćelap, Stefan; Brusilovsky, Peter; Klašnja-Milićević, Aleksandra; Ivanović, Mirjana

doi:10.1007/978-3-030-78292-4_6

Cited by 17 publications

(10 citation statements)

References 17 publications

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“…Although this suggests that these two characteristics are not the most important to take into account when designing explanations, we must remain cautious. For example, some work on explainable recommendations has shown that it is important to take prior knowledge into account (Barria-Pineda et al, 2021). In this study, students with low prior knowledge showed a higher level of trust in explained recommendations and were more willing to take these recommendations into account.…”

Section: Variablesmentioning

confidence: 66%

“…teacher, past students, similar students). Barria-Pineda et al (2021) provide explanations of rule-based recommendations for activities in a Java programming environment with a recommender module. The visual and verbal explanations show information about the link between learner's knowledge and prerequisites for recommendations.…”

Section: Ability To Explain the Reasoning Of White Box Modelsmentioning

confidence: 99%

“…In 2019, Arya et al designed a taxonomy of different explainability techniques to address the need to adapt explanations to a broader user audience than experts, with the example of a bank credit allocation algorithm that needs to be transparently explained to customers. In education, some works seek to generate explanations leading, for example, to explainable recommendations of learning resources (Abdi et al, 2020;Barria-Pineda et al, 2021;Palominos et al, 2019), to the optimal use of teachers' time to examine students' work (Darvishi et al, 2020), to the support of learners who need it most (Khosravi et al, 2021), to recommendations of pedagogical activities based on predictions (Pardo et al, 2016), or to improvement of learners' self-regulation (Afzaal et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Personal Characteristics and Explanations in Technology-Enhanced Environments: An Exploratory Study in Computer Science Education

Félix,

Amadieu,

Broisin

2024

Preprint

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The study of explainable artificial intelligence in education is a major preoccupation of the research community. In this paper, we introduce explanations in a competency-based system, in the context of computer education. Our overall aim is to increase learners' understanding of the levels of mastery of their competencies that are computed automatically, and thus to increase their trust in the system. In particular, we are investigating which of the students' personal characteristics affect their consultation and understanding of the explanations, and what impact these explanations have on their perception and behavior within the system. Our study focuses on 98 first-year computer science students in higher education and combines qualitative and quantitative analyses. The quantitative analyses show significant correlations between certain characteristics studied and the use, perception and understanding of explanations on the one hand, as well as learners' general perception of the system on the other hand. For example, students with a low perception of their ability to succeed are less likely to access and understand explanations, and students with low engagement and performance are less likely to understand explanations. Qualitative analyses, meanwhile, help us to identify certain learner needs in terms of the content, form, and timing of explanations: combining local and counterfactual explanations; combining textual and visual formats; providing explanations in real time.

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Section: Variablesmentioning

confidence: 66%

Section: Ability To Explain the Reasoning Of White Box Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Personal Characteristics and Explanations in Technology-Enhanced Environments: An Exploratory Study in Computer Science Education

Félix,

Amadieu,

Broisin

2024

Preprint

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“…We started our analysis by checking the relationship between the practice system usage and student learning. Following our prior research findings [3], we concentrated on success rate related features. We considered using the overall success rate and first-attempt success rate in both the SQL assessment tool (SAT) and SQL-Tutor as features.…”

Section: External Value Of Practice System Usagementioning

confidence: 99%

A Study of Worked Examples for SQL Programming

Akhüseyinoğlu

Hardt

Barria-Pineda

et al. 2022

Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1

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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. We report the results of the classroom study examining the usage and the impact of DBQA. Among other aspects, we explored the effect of textual step explanations provided by DBQA.

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“…The recommender systems (RSs) are widely adopted in internet applications to provide relevant items according to users' preference history [10,1,6,15]. In the online learning scenario, users are learners and items are exercises.…”

Section: Introductionmentioning

confidence: 99%

A Design of a Simple Yet Effective Exercise Recommendation System in K-12 Online Learning

Huang¹,

Liu²,

Chen³

et al. 2022

Lecture Notes in Computer Science

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We propose a simple but effective method to recommend exercises with high quality and diversity for students. Our method is made up of three key components: (1) candidate generation module;(2) diversity-promoting module; and (3) scope restriction module. The proposed method improves the overall recommendation performance in terms of recall, and increases the diversity of the recommended candidates by 0.81% compared to the baselines.

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Explainable Recommendations in a Personalized Programming Practice System

Cited by 17 publications

References 17 publications

Personal Characteristics and Explanations in Technology-Enhanced Environments: An Exploratory Study in Computer Science Education

Personal Characteristics and Explanations in Technology-Enhanced Environments: An Exploratory Study in Computer Science Education

A Study of Worked Examples for SQL Programming

A Design of a Simple Yet Effective Exercise Recommendation System in K-12 Online Learning

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