Towards Providing Feedback to Students in Absence of Formalized Domain Models

Groß, Sebastian; Mokbel, Bassam; Hammer, Barbara; Pinkwart, Niels

doi:10.1007/978-3-642-39112-5_79

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…1). The results show a positive impact of the instruction that was positively rated by the learners [10], and the study also confirmed that manual feedback generation indeed means a lot of work, and that the ITS feedback methods have been assessed as potentially helpful in most cases [7]. Finally, we conducted a field study in an introductory programming course for Java programming, comparing the appropriateness of different exemplar selection strategies (user or sample solutions, full or partial solutions) for feedback provision.…”

Section: Advances and Resultssupporting

confidence: 65%

“…We then conducted two Wizard-of-Oz studies [10,7] asking experts to simulate the behavior of a FIT ITS. In these studies, we examined two different example-based feedback provision strategies in the domain of Java programming: highlighting and/or contrasting similarities among learner solutions and sample solutions (as illustrated in Fig.…”

Section: Advances and Resultsmentioning

confidence: 99%

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Learning Feedback in Intelligent Tutoring Systems

et al. 2015

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Intelligent Tutoring Systems (ITSs) are adaptive learning systems that aim to support learners by providing one-on-one individualized instruction. Typically, instructing learners in ITSs is build on formalized domain knowledge and, thus, the applicability is restricted to well-defined domains where knowledge about the domain being taught can be explicitly modeled. For ill-defined domains, human tutors still by far outperform the performance of ITSs, or the latter are not applicable at all. As part of the DFG priority programme "Autonomous Learning", the FIT project has been conducted over a period of 3 years pursuing the goal to develop novel ITS methods, that are also applicable for ill-defined problems, based on implicit domain knowledge extracted from educational data sets. Here, machine learning techniques have been used to autonomously infer structures from given learning data (e.g., student solutions) and, based on these structures, to develop strategies for instructing learners.

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Section: Advances and Resultssupporting

confidence: 65%

Section: Advances and Resultsmentioning

confidence: 99%

Learning Feedback in Intelligent Tutoring Systems

et al. 2015

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“…Glassman et al [5] visualize the space of student solutions to Matlab programming problems in order to identify popular approaches for solving a problem. A number of recent papers have clustered students based on abstract syntax trees using distances in feature space [7,6], string edit distance [20,21] and tree edit distance [10], proposing to give feedback to exemplar submissions. These methods rely almost completely on syntactic analysis and do not explicitly relate form to function as in our work.…”

Section: Related Workmentioning

confidence: 99%

Codewebs

Nguyễn

Piech

Huang

et al. 2014

Proceedings of the 23rd International Conference on World Wide Web

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Massive open online courses (MOOCs), one of the latest internet revolutions have engendered hope that constant iterative improvement and economies of scale may cure the "cost disease" of higher education. While scalable in many ways, providing feedback for homework submissions (particularly open-ended ones) remains a challenge in the online classroom. In courses where the student-teacher ratio can be ten thousand to one or worse, it is impossible for instructors to personally give feedback to students or to understand the multitude of student approaches and pitfalls. Organizing and making sense of massive collections of homework solutions is thus a critical web problem. Despite the challenges, the dense solution space sampling in highly structured homeworks for some MOOCs suggests an elegant solution to providing quality feedback to students on a massive scale.We outline a method for decomposing online homework submissions into a vocabulary of "code phrases", and based on this vocabulary, we architect a queryable index that allows for fast searches into the massive dataset of student homework submissions. To demonstrate the utility of our homework search engine we index over a million code submissions from users worldwide in Stanford's Machine Learning MOOC and (a) semi-automatically learn shared structure amongst homework submissions and (b) generate specific feedback for student mistakes.Codewebs is a tool that leverages the redundancy of densely sampled, highly structured homeworks in order to forcemultiply teacher effort. Giving articulate, instant feedback is a crucial component of the online learning process and thus by building a homework search engine we hope to take a step towards higher quality free education.

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“…One of the key components in many introductory programming courses is hands-on practice with programming assignments that help students learn both syntax and semantics. A key part of this hands-on practice is feedback, which helps students to adjust their work to better match the expectations [1]- [3]. As an introductory programming course may receive hundreds or thousands of programming assignment submissions each week [4]- [7], writing feedback manually for each student is often not feasible.…”

Section: Introductionmentioning

confidence: 99%

Evaluating CodeClusters for Effectively Providing Feedback on Code Submissions

Koivisto

Hellas

2022

2022 IEEE Frontiers in Education Conference (FIE)

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Full research paper-Most introductory programming courses rely on the use of automated assessment for grading programming assignments. While such systems save teachers' time by eliminating manual grading, the submissions may not be reviewed by the teachers at all, losing valuable insight into how students solve the assignments. In this paper, we introduce CodeClusters which provides teachers' a quick overview of general patterns in code submissions. The main features of the system are full-text search and N-gram -based similarity detection model that can cluster and subset the code by various aspects such as AST similarity or software also has It has also an interface for streamlining the writing of feedback to multiple submissions at once. CodeClusters has been primarily designed to be used jointly with automated assessment systems where automated tests would assess the functionality of the code, and CodeClusters would be used for gaining a higherlevel view of programming patterns and for writing feedback to students. CodeClusters was evaluated in a think-aloud study with university lecturers responsible for programming courses at two research-first universities. The lecturers were pleased by the possibility of providing better feedback to students quickly, saw it could improve the quality of their courses over solely automated assessment, and expressed interest in using CodeClusters in their own courses.

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Towards Providing Feedback to Students in Absence of Formalized Domain Models

Cited by 4 publications

References 7 publications

Learning Feedback in Intelligent Tutoring Systems

Learning Feedback in Intelligent Tutoring Systems

Codewebs

Evaluating CodeClusters for Effectively Providing Feedback on Code Submissions

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