Paper "Towards the Automation of Grading Textual Student Submissions to Open-ended Questions" 

The second paper I want to share today is titled “Towards the Automation of Grading Textual Student Submissions to Open-ended Questions” and was published at the European Conference on Software Engineering Education (ECSEE) in 2020. DOI : doi.org/10.1145/3396802.339680 Preprint: brn.is/ecsee20
In this paper we present an algorithm using topic modeling for segmenting the submissions into smaller blocks.

Abstract–Growing student numbers at universities worldwide pose new challenges for instructors. Providing feedback to textual exercises is a challenge in large courses while being important for student’s learning success. Exercise submissions and their grading are a primary and individual communication channel between instructors and students. The pure amount of submissions makes it impossible for a single instructor to provide regular feedback to large student bodies.

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Employing tutors in the process introduces new challenges. Feedback should be consistent and fair for all students. Additionally, interactive teaching models strive for real-time feedback and multiple submissions.

We propose a support system for grading textual exercises using an automatic segment-based assessment concept. The system aims at providing suggestions to instructors by reusing previous comments as well as scores.

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The goal is to reduce the workload for instructors, while at the same time creating timely and consistent feedback to the students. We present the design and a prototypical implementation of an algorithm using topic modeling for segmenting the submissions into smaller blocks. Thereby, the system derives smaller units for assessment and allowing the creation of reusable and structured feedback.

We have evaluated the algorithm qualitatively by comparing automatically produced segments with manually produced segments created by humans. The results show that the system can produce topically coherent segments. The segmentation algorithm based on topic modeling is superior to approaches purely based on syntax and punctuation.

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