Dataset of Student Solutions to Algorithm and Data Structure Programming Assignments

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 956–962 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 956 Dataset of Student Solutions to Algorithm and Data Structure Programming Assignments Fynn Petersen-Frey1, Marcus Soll2, Louis Kobras3 Melf Johannsen4, Peter Kling5, Chris Biemann1 1 Language Technology Group, Dept. of Informatics, Universit¨at Hamburg 2 NORDAKADEMIE gAG Hochschule der Wirtschaft, K¨ollner Chaussee 11, 25337 Elmshorn 3 Fachbereich Informatik, Universit¨at Hamburg 4 Center for Optical Quantum Technologies, Fachbereich Physik, Universit¨at Hamburg 5 Theory of Efficient Algorithms Group, Dept. of Informatics, Universit¨at Hamburg {fynn.petersen-frey, louis.kobras, peter.kling, christian.biemann}@uni-hamburg.de marcus.soll@nordakademie.de, mjohanns@physnet.uni-hamburg.de Abstract We present a dataset containing source code solutions to algorithmic programming exercises solved by hundreds of Bachelor-level students at the Universit¨at Hamburg. These solutions were collected during the winter semesters 2019/2020, 2020/2021 and 2021/2022. The dataset contains a set of solutions to a total of 21 tasks written in Java as well as Python and a total of over 1500 individual solutions. All solutions were submitted through Moodle and the Coderunner plugin and passed a number of test cases (including randomized tests), such that they can be considered as working correctly. All students whose solutions are included in the dataset gave their consent into publishing their solutions. The solutions are pseudonymized with a random solution ID. Included in this paper is a short analysis of the dataset containing statistical data and highlighting a few anomalies (e.g. the number of solutions per task decreases for the last few tasks due to grading rules). We plan to extend the dataset with tasks and solutions from upcoming courses. Keywords: dataset, programming, algorithm, sourcecode, java, python 1. Introduction Many people believe that the ability to write computer programs is an important skill to form the world we are living in (K¨orber-Stiftung, 2017), yet only a small part of the German society is able to actually handle the activity of programming (Initiative D21, 2021). There- fore, the idea of using natural language (e.g. English or German language) which is then automatically trans- lated into computer code is quite old (Price et al., 2000; Begel, 2004). To improve these approaches, it would be useful to have datasets with tasks described in natural language and different valid computer programs which solve the task. Motivated by this, we created a new dataset consisting of student solutions to natural language task descriptions. With this dataset, we hope to enable future research on learning programming, algorithms and data structures both for students and in machine learning contexts. The dataset is available under the permissive CC BY-NC 4.0 license.1 The dataset contains algorithmic programming assign- ments formulated originally in German together with accompanying test cases and currently 1500+ source code solutions in Java and Python by students partici- pating in Bachelor-level algorithm and data structures courses at Universit¨at Hamburg. Only correct solutions from students who consented on both collection and dis- 1https://www.inf.uni-hamburg.de/en/ inst/ab/lt/resources/data/ad-lrec Implement a class ”Queue” that works like a queue (as described in the lecture). …

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