Automatic Feedback Provision in Teaching Computational Science
| Autor: | Thomas Kluyver, Hans Fangohr, Neil S. O'Brien, Ondrej Hovorka, Arti Kashyap, Nicholas Hale, Anil Prabhakar |
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| Rok vydání: | 2020 |
| Předmět: |
Correctness
Unit testing Programming education Computer science 05 social sciences 050301 education 020207 software engineering 02 engineering and technology Python (programming language) Computational science ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering Student learning 0503 education computer computer.programming_language |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030504359 ICCS (7) |
| DOI: | 10.1007/978-3-030-50436-6_45 |
| Popis: | We describe a method of automatic feedback provision for students learning computational science and data science methods in Python. We have implemented, used and refined this system since 2009 for growing student numbers, and summarise the design and experience of using it. The core idea is to use a unit testing framework: the teacher creates a set of unit tests, and the student code is tested by running these tests. With our implementation, students typically submit work for assessment, and receive feedback by email within a few minutes after submission. The choice of tests and the reporting back to the student is chosen to optimise the educational value for the students. The system very significantly reduces the staff time required to establish whether a student’s solution is correct, and shifts the emphasis of computing laboratory student contact time from assessing correctness to providing guidance. The self-paced nature of the automatic feedback provision supports a student-centred learning approach. Students can re-submit their work repeatedly and iteratively improve their solution, and enjoy using the system. We include an evaluation of the system from using it in a class of 425 students. |
| Databáze: | OpenAIRE |
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