Goal-oriented models for teaching and understanding data structures
Visualitza/Obre
10.1007/978-3-030-89022-3_19
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/359092
Tipus de documentText en actes de congrés
Data publicació2021
EditorSpringer Nature
Condicions d'accésAccés obert
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Abstract
Most computer science curricula include a compulsory course on data structures. Students are prone to memorise facts about data structures instead of understanding the essence of underlying concepts. This can be explained by the fact that learning the basics of each data structure, the difference with each other, and the adequacy of each of them to the most appropriate context of use, is far from trivial. This paper explores the idea of providing adequate levels of abstractions to describe data structures from an intentional point of view. Our hypothesis is that adopting a goal-oriented perspective could emphasise the main goals of each data structure, its qualities, and its relationships with the potential context of use. Following this hypothesis, in this paper we present the use of iStar2.0 to teach and understand data structures. We conducted a comparative quasi-experiment with undergraduate students to evaluate the effectiveness of the approach. Significant results show the great potential of goal modeling for teaching technical courses like data structures. We conclude this paper by reflecting on further teaching and conceptual modeling research to be conducted in this field.
CitacióFranch, X.; Ruiz, M. Goal-oriented models for teaching and understanding data structures. A: International Conference on Conceptual Modeling. "Conceptual Modeling: 40th International Conference, ER 2021: virtual event, October 18–21, 2021: proceedings". Springer Nature, 2021, p. 227-241. ISBN 978-3-030-89022-3. DOI 10.1007/978-3-030-89022-3_19.
ISBN978-3-030-89022-3
Versió de l'editorhttps://link.springer.com/chapter/10.1007/978-3-030-89022-3_19
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