Semi-automatic generation of textual exercises for software engineering education

被引:2
作者
Huber, Florian [1 ]
Hagel, Georg [1 ]
机构
[1] Kempten Univ Appl Sci, Fac Comp Sci, Kempten, Germany
来源
PROCEEDINGS OF THE 2022 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON 2022) | 2022年
关键词
Terms-UML class diagrams; software engineering education; deep learning; textual exercise generation;
D O I
10.1109/EDUCON52537.2022.9766802
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Creating class diagrams from given texts is an important skill for software engineering students in higher computer science education. Choosing the right diagram components and arranging them correctly regularly challenges students. Therefore, a thoughtful teaching approach is required from educators. Creating such exercises for different courses with different contexts is time-consuming for educators. In preparation for an upcoming exam, students are limited to few available exercise texts. To address these challenges, a deep learning model was trained to automatically generate textual exercises for software engineering education. To verify whether the model is suitable for educational purposes or not, a study with software engineering students has been conducted. The results show, that the texts are well understood according to grammar and sentence structure. Also, students found the created exercise useful and would like to use comparable exercises for exam preparation.
引用
收藏
页码:51 / 56
页数:6
相关论文
共 36 条
[1]  
Aldabe I, 2006, LECT NOTES COMPUT SC, V4053, P584
[2]  
Almeida J. J., 2013, 2013 8 IBERIAN C INF, P1
[3]  
[Anonymous], 2005, AUTOMATIC GENERATION
[4]   e-status:: An automatic web-based problem generator -: Applications to statistics [J].
Antonio Gonzalez, Jose ;
Munoz, Pilar .
COMPUTER APPLICATIONS IN ENGINEERING EDUCATION, 2006, 14 (02) :151-159
[5]  
Cable Baptiste, 2013, Artificial Intelligence in Education. Proceedings of 16th International Conference (AIED 2013): LNCS 7926, P679, DOI 10.1007/978-3-642-39112-5_87
[6]  
Combefis S., 2019, 6 INT C COMPUTER SCI, DOI [10.5121/csit.2019.91308, DOI 10.5121/CSIT.2019.91308]
[7]  
de Sande J. C. G., 2010, P ICERI2010 C
[8]  
Doring N., 2016, Forschungsmethoden und Evaluation in den Sozialund Humanwissen-schaften (Springer-Lehrbuch)
[9]  
Fenogenova A., 2016, AUTOMATIC GENERATION
[10]   Verb Tense Classification And Automatic Exercise Generation [J].
Ferreira, Kledilson ;
Pereira, Alvaro R., Jr. .
WEBMEDIA'18: PROCEEDINGS OF THE 24TH BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB, 2018, :105-108