Preview

Informatics and education

Advanced search

An intelligent model to assess the level of the computational algorithmic component of students’ computational thinking

https://doi.org/10.32517/0234-0453-2022-37-4-71-79

Abstract

The importance of developing students’ computational thinking (CT) determines the need to devise an objective and valid method for assessing its level. In this regard, it is relevant to build a universal model for diagnosing computational thinking on the basis of mathematical methods.

The article aims to develop a model for diagnosing students’ computational algorithmic thinking. This model makes it possible to automate assessment procedures and contributes to developing techniques for enhancing this cognitive ability of learners.

The diagnostic evaluation of students’ computational algorithmic component of CT includes an assessment of their subject knowledge, cognitive abilities, and cognitive activity. In this regard, we identified three diagnostic criteria. The content criterion (CC) is determined by the knowledge acquired through subject-specific learning. The operational criterion (OC) is a set of identified intellectual strategies necessary to solve problems using computers. The cognitive activity criterion (CAC) is a cognitive-psychological response to the cognitive process. It is manifested in the intensification of learning activity, a strong focus on a proactive attitude, independence, and a creative approach to learning. It is necessary to establish indicators to provide three assessment levels for each criterion, namely low, medium, and high. The integrated assessment of the level of the computational algorithmic component of a student’s CT is determined by the total of these three criteria values.

For the adopted diagnostic model, we have developed a method based on cluster analysis to determine the CT level. The object of recognition is a learner with an information vector (CC, OC, CAC). The weights of the vector elements are determined by experts. The array of students with their information vectors is divided into three classes: L1 (low level), L2 (medium level), L3 (high level). The diagnostics of the computational algorithmic component level of a particular student’s CT consists in determining their belonging to the corresponding group.

The method devised to assess the level of the computational algorithmic component of students’ CT was implemented as a software product. The proposed original model for diagnosing students’ CT provides an opportunity to automate assessment procedures. It also facilitates the integrated assessments of the diagnostic characteristics of the learning process.

The materials of this work are of interest to teachers and employees of the educational departments of universities, and also have theoretical significance for the development of the theory and practice of diagnosing educational results.

About the Authors

I. V. Bazhenova
Siberian Federal University
Russian Federation

Irina V. Bazhenova, Candidate of Sciences (Education), Associate Professor at the Basic Department of Computing and Information Technologies, Institute of Mathematics and Fundamental Informatics

Krasnoyarsk



M. M. Klunnikova
Siberian Federal University
Russian Federation

Margarita M. Klunnikova, Candidate of Sciences (Education), Associate Professor at the Basic Department of Computing and Information Technologies, Institute of Mathematics and Fundamental Informatics

Krasnoyarsk



N. I. Pak
Krasnoyarsk State Pedagogical University named after V. P. Astafiev
Russian Federation

Nikolai I. Pak, Doctor of Sciences (Education), Professor, Head of the Department of Informatics and Information Technologies in Education, Institute of Mathematics, Physics and Informatics

Krasnoyarsk



References

1. Dolganova O. V., Petrova O. O., Sharoxina E. V. Pedagogy: Lecture notes. Moscow, Eksmo; 2008. 190 p. (In Russian.)

2. Bazhenova I. V., Klunnikova M. M., Pak N. I., Pushkareva T. P., Khenner E. K. Cluster of disciplines as a platform for the development of students’ computational thinking. Krasnoyarsk, SFU; 2021, 184 p. (In Russian.)

3. Wing J. Research notebook: Computational thinking — What and Why? The Link. The Magazine of the Carnegie Mellon University School of Computer Science. Available at: http://www.cs.cmu.edu/link/research-notebook-computational-thinking-what-and-why

4. Lyon J. A., Fennel H. V., Magana A. J. Computational thinking in higher education: A review of the literature. Computer Applications in Engineering Education. 2020;(28):1174–1189. Available at: https://doi.org/10.1002/ cae.22295

5. Bazhenova I. V., Klunnikova M. M., Pak N. I. Schooluniversity cluster of disciplines developing the calculativealgorithmic component of computational thinking. Informatics and Education. 2021;36(3):42–49. (In Russian.) DOI: 10.32517/0234-0453-2021-36-3-42-49

6. Baird J. A., Andrich D., Hopfenbeck T. N., Stobart G. Assessment and learning: Fields apart? Assessment in Education: Principles, Policy & Practice. 2017;24(3):317–350. DOI: 10.1080/0969594X.2017.1319337

7. Deeley S. J. Using technology to facilitate effective assessment for learning and feedback in higher education. Assessment & Evaluation in Higher Education. 2017;43(3):439– 448. DOI: 10.1080/02602938.2017.1356906

8. Lau A. “Formative good, summative bad?” — A review of the dichotomy in assessment literature. Journal of Further and Higher Education. 2015;40(4):1–17. DOI: 10.1080/0309877X.2014.984600

9. Shmigirilova I. B., Rvanova A. S., Grigorenko O. V. Assessment in education: Current trends, problems and contradictions (review of scientific publications). Education and Science Journal. 2021;23(6):43–83. (In Russian.) DOI: 10.17853/19945639-2021-6-43-83

10. Glaser R. Evaluation of instruction and changing educational models. Proceedings of the Symposium on Problems in the Evaluation of Instruction. Los Angeles; 1967. 28 p.

11. Abekova Zh. A., Oralbaev A. B., Berdalieva M., Izbasarova Zh. K. Criteria-based assessment technology, methods of its application in the educational process. International Journal of Experimental Education. 2016;(2-2):215–218. (In Russian.) Available at: https: //expeducation.ru/ru/article/ view?id=9559

12. Stupnickaya M. Criteria-based assessment. Pedagogical Measurements. 2015;(1):52–74. (In Russian.)

13. Stagg-Taylor J. Cross-course assessment criteria: A study of criteria-based assessment developed for whole program use. ETL Conference; 2004. Available at: http:// hdl.handle.net/10072/2893

14. Ibarra-Sáiz M. S., Rodríguez-Gómez G., Boud D. The quality of assessment tasks as a determinant of learning. Assessment & Evaluation in Higher Education. 2021;46(6):943–955. DOI: 10.1080/02602938.2020.1828268

15. Efremova N. F. Conceptual model of evolution of competence of students. Modern high technologies. 2019;(7):169–174. (In Russian.) Available at: https://top-technologies.ru/ ru/article/view?id=37607

16. Bearman M., Dawson P., Bennett S., Hall M., Molloy E., Boud D., Joughin G. How university teachers design assessments: A cross-disciplinary study. Higher Education. 2017;74(1):49–64. DOI: 10.1007/s10734-016-0027-7

17. Fernández-Ruiz J., Panadero E., García-Pérez D. Assessment from a disciplinary approach: Design and implementation in three undergraduate programmes. Assessment in Education: Principles, Policy & Practice. 2021;28(5-6):703– 723. DOI: 10.1080/0969594X.2021.1999210

18. Rubinshtejn S. L. Fundamentals of General Psychology. Saint Petersburg, Piter; 2010. 720 p. (In Russian.)

19. Markelova O. V. Methodology for the development of cognitive activity of students of a technical school in the process of teaching informatics. Kand. ped. sci. diss.: 13.00.02. Krasnoyarsk; 2019. 191 p. (In Russian.)

20. Ignat’ev N. A. Cluster analysis and choice of standard objects in supervised pattern recognition problems. Computational Technologies. 2015;20(6):36–45. (In Russian.)


Review

For citations:


Bazhenova I.V., Klunnikova M.M., Pak N.I. An intelligent model to assess the level of the computational algorithmic component of students’ computational thinking. Informatics and education. 2022;37(4):71-79. (In Russ.) https://doi.org/10.32517/0234-0453-2022-37-4-71-79

Views: 318


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 0234-0453 (Print)
ISSN 2658-7769 (Online)