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FUZZY LOGIC APPROACH FOR PREDICTING STUDENT ACHIEVEMENT IN SCRATCH

Year 2024, Volume: 12 Issue: 2, 344 - 357, 01.06.2024
https://doi.org/10.36306/konjes.1372676

Abstract

21st-century skills such as critical thinking, problem-solving, and analytical thinking gained importance to survive in today’s world. There is growing research mostly focus on the prediction of students in higher education using machine learning and statistical models. However, predicting primary and middle school student’s performance also becomes important especially in learning computer programming. In this study, it was primarily proposed to a fuzzy logic system to predict student performance during the experiment then compare fuzzy logic prediction results to the experts’ results. Secondly, to test the theory that students’ interest in learning algorithms and coding can be increased using the creation of games in a visual programming tool for beginners. The fuzzy logic inference system has been employed to predict middle school student’s performance in the programming experiment which has been carried out using the Scratch environment with the participation of three different middle school students in Turkey. The success rate of three different middle school group success rates is estimated regarding task completion times, and the regression results with respect to the groups are %80, %97, %84.

References

  • J. H. Maloney, K. Peppler, Y. Kafai, M. Resnick, and N. Rusk, "Programming by choice: urban youth learning programming with scratch," in Proceedings of the 39th SIGCSE technical symposium on Computer science education, 2008, pp. 367-371.
  • M. Prensky, "Digital natives, digital immigrants part 2: Do they really think differently?," On the horizon, vol. 9, no. 6, pp. 1-6, 2001.
  • M. Resnick et al., "Scratch: programming for all," Communications of the ACM, vol. 52, no. 11, pp. 60-67, 2009.
  • W. Dann and S. Cooper, "Education Alice 3: concrete to abstract," Communications of the ACM, vol. 52, no. 8, pp. 27-29, 2009.
  • M. Mladenović, D. Krpan, and S. Mladenović, "Learning programming from Scratch," in International Conference on New Horizons in Education INTE, 2017.
  • W. Dann, D. Cosgrove, D. Slater, D. Culyba, and S. Cooper, "Mediated transfer: Alice 3 to java," in Proceedings of the 43rd ACM technical symposium on Computer Science Education, 2012, pp. 141-146.
  • A. K. Whitfield, S. Blakeway, G. E. Herterich, and C. Beaumont, "Programming, disciplines and methods adopted at Liverpool Hope University," Innovation in Teaching and Learning in Information and Computer Sciences, vol. 6, no. 4, pp. 145-168, 2007.
  • L. A. Zadeh, "Fuzzy sets," Information and control, vol. 8, no. 3, pp. 338-353, 1965.
  • H. Singh et al., "Real-life applications of fuzzy logic," Advances in Fuzzy Systems, vol. 2013, pp. 3-3, 2013.
  • S. A. Papert, Mindstorms: Children, computers, and powerful ideas. Basic books, 2020.
  • O. Yildiz, A. Bal, and S. Gulsecen, "Improved fuzzy modelling to predict the academic performance of distance education students," International Review of Research in Open and Distributed Learning, vol. 14, no. 5, pp. 144-165, 2013.
  • S. Ingoley and J. Bakal, "Use of fuzzy logic in evaluating students’ learning achievement," International Journal on Advanced Computer Engineering and Communication Technology (IJACECT), vol. 1, no. 2, pp. 47-54, 2012.
  • S. S. Jamsandekar and R. Mudholkar, "Performance evaluation by fuzzy inference technique," International Journal of Soft Computing and Engineering, vol. 3, no. 2, pp. 158-164, 2013.
  • Z. Yıldız and A. F. Baba, "Evaluation of student performance in laboratory applications using fuzzy decision support system model," in 2014 IEEE Global Engineering Education Conference (EDUCON), 2014: IEEE, pp. 1023-1027.
  • G. Jyothi, C. Parvathi, P. Srinivas, and S. Althaf, "Fuzzy expert model for evaluation of faculty performance in Technical educational Institutions," International Journal of Engineering Research and Applications, vol. 4, no. 5, pp. 41-50, 2014.
  • M. Eryılmaz and A. Adabashi, "Development of an intelligent tutoring system using bayesian networks and fuzzy logic for a higher student academic performance," Applied Sciences, vol. 10, no. 19, p. 6638, 2020.
  • D. Doz, D. Felda, and M. Cotič, "Combining Students’ Grades and Achievements on the National Assessment of Knowledge: A Fuzzy Logic Approach," Axioms, vol. 11, no. 8, p. 359, 2022.
  • N. U. Jan, S. Naqvi, and Q. Ali, "Using Fuzzy Logic for Monitoring Students Academic Performance in Higher Education," Engineering Proceedings, vol. 46, no. 1, p. 21, 2023.
  • M. Dhokare, S. Teje, S. Jambukar, and V. Wangikar, "Evaluation of academic performance of students using fuzzy logic," in 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2021: IEEE, pp. 1-5.
  • J. A. Rojas, H. E. Espitia, and L. A. Bejarano, "Design and optimization of a fuzzy logic system for academic performance prediction," Symmetry, vol. 13, no. 1, p. 133, 2021.
  • M. Abou Naaj, R. Mehdi, E. A. Mohamed, and M. Nachouki, "Analysis of the Factors Affecting Student Performance Using a Neuro-Fuzzy Approach," Education Sciences, vol. 13, no. 3, p. 313, 2023.
  • R. Mehdi and M. Nachouki, "A neuro-fuzzy model for predicting and analyzing student graduation performance in computing programs," Education and Information Technologies, vol. 28, no. 3, pp. 2455-2484, 2023.
  • R. Parkavi and P. Karthikeyan, "Predicting academic performance of learners with the three domains of learning data using neuro-fuzzy model and machine learning algorithms," Journal of Engineering Research, 2023.
  • M. Agaoglu, "Predicting instructor performance using data mining techniques in higher education," IEEE Access, vol. 4, pp. 2379-2387, 2016.
  • C.-T. Lye, L.-N. Ng, M. D. Hassan, W.-W. Goh, C.-Y. Law, and N. Ismail, "Predicting Pre-university student's Mathematics achievement," Procedia-Social and Behavioral Sciences, vol. 8, pp. 299-306, 2010.
  • L. A. Zadeh, "The concept of a linguistic variable and its application to approximate reasoning—I," Information sciences, vol. 8, no. 3, pp. 199-249, 1975.
  • E. H. Mamdani and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," International journal of man-machine studies, vol. 7, no. 1, pp. 1-13, 1975.
  • M. Sugeno, Industrial applications of fuzzy control. Elsevier Science Inc., 1985.
  • J. R. Jang, MATLAB: Fuzzy logic toolbox user's guide: Version 1. Math Works, 1997.
  • J. Yuan and R. T. Bowen, "Lifelong Kindergarten: Cultivating Creativity Through Projects, Passion, Peers, and Play," Interdisciplinary Journal of Problem-Based Learning, vol. 12, no. 2, p. 6, 2018.
  • K. Brennan and M. Resnick, "New frameworks for studying and assessing the development of computational thinking," in Proceedings of the 2012 annual meeting of the American educational research association, Vancouver, Canada, 2012, vol. 1, p. 25.
  • P. Byrne and G. Lyons, "The effect of student attributes on success in programming," in Proceedings of the 6th annual conference on Innovation and technology in computer science education, 2001, pp. 49-52.
  • G. White and M. Sivitanides, "An empirical investigation of the relationship between success in mathematics and visual programming courses," Journal of information systems education, vol. 14, no. 4, p. 409, 2003.
  • J. Bennedsen and M. E. Caspersen, "An investigation of potential success factors for an introductory model-driven programming course," in Proceedings of the first international workshop on Computing education research, 2005, pp. 155-163.
Year 2024, Volume: 12 Issue: 2, 344 - 357, 01.06.2024
https://doi.org/10.36306/konjes.1372676

Abstract

References

  • J. H. Maloney, K. Peppler, Y. Kafai, M. Resnick, and N. Rusk, "Programming by choice: urban youth learning programming with scratch," in Proceedings of the 39th SIGCSE technical symposium on Computer science education, 2008, pp. 367-371.
  • M. Prensky, "Digital natives, digital immigrants part 2: Do they really think differently?," On the horizon, vol. 9, no. 6, pp. 1-6, 2001.
  • M. Resnick et al., "Scratch: programming for all," Communications of the ACM, vol. 52, no. 11, pp. 60-67, 2009.
  • W. Dann and S. Cooper, "Education Alice 3: concrete to abstract," Communications of the ACM, vol. 52, no. 8, pp. 27-29, 2009.
  • M. Mladenović, D. Krpan, and S. Mladenović, "Learning programming from Scratch," in International Conference on New Horizons in Education INTE, 2017.
  • W. Dann, D. Cosgrove, D. Slater, D. Culyba, and S. Cooper, "Mediated transfer: Alice 3 to java," in Proceedings of the 43rd ACM technical symposium on Computer Science Education, 2012, pp. 141-146.
  • A. K. Whitfield, S. Blakeway, G. E. Herterich, and C. Beaumont, "Programming, disciplines and methods adopted at Liverpool Hope University," Innovation in Teaching and Learning in Information and Computer Sciences, vol. 6, no. 4, pp. 145-168, 2007.
  • L. A. Zadeh, "Fuzzy sets," Information and control, vol. 8, no. 3, pp. 338-353, 1965.
  • H. Singh et al., "Real-life applications of fuzzy logic," Advances in Fuzzy Systems, vol. 2013, pp. 3-3, 2013.
  • S. A. Papert, Mindstorms: Children, computers, and powerful ideas. Basic books, 2020.
  • O. Yildiz, A. Bal, and S. Gulsecen, "Improved fuzzy modelling to predict the academic performance of distance education students," International Review of Research in Open and Distributed Learning, vol. 14, no. 5, pp. 144-165, 2013.
  • S. Ingoley and J. Bakal, "Use of fuzzy logic in evaluating students’ learning achievement," International Journal on Advanced Computer Engineering and Communication Technology (IJACECT), vol. 1, no. 2, pp. 47-54, 2012.
  • S. S. Jamsandekar and R. Mudholkar, "Performance evaluation by fuzzy inference technique," International Journal of Soft Computing and Engineering, vol. 3, no. 2, pp. 158-164, 2013.
  • Z. Yıldız and A. F. Baba, "Evaluation of student performance in laboratory applications using fuzzy decision support system model," in 2014 IEEE Global Engineering Education Conference (EDUCON), 2014: IEEE, pp. 1023-1027.
  • G. Jyothi, C. Parvathi, P. Srinivas, and S. Althaf, "Fuzzy expert model for evaluation of faculty performance in Technical educational Institutions," International Journal of Engineering Research and Applications, vol. 4, no. 5, pp. 41-50, 2014.
  • M. Eryılmaz and A. Adabashi, "Development of an intelligent tutoring system using bayesian networks and fuzzy logic for a higher student academic performance," Applied Sciences, vol. 10, no. 19, p. 6638, 2020.
  • D. Doz, D. Felda, and M. Cotič, "Combining Students’ Grades and Achievements on the National Assessment of Knowledge: A Fuzzy Logic Approach," Axioms, vol. 11, no. 8, p. 359, 2022.
  • N. U. Jan, S. Naqvi, and Q. Ali, "Using Fuzzy Logic for Monitoring Students Academic Performance in Higher Education," Engineering Proceedings, vol. 46, no. 1, p. 21, 2023.
  • M. Dhokare, S. Teje, S. Jambukar, and V. Wangikar, "Evaluation of academic performance of students using fuzzy logic," in 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2021: IEEE, pp. 1-5.
  • J. A. Rojas, H. E. Espitia, and L. A. Bejarano, "Design and optimization of a fuzzy logic system for academic performance prediction," Symmetry, vol. 13, no. 1, p. 133, 2021.
  • M. Abou Naaj, R. Mehdi, E. A. Mohamed, and M. Nachouki, "Analysis of the Factors Affecting Student Performance Using a Neuro-Fuzzy Approach," Education Sciences, vol. 13, no. 3, p. 313, 2023.
  • R. Mehdi and M. Nachouki, "A neuro-fuzzy model for predicting and analyzing student graduation performance in computing programs," Education and Information Technologies, vol. 28, no. 3, pp. 2455-2484, 2023.
  • R. Parkavi and P. Karthikeyan, "Predicting academic performance of learners with the three domains of learning data using neuro-fuzzy model and machine learning algorithms," Journal of Engineering Research, 2023.
  • M. Agaoglu, "Predicting instructor performance using data mining techniques in higher education," IEEE Access, vol. 4, pp. 2379-2387, 2016.
  • C.-T. Lye, L.-N. Ng, M. D. Hassan, W.-W. Goh, C.-Y. Law, and N. Ismail, "Predicting Pre-university student's Mathematics achievement," Procedia-Social and Behavioral Sciences, vol. 8, pp. 299-306, 2010.
  • L. A. Zadeh, "The concept of a linguistic variable and its application to approximate reasoning—I," Information sciences, vol. 8, no. 3, pp. 199-249, 1975.
  • E. H. Mamdani and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," International journal of man-machine studies, vol. 7, no. 1, pp. 1-13, 1975.
  • M. Sugeno, Industrial applications of fuzzy control. Elsevier Science Inc., 1985.
  • J. R. Jang, MATLAB: Fuzzy logic toolbox user's guide: Version 1. Math Works, 1997.
  • J. Yuan and R. T. Bowen, "Lifelong Kindergarten: Cultivating Creativity Through Projects, Passion, Peers, and Play," Interdisciplinary Journal of Problem-Based Learning, vol. 12, no. 2, p. 6, 2018.
  • K. Brennan and M. Resnick, "New frameworks for studying and assessing the development of computational thinking," in Proceedings of the 2012 annual meeting of the American educational research association, Vancouver, Canada, 2012, vol. 1, p. 25.
  • P. Byrne and G. Lyons, "The effect of student attributes on success in programming," in Proceedings of the 6th annual conference on Innovation and technology in computer science education, 2001, pp. 49-52.
  • G. White and M. Sivitanides, "An empirical investigation of the relationship between success in mathematics and visual programming courses," Journal of information systems education, vol. 14, no. 4, p. 409, 2003.
  • J. Bennedsen and M. E. Caspersen, "An investigation of potential success factors for an introductory model-driven programming course," in Proceedings of the first international workshop on Computing education research, 2005, pp. 155-163.
There are 34 citations in total.

Details

Primary Language English
Subjects Electronics, Sensors and Digital Hardware (Other)
Journal Section Research Article
Authors

Ali Çetinkaya 0000-0002-7747-6854

Publication Date June 1, 2024
Submission Date October 8, 2023
Acceptance Date February 26, 2024
Published in Issue Year 2024 Volume: 12 Issue: 2

Cite

IEEE A. Çetinkaya, “FUZZY LOGIC APPROACH FOR PREDICTING STUDENT ACHIEVEMENT IN SCRATCH”, KONJES, vol. 12, no. 2, pp. 344–357, 2024, doi: 10.36306/konjes.1372676.