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Review of Technique and Algorithm for Educational Data Mining: Trend and Challenge in Games Design
Corresponding Author(s) : Ulfatun Nadifa
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 7, No. 1, February 2022
Abstract
This study reviews techniques and algorithm models often used in the analysis of educational data mining. The review in this study is based on previous studies to provide researchers knowledge about trends and challenges analysis Educational data mining in game design meaningful. However, there is a lot of games design developed without analysis Educational data mining which then will not answer the student problem. The analysis needed periodic data and developing the game required actual student conditions, this is a combination inseparable. Determine Research questions, Search Terms, and filtering for the selection and analysis of the article review. There are some student problems on analysis review, namely prediction student performance, student behavior, student at-riks, and student dropout. The number of Articles in the study was 33 with 21 Articles of research and 12 of Article review. The number of studies 8 with percent 38% used techniques Confusion matric with 33% percent used algorithms Decision Tree in 7 of studies. The section in this study consists of techniques evaluation, model selection, outcome, subject, and algorithm method. Which are recommended techniques and algorithms for analysis Educational data mining and in ideal game design to further research.
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- C. Iswarya, “Educational Data Mining Using Analysis Student Learning Process,” vol. 10, no. 6, pp. 407–410, 2021.
- Z. R. Anoopkumar M, “A Review on Data Mining techniques and factors used in Educational Data Mining to predict student amelioration,” Proc. 2016 Int. Conf. Data Min. Adv. Comput. SAPIENCE 2016, pp. 122–133, 2016. https://doi.org/10.1109/SAPIENCE.2016.7684113
- S. Pratama Wirya Atmaja, “Balancing Entertainment, Cost, and Educational Strength: A Design Framework for Medium-Coupling Educational Games,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, pp. 27–40, 2021. https://doi.org/10.22219/kinetik.v6i1.1158
- S. H. Dr. Radha Krishna Rambola, Mrunmayee Inamke, “Literature review- techniques and algorithms used for various applications of educational data mining (EDM).,” 2018 4th Int. Conf. Comput. Commun. Autom. ICCCA 2018, pp. 1–4, 2018. https://doi.org/10.1109/CCAA.2018.8777556
- K. S. Said A. Salloum, Muhammad Alshurideh, Ashraf Elnagar, “Mining in Educational Data: Review and Future Directions,” AICV, vol. 2, no. 1153, pp. 92–102, 2020. https://doi.org/10.1007/978-3-030-44289-7_9
- A. A. S. Ahmad Fairuzabadi, “An Overview Of Learning Support Factors On Mathematic Games,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 2, pp. 169–178, 2019. http://dx.doi.org/10.22219/kinetik.v4i2.761
- J. M. Steven Lehr, Hong Liu, Sean Klinglesmith, Alex Konyha Natalia Robaszewska, “Use Educational Data Mining to Predict Undergraduate Retention,” no. 1, pp. 2–4, 2016. https://doi.org/10.1109/ICALT.2016.138
- P. Rojanavasu, “Educational data analytics using association rule mining and classification,” ECTI DAMT-NCON 2019 - 4th Int. Conf. Digit. Arts, Media Technol. 2nd ECTI North. Sect. Conf. Electr. Electron. Comput. Telecommun. Eng., pp. 142–145, 2019. https://doi.org/10.1109/ECTI-NCON.2019.8692274
- M. Zaffar, M. A. Hashmani, and K. S. Savita, “Performance analysis of feature selection algorithm for educational data mining,” 2017 IEEE Conf. Big Data Anal. ICBDA 2017, vol. 2018-Janua, pp. 7–12, 2018. https://doi.org/10.1109/ICBDAA.2017.8284099
- M. V. H. Ms.Tismy Devasia, Ms.Vinushree T P, “Prediction of Students Performance using Educational Data Mining,” IEEE, vol. 1, no. 3, pp. 266–279, 2016. https://doi.org/10.1109/SAPIENCE.2016.7684167
- K. I. M. Ramaphosa, T. Zuva, and R. Kwuimi, “Educational Data Mining to Improve Learner Performance in Gauteng Primary Schools,” 2018 Int. Conf. Adv. Big Data, Comput. Data Commun. Syst. icABCD 2018, pp. 1–6, 2018. https://doi.org/10.1109/ICABCD.2018.8465478
- C. Jalota and R. Agrawal, “Analysis of Educational Data Mining using Classification,” in Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019, 2019, pp. 243–247. https://doi.org/10.1109/COMITCon.2019.8862214
- K. J. O. De Santos, A. G. Menezes, A. B. De Carvalho, and C. A. E. Montesco, “Supervised learning in the context of educational data mining to avoid university students dropout,” Proc. - IEEE 19th Int. Conf. Adv. Learn. Technol. ICALT 2019, vol. 2161–377X, pp. 207–208, 2019. https://doi.org/10.1109/ICALT.2019.00068
- Y. Pristyanto, I. Pratama, and A. F. Nugraha, “Data level approach for imbalanced class handling on educational data mining multiclass classification,” 2018 Int. Conf. Inf. Commun. Technol. ICOIACT 2018, vol. 2018-Janua, pp. 310–314, 2018. https://doi.org/10.1109/ICOIACT.2018.8350792
- anoushka panwar tanvi gera, “AFSA: A comprehensive analysis of educational big data using the advanced feature selection algorithm,” IEEE, vol. 7, pp. 5–9, 2021. https://doi.org/10.1109/ICACITE51222.2021.9404745
- B. Guo, R. Zhang, G. Xu, C. Shi, and L. Yang, “Predicting Students Performance in Educational Data Mining,” Proc. - 2015 Int. Symp. Educ. Technol. ISET 2015, pp. 125–128, 2016. https://doi.org/10.1109/ISET.2015.33
- C. C. Kiu, “Data Mining Analysis on Student’s Academic Performance through Exploration of Student’s Background and Social Activities,” Proc. - 2018 4th Int. Conf. Adv. Comput. Commun. Autom. ICACCA 2018, pp. 1–5, 2018. https://doi.org/10.1109/ICACCAF.2018.8776809
- M. Mimis, M. El Hajji, Y. Es-saady, A. Oueld Guejdi, H. Douzi, and D. Mammass, “A framework for smart academic guidance using educational data mining,” Educ. Inf. Technol., vol. 24, no. 2, pp. 1379–1393, 2019. https://doi.org/10.1007/s10639-018-9838-8
- T. Doleck, D. J. Lemay, R. B. Basnet, and P. Bazelais, “Predictive analytics in education: a comparison of deep learning frameworks,” Educ. Inf. Technol., vol. 25, no. 3, pp. 1951–1963, 2020. https://doi.org/10.1007/s10639-019-10068-4
- Q. Hu and H. Rangwala, “Towards Fair Educational Data Mining: A Case Study on Detecting At-risk Students,” Hu, Q., Rangwala, H. (2020). Towar. Fair Educ. Data Min. A Case Study Detect. At-risk Students. Proc. 13th Int. Conf. Educ. Data Min., no. Edm, pp. 431–437, 2020.
- O. Moscoso-Zea, P. Saa, and S. Luján-Mora, “Evaluation of algorithms to predict graduation rate in higher education institutions by applying educational data mining,” Australas. J. Eng. Educ., vol. 24, no. 1, pp. 4–13, 2019. https://doi.org/10.1080/22054952.2019.1601063
- V. Lampos, J. Mintz, and X. Qu, “An artificial intelligence approach for selecting effective teacher communication strategies in autism education,” npj Sci. Learn., vol. 6, no. 1, 2021. https://doi.org/10.1038/s41539-021-00102-x
- M. Rajathi and R. Murugesh, “Comparative Study of Binary Classification Algorithms to Analyze the Students ’ Performance on Virtual Machine,” vol. 10, no. 4, pp. 2017–2021, 2021.
- A. Abdulrahman Al-Noshan, M. Abdullah Al-Hagery, H. Abdulaziz Al-Hodathi, and M. Sulaiman Al-Quraishi, “Performance Evaluation and Comparison of Classification Algorithms for Students at Qassim University,” Int. J. Sci. Res., vol. 8, no. 11, pp. 1277–1282, 2018.
- R. Hasan, S. Palaniappan, S. Mahmood, A. Abbas, K. U. Sarker, and M. U. Sattar, “Predicting student performance in higher educational institutions using video learning analytics and data mining techniques,” Appl. Sci., vol. 10, no. 11, 2020. https://doi.org/10.3390/app10113894
- T. Elaf Abu Amrieh, I. Hamtini, and Aljarah, “mining educational data to predict student’s academic performance using esemble methods,” Int. J. Database Theory Appl., vol. 9, no. 8, pp. 119–136, 2016. http://dx.doi.org/10.14257/ijdta.2016.9.8.13
- F. Jauhari and A. A. Supianto, “Building student’s performance decision tree classifier using boosting algorithm,” Indones. J. Electr. Eng. Comput. Sci., vol. 14, no. 3, pp. 1298–1304, 2019. http://doi.org/10.11591/ijeecs.v14.i3.pp1298-1304
- A. A. Yahya and A. Osman, “A data-mining-based approach to informed decision-making in engineering education,” Comput. Appl. Eng. Educ., vol. 27, no. 6, pp. 1402–1418, 2019. https://doi.org/10.1002/cae.22158
- K. P. H. Khan, “A Survey on Analysis the Students Mind in Different Area,” Int. J. Sci. Res., vol. 7, no. 12, pp. 633–639, 2018.
- I. E. Moustafa M. Kurdi, Hatim Al-Khafagi, “Mining educational data to analyze students’ behavior and performance,” Proc. 2018 JCCO Jt. Int. Conf. ICT Educ. Training, Int. Conf. Comput. Arab. Int. Conf. Geocomputing, JCCO TICET-ICCA-GECO 2018, pp. 171–175, 2018. https://doi.org/10.1109/ICCA-TICET.2018.8726203
- S. Vandercruysse and J. Elen, “Instructional Techniques to Facilitate Learning and Motivation of Serious Games,” Instr. Tech. to Facil. Learn. Motiv. Serious Games, pp. 17–35, 2017. https://doi.org/10.1007/978-3-319-39298-1_2
- A. A. Syahidi, A. A. Supianto, T. Hirashima, and H. Tolle, “Learning Models in Educational Game Interactions : A Review,” vol. 3, no. June, pp. 11–29, 2021.
- C. Baek and T. Doleck, “Educational Data Mining versus Learning Analytics: A Review of Publications From 2015 to 2019,” Interact. Learn. Environ., vol. 0, no. 0, pp. 1–23, 2021. https://doi.org/10.1080/10494820.2021.1943689
References
C. Iswarya, “Educational Data Mining Using Analysis Student Learning Process,” vol. 10, no. 6, pp. 407–410, 2021.
Z. R. Anoopkumar M, “A Review on Data Mining techniques and factors used in Educational Data Mining to predict student amelioration,” Proc. 2016 Int. Conf. Data Min. Adv. Comput. SAPIENCE 2016, pp. 122–133, 2016. https://doi.org/10.1109/SAPIENCE.2016.7684113
S. Pratama Wirya Atmaja, “Balancing Entertainment, Cost, and Educational Strength: A Design Framework for Medium-Coupling Educational Games,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, pp. 27–40, 2021. https://doi.org/10.22219/kinetik.v6i1.1158
S. H. Dr. Radha Krishna Rambola, Mrunmayee Inamke, “Literature review- techniques and algorithms used for various applications of educational data mining (EDM).,” 2018 4th Int. Conf. Comput. Commun. Autom. ICCCA 2018, pp. 1–4, 2018. https://doi.org/10.1109/CCAA.2018.8777556
K. S. Said A. Salloum, Muhammad Alshurideh, Ashraf Elnagar, “Mining in Educational Data: Review and Future Directions,” AICV, vol. 2, no. 1153, pp. 92–102, 2020. https://doi.org/10.1007/978-3-030-44289-7_9
A. A. S. Ahmad Fairuzabadi, “An Overview Of Learning Support Factors On Mathematic Games,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 2, pp. 169–178, 2019. http://dx.doi.org/10.22219/kinetik.v4i2.761
J. M. Steven Lehr, Hong Liu, Sean Klinglesmith, Alex Konyha Natalia Robaszewska, “Use Educational Data Mining to Predict Undergraduate Retention,” no. 1, pp. 2–4, 2016. https://doi.org/10.1109/ICALT.2016.138
P. Rojanavasu, “Educational data analytics using association rule mining and classification,” ECTI DAMT-NCON 2019 - 4th Int. Conf. Digit. Arts, Media Technol. 2nd ECTI North. Sect. Conf. Electr. Electron. Comput. Telecommun. Eng., pp. 142–145, 2019. https://doi.org/10.1109/ECTI-NCON.2019.8692274
M. Zaffar, M. A. Hashmani, and K. S. Savita, “Performance analysis of feature selection algorithm for educational data mining,” 2017 IEEE Conf. Big Data Anal. ICBDA 2017, vol. 2018-Janua, pp. 7–12, 2018. https://doi.org/10.1109/ICBDAA.2017.8284099
M. V. H. Ms.Tismy Devasia, Ms.Vinushree T P, “Prediction of Students Performance using Educational Data Mining,” IEEE, vol. 1, no. 3, pp. 266–279, 2016. https://doi.org/10.1109/SAPIENCE.2016.7684167
K. I. M. Ramaphosa, T. Zuva, and R. Kwuimi, “Educational Data Mining to Improve Learner Performance in Gauteng Primary Schools,” 2018 Int. Conf. Adv. Big Data, Comput. Data Commun. Syst. icABCD 2018, pp. 1–6, 2018. https://doi.org/10.1109/ICABCD.2018.8465478
C. Jalota and R. Agrawal, “Analysis of Educational Data Mining using Classification,” in Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019, 2019, pp. 243–247. https://doi.org/10.1109/COMITCon.2019.8862214
K. J. O. De Santos, A. G. Menezes, A. B. De Carvalho, and C. A. E. Montesco, “Supervised learning in the context of educational data mining to avoid university students dropout,” Proc. - IEEE 19th Int. Conf. Adv. Learn. Technol. ICALT 2019, vol. 2161–377X, pp. 207–208, 2019. https://doi.org/10.1109/ICALT.2019.00068
Y. Pristyanto, I. Pratama, and A. F. Nugraha, “Data level approach for imbalanced class handling on educational data mining multiclass classification,” 2018 Int. Conf. Inf. Commun. Technol. ICOIACT 2018, vol. 2018-Janua, pp. 310–314, 2018. https://doi.org/10.1109/ICOIACT.2018.8350792
anoushka panwar tanvi gera, “AFSA: A comprehensive analysis of educational big data using the advanced feature selection algorithm,” IEEE, vol. 7, pp. 5–9, 2021. https://doi.org/10.1109/ICACITE51222.2021.9404745
B. Guo, R. Zhang, G. Xu, C. Shi, and L. Yang, “Predicting Students Performance in Educational Data Mining,” Proc. - 2015 Int. Symp. Educ. Technol. ISET 2015, pp. 125–128, 2016. https://doi.org/10.1109/ISET.2015.33
C. C. Kiu, “Data Mining Analysis on Student’s Academic Performance through Exploration of Student’s Background and Social Activities,” Proc. - 2018 4th Int. Conf. Adv. Comput. Commun. Autom. ICACCA 2018, pp. 1–5, 2018. https://doi.org/10.1109/ICACCAF.2018.8776809
M. Mimis, M. El Hajji, Y. Es-saady, A. Oueld Guejdi, H. Douzi, and D. Mammass, “A framework for smart academic guidance using educational data mining,” Educ. Inf. Technol., vol. 24, no. 2, pp. 1379–1393, 2019. https://doi.org/10.1007/s10639-018-9838-8
T. Doleck, D. J. Lemay, R. B. Basnet, and P. Bazelais, “Predictive analytics in education: a comparison of deep learning frameworks,” Educ. Inf. Technol., vol. 25, no. 3, pp. 1951–1963, 2020. https://doi.org/10.1007/s10639-019-10068-4
Q. Hu and H. Rangwala, “Towards Fair Educational Data Mining: A Case Study on Detecting At-risk Students,” Hu, Q., Rangwala, H. (2020). Towar. Fair Educ. Data Min. A Case Study Detect. At-risk Students. Proc. 13th Int. Conf. Educ. Data Min., no. Edm, pp. 431–437, 2020.
O. Moscoso-Zea, P. Saa, and S. Luján-Mora, “Evaluation of algorithms to predict graduation rate in higher education institutions by applying educational data mining,” Australas. J. Eng. Educ., vol. 24, no. 1, pp. 4–13, 2019. https://doi.org/10.1080/22054952.2019.1601063
V. Lampos, J. Mintz, and X. Qu, “An artificial intelligence approach for selecting effective teacher communication strategies in autism education,” npj Sci. Learn., vol. 6, no. 1, 2021. https://doi.org/10.1038/s41539-021-00102-x
M. Rajathi and R. Murugesh, “Comparative Study of Binary Classification Algorithms to Analyze the Students ’ Performance on Virtual Machine,” vol. 10, no. 4, pp. 2017–2021, 2021.
A. Abdulrahman Al-Noshan, M. Abdullah Al-Hagery, H. Abdulaziz Al-Hodathi, and M. Sulaiman Al-Quraishi, “Performance Evaluation and Comparison of Classification Algorithms for Students at Qassim University,” Int. J. Sci. Res., vol. 8, no. 11, pp. 1277–1282, 2018.
R. Hasan, S. Palaniappan, S. Mahmood, A. Abbas, K. U. Sarker, and M. U. Sattar, “Predicting student performance in higher educational institutions using video learning analytics and data mining techniques,” Appl. Sci., vol. 10, no. 11, 2020. https://doi.org/10.3390/app10113894
T. Elaf Abu Amrieh, I. Hamtini, and Aljarah, “mining educational data to predict student’s academic performance using esemble methods,” Int. J. Database Theory Appl., vol. 9, no. 8, pp. 119–136, 2016. http://dx.doi.org/10.14257/ijdta.2016.9.8.13
F. Jauhari and A. A. Supianto, “Building student’s performance decision tree classifier using boosting algorithm,” Indones. J. Electr. Eng. Comput. Sci., vol. 14, no. 3, pp. 1298–1304, 2019. http://doi.org/10.11591/ijeecs.v14.i3.pp1298-1304
A. A. Yahya and A. Osman, “A data-mining-based approach to informed decision-making in engineering education,” Comput. Appl. Eng. Educ., vol. 27, no. 6, pp. 1402–1418, 2019. https://doi.org/10.1002/cae.22158
K. P. H. Khan, “A Survey on Analysis the Students Mind in Different Area,” Int. J. Sci. Res., vol. 7, no. 12, pp. 633–639, 2018.
I. E. Moustafa M. Kurdi, Hatim Al-Khafagi, “Mining educational data to analyze students’ behavior and performance,” Proc. 2018 JCCO Jt. Int. Conf. ICT Educ. Training, Int. Conf. Comput. Arab. Int. Conf. Geocomputing, JCCO TICET-ICCA-GECO 2018, pp. 171–175, 2018. https://doi.org/10.1109/ICCA-TICET.2018.8726203
S. Vandercruysse and J. Elen, “Instructional Techniques to Facilitate Learning and Motivation of Serious Games,” Instr. Tech. to Facil. Learn. Motiv. Serious Games, pp. 17–35, 2017. https://doi.org/10.1007/978-3-319-39298-1_2
A. A. Syahidi, A. A. Supianto, T. Hirashima, and H. Tolle, “Learning Models in Educational Game Interactions : A Review,” vol. 3, no. June, pp. 11–29, 2021.
C. Baek and T. Doleck, “Educational Data Mining versus Learning Analytics: A Review of Publications From 2015 to 2019,” Interact. Learn. Environ., vol. 0, no. 0, pp. 1–23, 2021. https://doi.org/10.1080/10494820.2021.1943689