Exploring the Impact of Students Demographic Attributes on Performance Prediction through Binary Classification in the KDP Model

Issah Iddrisu; Peter Appiahene; Obed Appiah; Inusah Fuseini

doi:10.17977/um018v6i12023p24-40

Exploring the Impact of Students Demographic Attributes on Performance Prediction through Binary Classification in the KDP Model

Issah Iddrisu, Peter Appiahene, Obed Appiah, Inusah Fuseini

Abstract

During the course of this research, binary classification and the Knowledge Discovery Process (KDP) were used. The experimental and analytical capabilities of Rapid Miner's 9.10.010 instructional environment are supported by five different classifiers. Included in the analysis were 2334 entries, 17 characteristics, and one class variable containing the students' average score for the semester. There were twenty experiments carried out. During the studies, 10-fold cross-validation and ratio split validation, together with bootstrap sampling, were used. It was determined whether or not to use the Random Forest (RF), Rule Induction (RI), Naive Bayes (NB), Logistic Regression (LR), or Deep Learning (DL) methods. RF outperformed the other four methods in all six selection measures, with an accuracy of 93.96%. According to the RF classifier model, the level of education that a child's parents have is a major factor in that child's academic performance before entering higher education.

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R. Hasan, S. Palaniappan, S. Mahmood, K. U. Sarker, and A. Abbas, “Modelling and predicting student’s academic performance using classification data mining techniques,” Int. J. Bus. Inf. Syst., vol. 34, no. 3, pp. 403–422, 2020.

M. N. Yakubu and A. M. Abubakar, “Applying machine learning approach to predict students’ performance in higher educational institutions,” Kybernetes, vol. 51, no. 2, pp. 916–934, 2022.

M. Arashpour et al., “Predicting individual learning performance using machine-learning hybridized with the teaching-learning-based optimization,” Comput. Appl. Eng. Educ., vol. 31, no. 1, pp. 83–99, 2023.

D. M. Ahmed, A. M. Abdulazeez, D. Q. Zeebaree, and F. Y. H. Ahmed, “Predicting University’s Students Performance Based on Machine Learning Techniques,” 2021 IEEE Int. Conf. Autom. Control Intell. Syst. I2CACIS 2021 - Proc., no. August, pp. 276–281, 2021.

F. de Galiza Barbosa et al., “Genitourinary imaging,” Clinical PET/MRI. pp. 289–312, 2022.

F. Inusah, Y. M. Missah, N. Ussiph, and F. Twum, “Expert System in Enhancing Efficiency in Basic Educational Management using Data Mining Techniques,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 11, pp. 427–434, 2021.

F. Inusah, Y. M. Missah, U. Najim, and F. Twum, “Data Mining and Visualisation of Basic Educational Resources for Quality Education,” Int. J. Eng. Trends Technol., vol. 70, no. 12, pp. 296–307, Dec. 2022.

F. Inusah, Y. M. Missah, U. Najim, and F. Twum, “Integrating expert system in managing basic education: A survey in Ghana,” Int. J. Inf. Manag. Data Insights, vol. 3, no. 1, p. 100166, 2023.

F. Inusah, Y. M. Missah, U. Najim, and F. Twum, “Agile neural expert system for managing basic education,” Intell. Syst. with Appl., vol. 17, no. December 2022, p. 200178, 2023.

H. Drachsler and W. Greller, “Privacy and analytics - it’s a DELICATE issue a checklist for trusted learning analytics,” ACM Int. Conf. Proceeding Ser., vol. 25-29-Apri, no. April, pp. 89–98, 2016.

B. Owusu-Boadu, I. K. Nti, O. Nyarko-Boateng, J. Aning, and V. Boafo, “Academic Performance Modelling with Machine Learning Based on Cognitive and Non-Cognitive Features,” Appl. Comput. Syst., vol. 26, no. 2, pp. 122–131, 2021.

I. Issah, O. Appiah, P. Appiahene, and F. Inusah, “A systematic review of the literature on machine learning application of determining the attributes influencing academic performance,” Decis. Anal. J., vol. 7, no. March, p. 100204, 2023.

M. Tadese, A. Yeshaneh, and G. B. Mulu, “Determinants of good academic performance among university students in Ethiopia: a cross-sectional study,” BMC Med. Educ., vol. 22, no. 1, pp. 1–9, 2022.

F. Ouatik, M. Erritali, F. Ouatik, and M. Jourhmane, “Predicting Student Success Using Big Data and Machine Learning Algorithms,” Int. J. Emerg. Technol. Learn., vol. 17, no. 12, pp. 236–251, 2022.

S. Hussain and M. Q. Khan, “Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning,” Ann. Data Sci., no. Ml, 2021.

V. K. Pal and V. K. K. Bhatt, “Performance prediction for post graduate students using artificial neural network,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 7, pp. 446–454, 2019.

L. M. Abu Zohair, “Prediction of Student’s performance by modelling small dataset size,” Int. J. Educ. Technol. High. Educ., vol. 16, no. 1, 2019.

M. Pojon, “Using Machine Learning to Predict Student Performance,” Univ. Tampere, no. June, pp. 1–28, 2017.

B. Sekeroglu, K. Dimililer, and K. Tuncal, “Student Performance Prediction and Classification Using Machine Learning Algorithms,” in Proceedings of the 2019 8th International Conference on Educational and Information Technology, Mar. 2019, pp. 7–11.

M. N. Yakubu and A. M. Abubakar, “Applying machine learning approach to predict students’ performance in higher educational institutions,” Kybernetes, no. June, 2021.

F. Aman, A. Rauf, R. Ali, F. Iqbal, and A. M. Khattak, “A Predictive Model for Predicting Students Academic Performance,” in 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), Jul. 2019, pp. 1–4.

J. López-Zambrano, J. A. L. Torralbo, and C. Romero, “Early prediction of student learning performance through data mining: A systematic review,” Psicothema, vol. 33, no. 3, pp. 456–465, 2021.

A. I. Adekitan and E. Noma-Osaghae, “Data mining approach to predicting the performance of first year student in a university using the admission requirements,” Educ. Inf. Technol., vol. 24, no. 2, pp. 1527–1543, 2019.

Y. Altujjar, W. Altamimi, I. Al-turaiki, and M. Al-razgan, “Predicting Critical Courses Affecting Students Performance : A Case Study,” Procedia - Procedia Comput. Sci., vol. 82, no. March, pp. 65–71, 2016.

A. Ahadi, R. Lister, H. Haapala, and A. Vihavainen, “Exploring machine learning methods to automatically identify students in need of assistance,” ICER 2015 - Proc. 2015 ACM Conf. Int. Comput. Educ. Res., pp. 121–130, 2015.

D. T. Ha, C. N. Giap, P. T. T. Loan, and T. L. H. Huong, “An Empirical Study for Student Academic Performance Prediction Using Machine Learning Techniques,” Int. J. Comput. Sci. Inf. Secur., vol. 18, no. 3, pp. 21–28, 2020.

J. David and G. Anastasija, “Predicting Academic Performance Based on Students ’ Family Environment : Evidence for Colombia Using Classification Trees,” vol. 11, no. 3, pp. 299–311, 2019.

M. I. Al-Twijri and A. Y. Noaman, “A New Data Mining Model Adopted for Higher Institutions,” Procedia Comput. Sci., vol. 65, no. Iccmit, pp. 836–844, 2015.

A. Fernández, S. García, F. Herrera, and N. V. Chawla, “SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary,” J. Artif. Intell. Res., vol. 61, pp. 863–905, 2018.

P. D. Jenssen, T. Krogstad, and K. Halvorsen, “Community wastewater infiltration at 69 o northern latitude – 25 years of experience,” Soil Sci. Soc. Am. Onsite Wastewater Conf. , Albuquerque NM, 7-8 April 2014, no. April, pp. 7–8, 2014.

C. Anuradha and T. Velmurugan, “Fast Boost Decision Tree Algorithm: A novel classifier for the assessment of student performance in Educational data,” vol. 31, pp. 254–0223, 2016.

P. Bhatia, “Introduction to Data Mining,” Data Min. Data Warehous., pp. 17–27, 2019.

S. Samson, “Use of Data Mining For Determining Higher Education Students ’Performance,” St. Mary’s University, 2019.

J. Feng, “Predicting Students’ Academic Performance with Decision Tree and Neural Network,” University of Central Florida, 2019.

K. David Kolo, S. A. Adepoju, and J. Kolo Alhassan, “A Decision Tree Approach for Predicting Students Academic Performance,” Int. J. Educ. Manag. Eng., vol. 5, no. 5, pp. 12–19, 2015.

Y. Liu, S. Fan, S. Xu, A. Sajjanhar, S. Yeom, and Y. Wei, “Predicting Student Performance Using Clickstream Data and Machine Learning,” Educ. Sci., vol. 13, no. 1, 2023.

M. M. Z. Eddin, N. A. Khodeir, and H. A. Elnemr, “A Comparative Study of Educational Data Mining Techniques for skill-based Predicting Student Performance,” Int. J. Comput. Sci. Inf. Secur., vol. 16, no. 3, pp. 56–62, 2018.

P. Sokkhey and T. Okazaki, “Hybrid machine learning algorithms for predicting academic performance,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 1, pp. 32–41, 2020.

M. N. Yakubu, “Applying machine learning approach to predict students ’ performance in higher educational institutions,” no. June, 2021.

Y. Denny, H. Leslie, H. Spits, and W. Budiharto, “Systematic Literature Review on Abstractive Text Summarization,” no. November, 2021.

K. Blackmore and T. R. J. Bossomaier, “Comparison of See5 and J48.PART Algorithms for Missing Persons Profiling,” no. December, 2016.

F. Ofori, E. Maina, and R. Gitonga, “Using Machine Learning Algorithms to Predict Students’ Performance and Improve Learning Outcome: A Literature Based Review,” J. Inf. Technol., vol. 4, no. 1, pp. 2616–3573, 2020.

S. Agrawal, S. K., and A. K., “Using Data Mining Classifier for Predicting Student’s Performance in UG Level,” Int. J. Comput. Appl., vol. 172, no. 8, pp. 39–44, 2017.

D. Gašević, V. Kovanović, and S. Joksimović, “Piecing the learning analytics puzzle: a consolidated model of a field of research and practice,” Learn. Res. Pract., vol. 3, no. 1, pp. 63–78, 2017.

P. G. Sameer and S. R. Barahate, “Educational Data Mining – A New Approach to the Education Systems,” pp. 18–20, 2016.

A. S. Hashim, W. A. Awadh, and A. K. Hamoud, “Student Performance Prediction Model based on Supervised Machine Learning Algorithms,” IOP Conf. Ser. Mater. Sci. Eng., vol. 928, no. 3, 2020.

C. A. Palacios, J. A. Reyes-Suárez, L. A. Bearzotti, V. Leiva, and C. Marchant, “Knowledge discovery for higher education student retention based on data mining: Machine learning algorithms and case study in chile,” Entropy, vol. 23, no. 4, pp. 1–23, 2021.

D. T. Larose and C. D. Larose, “Data Mining and Predictive Analytics (Wiley Series on Methods and Applications in Data Mining): 9781118116197: Computer Science Books @ Amazon.com,” Wiley Ser., p. 794, 2015.

M. Maalouf, “Logistic regression in data analysis: an overview,” Int. J. Data Anal. Tech. Strateg., vol. 3, no. 3, p. 281, 2011.

K. J. Cios, W. Pedrycz, R. W. Swiniarski, and L. A. Kurgan, Data mining: A knowledge discovery approach. 2007.

Y. Chen et al., “Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential,” Geocarto Int., vol. 37, no. 19, pp. 5564–5584, 2022.

D. M. W. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” pp. 37–63, 2020.

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques. 2016.

M. Idris, S. Hussain, and N. Ahmad, “Relationship between Parents’ Education and their children’s Academic Achievement,” J. Arts Soc. Sci., vol. 7, no. 2, pp. 82–92, Dec. 2020.

DOI: http://dx.doi.org/10.17977/um018v6i12023p24-40