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Adaptive-CSSA: adaptive-chicken squirrel search algorithm driven deep belief network for student stress-level and drop out prediction with MapReduce framework

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Abstract

Stress is correlated with various illnesses that include diabetes, depression, and other chronic diseases and plays an important role in the emotional and physical well-being. In past years, number of student dropout from educational institute is rapidly maximizing. However, high rate of student dropout has been a major issue in various institutions. Dropout and stress prediction has received more attention in recent years. Previous literature studies applied machine learning algorithms to recognize the dropout and stress level of students, but there exist an issue of low accuracy and it leads to misidentification at learners. To overcome such problems and to create more accurate prediction result, a proposed method named Adaptive Chicken Squirrel Search Algorithm on the basis of Deep Belief Network (Adaptive-CSSA based DBN) is developed to predict the dropout and stress level of students based on the student performance behavior. The proposed prediction mechanism is designed with MapReduce framework by considering the mapper and the reducer functions. Here, the feature selection strategy is employed to select the unique features from student data that contains detailed information of students. Moreover, the proposed technique attained minimum MSE, RMSE, and MAPE as 0.025, 0.157, and 0.126 for dropout prediction and obtained lower MSE, RMSE, and MAPE value of 0.150, 0.387, and 0.326 for stress level prediction with student performance dataset.

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Data availability

The data underlying this article are available in Student Performance Data Set, "https://archive.ics.uci.edu/ml/datasets/student+performance#", accessed on June 2021.

References

  • Anson O, Bernstein J, Hobfoll SE (1984) Anxiety and performance in two ego threatening situations. J Pers Assess 48(2):168–172

    Article  Google Scholar 

  • Becker D, Kneip A, Patilea V (2021) Semiparametric inference for partially linear regressions with Box-Cox transformation, https://arxiv.org/abs/2106.10723

  • Cesare S, Xiang Y (2012) Software birthmark similarity, In: Software similarity and classification, Springer, London, pp. 63–70

  • Chander S, Vijaya P (2020) Tunicate swarm-based black hole entropic fuzzy clustering for data clustering using COVID data. In: The proceeding of IEEE 17th india council international conference (INDICON), IEEE, New Delhi, India

  • Chen J, Feng J, Sun X, Wu N, Yang Z, Chen S (2019) MOOC dropout prediction using a hybrid algorithm based on decision tree and extreme learning machine. Math Probl Eng. https://doi.org/10.1155/2019/8404653

    Article  Google Scholar 

  • Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113

    Article  Google Scholar 

  • Do QD, Tasanapradit P (2008) Depression and stress among the first-year medical students in the University of Medicine and Pharmacy, Hochiminh city, Vietnam. J Health Res 22:1–4

    Google Scholar 

  • Dupéré V, Leventhal T, Dion E, Crosnoe R, Archambault I, Janosz M (2014) Stressors and turning points in high school and dropout: a stress process, life course framework. Rev Educ Res 85(4):591

    Article  Google Scholar 

  • Dusselier L, Dunn B, Wang Y, Shelley iI MC, Whalen DF (2005) Personal, health, academic, and environmental predictors of stress for residence hall students. J Am Coll Health 54(1):15–24

    Article  Google Scholar 

  • Egilmez B, Poyraz E, Zhou W, Memik G, Dinda P, Alshurafa N (2017) “UStress: understanding college student subjective stress using wrist-based passive sensing, In: IEEE international conference on pervasive computing and communications workshops (PerCom Workshops), pp 673–678

  • Elagib SB, Najeeb AR, Hashim AH, Olanrewaju RF (2014) Big data analysis solutions using MapReduce framework, In: IEEE international conference on computer and communication engineering, pp 127–130

  • Gokulkumari G (2020) An overview of big data management and its applications. J Netw Commun Syst 3(3):11–20

    Google Scholar 

  • Guo J, Sun Z, Tang H, Jia X, Wang S, Yan X, Ye G, Wu G (2016) Hybrid optimization algorithm of particle swarm optimization and cuckoo search for preventive maintenance period optimization. Discrete Dyn Nat Soc. https://doi.org/10.1155/2016/1516271

    Article  MathSciNet  Google Scholar 

  • Hegde V, Prageeth PP (2018) Higher education student dropout prediction and analysis through educational data mining, In: IEEE 2nd international conference on inventive systems and control (ICISC), pp 694–699

  • Hussain S, Dahan NA, Ba-Alwib FM, Ribata N (2018) Educational data mining and analysis of students’ academic performance using WEKA. Indones J Electr Eng Comput Sci 9(2):447–459

    Google Scholar 

  • Hussain S, Muhsin ZF, Salal YK, Theodorou P, Kurtoğlu F, Hazarika GC (2019) Prediction model on student performance based on internal assessment using deep learning. Int J Emerg Technol Learn (iJET) 14(8):4–22

    Article  Google Scholar 

  • Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148–175

    Article  Google Scholar 

  • Kim BH, Vizitei E, Ganapathi V (2018) GritNet: Student performance prediction with deep learning, https://arxiv.org/abs/1804.07405

  • Kostopoulos G, Kotsiantis S, Ragos O, Grapsa TN, Early dropout prediction in distance higher education using active learning, In: IEEE 8th international conference on information, intelligence, systems and applications (IISA), pp 1–6

  • Kuo JY, Pan CW, Lei B (2017) Using stacked denoising autoencoder for the student dropout prediction, In: IEEE international symposium on multimedia (ISM), pp 483–488

  • Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization, In: International conference in swarm intelligence, Springer, Cham, pp 86-94

  • Mohd N, Yahya Y (2018) A data mining approach for prediction of students' depression using logistic regression and artificial neural network, In: Proceedings of the 12th international conference on ubiquitous information management and communication, pp 1–5

  • Mubarak AA, Cao H, Zhang W (2020) Prediction of students’ early dropout based on their interaction logs in online learning environment. Interact Learn Environ. https://doi.org/10.1080/10494820.2020.1727529

    Article  Google Scholar 

  • Patil SM, Raut CM, Pande AP, Yeruva AR, Morwani H (2022) An efficient approach for object detection using deep learning. J Pharm Negat Results 13(9):563–572

    Article  Google Scholar 

  • Qiu L, Liu Y, Hu Q, Liu Y (2019) Student dropout prediction in massive open online courses by convolutional neural networks. Soft Comput 23(20):10287–10301

    Article  Google Scholar 

  • Ramanathan L, Geetha A, Khalid M, Swarnalatha P (2017) Student performance prediction model based on lion-wolf neural network. Int J Intell Eng Syst 10(1):114–123

    Google Scholar 

  • Student Performance Data Set, "https://archive.ics.uci.edu/ml/datasets/student+performance#", Accessed on June 2021

  • Shaw A, Simsiri N, Deznaby I, Fiterau M, Rahaman T (2019) Personalized student stress prediction with deep multitask network, https://arxiv.org/abs/1906.11356

  • Stewart-Brown S, Evans J, Patterson J, Petersen S, Doll H, Balding J, Regis D (2000) The health of students in institutes of higher education: an important and neglected public health problem? J Public Health 22(4):492–499

    Article  Google Scholar 

  • Sun D, Mao Y, Du J, Xu P, Zheng Q, Sun H (2019) Deep learning for dropout prediction in MOOCs, In: IEEE eighth international conference on educational innovation through technology (EITT), pp 87–90

  • Vaughn MG, Roberts G, Fall AM, Kremer K, Martinez L (2020) Preliminary validation of the dropout risk inventory for middle and high school students,". Child Youth Serv Rev. https://doi.org/10.1016/j.childyouth.2020.104855

    Article  Google Scholar 

  • Veeramanickam MRM, Mohanapriya M, Pandey BK, Akhade S, Kale SA, Patil R, Vigneshwar M (2019) Map-reduce framework based cluster architecture for academic student’s performance prediction using cumulative dragonfly based neural network. Clust Comput 22(1):1259–1275

    Article  Google Scholar 

  • Verma G, Adhikari S, Khanduri V, Tandon S, Rawat S, Singh P (2020) Machine learning model for prediction of stress levels in students of technical education, In: The proceeding of international conference on applied mathematics and computational sciences

  • Wang X, Wu P, Liu G, Huang Q, Hu X, Xu H (2019) Learning performance prediction via convolutional GRU and explainable neural networks in e-learning environments. Computing 101(6):587–604

    Article  MathSciNet  Google Scholar 

  • Yu J, Liu G (2020) Knowledge extraction and insertion to deep belief network for gearbox fault diagnosis. Knowl-Based Syst 197:105883

    Article  Google Scholar 

  • Zhao Y, Wu J, Liu C (2014) Dache: a data aware caching for big-data applications using the MapReduce framework. Tsinghua Sci Technol 19(1):39–50

    Article  Google Scholar 

  • Zhu H, You X, Liu S (2019) Multiple ant colony optimization based on pearson correlation coefficient. IEEE Access 7:61628–61638

    Article  Google Scholar 

Download references

Acknowledgments

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

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This research did not receive any specific funding.

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Authors and Affiliations

  1. Department of CSE, Jawaharlal Nehru Technological University Anantapur, Ananathapuramu, 515002, India

    V. Kamakshamma

  2. Department of CSE, JNTUA College of Engineering, Ananthapuramu, Constituent College of Jawaharlal Nehru Technological University, Anantapur, Ananathapuramu, 515002, India

    K. F. Bharati

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  1. V. Kamakshamma
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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to V. Kamakshamma.

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Kamakshamma, V., Bharati, K.F. Adaptive-CSSA: adaptive-chicken squirrel search algorithm driven deep belief network for student stress-level and drop out prediction with MapReduce framework. Soc. Netw. Anal. Min. 13, 90 (2023). https://doi.org/10.1007/s13278-023-01090-z

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