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Machine learning based analysis of learner-centric teaching of punjabi grammar with multimedia tools in rural indian environment

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Abstract

The advent of multimedia and its reach to everyone has made a massive change in life. Multimedia content enhances the learning trend and is playing a pivotal role in making the teaching and learning process more learners centric. In the present manuscript, authors use machine learning methods to find the impact of multimedia led teaching in government schools of Punjab, India. They promote the use of computer-based teaching in Punjabi for teaching the syllabus in schools. The secondary class students of Patiala and Mohali government schools affiliated to Punjab School Education Board are participants of this study. The students are divided in two groups. Twenty two topics of Punjabi grammar syllabus are taught to two student groups separately using different instructional strategies (multimedia presentations and traditional lectures). Achievement test, before and after the teaching, are conducted for all participants. The results support the hypothesis that multimedia does make a difference in the overall learning of the students. The descriptive statistics of achievement score shows the improvement in marks obtained by students after technology driven teaching. The average marks scored by students taught through multimedia system is 52.24% more than taught through traditional method. Nine machine learning models are used to find effectiveness of multimedia-based learning. Results shows that AdaBoost performs well with an accuracy of 99.8% respective to others based on student learning strategies.

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References

  1. Acha J (2009) The effectiveness of multimedia programmes in children’s vocabulary learning. Br J Educ Technol 40(1):23–31

    Article  Google Scholar 

  2. Ali A, Zhu Y, Zakarya M (2021) Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks. Inf Sci 577:852–870

    Article  MathSciNet  Google Scholar 

  3. Altman NS (1992) An introduction to kernel and Nearest-Neighbor nonparametric regression. The American Statistician 46(3):175–185

    MathSciNet  Google Scholar 

  4. Bartlett RM, Strough J (2003) Multimedia versus traditional course instruction in introductory social psychology. Teach Psychol 30:335–338

    Article  Google Scholar 

  5. Bartlett PL, Traskin M (2007) Adaboost is consistent. J Mach Learn Res 8:2347–2368

    MathSciNet  MATH  Google Scholar 

  6. Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    Article  Google Scholar 

  7. Breiman L (2000) Some Infinity Theory for Predictor Ensembles. Technical Report 577, UC Berkeley

  8. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  9. Breiman L (2004) Consistency For a Simple Model of Random Forests. Technical Report 670, UC Berkeley

  10. Chambers JM (1992) Statistical models in s wadsworth & Brooks/Cole. Pacific Grove, California

    Google Scholar 

  11. Crosby ME, Stelovsky J (1995) From multimedia instruction to multimedia evaluation. Journal of Educational Multimedia and Hypermedia 4:147–162

    Google Scholar 

  12. Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1 (1):3–18

    Article  Google Scholar 

  13. Dey P, Bandyopadhyay S (2019) . Blended learning to improve quality of primary education among underprivileged school children in India 24 (3):1995–2016

    Google Scholar 

  14. Diebold FX, Mariano RS (2002) Comparing predictive accuracy. Journal of Business & Economic Statistics 20(1):134–144

    Article  MathSciNet  Google Scholar 

  15. Eisenbeis RA, Avery RB (1972) Discriminant analysis and classification procedures: theory and applications, Lexington, Mass.: D.C. Heath & Co.

  16. Embarak O (2021) A new paradigm through machine learning: A learning maximization approach for sustainable education. Procedia Comput Sci 191:445–450

    Article  Google Scholar 

  17. Fan G-F, Peng LL, Hong W-C, Sun F (2016) Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing 173(3):958–970

    Article  Google Scholar 

  18. Fan G-F, Qing S, Wang H, Hong W-C, Li H -J (2013) Support vector regression model based on empirical mode decomposition and auto regression for electric load forecasting. Energies 6:1887–1901

    Article  Google Scholar 

  19. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    Article  MathSciNet  Google Scholar 

  20. Friedman D, Geiger M (1997) Bayesian network classifiers. Mach Learn 29(23):131–163

    Article  Google Scholar 

  21. Glover KR, Bodzin A (2021) Learner-centric design of a hand hygiene serious simulation game for grade 12 emerging health professional students. TechTrends 65:379–393. https://doi.org/10.1007/s11528-020-00577-2

    Article  Google Scholar 

  22. Hong W-C, Fan G-F (2019) Hybrid empirical mode decomposition with support vector regression model for short term load forecasting. Energies 12:1093

    Article  Google Scholar 

  23. Huang W, Mille A (2006) ConKMel: A contextual knowledge management framework to support multimedia e-Learning. Multimed Tools Appl 30:205–219

    Article  Google Scholar 

  24. Iqbal MM, Farhan M, Jabbar S, Saleem Y, Khalid S (2019) Multimedia based IoT-centric smart framework for eLearning paradigm. Multimed Tools Appl 78(3):3087–3106

    Article  Google Scholar 

  25. Izenman J, Julian A (2013) Linear discriminant analysis. In: Izenman AJ (ed) Modern multivariate statistical techniques. Springer, New York, pp 237–280

  26. Jain Anil K, Jianchang Mao, Mohiuddin KM (1996) Artificial neural networks: A tutorial. Computer 29(3):31–44

    Article  Google Scholar 

  27. Jiang L, Zhang H, Cai Z (2009) A novel bayes model, hidden naive bayes. IEEE Transaction on Knowledge and Data Engineering 21(10):1361–1371

    Article  Google Scholar 

  28. Jo J, Park J, Ji H (2016) A study on factor analysis to support knowledge based decisions for a smart class. Inf Technol Manag 17:43–56

    Article  Google Scholar 

  29. Kim J, Mowat A, Phoole P, Kasabov N (2000) Linear and non-linear pattern recognition models for classification of fruit from visible–near infrared spectra. Chemometr Intell Lab Syst 51(2):201–216

    Article  Google Scholar 

  30. Krouska A, Troussas C, Sgouropoulou C (2021) Mobile game-based learning as a solution in COVID-19 era: Modeling the pedagogical affordance and student interactions. Educ Inf Technol. https://doi.org/10.1007/s10639-021-10672-3

  31. Kushik N, Yevtushenko N, Evtushenko T (2020) Novel machine learning technique for predicting teaching strategy effectiveness. Int J Inf Manag 53:101488

    Article  Google Scholar 

  32. Li EY (1994) Artificial Neural Networks and their Business Applications. Taiwan

  33. Li MW, Geng J, Hong WC (2019) Periodogram estimation based on LSSVR-CCPSO compensation for forecasting ship motion. Nonlinear Dyn 97:2579–2594

    Article  Google Scholar 

  34. Liu M, Hsiao YP (2002) Middle school students as multimedia designers: A project-based learning approach. J Interact Learn Res 13(4):311–337

    Google Scholar 

  35. Liu M, Rutledge K (1997) The effect of a “learner as multimedia designer” environment on at-risk high school students’ motivation and learning of design knowledge. J Educ Comput Res 16(2):145–177

    Article  Google Scholar 

  36. Malhotra S, Kumar A, Dutta R (2021) Effect of integrating IoT courses at the freshman level on learning attitude and behaviour in the classroom. Educ Inf Technol 26:2607–2621. https://doi.org/10.1007/s10639-020-10376-0

    Article  Google Scholar 

  37. Pea RD (1991) Learning through multimedia. IEEE Comput Graph Appl 4:58–66

    Article  Google Scholar 

  38. Quinlan JR (1986) Induction of decision trees. Mach Learn 1 (1):81–106

    Article  Google Scholar 

  39. Rish I (2014) An Empirical Study of the Naive Bayes Classifier, no. January

  40. Sarkar P (2020) Exploring design strategies for augmented reality learning experience in classrooms. In: 2020 IEEE international symposium on mixed and augmented reality adjunct (ISMAR-Adjunct), IEEE, pp 314–316

  41. Seber GAF, Lee AJ (2012) Linear regression analysis. John Wiley & Sons Publishers, USA

    MATH  Google Scholar 

  42. Soman KP, Loganathan L, Ajay V (2009) Machine learning with SVM and other kernel methods. PHI Learning, India

    Google Scholar 

  43. Tufte ER (1990) The visual display of quantitative information. Princeton, NJ: Princeton University

  44. Unlersen MF, Sabanci K (2016) The classification of diseased Trees by Using KNN and MLP Classification models according to the satellite Imagery. Int J Intell Syst Appl Eng 4(2):25–28

    Article  Google Scholar 

  45. Vapnik V, Chervonenkis A (1964) A note on one class of perceptrons. Autom Remote Control 25

  46. Vivekananda GN, Khapre S (2021) Multimedia-based English teaching and practical system. Aggress Violent Behav, 101706

  47. Weng F, Ho HJ, Yang RJ, Weng CH (2019) The influence of learning style on learning attitude with multimedia teaching materials. Eurasia Journal of Mathematics Science and Technology Education 15(1):1–9

    Google Scholar 

  48. Zhang G (2000) Neural networks for classification: A survey. IEEE Transactions System, Man, Cybernitics. Applications and Reviews 30(4):451–462

    Article  Google Scholar 

  49. Zhang Z, Ding S, Sun Y (2020) A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task. Neurocomputing 410:185–201

    Article  Google Scholar 

  50. Zhang Z, Hong WC (2019) Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm. Nonlinear Dyn 98:1107–1136

    Article  MathSciNet  Google Scholar 

  51. Zhang H, Mervin R, ND Mohammed BS (2021) Core competence-based English major practical teaching system. Aggress Violent Behav, 101683

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Thapar Institute of Engineering & Technology, Punjab, India

    Vinay Kumar & Nitin Saxena

  2. Plaksha University, Punjab, India

    Gittaly Dhingra

  3. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Reetu Malhotra

Authors
  1. Vinay Kumar
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  2. Gittaly Dhingra
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  3. Nitin Saxena
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  4. Reetu Malhotra
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Correspondence to Reetu Malhotra.

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Kumar, V., Dhingra, G., Saxena, N. et al. Machine learning based analysis of learner-centric teaching of punjabi grammar with multimedia tools in rural indian environment. Multimed Tools Appl 81, 40775–40792 (2022). https://doi.org/10.1007/s11042-022-12898-w

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  • DOI: https://doi.org/10.1007/s11042-022-12898-w

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