Skip to main content
Log in

A Hybrid Method Using Ensembles of Neural Network and Text Mining for Learner Satisfaction Analysis from Big Datasets in Online Learning Platform

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Computational intelligence approaches have proven to be effective in enhancing online learning systems. Although many studies have been conducted to reveal the learners’ satisfaction in online learning platforms, the use of machine learning in the analysis of big datasets for this aim has rarely been explored. In addition, although the analysis of online reviews on courses has been carried out in other fields, there are very few contributions in the area of online learning platforms. This study, therefore, aims to perform learner satisfaction analysis through the use of machine learning. We develop a new method using text mining and supervised learning techniques with the aid of the ensemble learning approach. A boosting approach, AdaBoost, is used in ANN for ensemble learning to improve its performance. We employ Artificial Neural Network (ANN) approach, dimensionality reduction and Latent Dirichlet Allocation (LDA) for textual data analysis. Principal Component Analysis (PCA) is used for data dimensionality reduction. We perform several experimental evaluations on the big datasets obtained from the online learning platforms. The accuracy and computation time of the proposed method are assessed on the obtained dataset. The method is compared with several machine learning approaches to show its effectiveness in big datasets analysis. The results showed that the method is effective in predicting learners’ satisfaction from online reviews. In addition, the proposed method outperform other classifiers, K-Nearest Neighbor (K-NN), Decision Trees (DT), Support Vector Machines (SVM) and Naïve Bayes (NB), in case of accuracy. The results are discussed and research implications from different perspectives are provided for future developments of educational decision support systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data Availibility

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ANN:

Artificial Neural Network

ACC:

Accuracy

BERT:

Bidirectional Encoder Representations from Transformers

PCA:

Principal Component Analysis

MOOCs:

Massive Open Online Courses

LDA:

Latent Dirichlet Allocation

S-O-R:

Stimulus–Organism–Response

RNN:

Recurrent Neural Networks

CO:

Course Organization

HE:

Helpfulness

HAN:

Hierarchical Attention Network

QV:

Quality of Video and Audio

QL:

Quality of Language

IE:

Instructor Expertise

IN:

Interactivity

CE:

Clarity of Explanation

QC:

Quality of Course Material

QA:

Quality of Course Assignments

SOM:

Self-Organizing Map

CNN:

Convolutional Neural Networks

FP:

False Positive

PR:

Precision

PDCA:

Plan, Do, Check, Act

FN:

False Negative

TN:

True Negative

TP:

True Positive

RE:

Recall

References

  1. Mehta A, Morris NP, Swinnerton B, Homer M (2019) The influence of values on e-learning adoption. Computers & Education. 141:103617. https://doi.org/10.1016/j.compedu.2019.103617

    Article  Google Scholar 

  2. Srivastava P (2019) Advantages & disadvantages of e-education & e-learning. Journal of Retail Marketing & Distribution Management. 2(3):22–27

    Google Scholar 

  3. Xing W, Du D (2018) Dropout prediction in MOOCs: using deep learning for personalized intervention. Journal of Educational Computing Research. 57(3):547–570. https://doi.org/10.1177/0735633118757015

    Article  Google Scholar 

  4. Vančová MH, Kovačičová Z (2018) Sharing knowledge and information through corporate e-learning. In: Kryvinska N, Gregus M (eds) Agile information business: exploring managerial implications. Springer Singapore, Singapore, pp 255–274

    Chapter  Google Scholar 

  5. El Mhouti A, Erradi M, Nasseh A (2018) Using cloud computing services in e-learning process: benefits and challenges. Educ Inf Technol 23(2):893–909. https://doi.org/10.1007/s10639-017-9642-x

    Article  Google Scholar 

  6. Lorenzo C, Lorenzo E (2020) Opening up higher education: an e-learning program on service-learning for university students. In: Karwowski W, Ahram T, Nazir S (eds) Advances in human factors in training, education, and learning sciences. Springer International Publishing, Cham, pp 27–38

    Chapter  Google Scholar 

  7. Al-Rahmi W, Aldraiweesh A, Yahaya N, Kamin YB, Zeki AM (2019) Massive open online courses (MOOCs): data on higher education. Data Brief 22:118–125. https://doi.org/10.1016/j.dib.2018.11.139

    Article  Google Scholar 

  8. Deng R, Benckendorff P, Gannaway D (2019) Progress and new directions for teaching and learning in MOOCs. Computers & Education. 129:48–60. https://doi.org/10.1016/j.compedu.2018.10.019

    Article  Google Scholar 

  9. Peng X, Xu Q (2020) Investigating learners’ behaviors and discourse content in MOOC course reviews. Computers & Education. 143:103673. https://doi.org/10.1016/j.compedu.2019.103673

    Article  Google Scholar 

  10. Jung Y, Lee J (2018) Learning engagement and persistence in massive open online courses (MOOCS). Computers & Education. 122:9–22. https://doi.org/10.1016/j.compedu.2018.02.013

    Article  Google Scholar 

  11. Moreno-Marcos PM, Alario-Hoyos C, Muñoz-Merino PJ, Kloos CD (2018) Prediction in MOOCs: a review and future research directions. IEEE Trans Learn Technol 12(3):384–401. https://doi.org/10.1109/TLT.2018.2856808

    Article  Google Scholar 

  12. Hew KF, Hu X, Qiao C, Tang Y (2020) What predicts student satisfaction with MOOCs: a gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education. 145:103724. https://doi.org/10.1016/j.compedu.2019.103724

    Article  Google Scholar 

  13. Deming DJ, Goldin C, Katz LF, Yuchtman N (2015) Can online learning bend the higher education cost curve? American Economic Review. 105(5):496–501. https://doi.org/10.1257/aer.p20151024

    Article  Google Scholar 

  14. Acemoglu D, Laibson D, List JA (2014) Equalizing superstars: the internet and the democratization of education. American Economic Review. 104(5):523–27. https://doi.org/10.1257/aer.104.5.523

    Article  Google Scholar 

  15. Corrin L, De Barba PG, Bakharia A (2017) Using learning analytics to explore help-seeking learner profiles in MOOCs. In: Proceedings of the seventh international learning analytics & knowledge conference. New York, NY, USA: Association for Computing Machinery. pp 424–428

  16. Khalil M, Ebner M (2016) Learning analytics in MOOCs: can data improve students retention and learning? In: EdMedia: World Conference on Educational Media and Technology. Vancouver, BC, Canada: Association for the Advancement of Computing in Education (AACE). pp 581–588

  17. Martín-Monje E, Castrillo MD, Mañana-Rodríguez J (2018) Understanding online interaction in language MOOCs through learning analytics. Comput Assist Lang Learn 31(3):251–272. https://doi.org/10.1080/09588221.2017.1378237

    Article  Google Scholar 

  18. Moreno-Marcos PM, Alario-Hoyos C, Muñoz-Merino PJ, Estevez-Ayres I, Kloos CD (2018) A learning analytics methodology for understanding social interactions in MOOCs. IEEE Trans Learn Technol 12(4):442–455. https://doi.org/10.1109/TLT.2018.2883419

    Article  Google Scholar 

  19. Shi L, Cristea AI. Andersson B, Johansson B, Carlsson S, Barry C, Lang M, Linger H, et al., editors.: Demographic indicators influencing learning activities in MOOCs: learning analytics of FutureLearn courses. Association for Information Systems

  20. Tabaa Y, Medouri A (2013) LASyM: a learning analytics system for MOOCs. International Journal of Advanced Computer Science and Applications. 4(5) https://doi.org/10.14569/IJACSA.2013.040516

  21. Dessí D, Dragoni M, Fenu G, Marras M, Recupero DR (2020) Deep learning adaptation with word embeddings for sentiment analysis on online course reviews. In: Agarwal B, Nayak R, Mittal N, Patnaik S (eds) Deep Learning-Based Approaches for Sentiment Analysis. Springer, Singapore, pp 57–83

    Chapter  Google Scholar 

  22. Liang J, Li C, Zheng L (2016) Machine learning application in MOOCs: dropout prediction. In: 11th international conference on computer science & education. IEEE. pp 52–57

  23. Jeong B, Yoon J, Lee JM (2019) Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. Int J Inf Manage 48:280–290. https://doi.org/10.1016/j.ijinfomgt.2017.09.009

    Article  Google Scholar 

  24. Jia S (2018) Leisure motivation and satisfaction: A text mining of yoga centres, yoga consumers, and their interactions. Sustainability. 10(12):4458. https://doi.org/10.3390/su10124458

    Article  Google Scholar 

  25. Liu J, Zhou Y, Jiang X, Zhang W (2020) Consumers’ satisfaction factors mining and sentiment analysis of B2C online pharmacy reviews. BMC Med Inform Decis Mak 20(1):194. https://doi.org/10.1186/s12911-020-01214-x

    Article  Google Scholar 

  26. Bi JW, Liu Y, Fan ZP, Cambria E (2019) Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. Int J Prod Res 57(22):7068–7088. https://doi.org/10.1080/00207543.2019.1574989

    Article  Google Scholar 

  27. Ma B, Zhang D, Yan Z, Kim T (2013) An LDA and synonym lexicon based approach to product feature extraction from online consumer product reviews. J Electron Commer Res 14(4):304–314

    Google Scholar 

  28. Moghaddam S, Ester M (2011) ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval. New York, NY, USA. pp 665–674

  29. Qiao Z, Zhang X, Zhou M, Wang GA, Fan W.: A domain oriented LDA model for mining product defects from online customer reviews. Hawaii

  30. Santosh DT, Babu KS, Prasad S, Vivekananda A (2016) Opinion mining of online product reviews from traditional LDA topic clusters using feature ontology tree and sentiwordnet. International Journal of Education and Management Engineering. 6(6):34–44. https://doi.org/10.5815/ijeme.2016.06.04

    Article  Google Scholar 

  31. Unankard S, Nadee W (2020) Topic detection for online course feedback using LDA. In: Popescu E, Hao T, Hsu TC, Xie H, Temperini M, Chen W (eds) Emerging technologies for education. Springer International Publishing, Cham, pp 133–142

    Chapter  Google Scholar 

  32. Yiran Y, Srivastava S (2019) Aspect-based sentiment analysis on mobile phone reviews with LDA. In: Proceedings of the 2019 4th international conference on machine learning technologies. New York, NY, USA: Association for Computing Machinery. pp 101–105

  33. Onan A (2021) Sentiment analysis on massive open online course evaluations: a text mining and deep learning approach. Comput Appl Eng Educ 29(3):572–589. https://doi.org/10.1002/cae.22253

    Article  Google Scholar 

  34. Ruipérez-Valiente JA, Halawa S, Slama R, Reich J (2020) Using multi-platform learning analytics to compare regional and global MOOC learning in the Arab world. Computers & Education. 146:103776. https://doi.org/10.1016/j.compedu.2019.103776

    Article  Google Scholar 

  35. Barthakur A, Kovanovic V, Joksimovic S, Siemens G, Richey M, Dawson S (2021) Assessing program-level learning strategies in MOOCs. Comput Hum Behav 117:106674. https://doi.org/10.1016/j.chb.2020.106674

    Article  Google Scholar 

  36. Moreno-Marcos PM, Munoz-Merino PJ, Maldonado-Mahauad J, Perez-Sanagustin M, Alario-Hoyos C, Kloos CD (2020) Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs. Computers & Education. 145:103728. https://doi.org/10.1016/j.compedu.2019.103728

    Article  Google Scholar 

  37. Zou W, Hu X, Pan Z, Li C, Cai Y, Liu M (2021) Exploring the relationship between social presence and learners’ prestige in MOOC discussion forums using automated content analysis and social network analysis. Comput Hum Behav 115:106582. https://doi.org/10.1016/j.chb.2020.106582

    Article  Google Scholar 

  38. Zhao Y, Wang A, Sun Y (2020) Technological environment, virtual experience, and MOOC continuance: a stimulus-organism-response perspective. Computers & Education. 144:103721. https://doi.org/10.1016/j.compedu.2019.103721

    Article  Google Scholar 

  39. Wang J, Xie H, Wang FL, Lee LK, Au OTS (2021) Top-N personalized recommendation with graph neural networks in MOOCs. Computers and Education: Artificial Intelligence. 2:100010. https://doi.org/10.1016/j.caeai.2021.100010

    Article  Google Scholar 

  40. Kumar P, Kumar N (2020) A study of learner’s satisfaction from MOOCs through a mediation model. Procedia Computer Science. 173:354–363. https://doi.org/10.1016/j.procs.2020.06.041

    Article  Google Scholar 

  41. de Barba PG, Malekian D, Oliveira EA, Bailey J, Ryan T, Kennedy G (2020) The importance and meaning of session behaviour in a MOOC. Computers & Education. 146:103772. https://doi.org/10.1016/j.compedu.2019.103772

    Article  Google Scholar 

  42. van de Oudeweetering K, Agirdag O (2018) Demographic data of MOOC learners: can alternative survey deliveries improve current understandings? Computers & Education. 122:169–178. https://doi.org/10.1016/j.compedu.2018.03.017

    Article  Google Scholar 

  43. Blagojević M, Micić Ž (2013) A web-based intelligent report e-learning system using data mining techniques. Computers & Electrical Engineering. 39(2):465–474. https://doi.org/10.1016/j.compeleceng.2012.09.011

    Article  Google Scholar 

  44. Tarus JK, Niu Z, Yousif A (2017) A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Futur Gener Comput Syst 72:37–48. https://doi.org/10.1016/j.future.2017.02.049

    Article  Google Scholar 

  45. Alkhattabi M, Neagu D, Cullen A (2011) Assessing information quality of e-learning systems: a web mining approach. Comput Hum Behav 27(2):862–873. https://doi.org/10.1016/j.chb.2010.11.011

    Article  Google Scholar 

  46. Shi D, Wang T, Xing H, Xu H (2020) A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning. Knowl-Based Syst 195:105618. https://doi.org/10.1016/j.knosys.2020.105618

    Article  Google Scholar 

  47. Rawat B, Samriya JK, Pandey N, Wariyal SC (2020) Enriching ‘user item rating matrix’ with resource description framework for improving the accuracy of recommendation in e-learning environment. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.09.701

    Article  Google Scholar 

  48. Aher SB, Lobo L (2013) Combination of machine learning algorithms for recommendation of courses in e-learning system based on historical data. Knowl-Based Syst 51:1–14. https://doi.org/10.1016/j.knosys.2013.04.015

    Article  Google Scholar 

  49. Park Y, Yu JH, Jo IH (2016) Clustering blended learning courses by online behavior data: a case study in a Korean higher education institute. The Internet and Higher Education. 29:1–11. https://doi.org/10.1016/j.iheduc.2015.11.001

    Article  Google Scholar 

  50. Kosztyán ZT, Orbán-Mihálykó É, Mihálykó C, Csányi VV, Telcs A (2020) Analyzing and clustering students’ application preferences in higher education. J Appl Stat 47(16):2961–2983. https://doi.org/10.1080/02664763.2019.1709052

    Article  MathSciNet  MATH  Google Scholar 

  51. Chaplot DS, Rhim E, Kim J (2015) Predicting student attrition in MOOCs using sentiment analysis and neural networks. In: Boticario J, Muldner K, editors. Proceedings of the workshops at the 17th international conference on artificial intelligence in education. vol. 1432. Madrid, Spain: CEUR-WS.org. pp 7–12

  52. Zhang Y, Jiang W (2018) Score prediction model of MOOCs learners based on neural network. Int J Emerg Technol Learn 13(10):171–182. https://doi.org/10.3991/ijet.v13i10.9461

    Article  Google Scholar 

  53. Yang TY, Brinton CG, Joe-Wong C, Chiang M (2017) Behavior-based grade prediction for MOOCs via time series neural networks. IEEE Journal of Selected Topics in Signal Processing. 11(5):716–728. https://doi.org/10.1109/JSTSP.2017.2700227

    Article  Google Scholar 

  54. Wang W, Yu H, Miao C (2017) Deep model for dropout prediction in MOOCs. In: Proceedings of the 2nd international conference on crowd science and engineering. New York, NY, USA: Association for Computing Machinery. pp 26–32

  55. Gandhi R, Raja T, Ruhil A, Kumar A (2010) Artificial neural network versus multiple regression analysis for prediction of lifetime milk production in Sahiwal cattle. J Appl Anim Res 38(2):233–237. https://doi.org/10.1080/09712119.2010.10539517

    Article  Google Scholar 

  56. Yaseen S, Al-Habaibeh A, Su D, Otham F (2013) Real-time crowd density mapping using a novel sensory fusion model of infrared and visual systems. Saf Sci 57:313–325. https://doi.org/10.1016/j.ssci.2013.03.007

    Article  Google Scholar 

  57. Chen G, Fidkowski KJ (2021) Output-based adaptive aerodynamic simulations using convolutional neural networks. Computers & Fluids. 223:104947. https://doi.org/10.1016/j.compfluid.2021.104947

    Article  MathSciNet  MATH  Google Scholar 

  58. Schapire RE (2013) Explaining adaboost. In: Schölkopf B, Luo Z, Vovk V, editors. Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik. Berlin, Heidelberg: Springer Berlin Heidelberg. pp 37–52

  59. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  60. Sarumi OA, Leung CK (2022) Adaptive machine learning algorithm and analytics of big genomic data for gene prediction. In: Mehta M, Fournier-Viger P, Patel M, Lin JCW (eds) Tracking and preventing diseases with artificial intelligence. Springer International Publishing, Cham, pp 103–123

    Chapter  Google Scholar 

  61. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1):37–52. https://doi.org/10.1016/0169-7439(87)80084-9

    Article  Google Scholar 

  62. Ho CTB, Wu DD (2009) Online banking performance evaluation using data envelopment analysis and principal component analysis. Computers & Operations Research. 36(6):1835–1842. https://doi.org/10.1016/j.cor.2008.05.008

    Article  MATH  Google Scholar 

  63. Di Tollo G, Tanev S, Liotta G, De March D (2015) Using online textual data, principal component analysis and artificial neural networks to study business and innovation practices in technology-driven firms. Comput Ind 74:16–28. https://doi.org/10.1016/j.compind.2015.08.006

    Article  Google Scholar 

  64. Li H, Sun J (2011) Empirical research of hybridizing principal component analysis with multivariate discriminant analysis and logistic regression for business failure prediction. Expert Syst Appl 38(5):6244–6253. https://doi.org/10.1016/j.eswa.2010.11.043

    Article  Google Scholar 

  65. Mamgain N, Sharma A, Goyal P (2014) Learner’s perspective on video-viewing features offered by MOOC providers: Coursera and edX. In: IEEE international conference on MOOC, innovation and technology in education. IEEE. pp 331–336

  66. Mukala P, Buijs JC, Leemans M, van der Aalst WM (2015) Learning analytics on coursera event data: a process mining approach. In: Proceedings of the 5th international symposium on data-driven process discovery and analysis. vol. 1527. Aachen, Germany: RWTH Aachen. pp 18–32

  67. Rossi LA, Gnawali O (2014) Language independent analysis and classification of discussion threads in Coursera MOOC forums. In: Proceedings of the 2014 IEEE 15th international conference on information reuse and integration. IEEE. pp 654–661

  68. Young JR Inside the Coursera contract: how an upstart company might profit from free courses. Retrieved from http://chronicle.com/article/How-an-Upstart-Company-Might/133065/?cid=at &utm_source=at &utm_medium=en

  69. Bunchongchit K, Wattanacharoensil W (2021) Data analytics of Skytrax’s airport review and ratings: Views of airport quality by passengers types. Research in Transportation Business & Management. 41:100688. https://doi.org/10.1016/j.rtbm.2021.100688

    Article  Google Scholar 

  70. Jannach D, Zanker M, Fuchs M (2014) Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations. Information Technology & Tourism. 14(2):119–149. https://doi.org/10.1007/s40558-014-0010-z

    Article  Google Scholar 

  71. Griffith DA (2008) Geographic sampling of urban soils for contaminant mapping: how many samples and from where. Environ Geochem Health 30(6):495–509. https://doi.org/10.1007/s10653-008-9186-5

    Article  Google Scholar 

  72. Locander DA, Weinberg FJ, Mulki JP, Locander WB (2015) Salesperson lone wolf tendencies: the roles of social comparison and mentoring in a mediated model of performance. Journal of Marketing Theory and Practice. 23(4):351–369. https://doi.org/10.1080/10696679.2015.1049680

    Article  Google Scholar 

  73. Serikova M, Sembiyeva L, Mussina A, Kuchukova N, Nurumov A (2018) The institutional model of tax administration and aspects of its development. Investment Management and Financial Innovations. 15(3):283–293. https://doi.org/10.21511/imfi.15(3).2018.23

    Article  Google Scholar 

  74. Feyisa TA (2017) Determinants of capital structure decisions among Ethiopian micro finance institutions: panel data evidence. Research Journal of Finance and Accounting. 8(4):8–21

    Google Scholar 

  75. Eckart L, Eckart S, Enke M (2021) A brief comparative study of the potentialities and limitations of machine-learning algorithms and statistical techniques. In: E3S Web of Conferences. vol. 266. EDP Sciences. pp 02001

  76. Hasnain M, Pasha MF, Ghani I, Imran M, Alzahrani MY, Budiarto R (2020) Evaluating trust prediction and confusion matrix measures for web services ranking. IEEE Access. 8:90847–90861. https://doi.org/10.1109/ACCESS.2020.2994222

    Article  Google Scholar 

  77. Ohsaki M, Wang P, Matsuda K, Katagiri S, Watanabe H, Ralescu A (2017) Confusion-matrix-based kernel logistic regression for imbalanced data classification. IEEE Trans Knowl Data Eng 29(9):1806–1819. https://doi.org/10.1109/TKDE.2017.2682249

    Article  Google Scholar 

  78. Alakus TB, Turkoglu I (2020) Comparison of deep learning approaches to predict COVID-19 infection. Chaos, Solitons & Fractals. 140:110120. https://doi.org/10.1016/j.chaos.2020.110120

    Article  MathSciNet  Google Scholar 

  79. Greevy E, Smeaton AF (2004) Classifying racist texts using a support vector machine. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval. New York, NY, USA: Association for Computing Machinery. pp 468–469

  80. Ardito C, Costabile MF, De Marsico M, Lanzilotti R, Levialdi S, Roselli T et al (2006) An approach to usability evaluation of e-learning applications. Univ Access Inf Soc 4(3):270–283. https://doi.org/10.1007/s10209-005-0008-6

    Article  Google Scholar 

  81. Attwell G (2006) Evaluating e-learning: a guide to the evaluation of e-learning. Evaluate Europe Handbook Series Volume 2, Perspektiven-Offset-Druck, Bremen, Germany, Available from http://www.pontydysgu.org/wpcontent/uploads/2007/11/eva_europe_vol2_prefinal.pdf. Accessed 22 Sept 2021

  82. Hogo MA (2010) Evaluation of e-learning systems based on fuzzy clustering models and statistical tools. Expert Syst Appl 37(10):6891–6903. https://doi.org/10.1016/j.eswa.2010.03.032

    Article  Google Scholar 

  83. Ozkan S, Koseler R (2009) Multi-dimensional students’ evaluation of e-learning systems in the higher education context: an empirical investigation. Computers & Education. 53(4):1285–1296. https://doi.org/10.1016/j.compedu.2009.06.011

    Article  Google Scholar 

  84. Huang R, Li B, Zhou L (2016) Information literacy instruction in Chinese universities: MOOCs versus the traditional approach. Library Hi Tech. 34(2):286–300. https://doi.org/10.1108/LHT-02-2016-0013

    Article  Google Scholar 

  85. Weinhardt JM, Sitzmann T (2019) Revolutionizing training and education? three questions regarding massive open online courses (MOOCs). Hum Resour Manag Rev 29(2):218–225. https://doi.org/10.1016/j.hrmr.2018.06.004

    Article  Google Scholar 

  86. Grover S, Franz P, Schneider E, Pea R (2013) The MOOC as distributed intelligence: dimensions of a framework & evaluation of MOOCs. In: Computer-supported collaborative learning conference CSCL. Madison, WI, USA: International Society of the Learning Sciences. pp 42–45

  87. Nkuyubwatsi B (2013) Evaluation of massive open online courses (MOOCs) from the learner’s perspective. University of Leicester

  88. Sanchez-Gordon S, Luján-Mora S (2020) Design, implementation and evaluation of MOOCs to improve inclusion of diverse learners. In: Association IRM (ed) Accessibility and diversity in education: breakthroughs in research and practice. IGI Global, pp 52–79

    Chapter  Google Scholar 

  89. Huda M, Anshari M, Almunawar MN, Shahrill M, Tan A, Jaidin JH et al (2016) Innovative teaching in higher education: the big data approach. The Turkish Online Journal of Educational Technology. 5(Special issue):1210–1216

    Google Scholar 

  90. Nazarenko MA, Khronusova TV (2017) Big data in modern higher education. benefits and criticism. In: 2017 International conference“ quality management, transport and information security, information technologies”(IT &QM &IS). IEEE. pp 676–679

  91. Pardos ZA (2017) Big data in education and the models that love them. Curr Opin Behav Sci 18:107–113. https://doi.org/10.1016/j.cobeha.2017.11.006

    Article  Google Scholar 

  92. Wang Y (2016) Big opportunities and big concerns of big data in education. TechTrends 60(4):381–384. https://doi.org/10.1007/s11528-016-0072-1

    Article  Google Scholar 

  93. Clarizia F, Colace F, De Santo M, Lombardi M, Pascale F, Pietrosanto A (2018) E-learning and sentiment analysis: a case study. In: Proceedings of the 6th international conference on information and education technology. New York, NY, USA: Association for Computing Machinery. pp 111–118

  94. Gurcan F, Ozyurt O, Cagitay NE (2021) Investigation of emerging trends in the e-learning field using latent Dirichlet allocation. International Review of Research in Open and Distributed Learning. 22(2):1–18. https://doi.org/10.19173/irrodl.v22i2.5358

    Article  Google Scholar 

  95. Jovanovic M, Vukicevic M, Milovanovic M, Minovic M (2012) Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study. International Journal of Computational Intelligence Systems. 5(3):597–610. https://doi.org/10.1080/18756891.2012.696923

    Article  Google Scholar 

  96. Özmen C, Streicher A, Zielinski A (2014) Using text segmentation algorithms for the automatic generation of e-learning courses. In: Proceedings of the Third Joint Conference on Lexical and Computational Semantics (* SEM 2014). Dublin, Ireland: Association for Computational Linguistics and Dublin City University. pp 132–140

  97. Nguyen QH, Ly HB, Le TT, Nguyen TA, Phan VH, Tran VQ et al (2020) Parametric investigation of particle swarm optimization to improve the performance of the adaptive neuro-fuzzy inference system in determining the buckling capacity of circular opening steel beams. Materials 13(10):2210. https://doi.org/10.3390/ma13102210

    Article  Google Scholar 

  98. Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480. https://doi.org/10.1109/5.58325

    Article  Google Scholar 

  99. MacQueen J et al (1967) Some methods for classification and analysis of multivariate observations. In: Le Cam LM, Neyman J (eds) The fifth Berkeley symposium on mathematical statistics and probability, vol 1. University of California Press, Berkeley, CA, USA, pp 281–297

    Google Scholar 

  100. Karahoca A, Karahoca D, İnce F (2009) ANFIS supported question classification in computer adaptive testing (CAT). In: Fifth international conference on soft computing, computing with words and perceptions in system analysis, decision and control. IEEE. pp 1–4

  101. Vasileva-Stojanovska T, Vasileva M, Malinovski T, Trajkovik V (2015) An ANFIS model of quality of experience prediction in education. Appl Soft Comput 34:129–138. https://doi.org/10.1016/j.asoc.2015.04.047

    Article  Google Scholar 

  102. Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies. San Diego, California: Association for Computational Linguistics. pp 1480–1489

  103. Sun F, Liu J, Wu J, Pei C, Lin X, Ou W, et al (2019) BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York, NY, USA: Association for Computing Machinery. pp 1441–1450

Download references

Acknowledgements

This work was funded by the Deanship of Scientific Research at Jouf University under grant No (DSR-2021-02-0357).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ahmed Alsayat.

Ethics declarations

Conflict of Interest

The authors have no conflict of interest to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix A Linear correlation among the input variables and input variables and output

Y

X

r

\({\hbox {r}}^{2}\)

Course organization

Helpfulness

0.0683

0.0047

Course organization

Quality of video and audio

0.0638

0.0041

Course organization

Quality of language

0.0870

0.0076

Course organization

Instructor expertise

0.0683

0.0047

Course organization

Interactivity

0.0701

0.0049

Course organization

Clarity of explanation

0.0677

0.0046

Course organization

Quality of course material

0.0637

0.0041

Course organization

Quality of course assignments

0.0845

0.0071

Helpfulness

Quality of video and audio

0.0700

0.0049

Helpfulness

Quality of language

0.0855

0.0073

Helpfulness

Instructor expertise

0.0640

0.0041

Helpfulness

Interactivity

0.0776

0.0060

Helpfulness

Clarity of explanation

0.0644

0.0041

Helpfulness

Quality of course material

0.0717

0.0051

Helpfulness

Quality of course assignments

0.0676

0.0046

Quality of video and audio

Quality of language

0.0663

0.0044

Quality of video and audio

Instructor expertise

0.0887

0.0079

Quality of video and audio

Interactivity

0.0590

0.0035

Quality of video and audio

Clarity of explanation

0.0640

0.0041

Quality of video and audio

Quality of course material

0.0775

0.0060

Quality of video and audio

Quality of course assignments

0.0695

0.0048

Quality of language

Instructor expertise

0.0688

0.0047

Quality of language

Interactivity

0.0594

0.0035

Quality of language

Clarity of explanation

0.0665

0.0044

Quality of language

Quality of course material

0.0710

0.0050

Quality of language

Quality of course assignments

0.0843

0.0071

Instructor expertise

Interactivity

0.0856

0.0073

Instructor expertise

Clarity of explanation

0.0655

0.0043

Instructor expertise

Quality of course material

0.0716

0.0051

Instructor expertise

Quality of course assignments

0.0776

0.0060

Interactivity

Clarity of explanation

0.0822

0.0068

Interactivity

Quality of course material

0.0610

0.0037

Interactivity

Quality of Course assignments

0.0558

0.0031

Clarity of explanation

Quality of course material

0.0664

0.0044

Clarity of explanation

Quality of course assignments

0.0775

0.0060

Quality of course Material

Quality of course assignments

0.0711

0.0051

Overall Rating (satisfaction)

Course organization

0.2776

0.0771

Overall rating (satisfaction)

Helpfulness

0.2732

0.0746

Overall rating (satisfaction)

Quality of video and audio

0.2749

0.0756

Overall rating (satisfaction)

Quality of language

0.2834

0.0803

Overall rating (satisfaction)

Instructor expertise

0.2854

0.0815

Overall rating (satisfaction)

Interactivity

0.2840

0.0807

Overall rating (satisfaction)

Clarity of explanation

0.2699

0.0728

Overall rating (satisfaction)

Quality of course material

0.2735

0.0748

Overall rating (satisfaction)

Quality of course assignments

0.2875

0.0826

Appendix B ANN Results

See Appendix Tables 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 and 20

Table 7 From input layer to hidden layer (5 hidden layers)
Table 8 From hidden layer to output layer (5 hidden layers)
Table 9 From input layer to hidden layer (7 hidden layers)
Table 10 From hidden layer to output layer (7 hidden layers)
Table 11 From input layer to hidden layer (8 hidden layers)
Table 12 From hidden layer to output layer (8 hidden layers)
Table 13 From input layer to hidden layer (9 hidden layers)
Table 14 From hidden layer to output layer (9 hidden layers)
Table 15 From input layer to hidden layer (10 hidden layers)
Table 16 From hidden layer to output layer (10 hidden layers)
Table 17 From input layer to hidden layer (11 hidden layers)
Table 18 From hidden layer to output layer (11 hidden layers)
Table 19 From input layer to hidden layer (12 hidden layers)
Table 20 From hidden layer to output layer (12 hidden layers)

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alsayat, A., Ahmadi, H. A Hybrid Method Using Ensembles of Neural Network and Text Mining for Learner Satisfaction Analysis from Big Datasets in Online Learning Platform. Neural Process Lett 55, 3267–3303 (2023). https://doi.org/10.1007/s11063-022-11009-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-022-11009-y

Keywords

Navigation