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Use of Behavior Dynamics to Improve Early Detection of At-risk Students in Online Courses

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Abstract

Online learning has developed rapidly, but the success rate is very low. Hence, it is of great significance to construct a learning result predicting model, and to quickly and accurately identify students at risk of failing their course. In order to mine the dynamic features of learning behaviors and use them to improve the accuracy of detection of at-risk students, we propose a long-short term memory (LSTM) network based approach to identify at-risk students. To validate the performance of this approach, we first extracted the behavior data of one course from a public dataset, and generate two types of datasets, the aggregated datasets and the sequential datasets. After that, we used eight classic machine learning methods to train predicting model on these datasets and explored whether the models trained on sequential datasets are more accurate than the models trained on aggregated datasets. The results show that the models trained on sequential datasets are more accurate when naïve Bayes, Classification and Regression Tree, Random Forest (RF), Iterative Dichotomiser 3 and Multilayer Perception are used. Finally, we used the LSTM to train predicting models on sequential datasets, and compared them with the best models trained by RF. The results show that the models trained by the LSTM are more accurate, which proves the effectiveness of the proposed approach at certain extent.

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References

  1. Deng RQ, Benckendorff P, Gannaway D (2019) Progress and new directions for teaching and learning in MOOCs. Comput Educ 129:48–60. https://doi.org/10.1016/j.compedu.2018.10.019

    Article  Google Scholar 

  2. Jayaprakash SM, Moody EW, Lauría JM, Regan R, Baron JD (2014) Early alert of academically at-risk students: an open source analytics initiative. J Learn Anal 1(1):6–47. https://doi.org/10.18608/jla.2014.11.3

    Article  Google Scholar 

  3. Hu YH, Lo CL, Shih SP (2014) Developing early warning systems to predict students’ online learning performance. Comput Hum Behav 36:469–478. https://doi.org/10.1016/j.chb.2014.04.002

    Article  Google Scholar 

  4. Chen WY, Brinton CG, Cao D, Mason-Singh A, Lu C, Chiang M (2018) Early detection prediction of learning outcomes in online short-courses via learning behaviors. IEEE Trans Learn Technol 12(1):44–58. https://doi.org/10.1109/TLT.2018.2793193

    Article  Google Scholar 

  5. Tempelaara DT, Rienties B, Giesber B (2015) In search for the most informative data for feedback generation: learning analytics in a data-rich context. Comput Hum Behav 47:157–167. https://doi.org/10.1016/j.chb.2014.05.038

    Article  Google Scholar 

  6. Zacharis NZ (2015) A multivariate approach to predicting student outcomes in web-enabled blended learning courses. Internet High Educ 27:44–53. https://doi.org/10.1016/j.iheduc.2015.05.002

    Article  Google Scholar 

  7. Ji WY (2016) Identifying significant indicators using LMS data to predict course achievement in online learning. Internet High Educ 29:23–30. https://doi.org/10.1016/j.iheduc.2015.11.003

    Article  Google Scholar 

  8. Romero C, López M, Luna J, Ventura S (2013) Predicting students’ final performance from participation in online discussion forums. Comput Educ 68:458–472. https://doi.org/10.1016/j.compedu.2013.06.009

    Article  Google Scholar 

  9. Li Q, Baker R (2018) The different relationships between engagement and outcomes across participant subgroups in massive open online courses. Comput Educ 127:41–65. https://doi.org/10.1016/j.compedu.2018.08.005

    Article  Google Scholar 

  10. Marbouti F, Diefes-Dux HA, Madhavan K (2016) Models for early prediction of at-risk students in a course using standards-based grading. Comput Educ 103:1–15. https://doi.org/10.1016/j.compedu.2016.09.005

    Article  Google Scholar 

  11. Howarda E, Meehana M, Parnell A (2018) Contrasting prediction methods for early warning systems at undergraduate level. Internet High Educ 37:66–75. https://doi.org/10.1016/j.iheduc.2018.02.001

    Article  Google Scholar 

  12. Pardo A, Han F, Ellis RA (2017) Combining university student self-regulated learning indicators and engagement with online learning events to predict academic performance. IEEE Trans Learn Technol 10(1):82–92. https://doi.org/10.1109/TLT.2016.2639508

    Article  Google Scholar 

  13. Gašević D, Dawson S, Rogersb T, Gasevic D (2016) Learning analytics should not promote one size fits all: the effects of instructional conditions in predicting academic success. Internet High Educ 28:68–84. https://doi.org/10.1016/j.iheduc.2015.10.002

    Article  Google Scholar 

  14. Fan YZ, Wang Q (2018) Prediction of academic performance and risk: a review of literature on predicative indicators in learning analytics. Distance Educ China 1:5–15 + 44 + 79 (in Chinese)

  15. Moreno-Marcos PM, Alario-Hoyos C, Muñoz-Merino PJ, Kloos CD (2019) 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 

  16. Agudo-Peregrina ÁF, Iglesias-Pradas S, Conde-González MÁ, Hernández-García Á (2014) Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Comput Hum Behav 31:542–550. https://doi.org/10.1016/j.chb.2013.05.031

    Article  Google Scholar 

  17. de Barba PG, Kennedy GE, Ainley MD (2016) The role of students’ motivation and participation in predicting performance on a MOOC. J Comput Assist Learn 32(3):218–231. https://doi.org/10.1111/jcal.12130

    Article  Google Scholar 

  18. Onah DFO, Sinclair J, Boyatt R (2014) Dropout rates of massive open online courses: behavioural patterns. In: Proceedings of the 6th international conference on education and new learning technologies (EDULEARN14), pp 5825–5834

  19. Conijn R, Snijders C, Kleingeld A, Matzat U (2017) Predicting student performance from LMS data: a comparison of 17 blended courses using Moodle LMS. IEEE Trans Learn Technol 10(1):17–29. https://doi.org/10.1109/TLT.2016.2616312

    Article  Google Scholar 

  20. Gray CC, Perkins D (2019) Utilizing early engagement and machine learning to predict student outcomes. Comput Educ 131:22–32. https://doi.org/10.1016/j.compedu.2018.12.006

    Article  Google Scholar 

  21. He J, Bailey J, Rubinstein BI, Zhang R (2015). Identifying at-risk students in massive open online courses. In: Proceedings of the 29th AAAI conference on artificial intelligence, pp 1749–1755

  22. Jiang S, Williams A, Schenke K, Warschauer M, O’Dowd D (2014) Predicting MOOC performance with week 1 behavior. In: Proceedings of the 7th international conference on educational data mining, pp 273–275

  23. Wang F, Chen L (2016) A nonlinear state space model for identifying at-risk students in open online courses. In: Proceedings of the 9th international conference on educational data mining, pp 527–532

  24. Hung JL, Wang MC, Wang S, Abdelrasoul M, Li Y, He W (2017) Identifying at-risk students for early interventions—a time-series clustering approach. IEEE Trans Emerg Top Comput 5(1):45–55. https://doi.org/10.1109/TETC.2015.2504239

    Article  Google Scholar 

  25. Botvinick MM, Plaut DC (2006) Short-term memory for serial order: a recurrent neural network model. Psychol Rev 113(2):201–233. https://doi.org/10.1037/0033-295X.113.2.201

    Article  Google Scholar 

  26. Fei M, Yeung DY (2015) Temporal models for predicting student dropout in massive open online courses. In: Proceedings of 2015 IEEE international conference on data mining workshop (ICDMW), pp 256–263

  27. Qiu L, Liu YS, Hu Q, Liu Y (2019) Student dropout prediction in massive open online courses by convolutional neural networks. Soft Comput 23:10287–10301. https://doi.org/10.1007/s00500-018-3581-3

    Article  Google Scholar 

  28. Arnold KE, Hall Y, Street SG, Lafayette W, Pistilli MD (2012) Course signals at Purdue: using learning analytics to increase student success. In: Proceedings of the 2nd international conference on learning analytics and knowledge, pp 267–270

  29. Kuzilek J, Hlosta M, Herrmannova D, Zdrahal Z, Wolf A (2015) OU analyse: analysing at-risk students at the open university. In: Learn analytics review, pp 1–16

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Acknowledgements

This work is supported by the Ministry of Education of Humanities and Social Science Project (No. 20YJCZH046), the Key Research and Development Program of Hubei Province (2020BAB017), Wuhan Science and Technology Program (2019010701011392), and Scientific Research Center Program of National Language Commission (ZDI135-135).

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Authors and Affiliations

  1. School of Education, South-Central University for Nationalities, Wuhan, China

    Huan Huang

  2. College of Computer, Hubei University of Education, Wuhan, China

    Shuai Yuan

  3. Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, School of Computer, National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, China

    Tingting He

  4. College of Computer Science, South-Central University for Nationalities, Wuhan, China

    Rui Hou

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  1. Huan Huang
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Correspondence to Shuai Yuan.

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Huang, H., Yuan, S., He, T. et al. Use of Behavior Dynamics to Improve Early Detection of At-risk Students in Online Courses. Mobile Netw Appl 27, 441–452 (2022). https://doi.org/10.1007/s11036-021-01844-z

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