Skip to main content
SpringerLink
Log in

Sequential Pattern Mining in Educational Data: The Application Context, Potential, Strengths, and Limitations

  • Chapter
  • First Online:
Educational Data Science: Essentials, Approaches, and Tendencies

Part of the book series: Big Data Management ((BIGDM))

Abstract

Increasingly, researchers have suggested the benefits of temporal analyses to improve our understanding of the learning process. Sequential pattern mining (SPM), as a pattern recognition technique, has the potential to reveal the temporal aspects of learning and can be a valuable tool in educational data science. However, its potential is not well understood and exploited. This chapter addresses this gap by reviewing work that utilizes sequential pattern mining in educational contexts. We identify that SPM is suitable for mining learning behaviors, analyzing and enriching educational theories, evaluating the efficacy of instructional interventions, generating features for prediction models, and building educational recommender systems. SPM can contribute to these purposes by discovering similarities and differences in learners’ activities and revealing the temporal change in learning behaviors. As a sequential analysis method, SPM can reveal unique insights about learning processes and be powerful for self-regulated learning research. It is more flexible in capturing the relative arrangement of learning events than the other sequential analysis methods. Future research may improve its utility in educational data science by developing tools for counting pattern occurrences as well as identifying and removing unreliable patterns. Future work needs to establish a systematic guideline for data preprocessing, parameter setting, and interpreting sequential patterns.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    The implementation of cSPADE in Python and R: https://pypi.org/project/pycspade/, https://CRAN.R-project.org/package=arulesSequences

Abbreviations

EDS:

Educational data science

LSA:

Lag-sequential analysis

MOOC:

Massive Open Online Course

SPM:

Sequential pattern mining

SRL:

Self-regulated learning

References

  1. Soderstrom, N.C., Bjork, R.A.: Learning versus performance. Perspect. Psychol. Sci. 10(2), 176–199 (2015)

    Article  Google Scholar 

  2. Hadwin, A.F.: Commentary and future directions: what can multi-modal data reveal about temporal and adaptive processes in self-regulated learning? Learn. Instr. 72(101), 287 (2021)

    Google Scholar 

  3. Knight, S., Wise, A.F., Chen, B.: Time for change: why learning analytics needs temporal analysis. J. Learn. Anal. 4(3), 7–17 (2017)

    Google Scholar 

  4. Molenaar, I., Wise, A.F.: Temporal aspects of learning analytics - grounding analyses in concepts of time. In: Lang, C., Siemens, G., Wise, A.F., Gašević, D., Merceron, A. (eds.) The Handbook of Learning Analytics, 2nd edn, pp. 66–76. SoLAR (2022)

    Google Scholar 

  5. Reimann, P.: Time is precious: variable- and event-centred approaches to process analysis in CSCL research. Int. J. Comput. Supp. Collab. Learn. 4(3), 239–257 (2009)

    Article  Google Scholar 

  6. Winne, P.H.: Improving measurements of self-regulated learning. Educ. Psych. 45(4), 267–276 (2010)

    Article  Google Scholar 

  7. Caglar Ozhan, S., Altun, A., Ekmekcioglu, E.: Emotional patterns in a simulated virtual classroom supported with an affective recommendation system. Br. J. Educ. Technol. 53(6), 1724–1749 (2022)

    Article  Google Scholar 

  8. Emara, M., Rajendran, R., Biswas, G., Okasha, M., Elbanna, A.A.: Do students’ learning behaviors differ when they collaborate in open-ended learning environments? Proc. ACM Hum. Comput. Interact. 2(CSCW), 49 (2018)

    Article  Google Scholar 

  9. Liu, T., Israel, M.: Uncovering students’ problem-solving processes in game-based learning environments. Comput. Educ. 182(104), 462 (2022)

    Google Scholar 

  10. Mishra, S., Majumdar, R., Kothiyal, A., Pande, P., Warriem, J.M.: Tracing embodied narratives of critical thinking. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds.) Artificial Intelligence in Education. AIED 2021 Lecture Notes in Computer Science, vol. 12749, pp. 267–272. Springer (2021)

    Google Scholar 

  11. Zheng, J., Li, S., Lajoie, S.P.: Diagnosing virtual patients in a technology-rich learning environment: a sequential mining of students’ efficiency and behavioral patterns. Educ. Inf. Technol. 27(3), 4259–4275 (2022)

    Article  Google Scholar 

  12. Zhu, G., Xing, W., Popov, V.: Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning. Internet High. Educ. 41, 51–61 (2019)

    Article  Google Scholar 

  13. Moon, J., Liu, Z.: Rich representations for analyzing learning trajectories: systematic review on sequential data analytics in game-based learning research. In: Tlili, A., Chang, M. (eds.) Data Analytics Approaches in Educational Games and Gamification Systems, pp. 27–53. Springer, Cham (2019)

    Chapter  Google Scholar 

  14. Van Laer, S., Elen, J.: Towards a methodological framework for sequence analysis in the field of self-regulated learning. Front. Learn. Res. 6(3), 228–249 (2018)

    Article  Google Scholar 

  15. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th International Conference on Data Engineering. IEEE (1995)

    Google Scholar 

  16. Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) Advances in Database Technology. EDBT 1996 Lecture Notes in Computer Science, vol. 1057, pp. 1–17. Springer, Cham (1996)

    Google Scholar 

  17. Zaki, M.J.: Sequence mining in categorical domains: incorporating constraints. In: Proceedings of the Ninth International Conference on Information and Knowledge Management, pp. 422–429. ACM (2000)

    Google Scholar 

  18. Lo, D., Khoo, S., Liu, C.: Efficient mining of recurrent rules from a sequence database. In: Haritsa, J.R., Kotagiri, R., Pudi, V. (eds.) Database Systems for Advanced Applications. DASFAA 2008 Lecture Notes in Computer Science, vol. 4947, pp. 67–83. Springer (2008)

    Chapter  Google Scholar 

  19. Ding, B., Lo, D., Han, J., Khoo, S.: Efficient mining of closed repetitive gapped subsequences from a sequence database. In: 2009 IEEE 25th International Conference on Data Engineering, pp. 1024–1035. IEEE (2009)

    Chapter  Google Scholar 

  20. Wu, Y., Zhu, C., Li, Y., Guo, L., Wu, X.: NetNCSP: nonoverlapping closed sequential pattern mining. Knowl. Based Syst. 196(105), 812 (2020)

    Google Scholar 

  21. Mudrick, N.V., Azevedo, R., Taub, M.: Integrating metacognitive judgments and eye movements using sequential pattern mining to understand processes underlying multimedia learning. Comput. Hum. Behav. 96, 223–234 (2019)

    Article  Google Scholar 

  22. Jian, P., Jiawei, H., Behzad, M.-A., Jianyong, W., Helen, P., Qiming, C., Umeshwar, D., Mei-Chun, H.: Mining sequential patterns by pattern-growth: the PrefixSpan approach. IEEE Trans. Knowl. Data Eng. 16(11), 1424–1440 (2004)

    Article  Google Scholar 

  23. Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 429–435. ACM (2002)

    Google Scholar 

  24. Ho, J., Lukov, L., Chawla, S.: Sequential pattern mining with constraints on large protein databases. In: Proceedings of the 12th International Conference on Management of Data, pp. 89–100. Computer Society of India (2005)

    Google Scholar 

  25. Mooney, C.H., Roddick, J.F.: Sequential pattern mining—approaches and algorithms. ACM Comput. Surv. 45(2), 19 (2013)

    Article  Google Scholar 

  26. Fournier-Viger, P., Lin, J.C., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Patt. Recogn. 1(1), 54–77 (2017)

    Google Scholar 

  27. Paquette, L., Bosch, N.: The invisible breadcrumbs of digital learning: how learner actions inform us of their experience. In: Matthew, M. (ed.) Handbook of Research on Digital Learning, pp. 302–316. IGI Global, Pennsylvania (2020)

    Chapter  Google Scholar 

  28. Winne, P.H.: Construct and consequential validity for learning analytics based on trace data. Comput. Hum. Behav. 112(106), 457 (2020)

    Google Scholar 

  29. Zhou, M., Xu, Y., Nesbit, J.C., Winne, P.H.: Sequential pattern analysis of learning logs: methodology and applications. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.D. (eds.) Handbook of Educational Data Mining, pp. 107–121. CRC Press, Boca Raton (2010)

    Google Scholar 

  30. Jiang, Y., Paquette, L., Baker, R.S., Clarke-Midura, J.: Comparing novice and experienced students within virtual performance assessments. In: Proceedings of the Eighth International Conference on Educational Data Mining, pp. 136–143. International Educational Data Mining Society (2015)

    Google Scholar 

  31. Kia, F.S., Teasley, S.D., Hatala, M., Karabenick, S.A., Kay, M.: How patterns of students dashboard use are related to their achievement and self-regulatory engagement. In: Proceedings of the Tenth International Conference on Learning Analytics and Knowledge, pp. 340–349. SoLAR (2020)

    Google Scholar 

  32. Kinnebrew, J.S., Loretz, K.M., Biswas, G.: A contextualized, differential sequence mining method to derive students’ learning behavior patterns. J. Educ. Data Min. 5(1), 190–219 (2013)

    Google Scholar 

  33. Kinnebrew, J., Mack, D., Biswas, G.: Mining temporally-interesting learning behavior patterns. In: Proceedings of the Sixth International Conference on Educational Data Mining, pp. 252–255. International Educational Data Mining Society (2013)

    Google Scholar 

  34. Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaiane, O.R.: Clustering and sequential pattern mining of online collaborative learning data. IEEE Trans. Knowl. Data Eng. 21(6), 759–772 (2009)

    Article  Google Scholar 

  35. Kang, J., Liu, M.: Investigating navigational behavior patterns of students across at-risk categories within an open-ended serious game. Technol. Knowl. Learn. (2020)

    Google Scholar 

  36. Swiecki, Z., Lian, Z., Ruis, A., Shaffer, D.W.: Does order matter? Investigating sequential and cotemporal models of collaboration. In: Lund, K., Niccolai, G.P., Lavoué, E., Hmelo-Silver, C., Gweon, G., Baker, M., Bailey, J. (eds.) The 13th International Conference on Computer Supported Collaborative Learning (CSCL), pp. 112–119. International Society of the Learning Sciences (2019)

    Google Scholar 

  37. Zheng, J., Xing, W., Zhu, G.: Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment. Comput. Educ. 136, 34–48 (2019)

    Article  Google Scholar 

  38. Chen, B., Resendes, M., Chai, C.S., Hong, H.: Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse. Interact. Learn. Environ. 25(2), 162–175 (2017)

    Article  Google Scholar 

  39. Elbadrawy, A., Karypis, G.: UPM: discovering course enrollment sequences associated with success. In: Proceedings of the Ninth International Conference on Learning Analytics and Knowledge, pp. 373–382. SoLAR (2019)

    Google Scholar 

  40. Jin, S.Y., Yei-Sol, W., Sang, J.P.: Mining course trajectories of successful and failure students: a case study. In: 2017 IEEE International Conference on Big Knowledge (ICBK), pp. 270–275. IEEE (2017)

    Google Scholar 

  41. Bhatt, C., Cooper, M., Jian, Z.: SeqSense: video recommendation using topic sequence mining. In: MultiMedia Modeling. MMM 2018 Lecture Notes in Computer Science, vol. 10705, pp. 252–263. Springer, Berlin (2018)

    Google Scholar 

  42. Wang, Y., Li, T., Geng, C., Wang, Y.: Recognizing patterns of student’s modeling behaviour patterns via process mining. Smart Learn. Environ. 6(1), 26 (2019)

    Article  Google Scholar 

  43. Anwar, T., Uma, V., Shahjad: Book recommendation for eLearning using collaborative filtering and sequential pattern mining. In: 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), pp. 1–6. IEEE (2020)

    Google Scholar 

  44. Sitanggang, I.S., Husin, N.A., Agustina, A., Mahmoodian, N.: Sequential pattern mining on library transaction data. In: Proceedings of the 2010 International Symposium on Information Technology (ITSim 2010), pp. 1–4. IEEE (2010)

    Google Scholar 

  45. Chen, C., Wang, W.: Mining effective learning behaviors in a web-based inquiry science environment. J. Sci. Educ. Technol. 29(4), 519–535 (2020)

    Article  Google Scholar 

  46. Chen, W., Niu, Z., Zhao, X., Li, Y.: A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web. 17(2), 271–284 (2014)

    Article  Google Scholar 

  47. Wong, J., Khalil, M., Baars, M., de Koning, B.B., Paas, F.: Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course. Comput. Educ. 140, 43–56 (2019)

    Article  Google Scholar 

  48. Knight, S., Martinez-Maldonado, R., Gibson, A., Shum, S.B.: Towards mining sequences and dispersion of rhetorical moves in student written texts. In: Proceedings of the Seventh International Learning Analytics and Knowledge Conference, pp. 228–232. SoLAR (2017)

    Google Scholar 

  49. McBroom, J., Yacef, K., Koprinska, I., Curran, J.R.: A data-driven method for helping teachers improve feedback in computer programming automated tutors. In: Artificial Intelligence in Education. 19th International Conference, AIED 2018. Proceedings: LNAI 10947, pp. 324–337. Springer (2018)

    Google Scholar 

  50. Mirzaei, M., Sahebi, S.: Modeling students’ behavior using sequential patterns to predict their performance. In: Artificial Intelligence in Education, AIED 2019 Lecture Notes in Artificial Intelligence, vol. 11626, pp. 350–353. Springer (2019)

    Google Scholar 

  51. Kang, J., Liu, M., Qu, W.: Using gameplay data to examine learning behavior patterns in a serious game. Comput. Hum. Behav. 72, 757–770 (2017)

    Article  Google Scholar 

  52. Taub, M., Azevedo, R.: Using sequence mining to analyze metacognitive monitoring and scientific inquiry based on levels of efficiency and emotions during game-based learning. J. Educ. Data Min. 10(3), 1–26 (2018)

    Google Scholar 

  53. Winne, P.H., Hadwin, A.F.: The weave of motivation and self-regulated learning. In: Schunk, D.H., Zimmerman, B.J. (eds.) Motivation and self-regulated learning: theory, research, and application, pp. 297–314. Lawrence Erlbaum Associates Publishers (2008)

    Google Scholar 

  54. Scherer, K.: Emotions are emergent processes: they require a dynamic computational architecture. Philos. Trans. R. Soc. 364, 3459–3474 (2009)

    Article  Google Scholar 

  55. Kinnebrew, J.S., Segedy, J.R., Biswas, G.: Integrating model-driven and data-driven techniques for analyzing learning behaviors in open-ended learning environments. IEEE Trans. Learn. Technol. 10(2), 140–153 (2017)

    Article  Google Scholar 

  56. Fatahi, S., Shabanali-Fami, F., Moradi, H.: An empirical study of using sequential behavior pattern mining approach to predict learning styles. Educ. Inf. Technol. 23(4), 1427–1445 (2018)

    Article  Google Scholar 

  57. Jaber, M., Wood, P.T., Papapetrou, P., Papapetrou, P.: A multi-granularity pattern-based sequence classification framework for educational data. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 370–378. IEEE (2016)

    Chapter  Google Scholar 

  58. Fournier-Viger, P., Faghihi, U., Nkambou, R., Nguifo, E.M.: Exploiting sequential patterns found in users’ solutions and virtual tutor behavior to improve assistance in ITS. Educ. Technol. Soc. 13(1), 13–24 (2010)

    Google Scholar 

  59. El-Ramly, M., Stroulia, E.: Analysis of web-usage behavior for focused web sites: a case study. J. Softw. Main. Evol. 16(1–2), 129–150 (2004)

    Article  Google Scholar 

  60. Tarus, J.K., Niu, Z., Yousif, A.: A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Fut. Gen. Comput. Syst. 72, 37–48 (2017)

    Article  Google Scholar 

  61. Slim, A., Heileman, G.L., Al-Doroubi, W., Abdallah, C.T.: The impact of course enrollment sequences on student success. In: Proceedings of 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), pp. 59–65. IEEE (2016)

    Google Scholar 

  62. Brown, J.S., Collins, A., Duguid, P.: Situated cognition and the culture of learning. Educ. Res. 18(1), 32–42 (1989)

    Article  Google Scholar 

  63. Kinnebrew, J.S., Segedy, J.R., Biswas, G.: Analyzing the temporal evolution of students’ behaviors in open-ended learning environments. Metacogn. Learn. 9(2), 187–215 (2014)

    Article  Google Scholar 

  64. Emara, M., Tscholl, M., Dong, Y., Biswas, G.: Analyzing students’ collaborative regulation behaviors in a classroom-integrated open ended learning environment. In: Smith, B.K., Borge, M., Mercier, E., Lim, K.Y. (eds.) Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning (CSCL), pp. 319–326. International Society of the Learning Sciences (2017)

    Google Scholar 

  65. Kinnebrew, J.S., Mack, D.L.C., Biswas, G., Chang, C.: A differential approach for identifying important student learning behavior patterns with evolving usage over time. In: Trends and Applications in Knowledge Discovery and Data Mining, pp. 281–292. Springer (2014)

    Chapter  Google Scholar 

  66. Biswas, G., Segedy, J.R., Bunchongchit, K.: From design to implementation to practice a learning by teaching system: Betty’s Brain. Int. J. Artif. Intell. Educ. 26(1), 350–364 (2016)

    Article  Google Scholar 

  67. Segedy, J.R., Biswas, G., Sulcer, B.: A model-based behavior analysis approach for open-ended environments. J. Educ. Technol. Soc. 17(1), 272–282 (2014)

    Google Scholar 

  68. He, Z., Zhang, S., Wu, J.: Significance-based discriminative sequential pattern mining. Exp. Syst. Appl. 122, 54–64 (2019)

    Article  Google Scholar 

  69. Zhang, Y., Paquette, L.: An effect-size-based temporal interestingness metric for sequential pattern mining. In: Rafferty, A.N., Whitehill, J., Cavalli-Sforza, V., Romero, C. (eds.) Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020), pp. 720–724. International Educational Data Mining Society (2020)

    Google Scholar 

  70. Sabourin, J., Mott, B., Lester, J.: Discovering behavior patterns of self-regulated learners in an inquiry-based learning environment. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) Artificial Intelligence in Education. AIED 2013 Lecture Notes in Computer Science, vol. 7926, pp. 209–218. Springer, Cham (2013)

    Google Scholar 

  71. Taub, M., Azevedo, R., Bradbury, A.E., Millar, G.C., Lester, J.: Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment. Learn. Instr. 54, 93–103 (2018)

    Article  Google Scholar 

  72. Taub, M., Azevedo, R.: How does prior knowledge influence eye fixations and sequences of cognitive and metacognitive SRL processes during learning with an Intelligent Tutoring System? Int. J. Artif. Intell. Educ. 29(1), 1–28 (2019)

    Article  Google Scholar 

  73. Martinez-Maldonado, R., Dimitriadis, Y., Martinez-Monés, A., Kay, J., Yacef, K.: Capturing and analyzing verbal and physical collaborative learning interactions at an enriched interactive tabletop. Int. J. Comput. Supp. Collab. Learn. 8(4), 455–485 (2013)

    Article  Google Scholar 

  74. Malekian, D., Bailey, J., Kennedy, G.: Prediction of students’ assessment readiness in online learning environments: the sequence matters. In: Proceedings of the Tenth International Conference on Learning Analytics and Knowledge, pp. 382–391. SoLAR (2020)

    Google Scholar 

  75. Sun, D., Cheng, G., Luo, H.: Analysing the evolution of student interaction patterns in a Massive Private Online Course. In: Interactive Learning Environments, pp. 1–14 (2022)

    Google Scholar 

  76. Fournier-Viger, P., Lin, J.C., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., Lam, H.T.: The SPMF open-source data mining library version 2. In: Berendt, B., Bringmann, B., Fromont, É., Garriga, G., Miettinen, P., Tatti, N., Tresp, V. (eds.) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2016 Lecture Notes in Computer Science, vol. 9853, pp. 36–40. Springer, Cham (2016)

    Google Scholar 

  77. Zimmerman, B.J.: Self-regulated learning and academic achievement: an overview. Educ. Psych. 25(1), 3–17 (1990)

    Article  Google Scholar 

  78. Bannert, M., Reimann, P., Sonnenberg, C.: Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacog. Learn. 9(2), 161–185 (2014)

    Article  Google Scholar 

  79. Paquette, L., Grant, T., Zhang, Y., Biswas, G., Baker, R.: Using epistemic networks to analyze self-regulated learning in an open-ended problem-solving environment. In: Advances in Quantitative Ethnography, pp. 185–201. Springer, Cham (2021)

    Chapter  Google Scholar 

  80. Bakeman, R., Quera, V.: Sequential Analysis and Observational Methods for the Behavioral Sciences. Cambridge University Press, Cambridge (2011)

    Book  Google Scholar 

  81. Bosch, N., Paquette, L.: What’s next? Sequence length and impossible loops in state transition measurement. J. Educ. Data Min. 13(1), 1–23 (2021)

    Google Scholar 

  82. Matayoshi, J., Karumbaiah, S.: Adjusting the L statistic when self-transitions are excluded in affect dynamics. J. Educ. Data Min. 12(4), 1–23 (2020)

    Google Scholar 

  83. Magnusson, M.S.: Discovering hidden time patterns in behavior: T-patterns and their detection. Behav. Res. Methods Instr. Comput. 32(1), 93–110 (2000)

    Article  Google Scholar 

  84. Wu, Y., Tong, Y., Zhu, X., Wu, X.: NOSEP: non-overlapping sequence pattern mining with gap constraints. IEEE Trans. Cyber. 48(10), 2809–2822 (2018)

    Article  Google Scholar 

  85. Kinnebrew, J.S., Killingsworth, S.S., Clark, D.B., Biswas, G., Sengupta, P., Minstrell, J., Martinez-Garza, M., Krinks, K.: Contextual markup and mining in digital games for science learning: connecting player behaviors to learning goals. IEEE Trans. Learn. Technol. 10(1), 93–103 (2017)

    Article  Google Scholar 

  86. Bazaldua, D.L., Baker, R., Pedro, M.O.: Comparing expert and metric-based assessments of association rule interestingness. In: Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of the Seventh International Conference on Educational Data Mining (EDM 2014), pp. 44–51. International Educational Data Mining Society (2014)

    Google Scholar 

  87. Merceron, A., Yacef, K.: Interestingness measures for association rules in educational data. In: Baker, R.S.J.D., Barnes, T., Beck, J.E. (eds.) Proceedings of the First International Conference on Educational Data Mining, pp. 57–66. International Educational Data Mining Society (2008)

    Google Scholar 

  88. Gao, C., Wang, J., He, Y., Zhou, L.: Efficient mining of frequent sequence generators. In: Proceedings of the 17th International Conference on World Wide Web, pp. 1051–1052. ACM (2008)

    Google Scholar 

  89. Wang, J., Han, J.: BIDE: efficient mining of frequent closed sequences. In: Proceedings of the 20th International Conference on Data Engineering, pp. 79–90. IEEE (2004)

    Chapter  Google Scholar 

  90. Reimann, P., Markauskaite, L., Bannert, M.: e-Research and learning theory: what do sequence and process mining methods contribute? Br. J. Educ. Technol. 45(3), 528–540 (2014)

    Article  Google Scholar 

Download references

Acknowledgments

This research was partially funded by the China Scholarship Council (grant number 201806040180).

Author information

Authors and Affiliations

  1. Institute of Artificial Intelligence in Education, South China Normal University, Guangdong, China

    Yingbin Zhang

  2. The Department of Curriculum and Instruction, College of Education, University of Illinois at Urbana-Champaign, Champaign, IL, USA

    Luc Paquette

Authors
  1. Yingbin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luc Paquette
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Artificial Intelligence in Education Lab, WOLNM & Sección de Estudios de Posgrado e Investigación, ESIME-Z, Instituto Politécnico Nacional, CDMX, Mexico

    Alejandro Peña-Ayala

Appendix

Appendix

1.1 R Code for the Synthetic Example

### install and load the R package that implements cSPADE

# install.packages('arulesSequences') library(arulesSequences)

### Create the sequences. Each row of data contains an event, its event ID, and sequence ID.

data <- data.frame( sequenceID = c(rep(1, 6), rep(2,2), rep(3, 5), rep(4, 4)), events = c( "read", "hint", "attempt", "note", "attempt", "attempt", "read", "attempt", "hint", "read", "attempt", "note", "attempt", “hint”, “note”, “read”, “attempt”) ) data$eventID <- 1:nrow(data)

### Convert the data to the basket format that cspade can handle

### Note that the first two columns should represent sequence ID and event ID

write.table(data[,c("sequenceID", "eventID", "events")], file = "formated_event_data.txt", sep=";", row.names = FALSE, col.names = FALSE, quote = FALSE) data_baskets <- read_baskets("formated_event_data.txt", sep = ";", info=c("sequenceID","eventID"))

### Apply cspade to data_baskets

freq_patterns <- cspade(data_baskets, parameter = list(support = 0.5, maxgap = 1))

### Inspect the frequent patterns. The minimum length of patterns in cspade is 1, so some patterns in freq_patterns are actually individual events.

inspect(freq_patterns)

### Convert the freq_patterns to a data.frame

freq_patterns_df <- as(freq_patterns, "data.frame")

1.2 Python Code for the Synthetic Example

### install and load the Python package that implements cSPADE

# !pip install Cython pycspade from pycspade.helpers import spade, print_result

### Create a list to represent the sequences

### The first, second, and third columns are the sequence ID, event ID, and events, respectively.

### At the time we wrote this example code, pycspade cannot handle events in string types.

### So we converted the events to integers: 1—read, 2—hint, 3—attempt, 4—note.

data = [ [1, 1, [1]], [1, 2, [2]], [1, 3, [3]], [1, 4, [4]], [1, 5, [3]], [1, 6, [3]], [2, 7, [1]], [2, 8, [3]], [3, 9, [2]], [3, 10, [1]], [3, 11, [3]], [3, 12, [4]], [3, 13, [3]], [4, 14, [2]], [4, 15, [4]], [4, 16, [1]], [4, 17, [3]] ]

### Apply cSPADE to the data

result = spade(data=data, support=0.5, maxgap = 1)

### Print the frequent patterns and interestingness measures

print_result(result)

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zhang, Y., Paquette, L. (2023). Sequential Pattern Mining in Educational Data: The Application Context, Potential, Strengths, and Limitations. In: Peña-Ayala, A. (eds) Educational Data Science: Essentials, Approaches, and Tendencies. Big Data Management. Springer, Singapore. https://doi.org/10.1007/978-981-99-0026-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-0026-8_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0025-1

  • Online ISBN: 978-981-99-0026-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation