Skip to main content
SpringerLink
Log in

Learning Analytics for Knowledge Creation and Inventing in K-12: A Systematic Review

  • Conference paper
  • First Online:
Intelligent Computing (SAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 508))

Included in the following conference series:

Abstract

This paper presents our systematic review of empirical learning analytic studies carried out at K-12 education with a specific focus on pedagogically innovative (constructive) approaches on technology-mediated learning, such as knowledge building, knowledge creation, and maker-centered learning and maker culture. After reading abstracts of identified 236 articles, we zoomed in on 22 articles. We identified three categories of studies: 1) articles oriented toward methodology development, 2) articles relying on digital tools (learning environments with LA functions) and 3) articles investigating the impact of LA.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

    http://www.scopus.com/.

  2. 2.

    http://www.webofknowledge.com/.

  3. 3.

    https://ccl.northwestern.edu/netlogo/.

References

  1. Aldowah, H., Al-Samarraie, H., Fauzy, W.M.: Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telematics Inform. 37, 13–49 (2019)

    Article  Google Scholar 

  2. Apiola, M., Sutinen, E.: Design science research for learning software engineering and computational thinking: four cases. Comput. Appl. Eng. Educ. 29(1), 1–19 (2020)

    Google Scholar 

  3. Apiola, M., Sutinen, E.: Mindset and study performance: new scales and research directions. In: Koli Calling 2020: Proceedings of the 20th Koli Calling International Conference on Computing Education Research, Koli Calling ’20, New York, Association for Computing Machinery (2020)

    Google Scholar 

  4. Arastoopour, G., et al.: Modeling and measuring high school students’ computational thinking practices in science. J. Sci. Educ. Technol. 29(1), 137–161 (2020)

    Article  Google Scholar 

  5. Berland, M., Davis, D., Smith, C.P.: Amoeba: designing for collaboration in computer science classrooms through live learning analytics. Int. J. Comput. Support. Collaborative Learn. 10(4), 425–447 (2015)

    Article  Google Scholar 

  6. Brasiel, S., Close, K., Jeong, S., Lawanto, K., Janisiewicz, P., Martin, T.: Measuring computational thinking development with the FUN! tool. In: Rich, P.J., Hodges, C.B. (eds.) Emerging Research, Practice, and Policy on Computational Thinking. ECTII, pp. 327–347. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52691-1_20

    Chapter  Google Scholar 

  7. Chen, B., Zhang, J.: Analytics for knowledge creation: towards epistemic agency and design-mode thinking. J. Learn. Anal. 3(2), 139–163 (2016)

    Article  Google Scholar 

  8. Cukurova, M., Luckin, R., Mavrikis, M., Millán, E.: Machine and human observable differences in groups’ collaborative problem-solving behaviours. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 17–29. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66610-5_2

    Chapter  Google Scholar 

  9. Dillenbourg, P.: What do you mean by collaborative learning? In: Dillenbourg, P. (ed.) Collaborative learning: Cognitive and Computational Approaches, pp. 1–19. Elsevier, Oxford (1999)

    Google Scholar 

  10. Ertmer, P.A., Newby, T.J.: Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Perform. Improv. Q. 26(2), 43–71 (2013)

    Article  Google Scholar 

  11. Gobert, J.D., Kim, Y.J., Sao Pedro, M.A., Kennedy, M., Betts, C.G.: Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld. Thinking Skills Creativity 18, 81–90 (2015)

    Article  Google Scholar 

  12. Hakkarainen, K., Hietajärvi, L., Alho, K., Lonka, K., Salmela-Aro, K.: Sociodigital Revolution: digital natives vs digital immigrants, vol. 22, pp. 918–923. Elsevier Scientific Publ. Co, Unknown, 2nd edn, February 2015

    Google Scholar 

  13. Hmelo-Silver, C.E., Duncan, R.G., Chinn, C.A.: Scaffolding and achievement in problem-based and inquiry learning: a response to kirschner, sweller, and. Educ. Psychol. 42(2), 99–107 (2007)

    Article  Google Scholar 

  14. Jonathan, C., Tan, J. P.-L., Koh, E., Caleon, I. S., Tay, S.H.: Enhancing students’ critical reading fluency, engagement and self-efficacy using self-referenced learning analytics dashboard visualizations. In: 25th International Conference on Computers in Education, pp. 457–462. New Zealand, Asia-Pacific Society for Computers in Education (2017)

    Google Scholar 

  15. Kesselbacher, M., Bollin, A.: Discriminating programming strategies in scratch: making the difference between novice and experienced programmers. In Proceedings of the 14th Workshop in Primary and Secondary Computing Education, WiPSCE 2019, New York, Association for Computing Machinery (2019)

    Google Scholar 

  16. Khanlari, A., Zhu, G., Scardamalia, M.: Knowledge building analytics to explore crossing disciplinary and grade-level boundaries. J. Learn. Anal. 6(3), 60–75 (2019)

    Google Scholar 

  17. Kirschner, P.A., Sweller, J., Clark, R.E.: Why minimal guidance during instruction does not work: an analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educ. Psychol. 41(2), 75–86 (2006)

    Article  Google Scholar 

  18. Kivunja, C.: Teaching students to learn and to work well with 21st century skills: unpacking the career and life skills domain of the new learning paradigm. Int. J. High. Educ. 4(1), 1–11 (2015)

    Google Scholar 

  19. Koh, E., Jonathan, C., Tan, J.: Exploring conditions for enhancing critical thinking in networked learning: findings from a secondary school learning analytics environment. Educ. Sci. 9(4), 287 (2019)

    Article  Google Scholar 

  20. Levy, S.T., Wilensky, U.: Mining students’ inquiry actions for understanding of complex systems. Comput. Educ. 56(3), 556–573 (2011)

    Article  Google Scholar 

  21. Li, S., Hietajärvi, L., Palonen, T., Salmela-Aro, K., Hakkarainen, K.: Adolescents’ social networks: exploring different patterns of socio-digital participation. Scandinavian J. Educ. Res. 61(3), 255–274 (2017)

    Article  Google Scholar 

  22. Liz-Domínguez, M., Caeiro-Rodríguez, M., Llamas-Nistal, M., Mikic-Fonte, F.: Systematic literature review of predictive analysis tools in higher education. Appl. Sci. 9(24), 5569 (2019)

    Article  Google Scholar 

  23. Manske, S., Hoppe, H.U.: The "Concept Cloud": supporting collaborative knowledge construction based on semantic extraction from learner-generated artefacts. In: 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT), pp. 302–306 (2016)

    Google Scholar 

  24. Ng, J., Hu, X., Luo, M., Chu, S.K.W.: Relations among participation, fairness and performance in collaborative learning with wiki-based analytics. Proc. Assoc. Inf. Sci. Technol. 56(1), 463–467 (2019)

    Article  Google Scholar 

  25. Packer, M.J., Goicoechea, J.: Sociocultural and constructivist theories of learning: ontology, not just epistemology. Educ. Psychol. 35(4), 227–241 (2000)

    Article  Google Scholar 

  26. Papamitsiou, Z., Economides, A.A.: Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. J. Educ. Technol. Soc. 17(4), 49–64 (2014)

    Google Scholar 

  27. Papert, S.: Teaching children to be mathematicians versus teaching about mathematics. Int. J. Math. Educ. Sci. Technol. 3(3), 249–262 (1972)

    Article  Google Scholar 

  28. Papert, S.: An exploration in the space of mathematics educations. Int. J. Comput. Math. Learn. 1(1), 95–123 (1996)

    Article  Google Scholar 

  29. Papert, S., Harel, I.: Situating constructionism. In: Papert, S., Harel, I. (eds.), Constructionism, vol. 36, pp. 1–11. Ablex Publishing, Norwood (1991)

    Google Scholar 

  30. Parker, J.N., Cardenas, E., Dorr, A.N., Hackett, E.J.: Using sociometers to advance small group research. Sociol. Methods Res. 49(4), 1064–1102 (2018)

    Article  MathSciNet  Google Scholar 

  31. Pentland, A.: Social Physics. Penguin Press, How Good Ideas Spread - The Lessons from a new Science (2014)

    Google Scholar 

  32. Reilly, J.M., Dede, C.: Differences in student trajectories via filtered time series analysis in an immersive virtual world. In: Proceedings of the 9th International Conference on Learning Analytics & Knowledge, LAK19, pp. 130–134, New York, Association for Computing Machinery (2019)

    Google Scholar 

  33. Ritella, G., Hakkarainen, K.: Instrumental genesis in technology-mediated learning: From double stimulation to expansive knowledge practices. Int. J. Comput Support. Collaborative Learn. 7(2), 239–258 (2012)

    Article  Google Scholar 

  34. Romero, C., Ventura, S.: Educational data mining and learning analytics: an updated survey. Data Min. Knowl. Dis. 10(3), e1355 (2020)

    Google Scholar 

  35. Root-Bernstein, R.: STEMM education should get “HACD." Science 361(6397), 22–23 (2018)

    Google Scholar 

  36. Glasner, R., Baraness, A.: Chapter 1: introduction. In: Alfonso’s Rectifying the Curved. SSHMPS, pp. 1–32. Springer, Cham (2021). https://doi.org/10.1007/978-3-319-77303-2_1

  37. Scaradozzi, D., Cesaretti, L., Screpanti, L., Mangina, E.: Identification of the students learning process during education robotics activities. Front. Robot. AI 7, 21 (2020)

    Article  Google Scholar 

  38. Scardamalia, M., Bereiter, C.: Knowledge building and knowledge creation: theory, pedagogy, and technology. In: Sawyer, K. (ed.), The Cambridge Handbook of the Learning Sciences, 2nd ed, pp. 397–417. Cambridge University Press (2014)

    Google Scholar 

  39. Schwarz, B.B., Prusak, N., Swidan, O., Livny, A., Gal, K., Segal, A.: Orchestrating the emergence of conceptual learning: a case study in a geometry class. Int. J. Comput. Support. Collaborative Learn. 13(2), 189–211 (2018). https://doi.org/10.1007/s11412-018-9276-z

    Article  Google Scholar 

  40. Schwendimann, B., et al.: Perceiving learning at a glance: a systematic literature review of learning dashboard research. IEEE Trans. Learn. Technol. 10(1), 30–41 (2017)

    Article  Google Scholar 

  41. Seitamaa, A.: Exploring Middle School Students’ Growth Mindsets in Relation to Educational and Sociodigital Activity. Master’s thesis, University of Helsinki, Faculty of Education (2021)

    Google Scholar 

  42. Shaffer, D.W.: Quantitative Ethnography. Cathcart Press, Madison (2017)

    Google Scholar 

  43. Shaffer, D.W., Collier, W., Ruis, A.R.: A tutorial on epistemic network analysis: analyzing the structure of connections in cognitive, social, and interaction data. J. Learn. Anal. 3(3), 9–45 (2016)

    Article  Google Scholar 

  44. Shum, S., Crick, R.: Learning Analytics for \(21^{st}\) Century Competencies. J. Learn. Anal. 3(2), 6–21 (2016)

    Article  Google Scholar 

  45. Shum, S.B., Ferguson, R.: Social Learning Analytics. J. Educ. Technol. Soc. 15(3), 3–26 (2012)

    Google Scholar 

  46. Skorton, D., Bear, A.: The Integration of the Humanities and Arts with Sciences, Engineering, and Medicine in Higher Education: Branches from the Same Tree. The National Academies Press, Washington (2018)

    Book  Google Scholar 

  47. Stahl, G., Hakkarainen, K.: Theories of CSCL. In: Cress, U., Rosé, C., Wise, A.F., Oshima, J. (eds.) International Handbook of Computer-Supported Collaborative Learning. CCLS, vol. 19, pp. 23–43. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-65291-3_2

    Chapter  Google Scholar 

  48. Tan, J., Koh, E., Ariffin, N., Teo, E., Tay, S., Singh, S.: Analytics environment (CoVAA) intervention: user experiences and reflections of teacher-practitioners. In: 26th International Conference on Computers in Education. Asia-Pacific Society for Computers in Education (2018)

    Google Scholar 

  49. Tan, J.P.-L., Yang, S., Koh, E., Jonathan, C.: Fostering 21st century literacies through a collaborative critical reading and learning analytics environment: User-perceived benefits and problematics. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, LAK 2016, pp. 430–434, New York, Association for Computing Machinery (2016)

    Google Scholar 

  50. Tripathi, P., Burleson, W.: Predicting creativity in the wild: experience sample and sociometric modeling of teams. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, CSCW 2012, pp. 1203–1212, New York, Association for Computing Machinery (2012)

    Google Scholar 

  51. van Leeuwen, A., Janssen, J., Erkens, G., Brekelmans, M.: Supporting teachers in guiding collaborating students: effects of learning analytics in cscl. Comput. Educ. 79, 28–39 (2014)

    Article  Google Scholar 

  52. van Leeuwen, A., Janssen, J., Erkens, G., Brekelmans, M.: Teacher regulation of multiple computer-supported collaborating groups. Comput. Hum. Behav. 52, 233–242 (2015)

    Article  Google Scholar 

  53. van Leeuwen, A., Rummel, N., van Gog, T.: What information should CSCL teacher dashboards provide to help teachers interpret CSCL situations? Int. J. Comput. Support. Collaborative Learn. 14(3), 261–289 (2019)

    Article  Google Scholar 

  54. Viberg, O., Hatakka, M., Bälter, O., Mavroudi, A.: The current landscape of learning analytics in higher education. Comput. Hum. Behav. 89, 98–110 (2018)

    Article  Google Scholar 

  55. Xhakaj, F., Aleven, V., McLaren, B.M.: Effects of a teacher dashboard for an intelligent tutoring system on teacher knowledge, lesson planning, lessons and student learning. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 315–329. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66610-5_23

    Chapter  Google Scholar 

  56. Zhu, G., Xing, W., Costa, S., Scardamalia, M., Pei, B.: Exploring emotional and cognitive dynamics of knowledge building in grades 1 and 2. User Model. User-Adap. Inter. 29(4), 789–820 (2019)

    Article  Google Scholar 

Download references

Acknowledgment

This research was supported by the Growing Mind-project (http://growingmind.fi), funded by the Strategic Research Council of the Academy of Finland.

Author information

Authors and Affiliations

  1. Department of Computing, University of Turku, Turku, Finland

    Mikko-Ville Apiola

  2. Department of Educational Sciences, University of Helsinki, Helsinki, Finland

    Sofia Lipponen, Aino Seitamaa, Tiina Korhonen & Kai Hakkarainen

Authors
  1. Mikko-Ville Apiola
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sofia Lipponen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aino Seitamaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tiina Korhonen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kai Hakkarainen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mikko-Ville Apiola .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Apiola, MV., Lipponen, S., Seitamaa, A., Korhonen, T., Hakkarainen, K. (2022). Learning Analytics for Knowledge Creation and Inventing in K-12: A Systematic Review. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 508. Springer, Cham. https://doi.org/10.1007/978-3-031-10467-1_15

Download citation

Publish with us

Policies and ethics

Search

Navigation