Skip to main content
SpringerLink
Log in

Learning Analytics: Using Data-Informed Decision-Making to Improve Teaching and Learning

  • Chapter
  • First Online:
Contemporary Technologies in Education

Abstract

This chapter describes three characteristics of Learning Analytics work that distinguish it from prior educational research to give readers a concise overview of what makes learning analytics a unique and especially promising technology to improve teaching and learning. Data used in learning analytics relate to the process of learning, can come from a variety of sources (in both virtual and physical learning environments), and are characterized by their large quantity and relatively small grain size. Analysisapproaches aim at detecting underlying patterns and relationships in the data and include prediction, structure discovery, temporal, language-based and visual methods. Pedagogical uses are what position learning analytics as more than simply a new set of methods but an impactful technology to drive data-informed decision-making through tailoring educational experiences, informing student self-direction, and supporting instructor planning and orchestration. The chapter concludes with an overview of the systemic and societal issues surrounding learning analytics use that frame how and to what extent they are able to affect education.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
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

References

  • ACM US Public Policy Council. (2017). Statement on algorithmic transparency and accountability. Washington, DC: ACM.

    Google Scholar 

  • Ahn, J. (2013). What can we learn from Facebook activity?: Using social learning analytics to observe new media literacy skills. In Proceedings of the third international conference on learning analytics & knowledge (pp. 135–144). Leuven: ACM.

    Chapter  Google Scholar 

  • Ali, L., Hatala, M., Gašević, D., & Jovanović, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58(1), 470–489.

    Article  Google Scholar 

  • Arnold, K. E. (2010). Signals: Applying academic analytics. Educause Quarterly, 33(1), 1–10.

    Google Scholar 

  • Baker, R., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (2nd ed., pp. 253–274). Cambridge, MA: Cambridge University Press.

    Chapter  Google Scholar 

  • Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.

    Google Scholar 

  • Baker, R. S., Hershkovitz, A., Rossi, L. M., Goldstein, A. B., & Gowda, S. M. (2013). Predicting robust learning with the visual form of the moment-by-moment learning curve. Journal of the Learning Sciences, 22(4), 639–666.

    Article  Google Scholar 

  • Baltrušaitis, T., Robinson, P., & Morency, L. P. (2016). Openface: An open source facial behavior analysis toolkit. In Proceedings of 2016 IEEE winter conference on applications of computer vision (pp. 1–10). Lake Placid: IEEE.

    Google Scholar 

  • Bergner, Y. (2017). Measurement and its uses in learning analytics. In Handbook of learning analytics (1st ed., pp. 35–48). Edmonton: SoLAR.

    Chapter  Google Scholar 

  • Brooks, C., Greer, J., & Gutwin, C. (2014). The data-assisted approach to building intelligent technology-enhanced learning environments. In J. A. Larusson & B. White (Eds.), Learning analytics (pp. 123–156). New York: Springer.

    Chapter  Google Scholar 

  • Chen, B., & Resendes, M. (2014). Uncovering what matters: Analyzing transitional relations among contribution types in knowledge-building discourse. In Proceedings of the fourth international conference on learning analytics & knowledge (pp. 226–230). Indianapolis: ACM.

    Chapter  Google Scholar 

  • Chen, B., & Zhang, J. (2016). Analytics for knowledge creation: Towards epistemic agency and design-mode thinking. Journal of Learning Analytics, 3(2), 139–163.

    Article  Google Scholar 

  • Chen, B., Wise, A. F., Knight, S., & Cheng, B. H. (2016). Putting temporal analytics into practice: The 5th international workshop on temporality in learning data. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 488–489). Edinburgh: ACM.

    Chapter  Google Scholar 

  • Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278.

    Article  Google Scholar 

  • Csikszentmihalyi, M., & Larson, R. (2014). Validity and reliability of the experience-sampling method. In Flow and the foundations of positive psychology (pp. 35–54). New York: Springer.

    Google Scholar 

  • Cuban, L. (2001). Oversold and underused: Computers in the classroom. Cambridge, MA: Harvard University Press.

    Book  Google Scholar 

  • Cui, Y., Jin, W. Q., & Wise, A. F. (2017). Humans and machines together: Improving characterization of large scale online discussions through dynamic interrelated post and thread categorization (DIPTiC). In Proceedings of learning at scale 2017 (pp. 217–219). Cambridge, MA: ACM.

    Google Scholar 

  • D’Angelo, C. M., Roschelle, J., & Bratt, H. (2015). Using students’ speech to characterize group collaboration quality. In Proceedings of the international conference on computer supported collaborative learning. Gothenburg: ISLS.

    Google Scholar 

  • Dawson, S. (2010). ‘Seeing’ the learning community: An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology, 41(5), 736–752.

    Article  Google Scholar 

  • Denley, T. (2013). Degree compass: A course recommendation system. Educause Review Online. https://er.educause.edu/articles/2013/9/degree-compass-a-course-recommendation-system

  • Dowell, N., Skrypnyk, O., Joksimović, S., Graesser, A. C., Dawson, S., Gašević, D., Vries, P. D., Hennis, T., & Kovanović, V. (2015). Modeling learners’ social centrality and performance through language and discourse. In Proceedings of the 8th international conference on educational data mining (pp. 250–257). New York: ACM.

    Google Scholar 

  • Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of recommender systems to support learning. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), Recommender systems handbook (pp. 421–451). New York: Springer.

    Chapter  Google Scholar 

  • Duval, E., & Verbert, K. (2012). Learning analytics. E-Learning and Education, 1(8). https://eleed.campussource.de/archive/8/3336

  • Ertmer, P. A. (1999). Addressing first-and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47–61.

    Article  Google Scholar 

  • Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., & Vuorikari, R. (2016). Research evidence on the use of learning analytics – Implications for education policy. In R. Vuorikari & J. Castaño Muñoz (Eds.), Joint research centre science for policy report; EUR 28294 EN; https://doi.org/10.2791/955210.

  • Fritz, J. (2011). Classroom walls that talk: Using online course activity data of successful students to raise self-awareness of underperforming peers. The Internet and Higher Education, 14(2), 89–97.

    Article  Google Scholar 

  • Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68–84.

    Article  Google Scholar 

  • Hecking, T., Chounta, I. A., & Hoppe, H. U. (2016). Investigating social and semantic user roles in MOOC discussion forums. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 198–207). New York: ACM.

    Chapter  Google Scholar 

  • Hero, A. O., & Rajaratnam, B. (2016). Foundational principles for large-scale inference: Illustrations through correlation mining. Proceedings of the IEEE, 104(1), 93–110.

    Article  Google Scholar 

  • Huberth, M., Chen, P., Tritz, J., & McKay, T. A. (2015). Computer-tailored student support in introductory physics. PLoS One, 10(9), e0137001.

    Article  Google Scholar 

  • Jayaprakash, S. M., Moody, E. W., Lauría, E. J., Regan, J. R., & Baron, J. D. (2014). Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics, 1(1), 6–47.

    Article  Google Scholar 

  • Jeong, H., Biswas, G., Johnson, J., & Howard, L. (2010). Analysis of productive learning behaviors in a structured inquiry cycle using hidden markov models. In Proceedings of the third international conference on educational data mining (pp. 81–90). Pittsburgh: EDM.

    Google Scholar 

  • Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC horizon report: 2016 higher education edition. Austin: The New Media Consortium.

    Google Scholar 

  • Joksimović, S., Kovanović, V., Jovanović, J., Zouaq, A., Gašević, D., & Hatala, M. (2015). What do cMOOC participants talk about in social media? A topic analysis of discourse in a cMOOC. In Proceedings of the fifth international conference on learning analytics & knowledge (pp. 156–165). Poughkeepsie: ACM.

    Google Scholar 

  • Joksimović, S., Manataki, A., Gašević, D., Dawson, S., Kovanović, V., & De Kereki, I. F. (2016). Translating network position into performance: Importance of centrality in different network configurations. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 314–323). Edinburgh: ACM.

    Google Scholar 

  • Klerkx, J., Verbert, K., & Duval, E. (2017). Learning analytics dashboards. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (1st ed., pp. 143–150). Edmonton: SoLAR.

    Chapter  Google Scholar 

  • Knight, S., & Littleton, K. (2015). Discourse-centric learning analytics: Mapping the terrain. Journal of Learning Analytics, 2(1), 185–209.

    Article  Google Scholar 

  • Knight, S., Wise, A. F., Chen, B., & Cheng, B. H. (2015). It’s about time: 4th international workshop on temporal analyses of learning data. In Proceedings of the fifth international conference on learning analytics & knowledge (pp. 388–389). Poughkeepise: ACM.

    Chapter  Google Scholar 

  • Kolb, D. A. (1984). Experiential education: Experience as the source of learning and learning science. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Kovanović, V., Joksimović, S., Gašević, D., Hatala, M., & Siemens, G. (2017). Content analytics: The definition, scope, and an overview of published research. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (1st ed., pp. 77–92). Edmonton: SoLAR.

    Chapter  Google Scholar 

  • Landauer, T. K., MacNamara, D. S., Dennis, S., & Kintsch, W. (Eds.). (2011). Handbook of latent semantic analysis. New York: Routledge.

    Google Scholar 

  • Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6, 70.

    Google Scholar 

  • Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439–1459.

    Article  Google Scholar 

  • McNamara, D. S., Crossley, S. A., & McCarthy, P. M. (2010). Linguistic features of writing quality. Written Communication, 27(1), 57–86.

    Article  Google Scholar 

  • McNamara, D., Allen, L., Crossley, S., Dascalu, M., & Perret, C. (2017). Natural language processing and learning analytics. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (1st ed., pp. 93–104). Edmonton: SoLAR.

    Chapter  Google Scholar 

  • Merceron, A., & Yacef, K. (2008). Interestingness measures for association rules in educational data. In Proceedings for the first international conference on educational data mining 2008 (pp. 57–66). Montreal: International Working Group on Educational Data Mining.

    Google Scholar 

  • Mu, J., Stegmann, K., Mayfield, E., Rosé, C., & Fischer, F. (2012). The ACODEA framework: Developing segmentation and classification schemes for fully automatic analysis of online discussions. International Journal of Computer-Supported Collaborative Learning, 7(2), 285–305.

    Article  Google Scholar 

  • Nwana, H. S. (1990). Intelligent tutoring systems: An overview. Artificial Intelligence Review, 4(4), 251–277.

    Article  Google Scholar 

  • Ochoa, X., & Worsley, M. (2016). Augmenting learning analytics with multimodal sensory data. Journal of Learning Analytics, 3(2), 213–219.

    Article  Google Scholar 

  • Papamitsiou, Z., & Economides, A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49–64.

    Google Scholar 

  • Parameswaran, A., Venetis, P., & Garcia-Molina, H. (2011). Recommendation systems with complex constraints: A course recommendation perspective. ACM Transactions on Information Systems (TOIS), 29(4), 20.

    Article  Google Scholar 

  • Pecaric, M., Boutis, K., Beckstead, J., & Pusic, M. (2017). A big data and learning analytics approach to process-level feedback in cognitive simulations. Academic Medicine, 92(2), 175–184.

    Article  Google Scholar 

  • Poon, L. K., Kong, S. C., Wong, M. Y., & Yau, T. S. (2017). Mining sequential patterns of students’ access on learning management system. In International conference on data mining and big data (pp. 191–198). Fukuoka: Springer.

    Chapter  Google Scholar 

  • Poquet, L., & Dawson, S. (2016). Untangling MOOC learner networks. In Proceedings of the sixth international conference on learning analytics and knowledge (pp. 208–212). Edinburgh: ACM.

    Google Scholar 

  • Prinsloo, P., & Slade, S. (2013). An evaluation of policy frameworks for addressing ethical considerations in learning analytics. In Proceedings of the third international conference on learning analytics and knowledge (pp. 240–244). Indianapolis: ACM.

    Chapter  Google Scholar 

  • Rabbany, R., Takaffoli, M., & Zaïane, O. R. (2011). Analyzing participation of students in online courses using social network analysis techniques. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, & J. Stamper (Eds.), Proceedings of the 4th international conference on educational data mining (pp. 21–30). EDM.

    Google Scholar 

  • Ritsos, P. D., & Roberts, J. C. (2014). Towards more visual analytics in learning analytics. In M. Phol & J. C. Roberts (Eds.), Proceedings of the EuroVis workshop on visual analytics (pp. 61–65). Swansea: Eurographics Association.

    Google Scholar 

  • Roll, I., MacFadyen, L. P., Ni, P., Cimet, M., Shiozaki, L., Paulin, D., & Harris, S. (2016). Questions, not answers: Boosting student participation in MOOC forums. In Proceedings of learning with MOOC IIIs (pp. 23–25). Philadelphia: LWMOOC.

    Google Scholar 

  • Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. (Eds.). (2010). Handbook of educational data mining. New York: CRC Press.

    Google Scholar 

  • Rosé, C. (2017). Discourse analytics. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (1st ed., pp. 105–114). Edmonton: SoLAR.

    Chapter  Google Scholar 

  • Rosé, C., Wang, Y. C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., & Fischer, F. (2008). Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning. International Journal of Computer-Supported Collaborative Learning, 3(3), 237–271.

    Article  Google Scholar 

  • Rummel, N., Walker, E., & Aleven, V. (2016). Different futures of adaptive collaborative learning support. International Journal of Artificial Intelligence in Education, 26(2), 784–795.

    Article  Google Scholar 

  • Schön, D. A. (1983). The reflective practitioner: How professionals think in action. New York: Basic Books.

    Google Scholar 

  • Sclater, N. (2014). Code of practice for learning analytics: A literature review of the ethical and legal issues. Bristol: JISC.

    Google Scholar 

  • Sclater, N. (2017). Learning analytics explained. New York: Routledge.

    Book  Google Scholar 

  • Serrano-Laguna, Á., Torrente, J., Moreno-Ger, P., & Fernández-Manjón, B. (2014). Application of learning analytics in educational videogames. Entertainment Computing, 5(4), 313–322.

    Article  Google Scholar 

  • Shneiderman, B. (2014). The big picture for big data: Visualization. Science, 343(6172), 730–730.

    Article  Google Scholar 

  • Shum, S. B., Knight, S., McNamara, D., Allen, L., Bektik, D., & Crossley, S. (2016). Critical perspectives on writing analytics. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 481–483). Edinburgh: ACM.

    Chapter  Google Scholar 

  • Siemens, G., Gašević, D., Haythornthwaite, C., Dawson, S., Buckingham Shum, S., Ferguson, R., Duval, E., Verbert, K., & Baker, R. S. (2011). Open learning analytics: An integrated & modularized platform. [Concept paper]. Society for Learning Analytics Research.

    Google Scholar 

  • Sinha, T., Jermann, P., Li, N., & Dillenbourg, P. (2014). Your click decides your fate: Inferring information processing and attrition behavior from MOOC video clickstream interactions. In Proceedings of the conference on empirical methods in natural language processing (EMNLP) workshop on modeling large scale social interaction in massively open online courses (pp. 3–14). Doha: ACL.

    Google Scholar 

  • Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529.

    Article  Google Scholar 

  • Slade, S., & Prinsloo, P. (2014). Student perspectives on the use of their data: Between intrusion, surveillance, and care. In Challenges for research into open & distance learning: Doing things better – Doing better things (pp. 291–300). Oxford: European Distance and E-Learning Network.

    Google Scholar 

  • Suthers, D. D., & Desiato, C. (2012). Exposing chat features through analysis of uptake between contributions. In Proceedings of the 45th Hawaii international conference on system science (pp. 3368–3377). Maui: IEEE.

    Google Scholar 

  • Suthers, D., Wise, A. F., Schneider, B., Shaffer, D. W., Hoppe, H. U., & Siemens, G. (2015). Learning analytics of and in mediational processes of collaborative learning. In O. Lindwall, P. Häkkinen, T. Koschmann, P. Tchounikine, & S. Ludvigsen (Eds.), Proceedings of the eleventh international conference on computer supported collaborative learning (Vol. I, pp. 26–30). Gothenburg: ISLS.

    Google Scholar 

  • Svihla, V., Wester, M. J., & Linn, M. C. (2015). Distributed revisiting: An analytic for retention of coherent science learning. Journal of Learning Analytics, 2(2), 75–101.

    Article  Google Scholar 

  • van Leeuwen, A. (2015). Learning analytics to support teachers during synchronous CSCL: Balancing between overview and overload. Journal of Learning Analytics, 2(2), 138–162.

    Article  Google Scholar 

  • Velazquez, E., Ratté, S., & de Jong, F. (2016). Analyzing students’ knowledge building skills by comparing their written production to syllabus. In Proceedings of the international conference on interactive collaborative learning (pp. 345–352). Belfast: Springer.

    Google Scholar 

  • Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500–1509.

    Article  Google Scholar 

  • Vytasek, J., Wise, A. F., & Woloshen, S. (2017). Topic models to support instructors in MOOC forums. In Proceedings of the seventh international conference on learning analytics & knowledge (pp. 610–611). Vancouver: ACM.

    Chapter  Google Scholar 

  • Whitelock, D., Twiner, A., Richardson, J. T., Field, D., & Pulman, S. (2015). OpenEssayist: A supply and demand learning analytics tool for drafting academic essays. In Proceedings of the fifth international conference on learning analytics & knowledge (pp. 208–212). Poughkeepsie: ACM.

    Chapter  Google Scholar 

  • Winne, P. H. (2010). Improving measurements of self-regulated learning. Educational Psychologist, 45(4), 267–276.

    Article  Google Scholar 

  • Winne, P. H. (2017). Leveraging big data to help each learner upgrade learning and accelerate learning science. Teachers College Record, 119(3), 1–24.

    Article  Google Scholar 

  • Wise, A. F., & Chiu, M. M. (2011). Analyzing temporal patterns of knowledge construction in a role-based online discussion. International Journal of Computer-Supported Collaborative Learning, 6(3), 445–470.

    Article  Google Scholar 

  • Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 5–13.

    Article  Google Scholar 

  • Wise, A. F., & Vytasek, J. M. (2017). Learning analytics implementation design. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (1st ed., pp. 151–160). Edmonton: SoLAR.

    Chapter  Google Scholar 

  • Wise, A. F., Speer, J., Marbouti, F., & Hsiao, Y. (2013). Broadening the notion of participation in online discussions: Examining patterns in learners’ online listening behaviors. Instructional Science, 41(2), 323–343.

    Article  Google Scholar 

  • Wise, A. F., Zhao, Y., & Hausknecht, S. N. (2014). Learning analytics for online discussions: Embedded and extracted approaches. Journal of Learning Analytics, 1(2), 48–71.

    Article  Google Scholar 

  • Wise, A. F., Vytasek, J. M., Hausknecht, S. N., & Zhao, Y. (2016). Developing learning analytics design knowledge in the “middle space”: The student tuning model and align design framework for learning analytics use. Online Learning, 20(2), 1–28.

    Article  Google Scholar 

  • Wise, A. F., Cui, Y., & Jin, W. Q. (2017). Honing in on social learning networks in MOOC forums: Examining critical network definition decisions. In Proceedings of the seventh international conference on learning analytics & knowledge (pp. 383–392). Vancouver: ACM.

    Chapter  Google Scholar 

  • Yang, D., Sinha, T., Adamson, D., & Rose, C. P. (2013). Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses. In Proceedings of the 2013 NIPS workshop on data-driven education. Lake Tahoe: NIPS Foundation.

    Google Scholar 

  • Zheng, A. (2015). Evaluating machine learning models. Boston: O’Reilly Media.

    Google Scholar 

  • Zhu, M., Bergner, Y., Zhang, Y., Baker, R. S. J. D., Wang, Y., Paquette, L., & Barnes, T. (2016). Longitudinal engagement, performance, and social connectivity: A MOOC case study using exponential random graph models. In Proceedings of the sixth international conference on learning analytics and knowledge (pp. 223–230). Edinburgh: ACM.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Learning Analytics Research Network (LEARN), New York University, NY, USA

    Alyssa Friend Wise

Authors
  1. Alyssa Friend Wise
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alyssa Friend Wise .

Editor information

Editors and Affiliations

  1. College of Education, Washington State University, Pullman, WA, USA

    Olusola O. Adesope  & A. G. Rud  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2019 The Author(s)

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wise, A.F. (2019). Learning Analytics: Using Data-Informed Decision-Making to Improve Teaching and Learning. In: Adesope, O.O., Rud, A.G. (eds) Contemporary Technologies in Education. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-89680-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-89680-9_7

  • Published:

  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-319-89679-3

  • Online ISBN: 978-3-319-89680-9

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics

Search

Navigation