Skip to main content

Abstract

Now, and in the times that follow, student education should focus on developing inclusive skills such as problem-solving and decision-making, where the role of the learning environment plays a crucial part, i.e., it is a process where the screen of the universe of discourse is accomplished in order to consider not only the complex relationships that flow among the objects that populate it, but also its inner structure, co-existing incomplete/unknown or even self-contradictory information or knowledge. As a result, we will focus on the development of an Intelligent Social Machine to assess Learning Environments in high schools, based on factors like School and Disciplinary Climates as well as Parental Involvement. The formal background will be to use Logic Programming to define its architecture based on a Deep Learning-Big Data approach to Knowledge Representation and Reasoning, complemented by an Evolutionary approach to Computing grounded on Virtual Intellects.

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

Access this chapter

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
Softcover Book
USD 169.99
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

References

  1. Kraft, M.A., Marinell, W.H., Yee, D.: School Organizational Contexts, Teacher Turnover, and Student Achievement: Evidence from Panel Data. March 2016, https://steinhardt.nyu.edu/scmsAdmin/media/users/sg158/PDFs/schools_as_organizations/SchoolOrganizationalContexts_WorkingPaper.pdf. Accessed 23 Jan 2018

  2. Richardson, C., Mishra, P.: Learning environments that support student creativity: developing the SCALE. Think. Skills Creat. 27, 45–54 (2018)

    Article  Google Scholar 

  3. Shernoff, D.J., et al.: Student engagement as a function of environmental complexity in high school classrooms. Learn. Instr. 43, 52–60 (2016)

    Article  Google Scholar 

  4. Poveya, J., et al.: The impact of parental involvement, parental support and family education on pupil achievements and adjustment: a literature review. Int. J. Educ. Res. 79, 128–141 (2016)

    Article  Google Scholar 

  5. Castro, M., et al.: Parental involvement on student academic achievement: a meta-analysis. Educ. Res. Rev. 14, 33–46 (2015)

    Article  Google Scholar 

  6. Thapa, A., Cohen, J., Guffey, S., Higgins-D’Alessandro, A.: A review of school climate research. Rev. Educ. Res. 83, 357–385 (2013)

    Article  Google Scholar 

  7. OECD: PISA 2015 Results (Volume II): Policies and Practices for Successful Schools, PISA. OECD Publishing, Paris (2016)

    Google Scholar 

  8. Fernandes, F., et al.: Artificial neural networks in diabetes control. In: Proceedings of the 2015 Science and Information Conference (SAI 2015), pp. 362–370. IEEE Edition (2015)

    Google Scholar 

  9. Silva, A., et al.: Length of stay in intensive care units – a case base evaluation. In: Fujita, H., Papadopoulos, G.A. (eds.) New Trends in Software Methodologies, Tools and Techniques, Frontiers in Artificial Intelligence and Applications, vol. 286, pp. 191–202. IOS Press, Amsterdam (2016)

    Google Scholar 

  10. Fernandes, A., Vicente, H., Figueiredo, M., Neves, M., Neves, J.: An adaptive and evolutionary model to assess the organizational efficiency in training corporations. In: Dang, T.K., et al. (eds.) Future Data and Security Engineering. LNCS, vol. 10018, pp. 415–428. Springer, Cham (2016)

    Chapter  Google Scholar 

  11. Neves, J., et al.: Evolutionary intelligence in asphalt pavement modelling and quality-of-information. Prog. Artif. Intell. J. Springer (2011). https://doi.org/10.1007/s13748-011-0003-5

    Article  Google Scholar 

  12. Florkowski, C.M.: Sensitivity, Specificity, Receiver-Operating Characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. Clin. Biochemist. Rev. 29(Suppl. 1), S83–S87 (2008)

    Google Scholar 

Download references

Acknowledgments

This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to José Neves .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Figueiredo, M., Vicente, H., Ribeiro, J., Neves, J. (2019). Awareness of School Learning Environments. In: Di Mascio, T., et al. Methodologies and Intelligent Systems for Technology Enhanced Learning, 8th International Conference. MIS4TEL 2018. Advances in Intelligent Systems and Computing, vol 804. Springer, Cham. https://doi.org/10.1007/978-3-319-98872-6_18

Download citation

Publish with us

Policies and ethics