Awareness of School Learning Environments

  • Margarida Figueiredo
  • Henrique Vicente
  • Jorge Ribeiro
  • José NevesEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 804)


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.


Artificial Intelligence Intelligent Learning Environments Logic Programming Knowledge Representation and Reasoning Evolutionary Computation Intelligent Social Machine 



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.


  1. 1.
    Kraft, M.A., Marinell, W.H., Yee, D.: School Organizational Contexts, Teacher Turnover, and Student Achievement: Evidence from Panel Data. March 2016, Accessed 23 Jan 2018
  2. 2.
    Richardson, C., Mishra, P.: Learning environments that support student creativity: developing the SCALE. Think. Skills Creat. 27, 45–54 (2018)CrossRefGoogle Scholar
  3. 3.
    Shernoff, D.J., et al.: Student engagement as a function of environmental complexity in high school classrooms. Learn. Instr. 43, 52–60 (2016)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 5.
    Castro, M., et al.: Parental involvement on student academic achievement: a meta-analysis. Educ. Res. Rev. 14, 33–46 (2015)CrossRefGoogle Scholar
  6. 6.
    Thapa, A., Cohen, J., Guffey, S., Higgins-D’Alessandro, A.: A review of school climate research. Rev. Educ. Res. 83, 357–385 (2013)CrossRefGoogle Scholar
  7. 7.
    OECD: PISA 2015 Results (Volume II): Policies and Practices for Successful Schools, PISA. OECD Publishing, Paris (2016)Google Scholar
  8. 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. 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. 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)CrossRefGoogle Scholar
  11. 11.
    Neves, J., et al.: Evolutionary intelligence in asphalt pavement modelling and quality-of-information. Prog. Artif. Intell. J. Springer (2011). Scholar
  12. 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

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Departamento de Química, Escola de Ciências e Tecnologia, Centro de Investigação em Educação e PsicologiaUniversidade de ÉvoraÉvoraPortugal
  2. 2.Departamento de Química, Escola de Ciências e Tecnologia, Centro de Química de ÉvoraUniversidade de ÉvoraÉvoraPortugal
  3. 3.Centro AlgoritmiUniversidade do MinhoBragaPortugal
  4. 4.Escola Superior de Tecnologia e Gestão, ARC4DigiT – Applied Research Center for Digital TransformationInstituto Politécnico de Viana do CasteloViana do CasteloPortugal

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