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Active and Agnostic: A Multidisciplinary Approach to Statistical Learning

Bezerra, Leonardo César Teonácio Vance, Eric A. ; Love, Kim ; Awe, O. Olawale

Promoting Statistical Practice and Collaboration in Developing Countries, 2022, p.341-350

CRC Press

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  • Título:
    Active and Agnostic: A Multidisciplinary Approach to Statistical Learning
  • Autor: Bezerra, Leonardo César Teonácio
  • Vance, Eric A. ; Love, Kim ; Awe, O. Olawale
  • É parte de: Promoting Statistical Practice and Collaboration in Developing Countries, 2022, p.341-350
  • Descrição: Statistical learning (SL) is naturally a multidisciplinary field, bridging technical concepts from statistics, math, and computer science with specialized application domain knowledge from fields as diverse as health, industry, sports, and social sciences. Yet, SL courses tend to follow the traditional methodology from technical sciences, with a strong emphasis on lectures. Furthermore, given the role of information technologies (ITs) in SL, students are expected to present above-average IT skills. In developing countries, skills such as computational thinking (CT), in particular computer programming, are often only available in university-level technical courses, which only a small fraction of the population can attend. These requirements combine to severely restrict access to SL courses and substantially reduce the benefits derived from its multidisciplinary nature. In this chapter, we describe a learning approach to leverage these multidisciplinary benefits. This approach is based on two principles: active learning and IT-agnostic educational objects. The first principle seeks to promote autonomy, with students organized in groups to present tutorials and develop projects. More importantly, these initiatives effectively help integrate students and bring them closer to the community and the market, with a portfolio that can be used in meetups, events, and/or job interviews. The second one seeks to include students from nontechnical fields, leveraging tools that reduce the need for an IT background. Specifically, we combine graphical user interface applications such as Spreadsheets and Orange3 with command-line interface Python libraries such as Pandas and Scikit-learn. More importantly, no educational object requires that students understand even basic computer programming concepts such as structured programming. Initial results from a pilot project in Brazil indicate the potential of this approach with high participation from students without a strong IT background. In addition, a growing set of educational object repositories are increasing the social impact of the project. In this chapter, the authors describe a learning approach to leverage these multidisciplinary benefits. In addition, a growing set of educational object repositories are increasing the social impact of the project. Several definitions of active learning have been proposed by various authors. Given the role of IT in most Statistical learning (SL) tools, traditional courses require above-average IT skills. More recent feedback provided by students led to a variant of this approach in which each student is assigned to two groups. Over the past year, the approach proposed in this chapter has been evaluated at Universidade Federal do Rio Grande do Norte, in Natal, Brazil. SL is a field in which multidisciplinarity is deeply rooted, leading to company teams composed of members who have contrasting and complementary backgrounds.
  • Editor: CRC Press
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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