The challenge of developing students'
statistical reasoning in times of Data Science:
Chilean experiences in Early Statistics
Raimundo Olfos & Soledad Estrella
Facultad de Ciencias, Pontificia Universidad Católica de Valparaíso, Chile
18.10.2022, APEC-KKU 2022

This research is supported by the ANID:
Fondef ID20i10070 y Fondecyt 1200346

Pontificia Universidad Católica de Valparaíso

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Teaching and learning data science• With the proliferation of big datain many
areas such as industry or social sciences,competent reasoning about data has
become even more critical (Biehler et al.,2018).
• From the perspective of statistics education,
data science requires students to gain "newskills" and reinforce informal inferential
reasoning by building inferences based ondata analysis.
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EXPANDING
FRAMEWORKS FORINVESTIGATING DATA

Frame
problem

Communicate✓ Analize and identify models that& proposeaddress the problema
action✓ Consider assumptions and context ofthe models✓ Recognize posible limitations✓ Analize and identify models that
address the problema✓ Consider assumptions and context ofthe models✓ Recognize posible limitations✓ Consider real-world phenomena &broader issues related to problema✓ Pose investigative question(s)✓ Anticipate potential data andstrategies

Consider &gather data✓DATA
INVESTIGATIONPROCESSFRAMEWORK

Considermodel

Key practices and processes for

K–12 data science education

Process data

Explore &visualize data

Lee et al. 2022
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Understandposibleattributes,measurements, and data collection
methods needed for the problema
Evaluate and use appropiate design andtechniques to collect or source data

✓ Organice, structure, clean, andtransform data in efficient and usefulways✓ Consider additional data cases orattributes.

✓ Construct meaningful visualizations,
static or Dynamic✓ Explore and analize data for potential
relationships or patterns that address
the problem

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Educating students in data science• ….goes beyond teaching aboutalgorithms,
skillsofmanipulating
datasets,selectingandapplying
appropriate
analyses, andcreating and interpreting visual
representations of data.

• It also involves raising a criticalunderstanding of how data areproduced and how they can beused for particular purposes,
including the role of context ininterpreting data. It emphasizes
developing an awareness for data
ethics, and considering theimplications for policy and society
when powerful algorithms areused.

Biehler et al. 2022
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Informal Statistical Inference (ISI)
Pose a question about
the population

C

Formulate a hypothesis
about the population

ommunicate
results

Inference fromreal samples topopulation

ICnfer about theollect

population

samples of data

Analyzesamples of data
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Design Considerations for the Statistical Learning Environment for
research into the teaching and learning of data science with
Lesson Study
These dimensions provide a wide spectrumof factors or starting points, which must be
considered and balanced in the design oflearning
environments
in statistics
education.
The goal of designing positive and effective
statistics learning environments is forstudents to develop a deep and meaningfulunderstanding of statistics and have theopportunity to develop their ability to think
and reason statistically (as data scientist).

Network of six interrelated dimensions of a learning environment.
Cobb & McClain, 2004; Ben-Zvi, Gravemeijer & Ainley, 2018; Ben-Zvi (2021);
Estrella (2014); Estrella et al. (2020)

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How?: playful situations, stories and apps• Games ------------------------------- ∙Stories (with artefacts) --------------------------∙ App----

It is important to craft a data story to convey insight about theproblem to the audience so they can make inferences and decisions.

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Grades K-3: Learning trajectory for the developmentof the Informal Inference Reasoning
Paso
Step 4.
4.
Paso
Step 3.

Paso
Step 2.
Paso
1.
Step 1.

Visualize andrecognizela
theVisualizary reconocervariation insamplesvariacióndethe
la muestras

Becomeaware of the
Tomarconciencia
delregularbehaviorofcomportamiento thesamplesde
bylasassigningaregularmuestrasconfidenceun
level
to eachasignando
nivel
deevent
confianza a cada evento

Generateenunciados
statements
Generarbeyondavailablemás
allá thede losdatos
data using expressions
disponibles
utilizando
of uncertainty
expresiones
deincertidumbre

Make aunaprediction
that
Hacer
predicción
contrasts
with thequecontraste
condatalosobtaineddatos obtenidos

How can we harvest the full potential of integrated pedagogies that provides a next step
forward in data science education?
In early age → Gradually with age → Formal learning
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The research and development (R & D) project:
Preschool Teachers Scaffolding in Mathematics

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Resources for teaching and learning

Communityof practice

Monitoring

Digital Plataform
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The last secquence of the 12 topics(built using Lesson Study)

MeasureIs the door wide enough?

• Recipe

PreKínder

Which worm is longest?

KínderAPEC KKU 2022

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Thank you!
estudiodeclases.cl

estadisticatemprana.cl
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References• -Olfos, R., Vergara, A., Estrella, S., & Goldrine, T. (2022). Impact of a theory-practice connecting scaffolding system on the abilityof preschool teachers-in-training to teach mathematics. Teaching and Teacher Education, 20,103887. https://doi.org/10.1016/j.tate.2022.103887• -Estrella, S., Méndez-Reina, M., Olfos, R., & Aguilera, J. (2022). Early statistics in kindergarten: Analysis of an educator’s
pedagogical content knowledge in lessons promoting informal inferential reasoning. International Journal for Lesson andLearning Studies, 11(1), 1-13. https://doi.org/10.1108/IJLLS-07-2021-0061• -Olfos, R., Goldrine, T., Morales, S., Estrella, S., Morales, G., Pinto, A., & Reyes, P. (2022). Pauta para evaluar la dimensión
práctica de la capacidad de enseñanza de la matemática en maestras de infantil en formación. PNA, Revista de Investigación
en Didáctica de la Matemática, 16(4), 343-364. https://doi.org/10.30827/pna.v16i4.22125 (Acceder a artículo).

• -Isoda, M., Olfos, R., Estrella, S., & Yamamoto-Baldin, Y. (2022). Duas contribuições da lesson study japonesa para a formaçãode professores de matemática: a terminologia eficaz para planejamento de aulas e como força promotora de grupos de estudo
sustentáveis. Educação Matemática em Revista, 23(1), 98-112. (Acceder a artículo).
• -Estrella, S., Vergara, A., & González, O. (2021). El desarrollo del sentido del dato: haciendo inferencias desde la variabilidad delos tsunamis en primaria/Developing data sense: making inferences from variability in tsunamis at primary school. Statistics
Education Research Journal, 20(2), 1-14. https://doi.org/10.52041/serj.v20i2.413. (Acceder a artículo español) (English) Scopus• -Isoda, M., Estrella, S., Zakaryan, D., Baldin, Y., Olfos, R., & Araya, R. (2021). Digital competence of a teacher involved in theimplementation of a cross-border lesson for classrooms in Brazil and Chile. International Journal for Lesson and Learning
Studies, 10(4), 362-377. https://doi.org/10.1108/IJLLS-05-2021-0045• -Estrella, S., Zakaryan, D., Olfos, R., & Espinoza, G. (2020). How teachers learn to maintain the cognitive demand of tasks
through Lesson Study. Journal of Mathematics Teacher Education, 23, 293 – 310. https://doi.org/10.1007/s10857-018-09423-y

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