Emerging Trends in Statistics Education
Visibilizing statistical concepts of very young students
Book chapter example (Leavy & Hourigan, 2018)
Leavy & Hourigan (2018, p. 95)
Analyzed 5-6-year-old inscriptions when collecting data by tracking the appearance of zoo animals in a video.
Argued inscriptions served as a record of the event and represented the beginnings of abstract thought.
How Kindergarten and Elementary School Students Understand the Concept of Classification (Guimaraes & Oliveira, 2018)
Introducing children to modeling variability (*Lehrer & English, 2018, p. 232)
Data modelling cycle used extensively by other researchers (e.g., Fielding-Wells (2018), Kazak et al. (2018))
Synthesized diverse research studies to show the potential of involving young students in an approximation to professional practice and simultaneously co-constructing statistical concepts.
“Schooling in the early years should support children to participate in practices of data modeling so that they are in a better position to appreciate and even participate in this increasingly data-centric world.” (p. 235)
Visibilizing statistical concepts
Visibilizing students’ statistical conceptions through interactive visual approaches is leading to some novel insights into their reasoning and conceptions.
The role of model comparison in young learners’ reasoning with statistical models and modeling (Dvir & Ben-Zvi, 2018)
Student conjecture: the longer the arm span, the greater the number of rope jumps
Pedagogical implication: Explicitly ask students to provide conjecture model and explicitly ask students to compare their conjecture model with actual data model
Students’ use of narrative when constructing statistical models in TinkerPlots (*Noll et al., 2018)
Using narrative theory, Noll et al. found that when introductory students constructed models in TinkerPlots they were influenced by the story of the situation, guessing musical notes correctly, and hence created and preferred models that were narrative in nature. Their data showed that the students’ models that followed a narrative path often led to productive statistical models.
“…the sharing and discussion of these statistical narratives we may be able to shape productive statistical thinking skills (modeling, inference, data organization, etc.), …” (p. 1278).
Pedagogical implications: Build on students’ preference for a narrative path.
Visibilizing statistical concepts and argumentation that have been absent in the curriculum
Makar & Rubin (2018) chapter on Learning about Statistical Inference
Trace how statistical inference ideas can be nurtured from a young age and grown across the curriculum towards simulation-based statistical inference methods prompted by Cobb (2007)
E,g., Biehler et al. (2018) chapter on Reasoning about Data
Visibilizing statistical concepts and argumentation that have been absent in the curriculum
What makes a good statistical question? (Arnold & Franklin, 2021)
Identify four question types necessary to foster productive statistical inquiry:
Foster and nurture the disposition to question and to interrogate data-based information
Emerging Trends in Statistics Education
Statistical literacy: Meanings, components, responsibilities (Gal, 2002)
Statistical literacy
People's ability to interpret and critically evaluate statistical information and data-related arguments in diverse contexts, and to communicate a reasoned opinion on the findings.
When students interact with data-based information need to jointly activate:
Knowledge Component
Literacy skills
Statistical knowledge
Mathematical knowledge
Contextual knowledge
List of critical “worry” questions
Dispositional Component
Critical stance
Beliefs and attitudes
Limited Action:
e.g., Callingham & Watson (2017)
Auckland University 2004
NZ curriculum 2007
ProCivicStat (2018)
(Built on Gal’s (2002) work)
ProCivicStat (2018)
2022
(Built on Gal’s (2002) work)
Provides instructional resources and conceptual frameworks for educators to design tasks based on ProCivicStat research
*Gal & Geiger (2022)updated Gal (2002)
Researched current statistical demands in the media
Main finding:
Primary means of communicating statistical information was text-based, written and spoken
Students need to identify and comprehend statistical information embedded in text, which is often conveyed implicitly and in everyday language
Learners should be able “to deconstruct rhetorical and argumentative styles they will encounter when reading and interpreting statistical messages” (ProCivicStat, 2018)
Souza & Araújo (2022) illustration of problem to be addressed
The pandemic and misinformation
Souza & Araújo, 2022
First Covid-19 case recorded March 12, 2020
Misinformation: Do the numbers make sense?
First Covid-19 case recorded March 12, 2020
Souza & Araújo, 2022
Misinformation and disinformation
Souza & Araújo, 2022
Urgent need for statistics educators to consider their unique role in assisting students to interrogate such information
… have the statistical knowledge to deconstruct and interpret statistical information embedded in text
Deconstructed the techniques journalist used to spread disinformation including language and tone to persuade people to adopt his beliefs
Social Justice
Zapata-Cardona (2018)
Socio-cultural perspective – immerse students in tasks where the data context allows them to “develop awareness of their surroundings and participation in the world”
(e.g., students investigated nutritious value of their lunch – “we are overconsuming calories” – critical citizen)
*Souza et al. (2020)
Creative insubordination theory – urge statistics educators to provide tools for students to become political activists
(e.g., School canteen food complaints reframed as food wastage, students used investigation findings to advocate for change)
Spatial Justice (Rubel et al., 2017
Students realized that compared to other neighborhoods, their community had more pawn shops, fewer bank facilities
Spatial data provides opportunities for students to glean geographical information to unlock stories in the data and to open their eyes to inequities in society
Social Statistics: Risk
Covid-19 crisis has raised awareness that our social coexistence and political decisions are essentially based on data, the weighing of risks … (Engel & Wilhelm, 2021)
Risk ideas need to be in the curriculum–research gap– good place to start–the risk-know-how framework (Brown et al., 2021)
(b) Find suitable and reliable risk information
(v) Understand recurrence intervals and averages. A 1-in-100-year flood is an average over the data we have, so two such floods could happen in back-to-back years, or 160 years apart.
How should a 1-in-a-100-year flood be communicated?
I am considering buying a property where I am told the flood risk is one in a 100 years
OR
I am considering buying a property where I am told there is almost a 10% chance of a flood happening in a ten-year period
Social statistics
Curriculum developers and researchers need to pay attention to critical statistical literacy (Weiland, 2017) by:
Assisting students to become knowledgeable citizens and advocates by learning how to investigate, communicate, and interrogate data-based situations as a producer and consumer of data
Conclusion
Across trends:
Acknowledgement: Access to technology
Recommendation: Get students “technology ready” using unplugged activities
Grade 6: Decision tree created with data cards (Podworny et al., 2022)
Social justice issues in a pencil-and-paper environment (Zapata-Cardona, 2018)
Algorithmic predictive modelling, start with data cards (ProDaBi Project)
Limitations
Possible future research avenues
Incorporation of other peoples’ views and cultures into statistics curricula (e.g., data sovereignty, Mātauranga Māori knowledge (NZ Ministry of Education, 2023), Pasifika Values (NZ Ministry of Education, 2020), lack of education on “cultural side of data and analytics” (Analyst large supermarket chain, NZSA newsletter, 2023))
Challenge for researchers and curriculum developers
To stay abreast of current and future developments in society and developments that will be relevant to students’ lives.
Dilemma:
How can we speed up our research, so it is timely?
Questions? Comments?
If you want to contact us about any issues raised, please email:
Gail (burrill@msu.edu)
Maxine (m.pfannkuch@auckland.ac.nz )