APEC XI FINAL 13.11.2016 v1 - home.kku.ac.th paper/apec2016... · APEC-KhonKaenUniversity International Symposium XI Workshop: Statistical Tasks for Implementing Cross-Border Lesson

OutlineOutline

APEC-Khon Kaen University International Symposium XI

Workshop: Statistical Tasks for Implementing Cross-Border Lesson Study on Energy Resiliency November 13, 2016 1

This workshop will be divided into two sessions:Session I:

– Statistical Literacy and Statistics Education– Understanding Variability– Analysis of Variability-related Tasks– Using the APEC Energy Database as a Resource for

Developing Statistical Tasks on Energy Resiliency

Session II:– Development of Statistical Tasks on Energy Resiliency by

the Participants– Participants’ Presentations of their Developed Statistical

Tasks

Statistical Literacy and Statistics EducationStatistical Literacy and Statistics Education






Why is statistics necessary?

• Today's society is a knowledge-based one, in whichstatistics plays an important role at personal,professional, national, global, and even educationallevels (Gal, 2004).

• Ordinary citizens must deal on a daily basis with a largevolume of varying information to draw conclusions,make predictions, describe trends, etc. (English &Watson, 2015; Shaughnessy, 2007).




Why is statistics necessary?

• All citizens need to be able to judge claims made by service providers, politicians and in the media.

• Some citizens will contribute by moving on to using formal statistics.




• Indeed, in today's society, it is more important thanever for all citizens to be able:– to explain, judge, and analyze varying information;– to provide good evidence-based arguments;– to critically evaluate data-based claims and

arguments coming from different sources ofinformation;

– to make the best possible decisions on the basis ofstatistical and probabilistic information.




• Therefore, these skills are regarded in many countries as an expectedoutcome of compulsory schooling and as a necessary component ofadults’ numeracy and literacy (Gal, 2004; English & Watson, 2015).

• For that reason, promotion of statistical literacy has been recentlysought by a number of school mathematics curricula worldwide.

• All these skills are related to being statistically literate.

• Promotion of statistical literacy aims at providing students with thestatistical knowledge and skills necessary to actively participate insociety by the moment they complete their compulsory education(Shaughnessy, 2007; Watson & Callingham, 2003; English & Watson,2015).


• The educational importance of producing studentsstatistically literate is particularly highlighted in themathematics curricula of Japan and Thailand.

• At senior high school level, in the latest Japanese Courseof Study (MEXT, 2008, 2009), one of the four strands inthe subject of Mathematics is “Analysis of Data” .

• In the case of Thailand, its latest Basic Education CoreCurriculum (2008) set 6 strands in the subject ofMathematics, being one of them “Data Analysis andProbability”.






• Exposure to statistics at secondary school is critical indeveloping students’ knowledge base and statistical literacyskills (Watson, 2006).

a. statistics at secondary school mathematics might be thelast and only statistics formal program taken by them.

b. for those students proceeding to tertiary education,exposure to statistics in secondary school would preparethem for statistics in college and university.




What are the “Big Ideas”behind Basic Statistical Understanding?

(Watson, 2013, 2014)




Are Five “Big Ideas” Enough?Variation

• Absolutely fundamental (Moore, 1990; Shaughnessy, 2007), and the property of Data that makes them interesting and requires Samples & Sampling.

Expectation• Complement to variation, “harness” variation.• Do this with:

- Trend/Center,- Models/Statistical models & modeling,- Association/covariation,- Comparing groups




Are Five “Big Ideas” Enough?Distribution

• The lens for viewing variation and confirming Expectation. The way to visualize Data.

Randomness• The foundation for probability, and the basis for Statistical

Inference and some aspects of Expectation. Also the basis for much of Samples & Sampling.

Informal inference• At school level develops the intuitions for using evidence

and acknowledges variation and uncertainty, leading to Statistical Inference.




a) the ability to interpret and critically evaluate statistical information, data-related arguments, or stochastic phenomena, which they may encounter in diverse contexts, and when relevant

b) the ability to discuss or communicate their reactions to such statistical information, such as their understanding of the meaning of the information, their opinions about the implications of this information, or their concerns regarding the acceptability of given conclusions.

Definition of Statistical Literacy for adults (Gal, 2004)


• According to Gal (2004), statistical literacy is the result of theinteraction between knowledge and dispositional elements.



Definition of Statistical Literacy for adults (Gal, 2004)




• The school curriculum should provide both− experience in the practice of statistics− opportunities to critically evaluate claims

of other students, science or social science reports, and media extracts

• Providing the first should assist with ability to do the second (Watson, 2014).

• But, how do we give students the opportunity to experience the “Practice of Statistics” in the classroom?




The Practice of Statistics: The PPDAC Model

Wild and Pfannkuch(1999) suggested the Investigative Cycle (better known as PPDAC Model) from their analysis of the work of students of statistics and practicing professional statisticians.




The Practice of Statistics: The PPDAC Model

• PPDAC is augmented with 3 other dimensions:

– Types of thinking: e.g., need for data, transnumeration, variation, context.

– Interrogative cycle: Generate, Seek, Interpret, Criticise, Judge.

– Dispositions: e.g., Scepticism, Imagination, Curiosity, Openness, Perseverance.




A Model for the Practice of Statistics at the school level (Watson, 2009)





• At school level, at least initially, some steps in the cycle are simplified, or include information provided for the student.

• Variation is important and explicit, because it is linked to every part of an investigation.

• Context is vital and is usually provided for students.• Analysis (A in PPDAC) is emphasized by highlighting

the importance of visualization and finding summary values, such as the mean or range.




• Informal inference is decision-making through an informal statistical investigation.

• Informal inference is based on a context and a meaningful question… – where evidence from the data is used– to make a generalization beyond the data– with an acknowledgement of uncertainty.

• It is informal because, at the school level, this does not involve the theory of t-tests and confidence intervals.





Hierarchical Framework for Statistical Literacy (Watson, 2006)

Three tiers for learning, applying, and assessing:• Tier 1: Understanding the terminology to be

used for statistical decision-making.• Tier 2: Interpreting the terminology in the

context presented.• Tier 3: Possessing ability and confidence to

challenge statements made in the context without proper statistical foundation.

Understanding VariabilityUnderstanding Variability






• Variability is regarded by many researchers as the veryreason for the existence of discipline of statistics (e.g.,Moore, 1990; Shaughnessy & Ciancetta, 2001).

• Variability is a characteristic of a statistical objectwhich accounts for its propensity to vary or change(Shaughnessy, 2007). Variation is the describing ormeasuring of that characteristic.


• Statistical literacy involves many skills related tovariation and variability, such as (Garfield & Ben-Zvi,2008; Watson, 2009):

– describing, measuring and representing variability,– using variability to make comparisons and informed

decisions,– recognizing variability in special kinds of

distributions, and– ability to identify, interpret, read, and explain

varying data, in order to make assessments,inferences and/or decisions.






• Energy resilience is the ability of an energy system to stabilize, provide and maintain an acceptable level of service within a given period of time in the face of various challenges to normal operation, after a sudden change of an indicator (Stoeglehner et al., 2016).

• “Resilient planning anticipates and accepts changing circumstances and, therefore, attempts to deal with uncertainties in advance” (p. 27)

• “Only by considering these perpetual processes of change, potential crises can be detected ex ante and the respective system might be prepared for no longer unexpected and convertible challenges” (p. 27)

Understanding VariabilityUnderstanding Variability• When analyzing data, people’s role is to be a “data detective”, to uncover

the stories that are hidden in the data (Shaughnessy, 2008).

• From a data-detectivepoint of view, studentsmust (e.g., Shaughnessy &Pfannkuch, 2002; Konold &Pollatsek, 2002; Pfannkuch& Ben-Zvi, 2011):

– deal with the variation,– look for patterns,– pose their own questions about the data,– interrogate the data,– learn new information about the real world

from the data (and vice versa),– generate hypotheses,– make judgments and predictions on the

basis of the data.

• Therefore, a continuous dialogue should exist between the data and context(Shaughnessy & Pfannkuch, 2002).

• Statistics is not about just numbers but about data, with data being"numbers in context".





Analysis of Variability-related Tasksusing Energy Data






• During the first part of this workshop, we discussed the modelsfor the Practice of Statistics at the school level developed byWild and Pfannkuch (1999) and Watson (2009).

• Challenging students with problems and questions in which theyhave to analyze data collected over time using graphicalrepresentations, provide students with opportunities toempower them as statistically literate citizens (e.g., English &Watson, 2015; Gal, 2004; Shaughnessy, 2007).




• As “data detectives”, people are expected:- to note important signals in the variability of the data,- to develop and share their reasoning about sources of

variation in data over time, and- to engage in informal inferential reasoning by using

data as evidence of estimations, predictions andgeneralizations (English & Watson, 2015; Makar &Rubin, 2009; Shaughnessy, 2007).

• Let us assume the role of “data detectives” again! This time with real-life energy data.




• According to Shaughnessy (2007),in order to analyze and interpretdata collected over time usinggraphical representations, peoplewill need to engage in thefollowing four levels of readinggraphs:

• When “reading behind the graph”, one might conjecture aboutthe possible causes that might affect the phenomenonrepresented over time (i.e., historical, economic, ordemographic potential sources of variation in the data), and usesome “data detective” skills to analyze graphical information.

• Reading the graph/data • Reading within the graph/data

• Reading beyond the graph/data• Reading behind the graph/data




• Shaughnessy (2007, p. 991) also listed the followingeight behaviors regarding understanding of graphs,considered to be critical to develop students’ basicgraph sense:




• Let us pose some questions to make students“read” this graph as “data detectives”!


• Any suggestion?

Energy Intensity is defined as the ratio of energy consumption to gross domestic product (GDP) and is an overall measure of how much energy is used to produce a unit of economic output in a country or a region.




• Energy Intensity is defined as the ratio of energy consumption to gross domestic product (GDP) and is an overall measure of how much energy is used to produce a unit of economic output in a country or a region.

• In economics, energy intensity is generally used to indicate how the energy efficiency is, it is, and the low energy intensity means high energy efficiency, while the high energy intensity means low energy efficiency.

• So, a high energy intensity means that few energy-saving technologies are present. Also, it means high price or cost of converting energy into GDP.




• Cornillie and Fankhauser (2004) argued that the two main factors influencing energy efficiency are higher energy prices and new technologies which increase the productivity of each unit of energy.

• Birol and Keppler (2000) also noted that higher energy prices can induce improvements in energy usage efficiency, thereby lowering energy intensity.

• Other key factors affecting energy intensity are capital investment and new construction, population and demographics, climate, technological innovation, energy policies and actions of national, state, and local governments.



• Using the graphical representation shown before, wecan ask students questions such as:- What was the overall pattern in the data?- Why might be the data varying in the way it is?- Why does the total Energy Intensity has a different

trend that the one shown by SE Asia and S America,but a similar one that the one shown by the otherregions?

- What might happened in the early 90’s in theregions under examination, to explain the behaviorof the Energy Intensity during that period?

- What might happen in the next five years, and why?




Using the APEC Energy Database as a Resource forDeveloping Statistical Tasks on Energy Resiliency

• We have access to the APEC Energy Database, compiledby the Asia Pacific Energy Research Centre (APERC).

• 100% real data, 100% free.

• http://www.ieej.or.jp/egeda/database/database-top.html







Questions and Comments are WelcomedQuestions and Comments are Welcomed



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APEC XI FINAL 13.11.2016 v1 - home.kku.ac.th paper/apec2016... · APEC-KhonKaenUniversity International Symposium XI Workshop: Statistical Tasks for Implementing Cross-Border Lesson