A MODELING APPROACH TO THE DEVELOPMENT OF STUDENTS’ INFORMAL INFERENTIAL REASONING

2017 ◽  
Vol 16 (2) ◽  
pp. 86-115
Author(s):  
HELEN M. DOERR ◽  
ROBERT DELMAS ◽  
KATIE MAKAR
Keyword(s):  
Teaching And Learning ◽  
Statistics Education ◽  
Model Development ◽  
Primary Students ◽  
Instructional Sequence ◽  
Inferential Reasoning ◽  
Research Journal ◽  
Informal Inferential Reasoning ◽  
Coherent Sequence ◽  
Models And Modeling

Teaching from an informal statistical inference perspective can address the challenge of teaching statistics in a coherent way. We argue that activities that promote model-based reasoning address two additional challenges: providing a coherent sequence of topics and promoting the application of knowledge to novel situations. We take a models and modeling perspective as a framework for designing and implementing an instructional sequence of model development tasks focused on developing primary students’ generalized models for drawing informal inferences when comparing two sets of data. This study was conducted with 26 Year 5 students (ages 10-11). Our study provides empirical evidence for how a modeling perspective can bring together lines of research that hold potential for the teaching and learning of inferential reasoning. First published November 2017 at Statistics Education Research Journal Archives

scholarly journals THE CHALLENGE OF PREPARING PRESERVICE TEACHERS TO TEACH INFORMAL INFERENTIAL REASONING

2010 ◽  
Vol 9 (1) ◽  
pp. 46-67
Author(s):  
AISLING M. LEAVY
Keyword(s):  
Preservice Teachers ◽  
Lesson Study ◽  
Statistics Education ◽  
Pedagogical Knowledge ◽  
Primary Students ◽  
Inferential Reasoning ◽  
Primary Classrooms ◽  
Research Journal ◽  
Informal Inferential Reasoning ◽  
Informal Inference

There is growing recognition of the importance of developing young students’ informal inferential reasoning (IIR). This focus on informal inference in school statistics has implications for teacher education. This study reports on 26 preservice teachers utilizing Lesson Study to support a focus on the teaching of IIR in primary classrooms. Participants demonstrated proficiency reasoning about the elements fundamental to informal inferential reasoning but had difficulties developing pedagogical contexts to advance primary students’ informal inferential reasoning. Specifically, issues emerged relating to data type, an excessive focus on procedures, locating opportunities for IIR, and a lack of justification and evidence-based reading. Focusing on the lesson as the unit of analysis combined with classroom-based inquiry supported the development of statistical and pedagogical knowledge. First published May 2010 at Statistics Education Research Journal: Archives

scholarly journals THE ROLE OF PROBABILITY IN DEVELOPING LEARNERS’ MODELS OF SIMULATION APPROACHES TO INFERENCE

2016 ◽  
Vol 15 (2) ◽  
pp. 216-238
Author(s):  
HOLLYLYNNE S. LEE ◽  
HELEN M. DOERR ◽  
DUNG TRAN ◽  
JENNIFER N. LOVETT
Keyword(s):  
Statistics Education ◽  
Model Development ◽  
Problem Situation ◽  
Task Sequence ◽  
The Core ◽  
Research Journal ◽  
Repeated Sampling ◽  
Sampling Approach ◽  
Models And Modeling

Repeated sampling approaches to inference that rely on simulations have recently gained prominence in statistics education, and probabilistic concepts are at the core of this approach. In this approach, learners need to develop a mapping among the problem situation, a physical enactment, computer representations, and the underlying randomization and sampling processes. We explicate the role of probability in this approach and draw upon a models and modeling perspective to support the development of teachers’ models for using a repeated sampling approach for inference. We explicate the model development task sequence and examine the teachers’ representations of their conceptualizations of a repeated sampling approach for inference. We propose key conceptualizations that can guide instruction when using simulations and repeated sampling for drawing inferences. First published November 2016 at Statistics Education Research Journal Archives

ASSESSMENT OF INFORMAL AND FORMAL INFERENTIAL REASONING: A CRITICAL RESEARCH REVIEW

2019 ◽  
Vol 18 (1) ◽  
pp. 8-25
Author(s):  
MARIA GUADALUPE TOBÍAS-LARA ◽  
ANA LUISA GÓMEZ-BLANCARTE
Keyword(s):  
Education Research ◽  
Data Analysis ◽  
Statistics Education ◽  
Research Review ◽  
Inferential Reasoning ◽  
Critical Research ◽  
Statistics Education Research ◽  
Research Journal ◽  
Informal Inferential Reasoning

As a contribution to the discussion on the assessment of informal inferential reasoning (IIR) and the transition from this to formal inferential reasoning (FIR), we present a review of research on how these two types of inferential reasoning have been conceptualized and assessed. Based on our review, we discuss the need to redefine the conceptions of IIR and FIR in order to create an integrated description of inferential reasoning that includes not only ideas of IIR and FIR, but also the whole activity of argumentation, which involves the production of both statistical and contextual reasons. Current descriptions of IIR and FIR list the facts that might be brought from data analysis to the process of inferential reasoning. The approach we propose considers how the facts, both statistical and contextual, can be used as arguments, leading to assessments of students’ inferential  reasoning focusing on articulating the statistical and contextual reasons students present to support an inference. First published May 2019 at Statistics Education Research Journal Archives

scholarly journals STUDENTS’ EXPRESSIONS OF UNCERTAINTY IN MAKING INFORMAL INFERENCE WHEN ENGAGED IN A STATISTICAL INVESTIGATION USING TINKERPLOTS

2016 ◽  
Vol 15 (2) ◽  
pp. 62-80
Author(s):  
ANA HENRIQUES ◽  
HÉLIA OLIVEIRA
Keyword(s):  
Education Research ◽  
Data Collection ◽  
Statistical Inference ◽  
Statistics Education ◽  
Statistical Investigation ◽  
Inferential Reasoning ◽  
Research Journal ◽  
Informal Inferential Reasoning ◽  
8Th Grade ◽  
Informal Inference

This paper reports on the results of a study investigating the potential to embed Informal Statistical Inference in statistical investigations, using TinkerPlots, for assisting 8th grade students’ informal inferential reasoning to emerge, particularly their articulations of uncertainty. Data collection included students’ written work on a statistical investigation as well as audio and screen records. Results show students’ ability to draw conclusions based on data, recognizing that these are constrained by uncertainty, and to use them to make inferences. However, few students used probabilistic language for describing their generalizations. These results highlight the need for working on probabilistic ideas within statistics, helping students to evolve from a deterministic perspective of inference to include uncertainty in their statements. First published November 2016 at Statistics Education Research Journal Archives

scholarly journals Moving beyond descriptive models: Research issues for design and implementation

2020 ◽  
pp. 5-20
Author(s):  
Jonas Bergman Ärlebäck ◽  
Helen M. Doerr
Keyword(s):  
Teaching And Learning ◽  
Explanatory Power ◽  
Model Development ◽  
Implementation Strategies ◽  
Central Research ◽  
Research Issues ◽  
Research Goal ◽  
Support Students ◽  
Descriptive Models ◽  
Models And Modeling

In this paper, we draw on a models and modeling perspective to describe the design of a sequence of tasks, known as a model development sequence, that has been used to research the teaching and learning of mathematics. A central research goal of a models and modeling perspective is the development of principles for the design of sequences of modeling tasks and for the teaching of such sequences. We extend our earlier research by elaborating how a model development sequence can be used to support students in developing models that are not only descriptive but also have explanatory power when connected to existing mathematical models. In so doing, we elaborate language issues about representations and context as well as the implementation strategies used by the teacher.

STUDENTS’ EMERGENT ARTICULATIONS OF STATISTICAL MODELS AND MODELING IN MAKING INFORMAL STATISTICAL INFERENCES

2017 ◽  
Vol 16 (2) ◽  
pp. 116-143
Author(s):  
HANA MANOR BRAHAM ◽  
DANI BEN-ZVI
Keyword(s):  
Statistical Models ◽  
Statistics Education ◽  
Fundamental Aspect ◽  
School Students ◽  
Real World Data ◽  
Research Journal ◽  
Statistical Inferences ◽  
Exploratory Data ◽  
The Relationship ◽  
Models And Modeling

A fundamental aspect of statistical inference is representation of real-world data using statistical models. This article analyzes students’ articulations of statistical models and modeling during their first steps in making informal statistical inferences. An integrated modeling approach (IMA) was designed and implemented to help students understand the relationship between sample and population, as well as reasoning with models and modeling. We explore the articulations of a pair of primary school students, who had previously participated in the Connections Project exploratory data analysis (EDA) activities, and suggest an emergent conceptual framework for reasoning with statistical models and modeling. We shed light on ideas of statistical models and modeling that can emerge among primary students and how they articulate those ideas. Implications for teaching and research are discussed. First published November 2017 at Statistics Education Research Journal Archives

scholarly journals ROLES OF TECHNOLOGY IN STUDENT LEARNING OF UNIVERSITY LEVEL BIOSTATISTICS

2014 ◽  
Vol 13 (1) ◽  
pp. 66-76
Author(s):  
WEILI XU ◽  
YUCHEN ZHANG ◽  
CHENG SU ◽  
ZHUANG CUI ◽  
XIUYING QI
Keyword(s):  
Student Learning ◽  
Undergraduate Students ◽  
Teaching And Learning ◽  
Statistics Education ◽  
Threshold Concepts ◽  
Research Journal ◽  
Technology In Teaching ◽  
University Level ◽  
Troublesome Knowledge ◽  
Improve Student Learning

This study explored threshold concepts and areas of troublesome knowledge among students enrolled in a basic biostatistics course at the university level. The main area of troublesome knowledge among students was targeted by using technology to improve student learning. A total of 102 undergraduate students who responded to structured questionnaires were included in this study. The results suggest that threshold concepts regarding “statistics” and “random sample” need to be better understood. “Confidence interval” and “hypothesis testing” were the two most frequent troublesome areas among the participants.The pedagogical role of technology in teaching and learning statistics, and the mechanisms whereby technology may improve student learning were discussed. First published May 2014 at Statistics Education Research Journal Archives

scholarly journals PUBLISHING IN SERJ: AN ANALYSIS OF PAPERS FROM 2002–2009

2011 ◽  
Vol 10 (2) ◽  
pp. 5-26
Author(s):  
ANDREW ZIEFFLER ◽  
JOAN GARFIELD ◽  
ROBERT C. DELMAS ◽  
LAURA LE ◽  
REBEKAH ISAAK ◽  
...  
Keyword(s):  
Education Research ◽  
Detailed Analysis ◽  
Teaching And Learning ◽  
Statistics Education ◽  
Publishing Research ◽  
Future Research ◽  
High Quality ◽  
Future Directions ◽  
Statistics Education Research ◽  
Research Journal

SERJ has provided a high quality professional publication venue for researchers in statistics education for close to a decade. This paper presents a review of the articles published to explore what they suggest about the field of statistics education, the researchers, the questions addressed, and the growing knowledge base on teaching and learning statistics. We present a detailed analysis of these articles in order to address the following questions: What is being published and why, who is publishing research in SERJ, how is the research being carried out, and what do the results suggest about future research? Implications for future directions in statistics education research are suggested. First published November 2011 at Statistics Education Research Journal: Archives

scholarly journals STATISTICS GRADUATE TEACHING ASSISTANTS’ BELIEFS, PRACTICES AND PREPARATION FOR TEACHING INTRODUCTORY STATISTICS

2017 ◽  
Vol 16 (1) ◽  
pp. 294-319
Author(s):  
NICOLA JUSTICE ◽  
ANDREW ZIEFFLER ◽  
JOAN GARFIELD
Keyword(s):  
Teaching And Learning ◽  
Online Survey ◽  
Teaching Assistants ◽  
Statistics Education ◽  
The United States ◽  
Graduate Teaching Assistants ◽  
Pedagogical Practices ◽  
Introductory Statistics ◽  
Graduate Teaching ◽  
Research Journal

Graduate teaching assistants (GTAs) are responsible for the instruction of many statistics courses offered at the university level, yet little is known about these students’ preparation for teaching, their beliefs about how introductory statistics should be taught, or the pedagogical practices of the courses they teach. An online survey to examine these characteristics was developed and administered as part of an NSF-funded project. The results, based on responses from 213 GTAs representing 38 Ph.D.–granting statistics departments in the United States, suggest that many GTAs have not experienced the types of professional development related to teaching supported in the literature. Evidence was also found to suggest that, in general, GTAs teach in ways that are not aligned with their own beliefs. Furthermore, their teaching practices are not aligned with professionally-endorsed recommendations for teaching and learning statistics. First published May 2017 at Statistics Education Research Journal Archives

scholarly journals INTERPRETATION OF STATISTICAL DATA: THE IMPORTANCE OF AFFECTIVE EXPRESSIONS

2017 ◽  
Vol 16 (1) ◽  
pp. 163-180
Author(s):  
TAMIRES QUEIROZ ◽  
CARLOS MONTEIRO ◽  
LILIANE CARVALHO ◽  
KAREN FRANÇOIS
Keyword(s):  
Undergraduate Students ◽  
Teaching And Learning ◽  
Statistical Data ◽  
Statistics Education ◽  
Affective Expression ◽  
Research Journal ◽  
Professional Careers ◽  
Complex Process ◽  
Research On Teaching ◽  
Affective Expressions

In recent years, research on teaching and learning of statistics emphasized that the interpretation of data is a complex process that involves cognitive and technical aspects. However, it is a human activity that involves also contextual and affective aspects. This view is in line with research on affectivity and cognition. While the affective aspects are recognized as important for the interpretation of data, they were not sufficiently discussed in the literature. This paper examines topics from an empirical study that investigates the influence of affective expression during the interpretation of statistical data by final-year undergraduate students of statistics and pedagogy. These two university courses have different curricular components, which are related to specific goals in the future professional careers of the students. The results suggest that despite differing academic backgrounds in both groups, the participants’ affective expressions were the most frequent type of category used during the interpretation of research assignments. First published May 2017 at Statistics Education Research Journal Archives

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