Focus on PostersP1: Monday 9th, 17:30-18:30
P2: Tuesday 10th, 17:30-18:30
Note: only the presenter is
Poster session P1 (Monday 9th, 17:30-18:30, Foyer)
|The Impact of Homework Feedback on Statistical Literacy in Austrian Computer Science Students
|This paper compares statistical literacy measured by a self-developed test between two types of feedback on homework assignments in a basic statistics course: (A) no immediate feedback from the lecturer and (B) immediate feedback from the lecturer. Group A consists of around 200 business computer science students and group B of around 200 computer science students. Each group was given a week to prepare a homework assignment (in total 10 assignments had to be prepared). The students of group A handed in the homework on paper and got back the corrected homework after a week. The students of group B presented the homework in class, so immediate feedback could be provided. At the end of the semester the statistical literacy of these two groups was compared.
|Learning Statistics from data
|Digging out information from data is essential for learning Statistics. Modern computing and communication technologies can make data collection very efficient. However, our ability to analyze and extract information from large data sets is hard-pressed to keep up with our capacity for data collection. It is know that all statistical methods are developed with some data analysis tasks in mind. However, when we teach these fabulous statistical methods, we usually apply a "topic-oriented" teaching scheme, which is useful to let students learn the skill of particular methods. However, it is hard to pass the original thoughts or reasons why we developed these methods. Here we like to share our experience with problem/data analysis oriented teaching arrangement for an introduction course in our data science program.
|Practice of Proactive Utilization of Mathematical Representation in Learning of Statistics
|By providing the children the trial and error training, I urge the children to make use of mathematical representation on their own initiative. So, as a developmental learning of statistics of the first year senior high school, I dealt with the transformation of the variable based on the roll of the dice. From the previous result, I asked the teacher to let the students discover the problem with the change of numbers/variables from the rolling of the dice. The students' trial and error development on the new data in the following cases, they analyzed and concluded: (1) new data obtained by adding constants all together, (2) new data obtained by multiplying data all. As a result of practice, I could encourage subjective use of mathematical representation.
|Context of statistical graphs in primary school textbooks in Costa Rica
|The aim of this research was to analyse the tasks including statistical graphs in primary education textbooks in Costa Rica. It is part of a more extensive project, where the way in which statistical graphs are presented in the textbooks in Costa Rica is analysed. In order to achieve this aim we performed a content analysis of all the activities (n=167) related to statistical graphs in the two books series (grades 1rst to 6th) which are most widely used in Costa Rica. Specifically we analyse the graph context, taking into account the categories proposed in the PISA tests, which have contributed to the current renewed interest in context-based education
|Learning of statistics in a Mexican telescondary school: impact of statistical enquiry in students outcomes
|Based on some guidelines of Wild and Pfannkuch (1999), Sovak (2003) and Makar (2008) for the implementation and evalution of statistical enquiry in the classroom, a didactic strategy was designed to improve the learning of statistics in third-grade students of a Mexican telesecondary -secondary school with televised classes-. We analyzed the impact of this strategy, for which a quasi-experimental study was carried out with three control groups and one experimental group. To evaluate the learning outcomes, a pretest and postest based on the CAOS-4 test were applied. The quantitative analysis performed shows an improvement in the learning of curricular statistical knowledge. This suggests that statistical enquiry is a method that helps, and motivates, students to learn the main subjects of statistics for the secondary level.
|The introduction of chemometrics course as a step forward to modernization of study curriculum at engineering faculties
|Chemometrics, as a relatively young scientific discipline, has developed during 1970s. In 1980s Chemometrics was distinguished as a scientific discipline and multidisciplinary approach in data analysis and processing particularly in chemistry, applying integrated mathematical and statistical methods. Since then, the application of chemometrics overcomes the frames of chemistry. Nowadays, chemometrics is being successfully used in engineering, including food engineering, pharmaceutical engineering, chemical engineering, agriculture and biotechnology. Considering the high position that chemometrics has on many universities in the world, the main aim of the present paper is to emphasize the importance of studying chemometrics at engineering faculties, the possibilities that chemometrics offers in fast and efficient data processing, prediction of outcomes and in saving time and financial resources.
|Modified Information Matrix Tests for Detecting Misspecification in the Random Effects of GLMMs
|Generalized linear mixed models (GLMMs) are commonly applied to regress a non-Gaussian clustered structure response for hierarchical data analysis and longitudinal studies. The normality assumption of the random-effects distribution in GLMMs is practically assumed, but it may be too restrictive to reveal the major feature of data. The test statistics are proposed based on a variety of modified information matrix tests introduced by White (1982), and their limiting chi-squared distributions are derived under the null hypothesis that the distribution of random-effects is corrected specified. Simulation results are presented under various configurations of practical relevance data generating mechanism with different modified matrices, and the power performance of the proposed tests are demonstrated. Furthermore, real longitudinal case studies are employed to illustrate the applications of proposed tests.
|Every learner is unique so why couldn’t their assignments be unique? The use of simulated datasets in inferential statistics courses.
|S. Jeanne Horst
|Data simulation is a technique used for robustness studies, model estimation, and investigation of new analytic methods. We incorporated simulated data into inferential statistics course assignments. Throughout the course, students completed formal assignments to answer identical research questions, performing analyses on their own unique instructor-provided simulated dataset. Using simulated data allowed the instructor to control outliers, assumption violations, and results. Course evaluation ratings of the assignments showed that students exhibited greater passion compared to more traditional assignments. Additionally, compared to traditional assignments, the technique minimized honor code violations by eliminating the ability to copy results and interpretations. To expedite the grading of unique analyses, solution syntax is developed that automates the process. Example assignments, solution syntax, and student evaluation of the assignments are presented.
|Developing secondary preservice mathematics teachers’ statistical knowledge for teaching
|The MODULE(S^2) project funded by the National Science Foundation in the United States has created teacher education curriculum materials that develop preservice secondary (grades 6-12) mathematics teachers’ statistical knowledge for teaching. This poster presentation will share the design principles of the project that detail our approach to developing teachers’ statistical content knowledge while at the same time developing their pedagogical content knowledge in ways that are closely tied to the practice of teaching statistics. Excerpts from the curriculum materials that illustrate these design principles will be included (for example, how we have teachers respond to students’ work), as will pilot research results on the materials’ effectiveness.
|Integration of Extensive Technology in a Canadian Service Statistics Course
|At Trent University (Ontario, Canada) we recently migrated our large first-year undergraduate service course in statistics to a randomization and simulation approach, with mandatory integration of R code and content for all students. In addition, we moved all homework to an online WeBWorK platform with weekly deliverables, and introduced a number of other technological changes, including videos of lectures and tutorials and an online persistent chat system. In this poster, we explore the results of this process, including student feedback, student performance, and general comments from the instructors and TAs for the two sections of the course. We conclude with some hard lessons learned, and some suggestions for any other instructor or institution thinking of implementing any or all of our recent changes.
|A study of practical statistics education using questionnaire survey, through the joint education of companies and universities
|It is a study on a lecture method of "Innovation-Challenge Program" which started last year from Faculty of Business at Aichi Shukutoku University, a part of marketing and statistical education. This program implements jointly by companies and universities in five groups. We show the case of a lecture method about financial institutions. In this lecture, each groups of five or six students conducted market research and proposed services according to customer's needs. They created question items, gathered data using the Internet, and made suggestions from the results of the aggregation. Moreover, we show the results of the analysis related to student evaluation for group study.
|University Math and Stats Support Centre and how to reduce a rate of unsuccessful studies
|Mathematics and Statistics underpin many university subjects in many disciplines (economics, engineering, biology, ...), but are often perceived as too difficult. This poses barriers to successful study. Furthermore, students often face difficulties with a data-driven approach in their final theses. Many higher education institutions throughout the world have established Mathematics and Statistics Support Centres (MSSC) to assist students with these issues. The first MSSC in the Czech Republic was established in 2016 at Masaryk University. Since then it has inspired other universities in the Czech Republic. It operates in a drop-in mode with support of staff-tutors and volunteering students. Since its establishment, there were more than 1000 visits related to study within the curriculum and for support on students' final theses.
|A novel way to teach biostatistics to public health students
|Public health students need conceptual knowledge to correctly apply biostatistical procedures. Categorical data analysis particularly presents conceptual hurdles to many students, but an unrealistic emphasis on conceptual knowledge allows unfamiliar mathematics to supplant logical reasoning. This presentation gives concrete examples of how I impart conceptual knowledge to public health students in a categorical data analysis course. By relying on high school algebra and first principles, I first demonstrate the algebraic connection between probabilities and logits. Then, via SAS software, I show students how to code the likelihood functions for several important models. This paves the way for students to appreciate abstract concepts like the deviance and the likelihood ratio statistic. The presentation illustrates the new teaching method by classroom-tested problems and their solutions by students.
|Information Request of the Society as a Condition for the Development of Statistical Literacy
|Information society as a new stage of social and economic systems development determines a special type of interaction between the official statistics and its users: the population, business, authority. The creation of official statistical information due to current users' requests raises the need, on the one hand, of adequate statistical literacy of users of information, and, on the other hand, the adaptation of official statistical methodology to user requests. Authors demonstrate the results of the interaction of the Moscow Analytical Centre with the Federal Service of State Statistics of Russia (Rosstat) of the methodology improvement for calculating earnings and income indicators. The conclusions include proposals for methodological development of official statistics in order to improve its effectiveness, taking into account the development of statistical literacy.
|Modelling and statistics in food product design - modernization of food engineering courses
|Modelling of food processes allows food engineers not only to understand these processes more clearly but also to control them more closely and make predictions about them. The application of predictive models in foods has emerged significantly in the last two decades due to development of computer science and statistical packages. The aim of defining predictive models in food science is to ensure safety and quality of food. Modelling could be a contemporary tool for designing food with higher and consistent quality. Applying modelling and statistical methods to food processing enables engineers to predict behavior of food products under different combinations of factors. This study presents the range of modelling techniques and their applications across the food chain for students at undergraduate food engineering courses.
|C-SOMAS: Measuring Classroom Characteristics
|The family of instruments known as SOMAS (Survey of Motivational Attitudes towards Statistics) will measure classroom characteristics in C-SOMAS. While the student and instructor instruments (S-SOMAS and I-SOMAS) are based on Expectancy-Value Theory (Eccles, 1983, Eccles &Wigfield, 2002), our model for the classroom characteristic instrument is broken into two factors with three elements in each. The two factors are split by the instructor’s locus of control. While influenced by the instructor, the first factor, institutional structures and characteristics, is not fully within the instructor’s control and will vary between instructors. The elements are institutional characteristics, course characteristics, and learning environment. Our link between I-SOMAS and C-SOMAS is the second factor, enacted classroom behaviors, which consists of general pedagogy practices, statistics-specific pedagogy practices and teacher-student relations.
|Basic statistics starts with bivariate for academic degree program
|In official statistics, bivariate statistics is used to start long-term statistical education such as covariance matrix. We propose that variance and covariance be taught simultaneously. Iterative proportional fitting (raking) is another form of statistics from two different sources or two different periods. Statistics can be introduced by using at minimum two sources of data or bivariate. Ever since computer time be affordable (inexpensive processing cost), statistics can be introduced in dimensionality reduction instead of merely generalization from univariate. Example is to start with structural equation modelling (SEM) concept then proceed with multiple regression. Example is to start with trivariate projection pursuit then apply principal component analysis (PCA). The views here are that of author and co-authors and not necessarily reflect official views.
|A web-based learning system for junior high school students and high school students
|A web-based learning system "Data-oriented Statistical System" or called DoSS@d (http://mo161.soci.ous.ac.jp/@d/index.html) has been developed mainly for educational use, which archives a large number of datasets and the corresponding analysis stories so that students can find suitable datasets for their purposes and learn practical data analysis based on the analysis stories. DoLStat@d, one of modules in DoSS@d, supplies several courses consisting of analysis stories suitable for learning purposes, e.g., marketing, visualization, and classification. In this study, new courses with "PPDAC cycle" for junior high school and high school students are developed. The contents are basic statistics and data visualization that junior high and high school students ordinally learn in mathematics and are based on PPDAC cycle.
|A case study on the development of statistical thinking using multivariate data: Based on Olympic Decathlon
|In highly-networked information society, statistical thinking to judgment and prediction based on data is required. To develop high school student's statistical thinking, we focused on teaching materials using multivariate data. The purpose of study is to clarify the students' process of using multivariate data and to examine the way of guidance to develop statistical thinking. To achieve them, firstly, we developed the teaching material using Olympic Decathlon data for Grade 10. Secondly, we analyzed small lesson about it for 7 students. The result, we revealed difficulty of focusing on the relevance of the data variables and statistical evidence. As conclusion, it is necessary to teach analyzing data qualitatively and quantitatively and data variables difference.
|Mastery-Based Grading in Introductory Statistics
|Mastery-based grading values iterative improvement and deep learning of course material over high-stakes cumulative assessments. This poster shares a method of adopting this grading method to a large introductory statistics college course with an audience of largely non-majors. Central to this method is outlining 15 concepts and tasks (called `standards) that, by the end of term, students should be able to articulate and appropriately apply. Each standard receives a level of Gold (meaning excellent), Silver (meaning mastery of the concept), Bronze (progressing towards understanding), or cannot be assessed. The level-based scoring system transparently signals to students where their understanding can be deepened. Evaluating each standard individually and giving students many opportunities to demonstrate their knowledge together incentivizes students to review their understanding of concept.
|Students competences in dealing with the classical and frequentist approach to probability
|The concept of probability is a key concept. The classical approach was defined by Laplace and prevailed in teaching probability at the beginning. The focus on the classical approach raised criticism and nowadays the frequentist approach is widely promoted for teaching probability. The aim of the project was to identify secondary students’ competences in dealing with the classical and the frequentist approach to probability. Therefore, approximately 500 students in Germany from grade 8, 9 and 10 performed a paper and pencil test. The analysis of the item difficulties was conducted with item response theory. The results showed that students had basic competences in dealing with the classical concept of probability but struggled to apply and comprehend the frequentist approach to probability.
|The construction of the central ideas of the sampling through technology in university students
|In this paper we present an instructional proposal for the understanding of some ideas related to sampling in university students, by applying the R software. These ideas are the sampling representativeness, the sampling variability and the distribution concept in four levels. The students in order to achieve their understanding, must establish differences between samples and populations; recognize differences and similarities between samples of a population. Then, they must distinguish between four levels of the data: distribution of the population (level 1), distribution of a sample (level 2), distribution of the random sample as a random n-dimensional variable(level 3) and distribution of sample statistics (level 4). The use of simulations and visualizations will help students to achieve an understanding of these ideas.
|Do standardized tests contribute to statistical education?
|In every country to measure student performance there are different national tests in different disciplines including mathematics. The statistic that is part of the Mathematics curriculum in standardized tests represents 25% or less of the test. The disaggregated results in general are not available for decision-making in the statistics as a discipline. Its availability would compare the performance of the students with the results of the international tests. This helps to define policies of education and training in the statistical discipline in order to be able to make decisions at national level. Such education and training should be aimed at teachers and students. A concrete example is presented with a Provincial and National test of Argentina,taken by students at the primary and secondary levels.
|What Would Fisher Do? A Useful Tool for Teaching the Model Construction Process
|Students often struggle converting study descriptions into plausible statistical models. What Would Fisher Do (WWFD) first appeared in Stroup (2013) as a tool to motivate the model construction process for generalized linear mixed models. Inspired by Fisher’s comments following Yates (1935) “Complex Experiments,” WWFD is a technique that organizes the design and treatment structures of the study. The result is a re-envisioned ANOVA table that provides a basis for constructing appropriate models. WWFD aids in understanding the difference between fixed and random effects, the impact of the response variable distribution on the model, and the role of the residual in context of different distributions. Examples of WWFD implementation in both undergraduate and graduate courses in statistical modeling and design of experiments are presented.
|Statistical Practices: What do Statisticians do?
|The demand for statistical skills is growing in many different fields and sectors, and the employment of statisticians is expected to increase drastically. However, the transition from learning to practicing statistics is challenging (Gibbons & MacGillivray, 2014) because professional statisticians are developing ways of reasoning and practices that grow out of experience (Pfannkuch & Wild, 2000). In order to investigate statistical practices that are developed at the workplace, statisticians were solicited to reflect on their experience. Each statistician sorted and identified prevailing practices performed at the workplace. The analysis revealed predominant and newly developed statistical skills. This identification of skills from the perspective of the statistical practitioners helps inform how to better promote an authentic experience of statistical practices throughout education.
|Dealing with Symbols with Multiple Meanings in Inferential Statistics
|Von Bing Yap
|A mathematical symbol can change meaning without warning, like in solving an optimization problem. The derivation of a maximum likelihood (ML) estimate inherits this issue, resulting in the parameter symbol θ acquiring multiple meanings in close proximity to each other. It is proposed that the instructor ought to explicitly indicate key points where the meaning of θ changes, and to introduce the hat notation θ-hat for the ML estimator carefully. In particular, the use of θ-hat; for a realization, i.e., an estimate, should be avoided. It will be demonstrated that words are powerful tools for untangling the overworked symbols, hence can be valuable for students to master the central ideas of inferential statistics.
|Effects and characteristics of representative values for the 6th grade in elementary school
|In light of the progress of our advanced information society, statistical education using ICT equipment based on the PPDAC cycle is taught in many countries. Recently, Japan has designated “representative value” as its educational content for 6th grade of elementary school, for example mean value, mode, and median. This research aims to identify the effect of learning and its characteristics by teaching representative values for histogram as educational content. As a result of this investigation, it was easy for 6th grade children to understand the mean value and mode. However, an incorrect recognition of the median was confirmed.
|Data Science Education using Statistical Graphic Concour
|It has been becoming important to consider effective Data Science education. To develop the student’s ability to process collected data and to explain obtained results so that everyone easily understands them, it is necessary to make students process real data. A higher motivation is very important for such education. One of successful approaches is to challenge to data competitions such as "Statistical Graphic Concour" in the classroom. The important aspects here are to collect suitable data for the purpose; to select contents to be resulted; to make several graphics have a story; and to visualize them understandable. In this study, a trial of Data Science education with the challenge to "Nation-wide Statistical Graphic Concour" designed for university students is introduced and the results are discussed.
|Comparison of assessment methods in an undergraduate biostatistics course
|Improving instructional methodologies often requires evaluation of assessment strategies. With the variety of assessment techniques in education, it can be difficult to determine the best one(s) for any particular course. Closed-book exams have traditionally been used to assess knowledge in biostatistics courses; recently, project-based learning has been incorporated to evaluate student learning. As biostatistics is an applied field, project-based learning may be better suited as an evaluation tool than exams. An initial sample of approximately 50 undergraduate biostatistics students had their baseline knowledge measured using a pre-test, and their post-course knowledge was assessed using a post-test. The 20 multiple-choice questions on the pre- and post-tests were repeated; comparison of change scores are used as the primary determination in effectiveness of assessment methods.
|Comparison of Campus and Online Sections of a Flipped Introductory Statistics Course
|Brigham Young University-Idaho (BYU-Idaho) embraces a Learning Model, where students prepare in advance of class meetings, teach one another, and reflect on their learning. Under the Learning Model, flipped courses are the standard. Students can enroll in either face-to-face or online sections of three different types of introductory statistics courses: Business Statistics, Biostatistics, and Social Science Statistics. Comparisons of exam scores and surveys illustrate differences between three different groups: on-campus students in face-to-face sections, on-campus students in online sections, and remote online students. Student performance and perception is compared for the various sub-populations.
|Validity Evidence Claims and Plan for the SOMAS Instruments
|The Surveys Of Motivational Attitudes towards Statistics (SOMAS) is a family of instruments being developed to measure what has previously been conceptualized as attitudes in statistics education. For the Student and Instructor instruments (S-SOMAS and I-SOMAS, respectively), the SOMAS project uses Eccles’s Expectancy-Value Theory (EVT). While other attitude surveys have used EVT in some capacity, EVT is central to the development of the S-SOMAS and I-SOMAS instruments. As part of the development process, a robust view of validity evidence has been adopted. This poster will articulate explicit claims and evidence to be gathered to support these claims using the validity framework proposed in the Standards for Educational and Psychological Testing (AERA, APA, & NCME, 2014).
|Adapting statistics education to a cognitively heterogeneous student population
|Historically, the introductory course in statistics at the Norwegian University of Life Sciences(NMBU), has been lecture based. Previous study at NMBU concluded that the course structure apparently disfavored certain cognitive types. Therefore the course was restructured into a student active learning course using flipped classroom. Output variables like exam scores, colloquium attendance and student evaluations were analyzed in light of cognitive information on the students as collected by an education test provided by the National Centre for Science Recruitment. One of the main findings shows that the previously negative effect of extraversion (E) disappeared in the flipped classroom course. In this paper we present several findings that indicate that additional adaptations should be made to reach an even wider group of the heterogeneous student mass.
Poster session P2 (Tuesday 10th, 17:30-18:30, Foyer)
|Use of Interactive Apps in Teaching Bayesian Statistics
|Use of statistical software is essential to the teaching and learning of Bayesian statistics. Effective use of statistical technologies, which help transcend the static pages of a textbook, have a great potential to make Bayesian theory and concepts more accessible through effective, dynamic, and interactive visualisation. This poster will present the development of specialised apps for teaching Bayesian statistics using Shiny, an open source web application framework for R. The apps were designed to dynamically visualise key Bayesian concepts covered in a first course. The apps allowed the instructor to develop students’ understanding through experimentation, whereby the instructor or students could vary input parameters (e.g. alter a prior distribution) and visualise the resulting effect (e.g. posterior distribution).
|Using concept questions to teach statistical terms
|Statistics, like any academic discipline, comes with its own set of technical terms. A number of these terms, such as normal and significant, are polysemic resulting in learners of statistics assigning the inappropriate lay meanings rather than statistical word sense. This presentation shows how concept-check questions can be used to convey and check meaning in a concise and clear manner. The interactive nature of concept questions enables active learning and engages learners in critical semantic analysis. Given the centrality of the meaning of key statistical terminology, conveying the meanings without distortion to students is essential. The denotative and connotative meanings of a lexical set of statistical terminology will be used as a vehicle to demonstrate the effectiveness of this teaching technique.
|Attitudes toward Statistics as moderator variables in the evaluation of activating methods in lectures on Statistics
|Many studies report heterogeneous attitudes toward statistics among bachelor students of different subjects with a tendency to negative attitudes toward statistics. The format of a lecture cannot adequately counteract this heterogeneity. Recent studies offer some suggestions how to actively integrate students in lectures. Particularly in the field of mobile learning, instruments are provided that are suitable for involving high amounts of students. Although evaluations show positive first results about these instruments it is not clear whether these instruments are particularly suitable for students with negative domain-specific attitudes. For the study presented here, n=315 students were asked to evaluate a unit with a smartphone-based clicker system. As a comparison, the technology-free Think-Pair-Share method was examined. The results focus on the moderating effect of attitudes toward statistics.
|A robust method to identify the statistical learning abilities of students
|Identification of the statistical learning abilities of students in a certain population is useful for statistics educators. The success of this task can not only help improve the performance of statistical learning for students, but can also help reduce the loading of educators. While we have collected many characteristics for a student that are useful for the identification of his/her learning ability, the process of constructing the prediction model can suffer the problem of outliers that can make the identification results totally biased. In this work, we propose a robust procedure for the identification of students that are less affected by the presence of outliers. The newly developed identification procedure is shown to well reflect the learning ability of a student.
|Molecular modeling and statistical software in modern organic chemistry teaching – Step forward to modernization of undergraduate studies at engineering faculties
|The application of computers and software is becoming inevitable in modern study curriculum at majority of faculties in the World. The application of computers in organic chemistry teaching has significantly contributed to the solution of the problems related to the presentation of statistical approaches in chemistry in a simple and understandable way. It also has encouraged the creativity and innovation of both teachers and students. The present study describes the analysis of the possibilities of application of different statistical software in basic and advanced organic chemistry teaching on undergraduate level at engineering faculties, the easy-to-understand software approaches in modern organic chemistry and visualization methods of molecular structures. This study also presents the analysis and interpretation of possible outcomes of these approaches in teaching.
|Teaching Graduate Students Sample Size Planning by Using R
|Instead of statistical test of equality of means, the test of equivalence of means has become more popular for clinical trials and pharmaceutical science, and it has recently been expanded into broader applications in many fields. However, the sample sizes planning using conventional techniques found in the literature on this topic have usually been under-valued with less statistical power than is required. For better preparing graduate students to design experiments and allocate required sample sizes for the experiments, the present study develops a new visualized method by using R to provide distinctive insights into effect sizes, the statistical power, and/or pre-specified equivalence boundaries to enhance comprehension of sample size planning.
|Another Look at the Box Model
|We revisit the box model, an analogy introduced by Freedman et al. (1978) to teach sampling distributions and inference. The idea is to represent a random phenomenon in terms of random draws of tickets from a box. In this way, random sampling from a population can be modeled in the same way as familiar phenomena like coin-tossing and card-shuffling. However, Freedman et al. present box models only as a thought experiment; calculations are still done using normal approximations. We argue that a simulation-based approach to box models correctly places the emphasis on the modeling rather than the calculations. Furthermore, we demonstrate how the box model is useful beyond an introductory course by showing how it can clarify discrete distributions in a probability course.
|Launching a statistical enquiry: Posing statistically worthwhile questions
|A recent approach to statistics education is situating the teaching and learning of statistics within cycles of statistical inquiry. Learners pose questions, plan and collect data, represent, analyse and interpret data. We focus on the first step – the preparation of prospective teachers to pose statistical questions. Posing worthwhile statistical questions is a critical step as they inform the types of data collected, determine the representations used and influence the interpretations that can be made. We report on an investigation of prospective elementary teachers in Ireland (n=200) and Germany (n=50) as they design statistical questions. Support was provided through tutorials, peer-feedback and expert-feedback. We describe the features of statistical questions posed, identify obstacles and difficulties experienced and evaluate the effectives of both peer and expert feedback.
|Technology in bi-dimensional statistics in High School textbooks
|María M. Gea
|The technology is recommended in the teaching of statistics and a resource for teachers is usually the textbook. The aim of this paper is to analyze the directions on the use of technology included in the Spanish textbooks directed to high school students in the topic of correlation and regression. We analysed eight textbooks in the Mathematics modality using content analysis in different analyses: a) the use of technology in the problem proposed and related procedures; 2) the references to Internet resources; and 3) the CD included in most of the books. We found variability in the use of technology, generally related to the use of calculators or to the spreadsheet and not to data sets that can be used in projects or to simulators.
|Student attitudes towards statistics at a South African university
|This study investigates the relationship between student attitudes towards Statistics and their performance in the Statistics course. We adopted the ‘SATS-36’ survey questionnaire to assess the attitudes of students towards Statistics. We used exploratory factor analysis to group the attitude responses according to factor loadings as was done in other studies using ‘SATS-36’. Moreover, we examined whether the attitudes to Statistics locally are related to demographic attributes, field of employment and academic exposure to Statistics.
|Consideration on Students’ Statistics Report about Social Justice
|I applied project teaching to graduation thesis (12th grade) on statistical literacy. The guideline was the following: (1) Report theme, (2) Motivation, (3) Method, (4) Arrangement of documents, (5) Consideration, (6) Reflections and comments. One student wrote the following report: Degrees of "cutting corners" of six television stations rebroadcasting programs in Japan. According to one viewer, the NHK educational broadcast had too many rebroadcasts about programs for children. The viewer pointed out that it looked like injustice. The student analyzed this objection based on ‘justice and structure (separated values)’. He also analyzed it by another viewpoint based on ‘caring and human connection (connected values)’. According to the public educators’view, it is interpreted that the integration of these two moral values was shown.
|Sharing statistical analysis techniques using dental practice data
|Statistics education has a vital role to play in all workplaces. Operational data can be turned into valuable knowledge. Dental practices are continually collecting patient data but it is often an underutilised resource. For practices which use digital systems, a large reserve of patient information is available. With the right knowledge, practice managers can gain insight into patient demographics to guide business decisions and improve patient care. We aimed to disseminate simple techniques to use data from practice management software and provide a guide to basic statistical analysis using Microsoft Excel, statistical software and tools available in freeware such as “R”. This allowed practice owners to understand and begin to use statistics and data analytic techniques and helped promote statistical literacy in the wider society.
|Datascientist education by e-Learning system
|In Febrary 14, 2017, i's Factory, Co. Ltd. offered datascientist education course by e-learning system (Japanese only). After that, over 100 students (businessperson, school students, etc.) already have learned our course. In Japan, there are the datascientist education course by schooling. But only we are providing the datascientist education course by e-learning system (Online). So, we make tuition fee reasonable (19,800 yen) and you can learn our course anytime, anywhere by accessing to the Internet with PC, smartphone, and tablet.
|Implementing Inverted Instruction in Undergraduate Introductory Statistics Courses
|This study was about an experimental design that compared different instructional styles in teaching introductory statistics to undergraduate students (N=270). Inverted classroom was the treatment, and the treatment effects were assessed against traditional classroom as control. Inverted classroom refers to the instructional practice where events that traditionally take place inside of the classroom now take place outside of the classroom and vice versa. Traditional classroom refers to statistics classes that are taught using the lecture method. This study aimed to provide insight into the effectiveness of inverted instruction and identify factors that facilitate or hinder this effectiveness. Given that inverted instruction is becoming popular, this study has a broader intellectual interest throughout higher education. This presentation would document this intervention and present preliminary results.
|Young learners’ reasoning with informal statistical models and modelling
|There has been a growing interest within the statistics education research community in statistical models – an object – and statistical modeling – the complementary process. Research has generally focused on older learners, such as high school and post-secondary school students, rather than learners at the primary school level. Our goal is to provide a framework that both describes and provides a tool for analyzing the reasoning that accompanies young students’ informal statistical modeling. This poster provides a brief description of some fundamental definitions, followed by our framework for young learners’ reasoning with informal statistical models and modeling. Our framework identifies three separate, but not independent, modeling sub-processes: the conjecture, data and comparison modeling processes. An illustrative example of its dual usefulness is also provided.
|Latent trait models highlight deficits in student understanding.
|Final exams are typically set in order to assess course content knowledge and to provide evidence that students have achieved at least some minimum level of competence in the learning outcomes. Exam papers are typically archived on completion but they contain abundant information that can highlight topics where there is either adequacy or a shortfall in understanding. Final exam papers from a first-year statistics unit were randomly selected. Using item response theory models, the probability of a correct response to each of 56 question items was obtained as a function of item difficult and student ability. Item difficulties were extracted to enable the ranking of question items from least to most difficult. Results of modelling and the impact on future teaching will be presented.
|Rethinking Exams in Large-Lecture Statistics Courses
|Exams are both time-consuming for instructors to grade and imperfect at assessing the depth of student knowledge. Exams that have to be completed in a short amount of time force students to quickly read the problems, determine the solution, and convey those solutions coherently. This artificial time constraint can be problematic for students. Why not think about the examination process differently? The author assigned a portion of some exams to groups and asked that these be completed outside of the classroom. Student often did the group portion by presenting solutions via video or as a result of using software to analyze their data. Discussion of these and other approaches along with some quantitative results will be presented.
|StatHand: An application to support students’ statistical decision making
|Quantitative research methods underpin professional competence across many disciplines. Despite this, many students struggle with the process of selecting appropriate statistics for common research questions, hypotheses and data types. StatHand (see https://stathand.net) is a free cross-platform application that aids this process by prompting students to focus systematically on each structural feature of their research problem. Student focus groups (N = 25) and instructor interviews (N = 9) support the subjective appeal and usability of StatHand. Furthermore, an experimental evaluation (N = 215) found that StatHand promotes decision making accuracy, reduces cognitive load, and is instructionally efficient relative to a range of alternative statistical decision making aids. StatHand can be readily integrated into a variety of classroom learning activities.
|The Effectiveness of Using ICT Technology in Statistics Education
|In mathematics education, utilization of ICT should aim to teach students how to discover new evidence present in large amounts of data through organization, analysis, and interpretation and how to understand trends within data. In order to achieve these goals, statistics instruction should not just have students calculate statistical values, rather it is also necessary to introduce teaching materials that have students consider data’s meaning and value from its context. I have found that having students present the results of their research to the class is an effective way to increase their efficiency in organizing, analyzing, and interpreting data.
|100% satisfactioned medical statistics seminar that healthcare workers really need at present
|Healthcare workers feel difficulty in statistical analysis of data. Approximately 70% of healthcare workers were working statistically in the routine work, of which about 30% were doing analyzes other than simple counting. In addition, only one responded "I am able to analyze". Therefore, we held a monthly seminar about data analysis method necessary for medical staff in the business. The purposes of the participants were "Analysis method using Excel", "Visualization of data, such as table and graph creation", etc. We emphasize analytical methods that can be completed only with Excel without using statistical software, and teach how to create comprehensible materials simply by devising from everyday work. Satisfaction from participants is high, and we will report the details from the seminar.
|An examination of computer versus tactile simulations for teaching sampling distributions in introductory Statistics
|Sampling distributions are a notoriously difficult topic for students in post-secondary introductory statistics courses. Much of the literature suggests utilizing computer simulation methods (CSMs) and, despite few empirical studies, incorporating hands-on simulation activities prior to CSMs as a pedagogical tool in teaching sampling distributions. In our pseudo-experiment performed at a large research university, we randomly assigned discussion sections to sequences of sampling distribution activities either using CSMs preceded by hands-on simulation activities, or CSMs alone. We found moderate evidence of a positive effect of hands-on simulations on exam scores. However, the analysis of the sampling distribution-specific exam questions, which vary in type (e.g., multiple choice or free response) across exams, requires the development of a new statistical methodology for longitudinal data of differing response distributions.
|Assessing High School Students' Statistical Literacy about the Measures of Central Tendency
|Statistics is increasingly considered an important outcome of schooling. To explore Chinese high school students’ statistical literacy about the measures of central tendency, this study examined a sample of eighty-three students’ responses to a two-tier fifteen-item instrument. Rasch analysis demonstrated adequate reliability and validity of this instrument to measure student performance. Considering Rasch difficulty estimates, classical difficulty estimates, and the most popular distractor, this study identified the students’ strength in calculating median and weighted mean. However, they struggled with understanding the mean of a random variable, distinguishing between sample mean and population mean, and applying the measures of central tendency in diverse contexts. Making connections between the statistics concepts appeared a challenge for the students. Implications for statistics education are discussed.
|Using Real Data on Beliefs about Maths to improve Primary Teachers Students' Capacities in Statistic Education
|The Survey for Mathematics Pedagogy and General Pedagogy Educators, developed by the International Association for the Evaluation of Educational Achievement - Teacher Education and Development Study in Mathematics in 2008, was initially passed to Mathematics, Mathematics Educators, but General Educators too. We have extended it to students also; we used only parts A and K and had got a sample of 135 tests with 10 questions about background and 34 questions on beliefs about Mathematics. Results of questionnaires were brought to students during several sessions on Statistic Education. Those students, who are being preparing to become Primary Teachers, did a complete statistic study from the original results, and gave special attention to making, reading and translating graphs. The whole process is presented in this work.
|Views of Benguet State University Employees on the Role of Statistics in the Workplace
|This study aimed to quantify the influence of subjects taught, educational background and length of service on viewing the importance and role of statistics in the workplace. Also this study aimed to measure the commonly used statistical tools/concepts among Benguet State University employees. Survey questionnaires were administered to randomly faculty and staff using stratified random sampling. Results showed that the respondents do not differ on their views of the importance and role of statistics when educational attainment was considered. However, respondents’ views differed across subjects taught and length of service. Also, the level of importance of statistics and its role in the workplace was above the moderate level. Among the topics/concepts that were considered widely useful and familiar among employees are mostly under descriptive statistics.
|Statistical Literacy through Guided Block Play: An Exploratory Multiple Case Study
|Misconceptions, anxiety, and negative attitudes impair adult learning of statistics. Courses in statistics often fail to impart conceptual understanding. Current guidelines suggest statistics learning begin with inquiry in primary school. Yet statistics learning might begin even earlier. Learning about distributions holistically through play might help convey the idea that a data set is an aggregate with emergent properties of shape, spread, and center, and help prevent misconceptions and anxiety in later years. What can a preliterate child learn about a frequency distribution? In this study, children as young as five manipulated blocks under the guidance of a tutor and created “embodied” frequency distributions. They found descriptive statistics, made X-plots and box plots. Only after sensorimotor experience with embodied statistical concepts did they perform statistical inquiry.
|Using Google Docs and Sheets to Design and Collect Data for Classroom Experiments
|The 2016 Guidelines for Assessment and Instruction in Statistics Education recommends the college professors use active learning pedagogies with real world data while using technology for data exploration and analysis. Pedagogies and technologies that actively engage students and use real world data have been shown to improve student engagement and achievement. Experiments offer an opportunity to actively engage students, generate real world data to analyze and organically integrate technology in the classroom. This poster describes some uses for Google Docs and Sheets that allow in-class collaboration in the design and implementation of experiments, as well as data collection and analysis.