Technology Innovations in Statistics Education

Technology has revolutionised society and it has revolutionised the way in which statistics, as a professional discipline, is done. The collection of data is growing exponentially both in relation to the quantity of data assembled on any particular measure and also in relation to the range of topics, and the measures, on which data is collected. Accessing data has become much simpler, and tools for exploring, manipulating and representing that data visually have multiplied, both in commercially available software and open-source freeware. However, the curriculum in schools in the UK is constrained by important factors which restrict the use of technology in assessment. The statistics curriculum is largely dull and does not address the core issues of most relevance in statistics today. Here, we explore ways in which technology can enhance the teaching of subjects in which statistics are used, and also the teaching of statistics within mathematics.

The concept of statistical literacy needs to be refreshed, regularly. Major changes in the ways that data can be accessed from government and non-government agencies allow everyone to access huge databases, to create new variables, and to explore new relationships. New ways of visualizing data provide further challenges and opportunities. The Open Data movement, and the rise of data driven journalism are increasing public access to large scale data via the media. Here, we map out some opportunities and potential pitfalls, and discuss the rebalancing of statistics curricula that is required. The most obvious challenge is the need to introduce students to the exploration and analysis of large scale multivariate data sets. The curriculum should also address issues of data provenance and quality. We present an example of our visualisations of complex multivariate data, used in classroom trials. General issues of pedagogy and curriculum innovation are discussed.

The need for people fluent in working with data is growing rapidly and enormously, but U.S. K–12 education does not provide meaningful learning experiences designed to develop understanding of data science concepts or a fluency with data science skills. Data science is inherently inter-disciplinary, so it makes sense to integrate it with existing content areas, but difficulties abound. Consideration of the work involved in doing data science and the habits of mind that lie behind it leads to a way of thinking about integrating data science with mathematics and science. Examples drawn from current activity development in the Data Games project shed some light on what technology-based, data-driven might be like. The project’s ongoing research on learners’ conceptions of organizing data and the relevance to data science education is explained.

Ordinarily, when a student plays a game on a computer, a great deal of data are generated, but never used. This paper describes a technological innovation: games designed for learning mathematics or statistics concepts in which success requires data analysis. These “Data Games” are small-scale, short, web-based games, embedded in a data analysis environment, suitable for  students in about year 7 onwards, and in teacher preparation. We discuss design for the games themselves, curriculum and assessment issues, and connections to research.

Hypothesis testing reasoning is recognized as a difficult area for students. Changing to a new paradigm for learning inference through computer intensive methods rather than mathematical methods is a pathway that may be more successful. To explore ways to improve students’ inferential reasoning at the Year 13 (last year of school) and introductory university levels, our research group developed new learning trajectories and dynamic visualizations for the randomization method. In this paper we report on the findings from a pilot study including student learning outcomes and on the modifications we intend to make before the main study. We discuss how the randomization method using dynamic visualizations clarifies concepts underpinning inferential reasoning and why the nature of the argument still remains a challenge.

The case is made for a new type of statistical master’s program called MSc in Research Methods.  The name of the course reflects the fact it is broader than one in statistics, partly because of the changing nature of research.  It is designed to be accessible to two types of students: those who have a mathematical background and those who have a more applied background from their first degree.  The program is intended primarily for working professionals so it is delivered in a way that is suitable for part-time students.  The implementation of an e-learning version of this course in Kenya is also described.

Technology has become an inseparable part of modern statistical practice (Gould, 2010), and, to a large extent, modern statistics courses. The literature on technology in statistics education has focused heavily on the role of technology for improving students’ understanding. However, limited research has examined the development of technological skills for “doing” statistics, e.g. using statistical packages. This paper proposes a distinction between these two roles of technology and how both benefit student learning. The paper then applies Kanfer and Ackerman’s (1989) integrative model of skill acquisition to explain the variability in students’ technological skill development. The ability to use statistical packages, arguably the most pervasive example of statistics technology, is used as an example to illustrate this model. The implications of the model are then discussed in the context of teaching technological skills in statistics courses. Future directions and challenges related to this area of are discussed

This discussion will summarize the two papers presented (Stern et al 2012; Baglin et al 2012) in 2012 IASE Roundtable Conference – “Technology in Statistics Education: Virtualities and Realities” – in Cebu, Philippines and the following discussions that took place after the presentations. In the last section a list of recommendations on learning and teaching and research will be provided.

The teaching and learning of statistics has evolved tremendously over the years owing to the reformation in statistics education and the advancement of technology that revolutionized the pedagogy in statistics classrooms. With technological tools students can focus in learning and understanding the important statistical concepts instead of concentrating on lengthy and repetitive calculations. Hand-held technologies such as the graphics calculators have paved the way for constructive and exciting learning experience. However, in a developing country like Malaysia the use of graphics calculators in statistics classrooms is not without challenges. This paper explores the advantages and limitations of the use of graphics calculators in the teaching of statistics in Malaysia.

Technology-assisted instruction is a core focus of educational reform in most disciplines. This exploratory study (N=227) examined instructors’ attitudes toward technology integration for the teaching of introductory statistics at the college level. Salient attitudinal elements (including perceived usefulness, self-efficacy, and comfort), which can serve as barriers to, and facilitators of, technology integration were identified. Additionally, a preliminary scale (ATTIS) for measuring instructors’ attitudes toward technology integration was developed with acceptable levels of internal reliability and validity. The results underscore the need for training and support for instructors, by way of workshops, modeling of best practices through team teaching and mentoring, and other targeted professional development activities.

It is well recognised that statistics teaching in Kenya needs to change, in both the course content and in the approaches to teaching.  Also clear is the important role that can be played through the recent wide availability of modern technology to students at all levels.  A wide range of resources are available and various initiatives have also recently been undertaken.  However, the system has remained resistant to change.  The case is made that teaching and learning of statistics could benefit from initiatives that cut across all educational levels from school through undergraduate to MSc and PhD.

Course projects have been argued to help develop students’ statistical thinking, but implementing authentic and realistic course projects still presents major challenges. This paper evaluated students’ experiences and perceptions of using an online simulated virtual environment, known as the Island, for implementing major course projects within an online masters level introductory statistics course. The use of the Island aimed to overcome significant practical and ethical constraints imposed on project-based work in online courses. The project required students to answer a self-posed research question by gathering and analysing data using methods covered in the course. The project was divided into two parts, a mid-semester proposal and an end of semester online presentation. Following completion of the projects, forty-two students responded to a questionnaire which rated their level of agreement to three aspects of using the Island: engagement, ease of use and contributes to understanding. Students were also asked to provide qualitative comments and five students participated in semi-structured interviews. Qualitative feedback was analysed to help explain the results from the quantitative questionnaire. In conclusion, perceptions of the use of the Island for project-based assessment were very positive. Qualitative feedback provided insight into how the Island-based projects may help to develop students’ statistical thinking.

At the 2012 IASE Roundtable, Thursday speakers covered diverse technological subjects in developed and developing countries. They demonstrated that the technological frontier varies based on current position and resources. Complexity and acclimation challenges affect all implementations. Discussion of several papers considered the foundation of statistics, whether data or mathematics made more sense and generated more beauty. Plenary discussion had two major topics – comparative benefits of real and realistic data, and ways to attract students to research in statistics.

In response to the need for reformed, outcome-based higher education statistics curricula in the Philippines, this paper draws from current research on the role of technology in statistics education and presents a framework for technology integration in teaching undergraduate and graduate-level statistics for non-majors. Anchored on the principles of Outcome-Based Education, this framework combines ideas from Pearson and Gallagher’s Gradual Release of Responsibility Model and Taggart’s Reflective Thinking Model to guide the attainment of the goals and intended learning outcomes for teaching statistics with technology as expanded opportunity and support for learning success. The Gradual Release of Responsibility Model describes how responsibility of learning shifts gradually over time from teacher to student ownership and from modeled and guided instruction to collaborative and independent learning.  The Reflective Thinking Model guides the course design where focus in teaching with technology moves from technical to contextual, and then to dialectical, in the transition from undergraduate to graduate -level statistics.

Online courses are becoming an increasingly more common option for college students and technology plays a critically important role. How can a course be taught in a way that engages the students so that they master the material as well as they would in a traditional classroom? In order to help accomplish these goals various technological packages must be chosen to bridge the gap between the traditional and online course. This paper will discuss the technological setup of an online Statistics course, and review the technology choices, implementations, and problems that arose. The paper will concentrate on the discussion of five areas: location of course, class conduct, communication, assessment and any additional hardware requirements.

Introductory remarks