Browsing by Author "Çetinkaya-Rundel, M"

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    Teaching Introductory Statistics with DataCamp
    (Journal of Statistics Education, 2020-01-02) Baumer, BS; Bray, AP; Çetinkaya-Rundel, M; Hardin, JS
    © 2020, © 2020 The Author(s). Published with license by Taylor and Francis Group, LLC. We designed a sequence of courses for the DataCamp online learning platform that approximates the content of a typical introductory statistics course. We discuss the design and implementation of these courses and illustrate how they can be successfully integrated into a brick-and-mortar class. We reflect on the process of creating content for online consumers, ruminate on the pedagogical considerations we faced, and describe an R package for statistical inference that became a by-product of this development process. We discuss the pros and cons of creating the course sequence and express our view that some aspects were particularly problematic. The issues raised should be relevant to nearly all statistics instructors. Supplementary materials for this article are available online.
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    Understanding the massive open online course (MOOC) student experience: An examination of attitudes, motivations, and barriers
    (Computers and Education, 2017-07-01) Shapiro, HB; Lee, CH; Wyman Roth, NE; Li, K; Çetinkaya-Rundel, M; Canelas, DA
    During the widespread development of open access online course materials in the last two decades, advances have been made in understanding the impact of instructional design on quantitative outcomes. Much less is known about the experiences of learners that affect their engagement with the course content. Through a case study employing text analysis of interview transcripts, we revealed the authentic voices of participants and gained a deeper understanding of motivations for and barriers to course engagements experienced by students participating in Massive Open Online Courses (MOOCs). We sought to understand why learners take the courses, specifically Introduction to Chemistry or Data Analysis and Statistical Inference, and to identify factors both inside and outside of the course setting that impacted engagement and learning. Thirty-six participants in the courses were interviewed, and these students varied in age, experience with the subject matter, and worldwide geographical location. Most of the interviewee statements were neutral in attitude; sentiment analysis of the interview transcripts revealed that 80 percent of the statements that were either extremely positive or negative were found to be positive rather than negative, and this is important because an overall positive climate is known to correlate with higher academic achievement in traditional education settings. When demographic data was added to the sentiment analysis, students who have already earned bachelor's degrees were found to be more positive about the courses than students with either more or less formal education, and this was a highly statistically significant result. In general, students from America were more critical than students from Africa and Asia, and the sentiments of female participants' comments were generally less positive than those of male participants. An examination of student statements related to motivations revealed that knowledge, work, convenience, and personal interest were the most frequently coded nodes (more generally referred to as “codes”). On the other hand, lack of time was the most prevalently coded barrier for students. Other barriers and challenges cited by the interviewed learners included previous bad classroom experiences with the subject matter, inadequate background, and lack of resources such as money, infrastructure, and internet access. These results are enriched by illustrative quotes from interview transcripts and compared and contrasted with previous findings reported in the literature, and thus this study enhances the field by providing the voices of the learners.
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    Web Scraping in the Statistics and Data Science Curriculum: Challenges and Opportunities
    (Journal of Statistics Education, 2020-01-01) Dogucu, M; Çetinkaya-Rundel, M
    © 2020, The Author(s). Published with license by Taylor and Francis Group, LLC. Best practices in statistics and data science courses include the use of real and relevant data as well as teaching the entire data science cycle starting with importing data. A rich source of real and current data is the web, where data are often presented and stored in a structure that needs some wrangling and transforming before they can be ready for analysis. The web is a resource students naturally turn to for finding data for data analysis projects, but without formal instruction on how to get that data into a structured format, they often resort to copy-pasting or manual entry into a spreadsheet, which are both time consuming and error-prone. Teaching web scraping provides an opportunity to bring such data into the curriculum in an effective and efficient way. In this article, we explain how web scraping works and how it can be implemented in a pedagogically sound and technically executable way at various levels of statistics and data science curricula. We provide classroom activities where we connect this modern computing technique with traditional statistical topics. Finally, we share the opportunities web scraping brings to the classrooms as well as the challenges to instructors and tips for avoiding them.