Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:p>Instructors at postsecondary institutions have designed a myriad of data science classes to keep up with the rise of big data. Businesses and companies have become increasingly interested in hiring people with strong data acquisition, management, and communication skills. Since data science as a field of study is relatively new, though it has deep connections to statistical studies, there are few comprehensive analyses of data science classes, majors, programs, and curricular goals. Through this research, we analyze how writing and communication are taught in undergraduate data science classes in the United States. We analyze the presence of writing and communication learning goals from course descriptions and course syllabi. These results show that most data science courses emphasize technical, computing skills over writing, and communication skills. We conclude with a set of actionable heuristics that emphasize integrating writing and communication into data science courses so that students are prepared to use these skills as responsible citizens.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Football, as one of the most popular sports, can provide exciting examples to motivate students learning statistics. In this paper, we analyzed the number of goals scored in the UEFA EURO 2020 final phase as well as the waiting times between goals, considering censored times. Such a dataset allows us to consider some aspects of count data taught at an introductory level (such as the Poisson distribution), as well as more advanced topics (such as survival analysis taking into account the presence of censored times). Employing data from the final phase of UEFA EURO 2020, depending on the course level, the student will acquire knowledge and understanding of a range of key topics and analytical techniques in statistics, develop knowledge of the theoretical assumption underlying them and learn the skills needed to model count data.</jats:p>

<jats:title>Abstract</jats:title><jats:p>This paper uses actual data on horse racing to illustrate probabilities, odds, and expected values, and offers cautionary remarks about applying textbook formulas to gambling on real‐world sporting events.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Readers are invited to participate in a data collection exercise that will be used subsequently in this series.</jats:p>

<jats:title>Abstract</jats:title><jats:p>In this paper, we provide instructors with an approach for a classroom activity for students in an introductory data science or statistics course who have little or no statistical programming experience. We designed this activity to help students improve their statistical literacy while exploring a social justice problem‐the gender wage gap. To minimize the challenges of developing statistical literacy in students who lack programming skills, we developed a web‐based data visualization application that does not require users to have any prior programming knowledge. The data in this visualization application comes from the March 2018 Current Population Uniform Extracts detailed by the Center for Economic Policy Research. Students can use the visualization application to create tables and plots to explore data on factors such as earnings and gender. Instructors can also use the application for other wage‐related variables, such as race, occupation and family size.</jats:p>

<jats:title>Abstract</jats:title><jats:p>When teaching statistics, educators sometimes overestimate their students' knowledge and abilities. This is due to the <jats:italic>curse of knowledge</jats:italic>, a cognitive bias that causes people—especially experts—to overestimate how likely others are to know and understand the same things as them. This can lead to various issues, including struggling to communicate with students, and making students feel less comfortable in the classroom. To address this, educators should first identify situations where this bias can affect their teaching. In doing so, they should consider relevant risk factors, and potentially also solicit feedback from relevant individuals. Then, educators can reduce this bias and its impact on their teaching by using techniques such as keeping the curse of knowledge and their audience in mind, assessing students' knowledge, assuming lack of knowledge unless there is strong evidence to the contrary, and avoiding saying that things are obvious.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Real‐world data are fundamental to modern teaching methodologies that aim to improve statistical knowledge and reasoning in students. Statistical information is encountered in everyday life, such as media articles and involves real‐world contexts. However, information could be biased or (mis)represented and students should be concerned about the validity of such articles, as well as the nature and trustworthiness of the evidence presented, while considering alternative interpretations of the findings conveyed to them. Statistics educators could make use of media articles to create opportunities for students to reflect on such (mis)representations and build statistical literacy. The purpose of this article is to show how information and data on the Omicron COVID‐19 variant have been (mis)represented in the media and by government entities. I also demonstrate how these examples may be utilized in the statistics classroom as they relate to concepts covered in most basic statistics courses.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Many college students consider statistical courses as frightening and demanding, yielding high anxiety and low competence, and correlating with maladaptive academic behaviors and low achievement. With undergraduate students, the present pre‐post study compared a supportive online teaching program utilizing mandatory statistical exercises (<jats:italic>n</jats:italic> = 37) with a no intervention, optional exercise statistics class (<jats:italic>n</jats:italic> = 32). We evaluated whether our statistics teaching intervention decreased test anxiety and academic procrastination and increased academic self‐efficacy and academic achievements. Results indicated a decrease in academic procrastination and test anxiety at course end for intervention group and an increase in test anxiety for control group. At the end of the course intervention group reported higher academic self‐efficacy and achievements. Teaching statistics using mandatory supportive activities might contribute to more positive psychological outcomes (eg, higher academic self‐efficacy and lower academic procrastination) and higher academic achievements.</jats:p>

<jats:title>Abstract</jats:title><jats:p>This study investigated first‐year undergraduate statistics students’ statistical problem‐solving skills on the probability of the union of two events, conditional probability, binomial probability distribution, probabilities for x‐limits using the z‐distribution, x‐limit associated with a given probability for a normal distribution, estimating the y‐value using a regression equation, and hypothesis testing for a single population mean when a population standard deviation is unknown. The study was a descriptive case study and employed a mixed‐method research approach. Data were collected through content analysis of a statistics course examination script of 120 first‐year undergraduate students of statistics in an open distance‐learning university in South Africa. Polya's Model of Problem Solving was used as the framework of analysis. The study revealed that the students, in general, had poor statistical problem‐solving skills.</jats:p>