Pub Date : 2019-08-30DOI: 10.1080/10691898.2019.1632759
M. McGee
Abstract In several sporting events, the winner is chosen on the basis of a subjective score. These sports include gymnastics, ice skating, and diving. Unlike for other subjectively judged sports, diving competitions consist of multiple rounds in quick succession on the same apparatus. These multiple rounds lead to an extra layer of complexity in the data, and allow the introduction of graphical constructs and interrater-agreement methods to statistics students. The data are sufficiently easy to understand for students in introductory statistics courses, yet sufficiently complex for upper level students. In this article, I present data from a high-school diving competition that allows for investigation in graphical methods, data manipulation, and interrater agreement methods. I also provide a list of questions for exploration at the end of the document to suggest how an instructor can effectively use the data with students. These questions are not meant to be exhaustive, but rather generative of ideas for an instructor using the data in a classroom setting. Supplementary materials for this article are available online.
{"title":"Deep Dive Into Visual Representation and Interrater Agreement Using Data From a High-School Diving Competition","authors":"M. McGee","doi":"10.1080/10691898.2019.1632759","DOIUrl":"https://doi.org/10.1080/10691898.2019.1632759","url":null,"abstract":"Abstract In several sporting events, the winner is chosen on the basis of a subjective score. These sports include gymnastics, ice skating, and diving. Unlike for other subjectively judged sports, diving competitions consist of multiple rounds in quick succession on the same apparatus. These multiple rounds lead to an extra layer of complexity in the data, and allow the introduction of graphical constructs and interrater-agreement methods to statistics students. The data are sufficiently easy to understand for students in introductory statistics courses, yet sufficiently complex for upper level students. In this article, I present data from a high-school diving competition that allows for investigation in graphical methods, data manipulation, and interrater agreement methods. I also provide a list of questions for exploration at the end of the document to suggest how an instructor can effectively use the data with students. These questions are not meant to be exhaustive, but rather generative of ideas for an instructor using the data in a classroom setting. Supplementary materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"608 ","pages":"275 - 287"},"PeriodicalIF":2.2,"publicationDate":"2019-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1632759","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41310118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-28DOI: 10.1080/10691898.2019.1647008
Basil Conway, W. Gary Martin, Marilyn E. Strutchens, M. Kraska, Huajun Huang
Abstract The purpose of this study was to study the impact of conformity to statistical reasoning learning environment (SRLE) principles on students’ statistical reasoning in advanced placement statistics courses. A quasi-experimental design was used to compare teachers’ levels of conformity to SRLE principles through a matching process used to mitigate the effects of nonrandom assignment. This matching process resulted in five pairs of similar teachers and schools who differed in self-reported beliefs in the effectiveness and application of SRLE principles. Increases in students’ statistical reasoning were found at varying levels in both high and low conformity classrooms. Improvements among teachers with low conformity to SRLE principles were less varied and consistent with national averages for improvement by college students. Improvements in classes with high conformity to SRLE principles were more varied. Students of two teachers with high levels of conformity to SRLE principles showed large levels of improvement in statistical reasoning in comparison to national results. While the comparison between classrooms conformity to SRLE principles revealed no statistically significant differences in students’ statistical reasoning ability, deeper analysis suggests that beliefs and practices aligned with SRLE principles have potential to increase students’ statistical reasoning at rates above national averages.
{"title":"The Statistical Reasoning Learning Environment: A Comparison of Students’ Statistical Reasoning Ability","authors":"Basil Conway, W. Gary Martin, Marilyn E. Strutchens, M. Kraska, Huajun Huang","doi":"10.1080/10691898.2019.1647008","DOIUrl":"https://doi.org/10.1080/10691898.2019.1647008","url":null,"abstract":"Abstract The purpose of this study was to study the impact of conformity to statistical reasoning learning environment (SRLE) principles on students’ statistical reasoning in advanced placement statistics courses. A quasi-experimental design was used to compare teachers’ levels of conformity to SRLE principles through a matching process used to mitigate the effects of nonrandom assignment. This matching process resulted in five pairs of similar teachers and schools who differed in self-reported beliefs in the effectiveness and application of SRLE principles. Increases in students’ statistical reasoning were found at varying levels in both high and low conformity classrooms. Improvements among teachers with low conformity to SRLE principles were less varied and consistent with national averages for improvement by college students. Improvements in classes with high conformity to SRLE principles were more varied. Students of two teachers with high levels of conformity to SRLE principles showed large levels of improvement in statistical reasoning in comparison to national results. While the comparison between classrooms conformity to SRLE principles revealed no statistically significant differences in students’ statistical reasoning ability, deeper analysis suggests that beliefs and practices aligned with SRLE principles have potential to increase students’ statistical reasoning at rates above national averages.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"171 - 187"},"PeriodicalIF":2.2,"publicationDate":"2019-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1647008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49087931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-05DOI: 10.1080/10691898.2019.1637801
A. Brisbin, Erica Maranhao do Nascimento
Abstract Practice problems and worked examples are both well-established teaching techniques. Research in math and physics suggests that having students study worked examples during their first contact with new material, instead of solving practice problems, can be beneficial to their subsequent performance, possibly due to the reduced cognitive load required to study examples compared to generating solutions. However, there is minimal research directly comparing these teaching methods in introductory statistics. In this study, we chose six pairs of introductory statistics topics of approximately equal difficulty from throughout the semester. After an initial mini-lecture, one topic from each pair was taught using practice problems; the other was taught by having students read worked examples. Using Bayesian and frequentist analyses, we find that student performance is better after reading worked examples. This may be due to worked examples slowing the process of forgetting. Surprisingly, there is also strong evidence from in-class surveys that students experience greater frustration when reading worked examples. This could indicate that frustration is not an effective proxy for cognitive load. Alternatively, it could indicate that classroom supports during in-class problem-solving were effective in reducing the cognitive load of practice problems below that of interpreting written statistical explanations.
{"title":"Reading Versus Doing: Methods of Teaching Problem-Solving in Introductory Statistics","authors":"A. Brisbin, Erica Maranhao do Nascimento","doi":"10.1080/10691898.2019.1637801","DOIUrl":"https://doi.org/10.1080/10691898.2019.1637801","url":null,"abstract":"Abstract Practice problems and worked examples are both well-established teaching techniques. Research in math and physics suggests that having students study worked examples during their first contact with new material, instead of solving practice problems, can be beneficial to their subsequent performance, possibly due to the reduced cognitive load required to study examples compared to generating solutions. However, there is minimal research directly comparing these teaching methods in introductory statistics. In this study, we chose six pairs of introductory statistics topics of approximately equal difficulty from throughout the semester. After an initial mini-lecture, one topic from each pair was taught using practice problems; the other was taught by having students read worked examples. Using Bayesian and frequentist analyses, we find that student performance is better after reading worked examples. This may be due to worked examples slowing the process of forgetting. Surprisingly, there is also strong evidence from in-class surveys that students experience greater frustration when reading worked examples. This could indicate that frustration is not an effective proxy for cognitive load. Alternatively, it could indicate that classroom supports during in-class problem-solving were effective in reducing the cognitive load of practice problems below that of interpreting written statistical explanations.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"154 - 170"},"PeriodicalIF":2.2,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1637801","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43029490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-10DOI: 10.1080/10691898.2019.1669506
Roberto Rivera, Mario Marazzi, P. Torres-Saavedra
Abstract The 2016 Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report emphasized six recommendations to teach introductory courses in statistics. Among them: use of real data with context and purpose. Many educators have created databases consisting of multiple datasets for use in class; sometimes making hundreds of datasets available. Yet “the context and purpose” component of the data may remain elusive if just a generic database is made available. We describe the use of open data in introductory courses. Countries and cities continue to share data through open data portals. Hence, educators can find regional data that engage their students more effectively. We present excerpts from case studies that show the application of statistical methods to data on: crime, housing, rainfall, tourist travel, and others. Data wrangling and discussion of results are recognized as important case study components. Thus, the open data based case studies attend most GAISE College Report recommendations. Reproducible R code is made available for each case study. Example uses of open data in more advanced courses in statistics are also described. Supplementary materials for this article are available online.
{"title":"Incorporating Open Data Into Introductory Courses in Statistics","authors":"Roberto Rivera, Mario Marazzi, P. Torres-Saavedra","doi":"10.1080/10691898.2019.1669506","DOIUrl":"https://doi.org/10.1080/10691898.2019.1669506","url":null,"abstract":"Abstract The 2016 Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report emphasized six recommendations to teach introductory courses in statistics. Among them: use of real data with context and purpose. Many educators have created databases consisting of multiple datasets for use in class; sometimes making hundreds of datasets available. Yet “the context and purpose” component of the data may remain elusive if just a generic database is made available. We describe the use of open data in introductory courses. Countries and cities continue to share data through open data portals. Hence, educators can find regional data that engage their students more effectively. We present excerpts from case studies that show the application of statistical methods to data on: crime, housing, rainfall, tourist travel, and others. Data wrangling and discussion of results are recognized as important case study components. Thus, the open data based case studies attend most GAISE College Report recommendations. Reproducible R code is made available for each case study. Example uses of open data in more advanced courses in statistics are also described. Supplementary materials for this article are available online.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"198 - 207"},"PeriodicalIF":2.2,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1669506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45128754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-04DOI: 10.1080/10691898.2019.1629852
J. Immekus
Abstract This article examines the integration of cognitive psychology research and technology within existing frameworks of statistics course design and implementation for a sequence of flipped graduate-level courses. Particular focus is the use of the principles of spacing and retrieval practice within the flipped classroom format as strategic approaches to curriculum design and instructional delivery within and across courses. The reporting of student perceptions regarding their engagement in learning, statistical thinking and practice, and course components that contributed to their learning serves to shed light on ways educators can bridge theory to practice in statistics education at the graduate-level.
{"title":"Flipping Statistics Courses in Graduate Education: Integration of Cognitive Psychology and Technology","authors":"J. Immekus","doi":"10.1080/10691898.2019.1629852","DOIUrl":"https://doi.org/10.1080/10691898.2019.1629852","url":null,"abstract":"Abstract This article examines the integration of cognitive psychology research and technology within existing frameworks of statistics course design and implementation for a sequence of flipped graduate-level courses. Particular focus is the use of the principles of spacing and retrieval practice within the flipped classroom format as strategic approaches to curriculum design and instructional delivery within and across courses. The reporting of student perceptions regarding their engagement in learning, statistical thinking and practice, and course components that contributed to their learning serves to shed light on ways educators can bridge theory to practice in statistics education at the graduate-level.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"79 - 89"},"PeriodicalIF":2.2,"publicationDate":"2019-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1629852","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45408750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-04DOI: 10.1080/10691898.2019.1646538
Allan Rossman, Gail Burrill
Gail Burrill is a Mathematics Specialist in the Program in Mathematics Education at Michigan State University. She previously served as secondary teacher and department chair. She was President of the National Council of Teachers of Mathematics and is currently President of the International Association for Statistical Education. She is a Fellow of the American Statistical Association. This interview took place via email on January 1–July 14, 2019.
{"title":"Interview With Gail Burrill","authors":"Allan Rossman, Gail Burrill","doi":"10.1080/10691898.2019.1646538","DOIUrl":"https://doi.org/10.1080/10691898.2019.1646538","url":null,"abstract":"Gail Burrill is a Mathematics Specialist in the Program in Mathematics Education at Michigan State University. She previously served as secondary teacher and department chair. She was President of the National Council of Teachers of Mathematics and is currently President of the International Association for Statistical Education. She is a Fellow of the American Statistical Association. This interview took place via email on January 1–July 14, 2019.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"128 - 134"},"PeriodicalIF":2.2,"publicationDate":"2019-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1646538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59859470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-04DOI: 10.1080/10691898.2019.1623136
Donghui Yan, Gary E. Davis
Abstract Data science is a discipline that provides principles, methodology, and guidelines for the analysis of data for tools, values, or insights. Driven by a huge workforce demand, many academic institutions have started to offer degrees in data science, with many at the graduate, and a few at the undergraduate level. Curricula may differ at different institutions, because of varying levels of faculty expertise, and different disciplines (such as mathematics, computer science, and business) in developing the curriculum. The University of Massachusetts Dartmouth started offering degree programs in data science from Fall 2015, at both the undergraduate and the graduate level. Quite a few articles have been published that deal with graduate data science courses, much less so dealing with undergraduate ones. Our discussion will focus on undergraduate course structure and function, and specifically, a first course in data science. Our design of this course centers around a concept called the data science life cycle. That is, we view tasks or steps in the practice of data science as forming a process, consisting of states that indicate how it comes into life, how different tasks in data science depend on or interact with others until the birth of a data product or a conclusion. Naturally, different pieces of the data science life cycle then form individual parts of the course. Details of each piece are filled up by concepts, techniques, or skills that are popular in industry. Consequently, the design of our course is both “principled” and practical. A significant feature of our course philosophy is that, in line with activity theory, the course is based on the use of tools to transform real data to answer strongly motivated questions related to the data.
数据科学是一门学科，为分析数据的工具、价值或见解提供原则、方法和指导方针。在巨大的劳动力需求的推动下，许多学术机构开始提供数据科学学位，其中许多是研究生学位，也有一些是本科学位。不同机构的课程可能会有所不同，因为不同的教师专业水平和不同的学科(如数学、计算机科学和商业)在开发课程。马萨诸塞大学达特茅斯分校(University of Massachusetts Dartmouth)从2015年秋季开始提供数据科学的本科和研究生学位课程。已经发表了不少关于研究生数据科学课程的文章，而关于本科生数据科学课程的文章就少得多了。我们的讨论将集中在本科课程的结构和功能，特别是数据科学的第一门课程。我们这门课程的设计围绕着一个叫做数据科学生命周期的概念。也就是说，我们将数据科学实践中的任务或步骤视为形成一个过程，由状态组成，这些状态表明它是如何产生的，数据科学中的不同任务是如何依赖或相互作用的，直到数据产品或结论的诞生。当然，数据科学生命周期的不同部分构成了课程的各个部分。每件作品的细节都由工业中流行的概念、技术或技能填充。因此，我们的课程设计既“有原则”又实用。我们课程理念的一个显著特点是，与活动理论一致，课程基于使用工具转换真实数据，以回答与数据相关的强烈动机问题。
{"title":"A First Course in Data Science","authors":"Donghui Yan, Gary E. Davis","doi":"10.1080/10691898.2019.1623136","DOIUrl":"https://doi.org/10.1080/10691898.2019.1623136","url":null,"abstract":"Abstract Data science is a discipline that provides principles, methodology, and guidelines for the analysis of data for tools, values, or insights. Driven by a huge workforce demand, many academic institutions have started to offer degrees in data science, with many at the graduate, and a few at the undergraduate level. Curricula may differ at different institutions, because of varying levels of faculty expertise, and different disciplines (such as mathematics, computer science, and business) in developing the curriculum. The University of Massachusetts Dartmouth started offering degree programs in data science from Fall 2015, at both the undergraduate and the graduate level. Quite a few articles have been published that deal with graduate data science courses, much less so dealing with undergraduate ones. Our discussion will focus on undergraduate course structure and function, and specifically, a first course in data science. Our design of this course centers around a concept called the data science life cycle. That is, we view tasks or steps in the practice of data science as forming a process, consisting of states that indicate how it comes into life, how different tasks in data science depend on or interact with others until the birth of a data product or a conclusion. Naturally, different pieces of the data science life cycle then form individual parts of the course. Details of each piece are filled up by concepts, techniques, or skills that are popular in industry. Consequently, the design of our course is both “principled” and practical. A significant feature of our course philosophy is that, in line with activity theory, the course is based on the use of tools to transform real data to answer strongly motivated questions related to the data.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"99 - 109"},"PeriodicalIF":2.2,"publicationDate":"2019-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1623136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48867212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-04DOI: 10.1080/10691898.2019.1623137
B. Chance, Shea Reynolds
Abstract Through a series of explorations, this article will demonstrate how the Kentucky Derby winning times dataset provides various opportunities for introductory and advanced topics, from data processing to model building. Although the final goal may be a prediction interval, the dataset is rich enough for it to appear in several places in an introductory or second course in statistics. After adjusting for the change in track length and track condition, winning speed has an interesting nonlinear trend, with one notable outlier. Student investigations can range from validating the phrase “the most exciting two minutes in sports” to predicting the winning speed of next year’s race using parallel polynomial models.
{"title":"Predicting the Kentucky Derby Winner! Sort of","authors":"B. Chance, Shea Reynolds","doi":"10.1080/10691898.2019.1623137","DOIUrl":"https://doi.org/10.1080/10691898.2019.1623137","url":null,"abstract":"Abstract Through a series of explorations, this article will demonstrate how the Kentucky Derby winning times dataset provides various opportunities for introductory and advanced topics, from data processing to model building. Although the final goal may be a prediction interval, the dataset is rich enough for it to appear in several places in an introductory or second course in statistics. After adjusting for the change in track length and track condition, winning speed has an interesting nonlinear trend, with one notable outlier. Student investigations can range from validating the phrase “the most exciting two minutes in sports” to predicting the winning speed of next year’s race using parallel polynomial models.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"120 - 127"},"PeriodicalIF":2.2,"publicationDate":"2019-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1623137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41598298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-04DOI: 10.1080/10691898.2019.1644052
J. Witmer
{"title":"Note From the Editor","authors":"J. Witmer","doi":"10.1080/10691898.2019.1644052","DOIUrl":"https://doi.org/10.1080/10691898.2019.1644052","url":null,"abstract":"","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"59 - 59"},"PeriodicalIF":2.2,"publicationDate":"2019-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1644052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48369107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-04DOI: 10.1080/10691898.2019.1608874
Tommy Soesmanto, S. Bonner
Abstract In recent years, the Australian tertiary education sector embraced the gradual adaption of the dual mode system in course delivery in universities and higher degree education providers. In such systems, students have the option, as well as the flexibility, to undertake the same course in a face-to-face (F2F) environment and/or an online environment. This article presents an evaluation of the dual mode design of a first-year business statistics course delivered at the Griffith University. In this article, we discuss the various aspects of the dual mode design in the course, emphasizing the use of consistent teaching strategies for the F2F and online student cohorts. Moreover, we present a comparative analysis of learning satisfaction and academic performance of the two cohorts within the dual mode system. Using t-tests, nonparametric tests, and propensity score matching estimators we provide new insights into dual mode course design. Our results suggest no significant difference in student experiences and outcomes. Discussion and analysis presented in this article is useful as feedback for further improvement in teaching strategies in the delivery of dual mode courses.
{"title":"Dual Mode Delivery in an Introductory Statistics Course: Design and Evaluation","authors":"Tommy Soesmanto, S. Bonner","doi":"10.1080/10691898.2019.1608874","DOIUrl":"https://doi.org/10.1080/10691898.2019.1608874","url":null,"abstract":"Abstract In recent years, the Australian tertiary education sector embraced the gradual adaption of the dual mode system in course delivery in universities and higher degree education providers. In such systems, students have the option, as well as the flexibility, to undertake the same course in a face-to-face (F2F) environment and/or an online environment. This article presents an evaluation of the dual mode design of a first-year business statistics course delivered at the Griffith University. In this article, we discuss the various aspects of the dual mode design in the course, emphasizing the use of consistent teaching strategies for the F2F and online student cohorts. Moreover, we present a comparative analysis of learning satisfaction and academic performance of the two cohorts within the dual mode system. Using t-tests, nonparametric tests, and propensity score matching estimators we provide new insights into dual mode course design. Our results suggest no significant difference in student experiences and outcomes. Discussion and analysis presented in this article is useful as feedback for further improvement in teaching strategies in the delivery of dual mode courses.","PeriodicalId":45775,"journal":{"name":"Journal of Statistics Education","volume":"27 1","pages":"90 - 98"},"PeriodicalIF":2.2,"publicationDate":"2019-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10691898.2019.1608874","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47792661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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