Undergraduate Statistics Course Research Articles

Age Guessing: A Game to Introduce Fundamental Statistical Concepts

We develop a spreadsheet-based game to illustrate fundamental statistical concepts in the first class of an undergraduate Statistics course to motivate students about the topics that they will learn in upcoming classes. This game has been implemented by Google Forms and Google Sheets and can be played in both online and in-person classes of small and large sizes. Statistics is one of the most anxiety-inducing courses for undergraduate students, especially if mathematics is not the focus of their program. Negative anecdotes about the course, mathematics anxiety, and not knowing what the course is exactly about and how practical it can be are among the reasons that contribute to statistics anxiety. The first class provides a good opportunity for an instructor to mitigate these negative impressions and to set a positive attitude toward the course. A pre- and post-game group discussion that we have conducted systematically for six years suggests that the game addresses the students’ negative impression about the course and helps them gain a clearer understanding of the tools and skills they will learn in Statistics. Supplementary materials for this article are available online.

Inquiry dynamics at the crossroads of descriptive and inferential statistics

The fast evolution of statistics caused by technological developments is the basis of numerous challenges in undergraduate courses. Project-based learning (PBL) and similar methodologies have been proposed by various authors to develop students’ ‘statistical sense’. This paper presents a case study regarding implementing three study and research paths (SRPs) in an undergraduate statistics course. SRPs are inquiry-based teaching devices developed in the Anthropological Theory of the Didactic. This study aims to analyse the inquiry dynamics that SRP implementations promote, the influences of teachers’ interventions and the role of external instances. The way statistical knowledge evolves and links descriptive and inferential statistics is also examined. The results of this study suggest that SRPs can contribute to the self-sustainability of the inquiry process, which is not often the case in other approaches such as PBL. This study also highlights the use of question–answer maps to analyse the dynamics of the inquiry process in terms of the connectivity of the knowledge involved. Future research points to the study of the conditions needed to generate more adidacticity in PBL proposals.

STATISTICS ATTITUDES AFTER USING GUIDED PROJECT-BASED LEARNING AS AN ANDRAGOGICAL STRATEGY IN A GRADUATE STATISTICS COURSE

This study builds on previous studies that have examined guided project-based learning in undergraduate statistics courses to examine students’ attitudes toward statistics after participating in a graduate-level statistics course that used guided project-based learning as an andragogical technique. This phenomenological qualitative case study utilized multiple student interviews and reflections over a semester-long statistics course in a doctoral education degree program. The results showed that guided project-based learning immersed students in the quantitative inquiry process and emboldened them to read and use statistics in their academic and professional lives. It also revealed several elements of guided project-based learning that are important for instructors looking to implement this approach in their own courses.

Repurposing a Peer-Reviewed Publication to Engage Students in Statistics: An Illustration of Study Design, Data Collection, and Analysis

Engaging and motivating students in undergraduate statistics courses can be enhanced by using topical peer-reviewed publications for analyses as part of course assignments. Given the popularity of on-campus therapy dog stress-reduction programs, this topic fosters buy-in from students whilst providing information regarding the importance of mental health and well-being as it impacts learning. This article describes how instructors can use a study on the benefits of human–dog interactions to teach students about study design, data collection and ethics, and hypothesis testing. The data and research questions are accessible to students without requiring detailed subject-area knowledge. Students can think carefully about how to collect and analyze data from a randomized controlled trial with two-sample hypothesis tests. Instructors can use these data for short in-class examples or longer assignments and assessments, and throughout this article, we suggest activities and discussion questions. Supplementary materials for this article are available online.

Challenges and Successes of Emergency Online Teaching in Statistics Courses

As the COVID-19 pandemic took hold in early months of 2020, education at all levels was pushed to emergency fully remote, online formats. This emergency shift affected all aspects of teaching and learning with very little notice and often with limited resources. Educators were required to convert entire courses online and shift to remote instructional approaches practically overnight. Students found themselves enrolled in online courses without choice and struggling to adjust to their new learning environments. This article highlights some of the challenges and successes of teaching emergency online undergraduate statistics courses. In particular, we discuss challenges and successes related to (a) technology, (b) classroom community and feedback, and (c) student-content engagement. We also reflect on the opportunity to continue to enhance and enrich the learning experiences of our students by using some of the lessons learned from emergency online teaching as new permanent online statistics courses are developed and/or moved back into the classroom.

Eliminating ANOVA Hand Calculations Predicts Improved Mastery in an Undergraduate Statistics Course

Background Presumably, most statistics professionals use a software program to perform statistical analyses, yet statistics textbooks and classes commonly teach hand calculations. Objective This study examined whether, in an undergraduate psychology statistics course, hand calculations related to students' conceptual and applied analysis of variance (ANOVA) literacy. Method Quasi-experimentally, we compared 95 students’ mastery of one-factor between- and within-subjects ANOVAs, using the unit quiz and SPSS data analytics lab, between sections which did and did not include hand calculations. Results Controlling for GPA, gender, ethnicity, major, age, and math preparation to offset nonrandom assignment to section, eliminating ANOVA hand calculations predicted better performance on the quiz by 8% and on the SPSS lab by 6%. Conclusion Shifting instructional focus away from hand calculations could enhance students’ conceptual and practical understanding of one-factor ANOVAs. Teaching Implications Given time limitations, instructors might forgo teaching ANOVA hand calculations and focus on conceptual information and software applications.

Assessing impact of problem-based learning using data mining to extract learning patterns

PurposeThis paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India.Design/methodology/approachStudy employs an in situ experiment using a conceptual model based on learning theory. The participant's end-of-semester GPA is Performance Indicator. Integrating PBL with classroom teaching is unique instructional approach to this study. An unsupervised and supervised data mining approach to analyse PBL impact establishes research conclusions.FindingsThe administration of PBL results in improved learning patterns (above-average) for students with medium attendance. PBL, Gender, Math background, Board and discipline are contributing factors to students' performance in the decision tree. PBL benefits a student of any gender with lower attendance.Research limitations/implicationsThis study is limited to course students from one institute and does not consider external factors.Practical implicationsResearchers can apply learning patterns obtained in this paper highlighting PBL impact to study effect of every innovative pedagogical study. Classification of students based on learning behaviours can help facilitators plan remedial actions.Originality/value1. Clustering is used to extract student learning patterns considering dynamics of student performances over time. Then decision tree is utilized to elicit a simple process of classifying students. 2. Data mining approach overcomes limitations of statistical techniques to provide knowledge impact in presence of demographic characteristics and student attendance.

Insights from DataFest point to new opportunities for undergraduate statistics courses: Team collaborations, designing research questions, and data ethics

AbstractAs the field of data science evolves with advancing technology and methods for working with data, so do the opportunities for re‐conceptualizing how we teach undergraduate statistics and data science courses for majors and non‐majors alike. In this paper, we focus on three crucial components for this re‐conceptualization: Developing research questions, professional ethics, and team collaborations. We share vignettes from two teams of undergraduate statistics or data science majors at two different stages of their development (novice and expert) while they worked on a DataFest data challenge. These vignettes shed light on opportunities for re‐conceptualizing introductory courses to give more attention to issues of the process of developing focused research questions when given a complex data set, professional ethics and bias, and how to collaborate effectively with others. We provide some implications for teaching and learning as well as an example activity for educators to use in their courses.

INITIAL ATTITUDES TOWARD STATISTICS ARE BETTER IN TRADITIONAL COMPARED TO ONLINE COURSES, AT LEAST UNTIL COVID-19

We examined students’ initial and concluding attitudes toward statistics based on course delivery methods. Students enrolled in either traditional or online undergraduate statistics courses (N = 196) completed the Survey of Attitudes Toward Statistics-36. At the beginning of the semester, students in traditional courses felt better about the course and believed it would be easier, compared to students taking statistics online. Attitude differences, however, were mitigated as traditional courses were forced online by the pandemic, and distinct attitudinal differences were not observed at the semester’s end. With limited offerings and restrictions on the delivery of traditional courses in the COVID-19 era, statistics educators should be cognizant of student attitudes, their potential for change, and how to best influence positive attitude shifts for different instructional formats.

An exploration of course and cohort communication spaces in Discord, Teams, and Moodle

This research examined the impact of supplementing a learning management system, Moodle, with communication tools, Discord and Teams, to support communication in blended and distance undergraduate courses in computer science, information technology, mathematics and statistics at a New Zealand university with well-established use of Moodle. Nineteen students participated in semi-structured interviews. Findings show that adding Discord or Teams increases information and knowledge exchange and helps students to connect with peers and teachers in their courses. Teams was beneficial particularly in settings with formal group work. Discord, which enables both students and staff to set up additional communication channels that are not restricted to course enrolments, was instrumental in connecting students across year levels with peers and alumni. This enabled discussions on course selection, career options and disciplinary topics beyond the course curriculum. Importantly, these beyond-course communication spaces nurtured belonging to wider discipline and study communities. The research establishes the importance of the increased levels of communication by highlighting the effects on student learning and connections to others. Looking beyond the specific tools, the level of formality and the degree of student co-ownership are identified as key factors in supporting the within- and beyond-course communication spaces. Implications for practice or policy: Learning management systems provide valuable course support but do not meet all learning and teaching communication needs, partly due to their formal and university-controlled nature. Educators and students benefit from using strong chat tools with improved information and knowledge exchange. Both educators and students experience enhanced belonging when using collaboration and chat tools. Students value Discord’s informality and student-led characteristics. Educators who use Teams to support formal group work must carefully manage integration with Moodle.

A simple consistent Bayes factor for testing the Kendall rank correlation coefficient

In this paper, we propose a simple and easy-to-implement Bayesian hypothesis test for the presence of an association, described by Kendall's τ coefficient, between two variables measured on at least an ordinal scale. Owing to the absence of the likelihood functions for the data, we employ the asymptotic sampling distributions of the test statistic as the working likelihoods and then specify a truncated normal prior distribution on the noncentrality parameter of the alternative hypothesis, which results in the Bayes factor available in closed form in terms of the cumulative distribution function of the standard normal distribution. Investigating the asymptotic behaviour of the Bayes factor we find the conditions of the priors so that it is consistent to whichever the hypothesis is true. Simulation studies and a real-data application are used to illustrate the effectiveness of the proposed Bayes factor. It deserves mentioning that the proposed method can be easily covered in undergraduate and graduate courses in nonparametric statistics with an emphasis on students' Bayesian thinking for data analysis.

Comparison of Students’ Performance Using Web-Based and Paper-Based Homework in an Undergraduate Statistics Course

Our institution is heavily invested in improving the learning outcomes of our students, especially in core courses like statistics which is taken by majority of freshmen students. As such, we have been reviewing our existing pedagogy and exploring novel ways to improve students experience in statistics course. In this study we compare web-based vs paper-based traditional homework models and their impact on students’ performance in an undergraduate statistics course. In web-based model, students used MyStatLab to complete the homework assignments along with additional resources offered by this platform. On the other hand, in paper-based model students used traditional approach in completing the homework and was manually graded by the instructor. Our analysis indicated that there is no statistically significant difference in students’ final course grades between the two modalities. Additionally, we conducted a chi-square test of independence and found no statistically significant relationship between students’ final course grades and homework models. However, our analysis concluded that student performance and perceptions improved with online homework model compared to the traditional model. Keywords: Web-based online homework, Traditional homework, Student performance, Undergraduate Statistics DOI: 10.7176/JEP/13-27-09 Publication date: September 30 th 2022

Questionnaire design and sampling procedures for business and economics students: a research-oriented, hands-on course

Introductory undergraduate statistics courses widely focus on statistical concepts or software-based data analysis. Despite the fact that the analysis of real data has shown to enhance students' engagement, the step of data collection is often neglected. Once students know the challenges of data collection, they are more aware of potential imperfections, such as a lack of representativeness, during data analysis. In this paper, we present a course that closes the gap allowing Business and Economics students to conduct a full survey under realistic conditions including questionnaire design, sampling, and data analysis. It entangles theory and application by combining course-based research experiences with cooperative learning and a flipped classroom approach. Students do not only obtain competences in the field of statistics, they also gain experiences and self-confidence for future research projects because the lecturer acts as a mentor guiding the students throughout the project. Although statistics is usually an unpopular field for Business and Economics students, their motivation was high throughout the semester as they acted as researchers who analysed a specific research question. This is in agreement with student feedback, which highlights the promotion of research-related competences and self-efficacy within the course.

E-learning with multiple-try-feedback: Can hints foster students’ achievement during the semester?

E-learning opportunities have become an increasingly important component of university education. Various laboratory studies have shown that e-learning environments can meaningfully enhance learning by incorporating various interventions and design choices (e.g., providing feedback and scaffolds). However, many computer-based interventions have not yet been applied in authentic university courses, raising questions about whether and how the provision of certain forms of feedback works and scales in an applied context. In this paper, we addressed this research gap. Specifically, we investigated whether including an elaborative component (hints) in multiple-try feedback increases student learning in e-learning exercises in an undergraduate statistics course. In one exercise, after completing a statistical problem, one group received feedback that conveyed knowledge about the correct response, while the other group additionally received elaborative feedback in the form of hints. We conducted an experimental comparison of these two types of feedback with third-semester sociology students in the tutorial component of an introductory statistics course. The results show that additional feedback helps students perform better during the session and on a delayed test one week later. Implications for further research and the application of such e-learning environments in university settings are discussed.

A Data-First Approach to Learning Real-World Statistical Modeling

This paper presents a novel design for an upper-level undergraduate statistics course structured around data rather than methods. The course is designed around curated datasets to reflect real-world data science practice and engages students in experiential and peer learning using the data science competition platform Kaggle. Peer learning is further encouraged by patterning the course after a genetic algorithm: students have access to each other’s solutions, allowing them to learn from what others have done and figure out how to improve upon previous work from week to week. Implementation details for the course are provided, and course efficacy is assessed using a survey of students and a focus group. Student responses suggest that the structure of the course contributed to narrowing the perceived gap between low- and high-performing students, that desired learning outcomes were successfully achieved, and that a data-first approach to learning statistics is effective for learning.

APPLICATIONS OF ITEM RESPONSE THEORY MODELS TO ASSESS ITEM PROPERTIES AND STUDENTS’ ABILITIES IN DICHOTOMOUS RESPONSES ITEMS

A test is a tool meant to measure the ability level of the students, and how well they can recall the subject matter, but items making up a test may be defectives, and thereby unable to measure students’ ability or traits satisfactorily as intended if proper attention is not paid to item properties such as difficulty, discrimination, and pseudo guessing indices (power) of each item. This could be remedied by item analysis and moderation. It is a known fact that the absence or improper use of item analysis could undermine the integrity of assessment, certification and placement in our educational institutions. Both appropriateness and spread of items properties in accessing students’ abilities distribution, and the adequacy of information provided by dichotomous response items in a compulsory university undergraduate statistics course which was scored dichotomously, and analyzed with stata 16 SE on window 7 were focused here. In view of this, three dichotomous Item Response Theory (IRT) measurement models were used in the context of their potential usefulness in an education setting such as in determining these items properties. Ability, item discrimination, difficulty, and guessing parameters as unobservable characteristics were quantified with a binary response test, then discrete item response becomes an observable outcome variable which is associated with student’s ability level is thereby linked by Item Characteristic Curves that is defined by a set of item parameters that models the probability of observing a given item response by conditioning on a specific ability level. These models were used to assess each of the three items properties together with students’ abilities; then identified defective items that were needed to be discarded, moderated, and non-defectives items as the case may be while some of these chosen items were discussed based on underlining models. Finally, the information provided by these items was also discussed.

Undergraduate Students’ Attitudes toward Statistics: How Excel Makes a Student’s Life Easier

ABSTRACT Purpose This study discusses how Excel program can improve attitudes toward statistics among undergraduate social work students in the context of experiential learning. According to the experiential learning approach, instructors can take diverse teaching techniques such as learning by doing, simulating, and participating in activities to maximize students’ learning. The researchers used Microsoft Excel as a statistical analysis software program to increase students’ engagement and participations in an undergraduate statistics course. Method The Students Attitude Toward Statistics was measured to compare students’ attitude toward Statistics course before and after the course. Results The findings of our pretest and posttest scores suggested that the use of the Excel program was following by a reduction in students’ anxiety. Discussion Due to the small sample size and other limitations of this study, the results should be interpreted with caution.

Your online textbook is ready: a shareable, interactive online textbook in response to COVID-19 lockdowns

This article discusses the development of an interactive online textbook for undergraduate teaching, in response to moving teaching online due to COVID-19. The development took place initially for a university with less than 5% of students studying online in 2019. The online textbook was incorporated into a large, diverse, multidisciplinary and predominantly first-year undergraduate research design and statistics course. Digital tools were embedded into the textbook to facilitate student engagement in lieu of face-to-face contact, including online crosswords for low-level statistical literacy, drop-and-drag activities, screencasts, exercises, animations, and videos. The textbook was prepared in markdown format. This approach allows for instant updating of material and provides focussed URLs that educators can use to point students directly to specific learning outcomes. Despite the textbook being developed in response to restricted teaching conditions, it has provided a cost-effective resource, targeted at teaching research design and basic statistics concepts, interpretation, and communication. Having returned to face-to-face teaching, the text is now fully integrated into the course. Importantly, the online textbook is freely available for other educators to use, either in its existing form, or by forking (on Github) the textbook sources to create a bespoke online textbook for other courses.

Statistics Instructors' Perceptions of Statistics Literacy in Different Undergraduate Programs

The present study aimed to investigate the statistical literacy perceptions of instructors who teach undergraduate statistics courses in different disciplines. Instructors were asked questions on their statistical literacy definitions, organization of the course content, topics emphasized or avoided by instructors in teaching statistics, and instructors post-course expectations from their students. Qualitative data gathered from interviews were analyzed and categorized under five main themes. The instructors' ultimate expectations from the students and the topics they emphasized during the courses are mostly stated in relation with statistical literacy. They provided less information related statistical literacy about two themes: course content and the issues they avoid. Recommendations from this study include reviewing and revising statistics course content and methods to reveal the components of statistical literacy.

New Statistics, Old Schools: An Overview of Current Introductory Undergraduate and Graduate Statistics Pedagogy Practices

Background A philosophical shift in statistics regarding emphasis on “New Statistics” (NS; Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7-29.) over conventional null hypothesis significance testing (NHST) raises the question of appropriate material coverage in undergraduate statistics courses. Objective We examined current practices in statistics pedagogy at the graduate and undergraduate levels for both NS and NHST. Method Using an online survey of a nationwide sample of current graduate students ( n = 452) and graduate faculty ( n = 162), we examined statistics pedagogy and perceptions of best approaches for teaching undergraduate statistics. Results In undergraduate statistics courses, coverage of NS material involves modest instruction in effect sizes and confidence intervals, while NHST remains dominant. Graduate courses have more balanced coverage. Effect size estimation was regarded as the most important NS knowledge for success in graduate school and the topic most in need of increased undergraduate coverage. Conclusion Undergraduate statistics courses could increase NS coverage, particularly effect size estimation, to better align with and prepare students for graduate work. Teaching Implications This research summarizes graduate program expectations and graduate student experiences regarding undergraduate statistics that current instructors can use to shape the content of their classes.