Statistics Course Research Articles

The effect of students’ attitudes and concerns on statistical concepts performance: A SEM approach

The purpose of this study was to investigate how the attitudes and concerns of higher learning students made an impact on their performance in the fundamental concepts of the statistics course. Despite being classified as one of the important areas of scientific, industrial, or social problems, students generally perceive statistics as a challenging course to understand. This negative perception leads to a set of complex emotional reactions that may cause great discomfort, resulting in negative consequences, such as anxiety. A sample of 248 UiTM Pahang Branch students, enrolled in a basic statistics course, was randomly selected using a stratified sampling technique. This study employed Structural Equation Modeling (SEM) method to assess the linear relationship model between students' attitudes and concerns, and their performance in statistics examinations. The findings indicated that both students' attitudes and concerns showed a significant negative influence on their performance in statistics examinations, resulting in poor academic performance. Hence, to foster students' academic success in statistics courses, it is essential to cultivate a positive attitude and enhance their confidence levels.

Creating simulation applets to teach statistics

The purpose of this study is whether the use of software for simulating concepts of statistics and probability theory during a semester introductory statistics course ensured greater success in the final exam of the course for students who used it, compared to students who used general statistical software. The simulation software used was created for this purpose based on the average level of understanding of the students who attended the course. The students who attended the course were divided into two groups in a way that ensured that the two groups had proportionally about the same level of statistical knowledge, mathematical background, and about the same number of students in terms of origin and gender. One group used simulation software throughout the semester, while the other used general statistical software. The simulation software used in this survey was created for this purpose based on the average level of understanding of the students who attended the course. At the end of the semester after the final exam, various factors were considered and students' final exam performance (success rates), were compared based on these factors. It appeared that success in the final exam when it comes to similar populations as the two groups of students who attended the course has a strong dependence on the factor of using simulation software.

Designing OER with Equity: An Example of Situating Equity in a Community College Statistics Course Redesign

Designing OER with Equity: An Example of Situating Equity in a Community College Statistics Course Redesign

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.

THE ROLE OF SCIENTIFIC CALCULATORS IN IMPROVING STATISTICS LEARNING

In its development, Business Statistics has used several types of technology to facilitate the learning process for students, such as statistical software, spreadsheets, calculators, multimedia materials, and data repositories. The calculator includes computer technology with a simple version but allows students to experience an active learning process. Thus, the calculator media was chosen as the main learning media in business statistics courses, including in evaluating student abilities. Therefore, this study aims to determine the effectiveness of using calculators in Business Statistics courses. The research was conducted using a mix method, namely qualitative (questionnaire) and quantitative (experimental). The results showed that there was an increase in the average value of Simple Linear Regression for groups of students who used statistical applications on calculators compared to groups who did not use calculators. Based on the results of the questionnaire and suggestions, it was found that 87.7% of students considered it easy to understand the Business Statistics application questions, when using the statistical application on a calculator. Suggestions made by students were the availability of guidelines for using statistical calculator applications for practice, and the availability of calculators in the research laboratory that students could use for practice

Building skills for the future of work: Students’ perspectives on emerging jobs in the Data and AI Cluster through artificial intelligence in education

The main goal of this paper is to supplement the existing literature with new knowledge in the field of artificial intelligence and education, which relates to the importance of courses in statistics, quantitative methods, and students’ perspectives about emerging jobs in the Data and AI Cluster. A multidimensional model of the perceived usefulness of artificial intelligence in students’ perspective about emerging jobs in the Data and AI Cluster was formed; it includes constructs students’ knowledge of the meaning of “artificial intelligence”, their perception of its usefulness in their studies, the perceived ease of use of AI, the perceived usefulness of statistics and quantitative methods, students’ perspective on work skills for the future, and their perspective on emerging jobs in the Data and AI Cluster. The empirical research included 197 undergraduate and postgraduate students from the University of Maribor, Faculty of Economics and Business in Slovenia, who had prior knowledge of statistics obtained during their studies. The data were analyzed using structural equation modeling. The main findings of our research are important for curricula development and stress these implications: emphasis on teaching the meaning and importance of AI, integration of AI in coursework, strengthening quantitative skills and developing future work skills that are aligned with emerging trends in the Data and AI Cluster.

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.

The Applicability of Peer Teaching in a Post-Graduate Business Course

This study demonstrates the interrelation between peer teaching and six grading criteria used in a graduate business program. Twenty (20) enrolled post-graduate students in the Advanced Statistics course were assigned to five (5) peer groups participating in the study. The quantitative graphical method was used. Specifically, the Correspondence Analysis (CA) two-way contingency table describes the association between the level of peer-teaching and its applicability to the six grading criteria. Based on the CA result, there was a clear separation among the peer groupings and the six grading criteria. Peer group D find peer teaching applicable to written content and exam. Peer group B considered peer-teaching moderately applicable to research while highly applicable in language content. For students in peer group C, moderate peer teaching is highly applicable to oral content. However, peer group A regarded peer teaching as not applicable to written skills. The correspondence analysis provided valuable insights into the relationships between peer teaching and six grading criteria. This study confirms that the level of peer teaching possesses a specific level of applicability in the six grading criteria, with the peer groupings exhibiting distinct patterns.

Development of a Week-Long Mathematics Intervention for Incoming Chemistry Graduate Students.

A student-led mathematics bootcamp has been designed and implemented to help foster community building, improve confidence in mathematical skills, and provide mathematical resources for incoming physical chemistry doctoral students. The bootcamp is held immediately before the start of the first semester of graduate school and uses an active learning approach to review and practice undergraduate-level mathematics problems over 5 days in small student groups. This work includes the development and presentation of a new, publicly available mathematics curriculum for the bootcamp on select mathematics topics, including calculus, linear algebra, functions, differential equations, statistics, and coding in Python, aiming at improving students' confidence and learning experiences in graduate quantum mechanics and statistical physics courses. Surveys before and after the bootcamp showed an increase in students' confidence in problem-solving in key mathematical areas and social aspects of peer-led group learning. Qualitative and quantitative analyses demonstrate that the bootcamp reduced prior inequities in students' confidence metrics based on gender and mathematical background.

Bayesian Formula and Its Application Analysis

Bayesian formula is a very important and widely used formula in probability and statistics courses, which can simplify the probability calculation of certain complex events. This article not only introduces the relevant theories of Bayesian formula, but also explores its practical applications in daily life through examples.

Accounting Student Perceptions toward Online Flipped Statistics Course and Effective Methods to Promote Engagement, Increase Satisfaction, and Improve Self-Efficacy

The COVID-19 pandemic has motivated many educational institutions in the world to adapt their delivery method from conventional (offline) teaching/learning to online methods, and one of the methods is the Flipped Classroom. Nonetheless, the shift from offline classroom instruction to online learning must ensure its long-term quality, which can be effectively delivered to students. Thus, this study investigates the effectiveness of the an online flipped classroom teaching model as perceived by learners, applied due to the COVID-19 pandemic. The sample for this study includes 41 accounting students taking an online statistics course in their 5th and 7th semesters in a private university in Indonesia. The methods are both quantitative—employed through a questionnaire—and qualitative through applied semi-structured interviews as well as for analyzing the collected corpus. Ultimately, the study concludes that, in terms of student engagement, satisfaction, and efficacy, the flipped classroom method is effective. Lastly, the significance of the research shows recommendations for improving the application of the flipped classroom approach in the current context of online learning amidst the prevailing policy in Indonesia and beyond.

Teaching basic statistic concepts to student classes with diverse mathematical background using specialized applets

This study examines whether the use of specialized software applications can have an effective role in understanding and clarifying concepts of the basic measures of central tendency and dispersion, so that the advantages over traditional teaching are clear. Higher education students need to have a deep understanding of the concepts of theory of probability, descriptive statistics measures and statistical conclusions in order to be able to be used in their studies and in their work. For this purpose during an introductory course in Statistics at a University of Applied Sciences, two separate groups of students consisting of randomly selected members were given the same worksheet. One of these groups was additionally given specialized application software developed to visualize these concepts in order to answer the questions on the worksheet. The investigated case is whether the appropriate use of specialized software can help to effectively understand and interpret basic descriptive statistic concepts in a realistic application. The main conclusion is that appropriate specialized software applications can lead to the deepening of statistic concepts by students and in general promote the statistical literacy to a much greater extent than traditional teaching.

How Fun Overcame Fear: The Gamification of a Statistics Course

How Fun Overcame Fear: The Gamification of a Statistics Course

Active-Learning Class Activities and Shiny Applications for Teaching Support Vector Classifiers

Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is unconventional to many students in a second or third course in statistics or data science. As a result, this topic is often not as intuitive to students as some of the more traditional statistical modeling tools. In order to facilitate students’ mastery of the topic and promote active learning, we developed some in-class activities and their accompanying Shiny applications for teaching support vector classifiers. The designed course materials aim at engaging students through group work and solidifying students’ understanding of the algorithm via hands-on explorations. The Shiny applications offer interactive demonstration of the changes of the components of a support vector classifier when altering its determining parameters. With the goal of benefiting the broader statistics and data science education community, we have made the developed Shiny applications publicly available. In addition, a detailed in-class activity worksheet and a real data example are also provided in the online supplementary materials.

Introducing Variational Inference in Statistics and Data Science Curriculum

Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in both undergraduate and graduate statistics and data science curricula due to their wide range of applications. In this article, we present a one-week course module for students in advanced undergraduate and applied graduate courses on variational inference, a popular optimization-based approach for approximate inference with probabilistic models. Our proposed module is guided by active learning principles: In addition to lecture materials on variational inference, we provide an accompanying class activity, an R shiny app, and guided labs based on real data applications of logistic regression and clustering documents using Latent Dirichlet Allocation with R code. The main goal of our module is to expose students to a method that facilitates statistical modeling and inference with large datasets. Using our proposed module as a foundation, instructors can adopt and adapt it to introduce more realistic case studies and applications in data science, Bayesian statistics, multivariate analysis, and statistical machine learning courses.

Perceived Creativity Fostering Teacher Behavior and Student Engagement in Statistic Course: Academic Self Efficacy as Mediator

Perceived Creativity Fostering Teacher Behavior and Student Engagement in Statistic Course: Academic Self Efficacy as Mediator

Pengaruh Pemanfaatan Hybrid Learning terhadap Motivasi dan Prestasi Belajar Mahasiswa di Masa Post Pandemi

Teaching and learning activities in statistics courses are classified as low, this is known from the results of exams held by many students who get low marks. This study aims to determine the effect of using hybrid learning on student motivation and achievement during the post-pandemic period at STMIK Jaya Nusa. This research is quantitative with the census method. The population is students who take statistics courses as many as 70 people. The results of the study show that (1) there is a positive influence between the use of hybrid learning and student learning motivation in the post-pandemic period. (2) There is no positive effect between the use of hybrid learning and student achievement in the post-pandemic period.

Reducing statistics anxiety and academic procrastination among Israeli students: A pilot program

AbstractMany 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 (n = 37) with a no intervention, optional exercise statistics class (n = 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.

How Fun Overcame Fear: The Gamification of a Graduate-Level Statistics Course

Innovative instructional methods can help improve student engagement and learning outcomes when teaching difficult subjects, such as statistics. This instructional innovation article illustrates how gamification can be applied in management education to improve students’ learning experience, engagement, and acquisition of knowledge. Our purpose is to demonstrate how gamification is not only a powerful way to build on the use of games and game thinking in our field, but also a versatile application of education technology that could potentially enhance the way management knowledge is taught. Furthermore, it is a low-risk way for management educators to join and contribute to the larger virtual revolution. We document the process of combining the Technology, Pedagogy, and Content Knowledge (TPACK) competency framework and the Mechanics, Dynamics, and Aesthetics (MDA) design framework to create both theoretically and practically motivated gamification designs in a graduate-level statistics class. With student data and feedback, we demonstrate that gamification helped create a positive learning experience, facilitated interactions in the course, and assisted the learning of statistical knowledge. We offer suggestions and concrete examples for interested educators to implement gamification in their courses.

Analysis of Error in Working of Questions on the Semester Final Exam of Mathematics Statistics for Students of UIN Saizu Purwokerto

Learning evaluation of the certain subject is important to be done in university. It is to be able to see, improve and increase the quality of learning outcomes. One form of evaluation for a Mathematics Statistics course is the final semester exam, abbreviated as UAS. Purpose of this article to determine the error type of students in working the final semester exam, percentage of students who do mistakes and amount of error on each type, as well the most mistakes . Research used a quantitative descriptive method imposed on all of population as many as 98 respondents. Data collection used documentation and the data analysis that is statistics descriptive. Research results showed that as many as 14 type of error with one among them is findings a new of error type. Percentage of students do error on each type is the concept error 96.94%, principle 92.86%, fact 20.41%, conclusion no used correct reasoning 8.16% , error because procedure or algoritm 4.08%, skill error 21.43%, error without pattern 37.76%, technical 3.06%, and error in mark 5.1%, and successive types of error that is data error , operation , write the exact same long answer to two different questions, language interpretation, as well finishing error each of which is 1.02%. The same as in according of the type errors are found percentage of the number of errors that is 40.12%, 28, 09%, 6.17%, 2.47% , 1.23%, 6.48%, 11.42%, 0.93%, 1.54%, and the others is 0.31% respectively. Most mistakes is the concept error.