Statistics Course Research Articles

Pengaruh Minat Belajar Siswa Terhadap Hasil Belajar Statistik di SDN Wirun 3

This research aims to investigate the influence of students' interest in learning on statistics learning outcomes at SDN Wirun 3. The research uses a correlational approach to evaluate the relationship between students' interest in learning and statistics learning achievement. Data collection methods include surveys, academic records, and observations to understand students' learning interests. Data analysis uses correlation and regression techniques to assess the strength of the relationship between interest in learning and statistical learning achievement as well as other factors that influence it. The results of this work show a strong positive relationship between students' learning interest and their achievement in statistics courses, with approximately 49.8% of the variation in statistics achievement being explained by the level of learning interest. The data shows a high level of enthusiasm for learning among students, with average scores for interest in learning quite high and academic performance in moderate but strong statistics. The correlation coefficient of 0.706 between interest in learning and academic achievement indicates a significant correlation, underscoring the importance of building interest in learning to improve educational outcomes. These results confirm that increasing interest in learning can be a key strategy to advance students' academic achievement, especially in the subject of statistics.

Success in a Statistics Course: How Important is Grit?

Success in a statistics course depends on many factors including student attitudes and behaviors. In the first of two studies we had students complete a self-assessment at the beginning of the semester. The responses were later evaluated to determine what characteristics distinguish stronger students from weaker students. In the second study, assessments were made at the end of the semester. The students’ self-reflections were correlated with expected course grades and, as expected, stronger students reported behaviors consistent with a stronger work ethic. To the extent that pre-course and post-course assessments reveal student characteristics associated with academic success, the information may be useful in identifying promising students and in further developing those traits in all students.

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.

PREDICTIVE DISTRIBUTION TO DETERMINE LEARNING MODEL AT THE STRATEGIC COMPETENCE LEVEL OF STUDENTS IN STATISTICS GROUP COURSE

The problem of this research comes from a situation or condition that is not static. The description of these problems is the condition of the learning system, which tends to change due to the Covid-19 pandemic, causing learning conditions to be dynamic. From a statistical perspective, the dynamic situation can be modeled using a predictive distribution approach, so its characteristics can be studied. The purpose is to provide policy recommendations on appropriate learning models for lecturers in improving students' strategic competence, which is an ability that students need to master in solving various mathematical problems. The main discussion of this paper consists of three parts: clustering, predictive distribution, and statistical inference. The purpose of clustering is to group students based on test results to determine the level of strategic competence. In addition, clustering is also used as an initial process to predict students' strategic competence level if the learning used is still the same. The benefits of statistical inference in the distribution procedure in this study are used to determine the type of data distribution from each arrival of new information or data. The results of the statistical inference determine whether or not it is necessary to update the learning model of the lecturer. This research produce a new alternative statistical inference needed to make decisions. Based on the simulation results and discussion, the use of a predictive distribution approach to predict dynamic data is very appropriate. Distribution approach can use for detecting changes in new data distribution with historical data for the dynamic condition. If the changes are insignificant, direct instruction can still be used for the learning model in statistics course. A new learning model is recommended for the statistics group course at a higher level when the changes are significant.

Case Article—Racial Bias in Automated Traffic Law Enforcement and the Price of Unjustness

This case study has been developed for students to practice their data analysis and optimization skills in a contemporary societal issue: that of injustice in automated traffic law enforcement. Specifically, this case study is for students of modern data analysis and statistical modeling courses that focus on hypothesis testing; it also has a component for students in optimization and mathematical modeling courses that focus on linear and network optimization. The case study has been used since Spring 2023 in a combination of two courses from the Industrial Engineering (Analysis of Data, an introduction to probability and statistics) and Civil Engineering (Transportation Systems, an introduction to mathematical modeling and optimization for civil engineers with a focus on transportation) curricula. History: This paper has been accepted for the INFORMS Transactions on Education Special Issue on DEI in ORMS Classrooms. Supplemental Material: The Teaching Note and data files are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

Case—Racial Bias in Automated Traffic Law Enforcement and the Price of Unjustness

This case study has been developed for students to practice their data analysis and optimization skills in a contemporary societal issue: that of injustice in automated traffic law enforcement. Specifically, this case study is for students of modern data analysis and statistical modeling courses that focus on hypothesis testing; it also has a component for students in optimization and mathematical modeling courses that focus on linear and network optimization. The case study has been used since Spring 2023 in a combination of two courses from the Industrial Engineering (Analysis of Data, an introduction to probability and statistics) and Civil Engineering (Transportation Systems, an introduction to mathematical modeling and optimization for civil engineers with a focus on transportation) curricula. History: This paper has been accepted for the INFORMS Transactions on Education Special Issue on DEI in ORMS Classrooms.

The Statistical Literacy of Mathematics Education Students: An Investigation on Understanding the Margin of Error

Understanding the margin of error (MoE) as a part of statistical literacy which is useful for the public to select credible information from various surveys and polls. The study aims to reveal the levels of statistical literacy of mathematics education students, especially in understanding MoE, and compare them based on four variables: gender, enrollment in a statistics course, year in the program, and type of university. The online survey research involved undergraduate students of the mathematics education study program from 21 universities in Indonesia’s western, central, and eastern regions as the sample (n = 970). Descriptive statistics was used to describe the literacy levels and inferential statistics (t-test and F-test) to compare them based on the four variables. The results of the study reveal that: (1) student literacy in understanding the MoE concept is dominant at the non-literate level; and (2) there are significant differences in students’ literacy levels in terms of gender, enrollment in a statistics course, year in the program, and type of university. The study indicates that the literacy of mathematics education students is still low, so the statistics course is expected to focus more on developing statistical literacy.

GRIT AKADEMIK MAHASISWA: (DEFERENSIASI GENDER PADA PEMBELAJARAN ONLINE STATISTIKA DI ERA REVOLUSI INDUSTRI 4.0)

This research aims to determine differences in students' academic grit in online statistics courses based on gender. This research was conducted in 2021 on odd semester Islamic Psychology Study Program students. The population is students who have taken statistics and advanced statistics courses in even and odd semesters, totaling 60 students. The sampling technique used is a total sample, where all members of the population are used as sample members. Data was collected using an academic grit measuring tool in which there are 12 statement items. Data were analyzed using an independent difference test (independent t-test). The results of this research are that there are differences in students' academic grit during online statistics lectures based on gender. The research proportion value obtained was 0.006, where this value was smaller than the research significance value (0.05). This difference is a separate note as an effort to relate it to competence in the era of industrial revolution 4.0. Statistical skills are one of the skills that must be possessed in the era of industrial revolution 4.0. This is because with the existence of "Big Data", skills are needed in how to collect data, process data, compile data, present data, and analyze data well so as to produce correct conclusions on the data. In the future, statistics learning needs to be strengthened from a practical perspective so that students are skilled in managing "Big Data".

Statistics anxiety or statistics fear? A reinforcement sensitivity theory perspective on psychology students’ statistics anxiety, attitudes, and self-efficacy

This research investigates the role of reinforcement sensitivity theory (RST) in statistics education among two distinct samples of undergraduate psychology students. In Study 1, 318 students in a third-year statistics course completed self-report measures of RST, anxiety, attitudes, and self-efficacy concerning the study of statistics. In Study 2, 577 students from first-, second-, and third-year statistics courses participated. Controlling for age and gender, both studies found students who were high in goal-drive persistence reported lower statistics anxiety, higher statistics self-efficacy and more favourable attitudes toward learning statistics, while students who were high in the tendency to avoid threat reported higher statistics anxiety. Those with a more sensitive behavioural inhibition system reported greater statistics anxiety and less favourable attitudes, in Study 2 but not Study 1. Results indicate that RST dimensions account for as much as 23% of additional variance in statistics anxiety, 18% in statistics self-efficacy, and 11% in attitudes, after controlling for age and gender. These studies highlight the utility of RST dimensions, notably goal-drive persistence, threat sensitivity, and behavioural inhibition, in providing critical information as to personality differences among students that need to be considered when developing programs targeting statistics anxiety, attitudes, and self-efficacy.

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.

Students' Ability to Understand Statistics Subjects: An Analytical Review

This research aims to assess the comprehension skills of students in the statistics course at the Management Study Program, Universitas Islam Kadiri, Kediri. A survey method was employed to gather diverse information from 30 students in Semester III class A5 enrolled in the statistics course during the academic year 2022/2023. The research was conducted from July to November 2023, utilizing purposive sampling. Data collection involved a test comprising 10 multiple-choice questions, focusing on stages such as reading comprehension, understanding, transformation, process skills, and selecting the correct answers. Qualitative descriptive analysis was applied during and after data collection. The research findings indicate that students' comprehension skills in the statistics course fall into the high category, with 62% of students answering correctly. Common errors occurred in stages such as reading comprehension, understanding, transformation, process skills, and answer selection. Suggestions are given to students to study more diligently, actively participate in learning activities, and overcome difficulties by finding ways to make statistics materials more comprehensible. The Management Study Program is advised to pay special attention to challenging statistics topics to optimize students' learning outcomes.

Identification of the determinants of students’ preferences in their choice of study mode in economics majors

The COVID-19 pandemic affected the methods and results of teaching, posed many challenges, and changed the approach to education, providing an opportunity to review and reconsider the pros and cons of different modes of education. The aim of the study discussed in the article is to determine the factors influencing students’ preferences for taking different classes remotely or onsite. The data were obtained through a survey sent to the largest economics universities in Poland, which distributed the link to the survey among their students via email. The study was extended over the period from 4th November 2020 to 31st March 2021 and a total of 604 students took part in it. The authors compared the results relating to general and statistics courses, taking into account the following factors: efficiency, fatigue, concentration, students’ activity, workload, quality of lecturers’ preparation for classes and the materials used, as well as the availability of the teacher. Statistical methods were used for the analysis, including non-parametric tests and a generalised linear model (GLM). The respondents considered distance learning more complicated and time-consuming than onsite studying. Students were less active during online statistics courses, which were perceived as more tiring and less effective compared to general courses conducted remotely. The respondents declared a greater need for contact with the lecturers of general courses than the statistics ones. This was due to the lower quality of the provided teaching materials and lower availability of the lecturers of general courses. The ability to focus proved a decisive factor in the choice of the mode of courses in statistics. As regards general subjects, the choices were determined by the degree of fatigue.

Grasping the STARS: A comprehensive study on statistics—Anxiety levels among engineering students

Engineering students often face the challenge of comprehending and applying statistical concepts to evaluate and assess research. This research seeks to investigate the factor structure of Statistics Anxiety Rating Scale (STARs), which comprises six domains with 51 items. The questionnaire was administered to engineering students currently enrolled in a statistics class, and the data were analyzed using Statistical Package for the Social Sciences. The study sheds light on the factors that contribute to students’ statistics anxiety when taking a statistics course. The findings highlight the elements that influence statistics anxiety, educators and institutions can develop targeted interventions and support mechanisms to enhance students’ statistical competence and overall learning experience. This research not only addresses the unique challenges faced by engineering students but also holds implications for fostering statistical literacy across various disciplines.

Latine Students’ Motivational and Emotional Experiences Related to Their Introductory Statistics Course: Differences by Institution Type Necessitate Tailored Interventions

The study compared concerns, anxiety, mindsets, and belonging in introductory statistics among Latine students across three higher education institutions. Students at the Predominantly White Institution voiced more concerns about R-coding and lack of prior knowledge. Students at the Hispanic-Serving Institutions voiced more concerns about math anxiety and social comparison and had more fixed mindsets, yet higher levels of belonging—pointing toward the value of exploring Latine students’ experiences across different contexts and providing tailored interventions.

PELAKSANAAN PERKULIAHAN STATISTIKA DASAR DENGAN MODEL PROJECT BASED LEARNING

In digital era, students are expected to have 21st century skills to face global challenges. One of the learning models that support 21st century skills is Project Based Learning (PjBL). This research is descriptive research to describe the implementation of mathematics learning with the PjBL model in the Basic Statistics course. Fourteen students who took the Basic Statistics course were the subjects in this study. The project is a Basic Statistics project that integrates data presentation material, frequency distribution, centering measures, and data distribution measures. Data were collected using interviews, observations, self-assessment, oral presentation, peer review and final report. The implementation of learning is carried out online using google classroom, google form, zoom meeting and whatsapp group. The results of the implementation of mathematics learning with the PjBL model in the Basic Statistics course can be said to be good with an average score of 82.5. Of the fourteen students who worked on the project, five students or 36% had completed the project very well. A total of nine students or 64% have completed the math project well from the stages of planning, implementation and collecting reports. In this study, Information and Communication Technology (ICT) plays a very important role in the stages of design, implementation, and final report collection in project completion.

Teaching Students to Read COVID-19 Journal Articles in Statistics Courses

Statistics is interdisciplinary and the practical application of statistical methods in various areas prompts undergraduates to learn more about statistics and better understand complex methods. This article presents a classroom teaching design that guides students in reading COVID-19 literature. The activities presented encourage peer-peer and student-instructor interaction, which can be modified based on the type of course offered as well as the level and major of students by changing the articles required for reading. The teaching activities are designed with evidence-based teaching strategies such as scaffolding and connecting the activities to learning outcomes aligned with levels of Bloom’s Taxonomy. In our experience, the COVID-19 literature teaching activity improves students’ learning interest, cultivates students’ critical thinking, and enhances students’ understanding of theoretical methods. Supplementary materials for this article are available online.

Teaching advanced statistical methods to postgraduate novices: a case example

Higher degree research students in education are largely underprepared for understanding or employing statistical data analysis methods. This is despite their need to read literature in their field which will indubitably include such research. This weakness may result in students choosing to use qualitative or interpretivist methodologies, even though education data are highly complex requiring sophisticated analysis techniques to properly evaluate the impact of nested data, multi-collinear factors, missing data, and changes over time. This paper describes a research methods course at a research-intensive university designed for students in a thesis-only degree program. The course emphasizes the logic and conceptual function of statistical methods and exposes students to hands-on tutorials in which students are required to conduct analyses with open-access data. The first half of the 12-week course focuses on core knowledge, normally taught in first-year probability and statistics courses. The second half focuses on introducing and modeling advanced statistical methods needed to handle complex problems and data. The course outline is provided along with descriptions of teaching and assessments. This exemplar functions as a potential model of how relative novices in statistical methods can be introduced to a conceptual use of statistical methods to raise the credibility of research.

The Development of U-Learning Innovation to Promote Research Potential: Analysis by Advanced Statistics, Srinakharinwirot University, Thailand

Although the COVID-19 pandemic required teachers to adjust their teaching methods, there remains a lack of high-quality and inclusive instructional media for theoretical and practical content. This study analyzed the instructional management, design, and development of ubiquitous-learning (U-Learning) to promote the research potential: data analysis for advanced statistics course at Srinakharinwirot University, Thailand. The research participants were divided into three assessment groups: 10 graduate students, 5 statistics experts, and 20 course users. The qualitative data were analyzed using content analysis while the quantitative data were analyzed using arithmetic means and standard deviations. The study found that learning innovation (C-TAPE) was composed of five main components, namely, content, teaching, activity, practice, and evaluation. Additionally, the statistics experts and course users had the highest score for learning innovation efficiency among the assessment groups.

Flipping the Online Classroom to Teach Statistical Data Analysis Software: A Quasi-Experimental Study

The flipped classroom (FC) is a pedagogical model with an active learning concept and a hybrid course design that reverses the typical lecture process. Given the widespread use of online delivery methods, there is a need to explore the FC in delivering statistics courses. This study employs a prospective quasi-experimental study design to evaluate the effectiveness of the flipped method and to compare it with the traditional method of teaching statistical data analysis software. Each of the 71 students enrolled in a medicine program was allocated to either a traditional classroom or a FC. The difference between the two teaching methods is evaluated using overall assessment performance as the main outcome measure. Our results show that the teaching method had a large and significant effect on assessment performance, with the FC method exhibiting a higher student overall performance than the traditional classroom. The data suggest integrating the FC in teaching statistical data analysis software is a helpful alternative to the traditional classroom. This study may serve as a guide and inspiration for educators in statistics courses to incorporate the concept of flipped learning, so as to engage students in a more active learning experience.

统计学课程思政案例教学模式的设计与实施

统计学课程思政案例教学模式的设计与实施