This paper discusses student-centered learning within the context of an introductory statistics course.
This paper discusses student-centered learning within the context of an introductory statistics course.
Random assignment is one of the more difficult concepts in introductory statistics classes. Many textbook authors admonish students to check on the comparability of two randomly assigned groups by conducting statistical tests on pretest means to determine if randomization worked. A Monte Carlo study was conducted on a sample of n = 2 per group, where each participant's personality profile was represented by 7,500 randomly selected and assigned scores. These values were obtained from real data sets from applied education and psychology research. Then, independent samples t-tests were conducted at the 0.01 alpha level on these scores. Results demonstrated that x-bar(1) does not equal x-bar(2) for only 33 out of 7,500 variables, indicating that random assignment was successful in equating the two groups on 7,467 variables, even with a sample size of n = 2. The students' focus is redirected from the ability of random assignment to create comparable groups to testing the claims of randomization schemes.
This report discusses how factors such as sex, major, mathematics background, and dominant learning style can affect student performance in statistics. It is almost unarguable that the introductory statistics course is the most widely feared course on most university campuses. Dropout and failure rates are extremely high. Students come into the course with low expectation of success., and I have often wondered and talked with colleagues about this fear and lack of success. Can we identify any factors that affect our students' performance in the "Introduction to Statistics" course? Can we determine "what makes a student's statistical clock tick?" Or perhaps more precisely, "what prevents a student's statistical clock from ticking?" Do factors such as sex, major [field of study], class [freshman (first year), sophomore (second year), junior (third year), senior (fourth year)] or mathematics background have a bearing on student performance? For the above factors, no big surprise were found. But another factor, suggested to me by a colleague in the Psychology Department produced a rather stunning result. That factor is the student's dominant learning style.
The purpose of this study was to evaluate the stability of attitudes toward the course and field of statistics over a year's time. Students were surveyed at the end of an intermediate (second) coure in statistics and again one year later. In line with previous studies, attitude scale scores were correlated with course grade. It was expected that this correlation would be positive but low as was found by others. Attitude scores were also correlated with GRE scores.
This study approached the investigation of attitudes toward statistics from the perspective of Rosenberg and Hovland's (1960) hierarchical, multicomponent model. In this model, cognition, affect, and behavior are considered interrelated first-order-factors with attitude a single second-order factor. In the present study, affective, cognitive, and behavioral components of attitude were assessed. Two other assessment tools were also administered for comparison purposes (Wise's ATA and a semantic differential measure). Data were gathered from 2 classes (n = 47) attended by master's and doctoral students in education, social work, and speech communication. Results suggest the strongest association is between affective and cognitive components but the greatest temporal stability was found for the behavioral component. Consistency in ratings of affect and cognition was not predictive of behavior nor was locus of attitude formation.
The effects of previous mathematics, statistics, and computer science coursework; attitudes toward statistics and computers; and mathematics ability on statistics achievement were studied for 289 college students over four semesters. A secondary purpose of the study was to determine the effect of the computer laboratory component of an inferential statistics class on students' end of course attitudes. No statistically significant differences were found between students taught with a computer laboratory and those taught without the computer component for attitudes toward statistics, but those taught by computer exhibited more positive attitudes toward the computer and less statistical anxiety at the end of the course.
This paper reviews the literature on factors affecting students' performance in undergraduate statistics courses for the social sciences. Factors studied include anxiety, attitude, computer experience, and gender identity.
A multifactorial scale of attitudes toward statistics was developed, and factors related to attitudes toward statistics (objective and subjective mathematics background, anxiety, spatial ability, expectations, motivation, attitutdes toward computers, teacher and course evaluation, sex and sex-role stereotypes, and major) were investigated for college students in Spain. Regression analyses determined predictors of attitudes toward statistics. Predictive factors before the course included expectations of success and failure; attitudes toward computers; objective and subjective background; motivation; and state anxiety. Predictive factors at the end of the course included: expectations of success and failure; subjective and objective background; level of the subject; and teacher and course evaluation.
The purpose of this study was to extend the evaluation of Wise's Attitudes Toward Statistics (ATS) scale by examining responses in relation to (a) its factor structure and (b) the correlation of ATS subscale scores with students' grades in statistics courses at several levels of graduate study, students' sex (which was found to be a useful predictor by Woehlke & Leitner, 1980), and scores on measures of basic mathematics and comprehension of statistical terminology.
The purposes of this study were to develop an instrument to measure students' attitudes toward statistics (STATS), and to define the underlying dimensions that comprise the STATS. Six factors seem applicable: students' interest and future applicability, relationship and impact of the instructor, attitude toward statistical tools, self-confidence, parental influence, and initiative and extra effort in learning statistics.