The efficacy of an interactive computer system for teaching developmental mathematics to college students

Many students are entering colleges and universities in the United States underprepared in mathematics. National statistics indicate that only approximately one-third of students in developmental mathematics courses pass. When underprepared students repeatedly enroll in courses that do not count toward their degree, it costs them money and delays graduation. This study investigated a possible solution to this problem: Whether using a particular computer assisted learning strategy combined with using mastery learning techniques improved the overall performance of students in a developmental mathematics course. Participants received one of three teaching strategies: (a) group A was taught using traditional instruction with mastery learning supplemented with computer assisted instruction, (b) group B was taught using traditional instruction supplemented with computer assisted instruction in the absence of mastery learning and, (c) group C was taught using traditional instruction without mastery learning or computer assisted instruction. Participants were students in MAT1033, a developmental mathematics course at a large public 4-year college. An analysis of covariance using participants' pretest scores as the covariate tested the null hypothesis that there was no significant difference in the adjusted mean final examination scores among the three groups. Group A participants had significantly higher adjusted mean posttest score than did group C participants. A chi-square test tested the null hypothesis that there were no significant differences in the proportions of students who passed MAT1033 among the treatment groups. It was found that there was a significant difference in the proportion of students who passed among all three groups, with those in group A having the highest pass rate and those in group C the lowest. A discriminant factor analysis revealed that time on task correctly predicted the passing status of 89% of the participants. ^ It was concluded that the most efficacious strategy for teaching developmental mathematics was through the use of mastery learning supplemented by computer-assisted instruction. In addition, it was noted that time on task was a strong predictor of academic success over and above the predictive ability of a measure of previous knowledge of mathematics.^

The knowledge and use of critical thinking teaching strategies of faculty in associate degree nursing education programs

The purpose of this study was to determine the knowledge and use of critical thinking teaching strategies by full-time and part-time faculty in Associate Degree Nursing (ADN) programs. Sander's CTI (1992) instrument was adapted for this study and pilottested prior to the general administration to ADN faculty in Southeast Florida. This modified instrument, now termed the Burroughs Teaching Strategy Inventory (BTSI), returned reliability estimates (Cronbach alphas of .71, .74, and .82 for the three constructs) comparable to the original instrument. The BTSI was administered to 113 full-time and part-time nursing faculty in three community college nursing programs. The response rate was 92% for full-time faculty (n = 58) and 61 % for part-time faculty (n = 55). The majority of participants supported a combined definition of critical thinking in nursing which represented a composite of thinking skills that included reflective thinking, assessing alternative viewpoints, and the use of problem-solving. Full-time and part-time faculty used different teaching strategies. Fulltime faculty most often used multiple-choice exams and lecture while part-time faculty most frequently used discussion within their classes. One possible explanation for specific strategy choices and differences might be that full-time faculty taught predominately in theory classes where certain strategies would be more appropriate and part-time faculty taught predominately clinical classes. Both faculty types selected written nursing care plans as the second most effective critical thinking strategy. Faculty identified several strategies as being effective in teaching critical thinking. These strategies included discussion, case studies, higher order questioning, and concept analysis. These however, were not always the strategies that were used in either the classroom or clinical setting. Based on this study, the author recommends that if the profession continues to stress critical thinking as a vital component of practice, nursing faculty should receive education in appropriate critical teaching strategies. Both in-service seminars and workshops could be used to further the knowledge and use of critical thinking strategies by faculty. Qualitative research should be done to determine why nursing faculty use self-selected teaching strategies.

An Innovative Approach to Teaching Structural Induction for Computer Science

Proofs by induction are central to many computer science areas such as data structures, theory of computation, programming languages, program efficiency-time complexity, and program correctness. Proofs by induction can also improve students’ understanding of and performance with computer science concepts such as programming languages, algorithm design, and recursion, as well as serve as a medium for teaching them. Even though students are exposed to proofs by induction in many courses of their curricula, they still have difficulties understanding and performing them. This impacts the whole course of their studies, since proofs by induction are omnipresent in computer science. Specifically, students do not gain conceptual understanding of induction early in the curriculum and as a result, they have difficulties applying it to more advanced areas later on in their studies. The goal of my dissertation is twofold: 1. identifying sources of computer science students’ difficulties with proofs by induction, and 2. developing a new approach to teaching proofs by induction by way of an interactive and multimodal electronic book (e-book). For the first goal, I undertook a study to identify possible sources of computer science students’ difficulties with proofs by induction. Its results suggest that there is a close correlation between students’ understanding of inductive definitions and their understanding and performance of proofs by induction. For designing and developing my e-book, I took into consideration the results of my study, as well as the drawbacks of the current methodologies of teaching proofs by induction for computer science. I designed my e-book to be used as a standalone and complete educational environment. I also conducted a study on the effectiveness of my e-book in the classroom. The results of my study suggest that, unlike the current methodologies of teaching proofs by induction for computer science, my e-book helped students overcome many of their difficulties and gain conceptual understanding of proofs induction.

The relationship between the teacher's experience, the teacher's college major, and the teacher's level of education in predicting classroom attitudes in high school science students

A review of the literature reveals few research has attempted to demonstrate if a relationship exists between the type of teacher training a science teacher has received and the perceived attitudes of his/her students. Considering that a great deal of time and energy has been devoted by university colleges, school districts, and educators towards refining the teacher education process, it would be more efficient for all parties involved, if research were available that could discern if certain pathways in achieving that education, would promote the tendency towards certain teacher behaviors occurring in the classroom, while other pathways would lead towards different behaviors. Some of the teacher preparation factors examined in this study include the college major chosen by the science teacher, the highest degree earned, the number of years of teaching experience, the type of science course taught, and the grade level taught by the teacher. This study examined how the various factors mentioned, could influence the behaviors which are characteristic of the teacher, and how these behaviors could be reflective in the classroom environment experienced by the students. The instrument used in the study was the Classroom Environment Scale (CES), Real Form. The measured classroom environment was broken down into three separate dimensions, with three components within each dimension in the CES. Multiple Regression statistical analyses examined how components of the teachers' education influenced the perceived dimensions of the classroom environment from the students. The study occurred in Miami-Dade County Florida, with a predominantly urban high school student population. There were 40 secondary science teachers involved, each with an average of 30 students. The total number of students sampled in the study was 1200. The teachers who participated in the study taught the entire range of secondary science courses offered at this large school district. All teachers were selected by the researcher so that a balance would occur in the sample between teachers who were education major versus science major. Additionally, the researcher selected teachers so that a balance occurred in regards to the different levels of college degrees earned among those involved in the study. Several research questions sought to determine if there was significant difference between the type of the educational background obtained by secondary science teachers and the students' perception of the classroom environment. Other research questions sought to determine if there were significant differences in the students' perceptions of the classroom environment for secondary science teachers who taught biological content, or non-biological content sciences. An additional research question sought to evaluate if the grade level taught would affect the students' perception of the classroom environment. Analysis of the multiple regression were run for each of four scores from the CES, Real Form. For score 1, involvement of students, the results showed that teachers with the highest number of years of experience, with masters or masters plus degrees, who were education majors, and who taught twelfth grade students, had greater amounts of students being attentive and interested in class activities, participating in discussions, and doing additional work on their own, as compared with teachers who had lower experience, a bachelors degree, were science majors, and who taught a grade lower than twelfth. For score 2, task orientation, which emphasized completing the required activities and staying on-task, the results showed that teachers with the highest and intermediate experience, a science major, and with the highest college degree, showed higher scores as compared with the teachers indicating lower experiences, education major and a bachelors degree. For Score 3, competition, which indicated how difficult it was to achieve high grades in the class, the results showed that teachers who taught non-biology content subjects had the greatest effect on the regression. Teachers with a masters degree, low levels of experience, and who taught twelfth grade students were also factored into the regression equation. For Score 4, innovation, which indicated the extent in which the teachers used new and innovative techniques to encourage diverse and creative thinking included teachers with an education major as the first entry into the regression equation. Teachers with the least experience (0 to 3 years), and teachers who taught twelfth and eleventh grade students were also included into the regression equation.

Utilizing Traditional Cognitive Measures of Academic Preparation to Predict First-Year Science, Technology, Engineering, and Mathematics (STEM) Majors' Success in Math and Science Courses

For the past several years, U.S. colleges and universities have faced increased pressure to improve retention and graduation rates. At the same time, educational institutions have placed a greater emphasis on the importance of enrolling more students in STEM (science, technology, engineering and mathematics) programs and producing more STEM graduates. The resulting problem faced by educators involves finding new ways to support the success of STEM majors, regardless of their pre-college academic preparation. The purpose of my research study involved utilizing first-year STEM majors’ math SAT scores, unweighted high school GPA, math placement test scores, and the highest level of math taken in high school to develop models for predicting those who were likely to pass their first math and science courses. In doing so, the study aimed to provide a strategy to address the challenge of improving the passing rates of those first-year students attempting STEM-related courses. The study sample included 1018 first-year STEM majors who had entered the same large, public, urban, Hispanic-serving, research university in the Southeastern U.S. between 2010 and 2012. The research design involved the use of hierarchical logistic regression to determine the significance of utilizing the four independent variables to develop models for predicting success in math and science. The resulting data indicated that the overall model of predictors (which included all four predictor variables) was statistically significant for predicting those students who passed their first math course and for predicting those students who passed their first science course. Individually, all four predictor variables were found to be statistically significant for predicting those who had passed math, with the unweighted high school GPA and the highest math taken in high school accounting for the largest amount of unique variance. Those two variables also improved the regression model’s percentage of correctly predicting that dependent variable. The only variable that was found to be statistically significant for predicting those who had passed science was the students’ unweighted high school GPA. Overall, the results of my study have been offered as my contribution to the literature on predicting first-year student success, especially within the STEM disciplines.