Technology Solutions for Developmental Math: An Overview of Current and Emerging Practices

Reviews current practices in and strategies for incorporating innovative technology into the teaching of remedial math at the college level. Outlines challenges, emerging trends, and ways to combine technology with new concepts of instructional strategy

Using Experiential and Collaborative Learning to Promote Careers in Engineering

This paper discusses the design of the Girls Accelerating and Learning STEM (G.A.L.S.) one-week residential summer camp designed to encourage young girls to pursue engineering careers. Specifically, the camp exposed participants to the fields of computer science and engineering using experiential learning to develop participant interest and skills. At the end of the program, students participated in team competitions and presented their work. The end-of-program survey data showed that the G.A.L.S. camp had an impact on the enhancement of student interest in engineering as a potential career. The paper will provide details on the program components, and further discuss the impacts of the program and how it can be used as a model for future programs.Cockrell School of Engineerin

Computer Programming Effects in Elementary: Perceptions and Career Aspirations in STEM

The development of elementary-aged students’ STEM and computer science (CS) literacy is critical in this evolving technological landscape, thus, promoting success for college, career, and STEM/CS professional paths. Research has suggested that elementary- aged students need developmentally appropriate STEM integrated opportunities in the classroom; however, little is known about the potential impact of CS programming and how these opportunities engender positive perceptions, foster confidence, and promote perseverance to nurture students’ early career aspirations related to STEM/CS. The main purpose of this mixed-method study was to examine elementary-aged students’ (N = 132) perceptions of STEM, career choices, and effects from pre- to post-test intervention of CS lessons (N = 183) over a three-month period. Findings included positive and significant changes from students’ pre- to post-tests as well as augmented themes from 52 student interviews to represent increased enjoyment of CS lessons, early exposure, and its benefits for learning to future careers

The Case for Improving U.S. Computer Science Education

Despite the growing use of computers and software in every facet of our economy, not until recently has computer science education begun to gain traction in American school systems. The current focus on improving science, technology, engineering, and mathematics (STEM) education in the U.S. school system has disregarded differences within STEM fields. Indeed, the most important STEM field for a modern economy is not only one that is not represented by its own initial in "STEM" but also the field with the fewest number of high school students taking its classes and by far has the most room for improvement—computer science

Living-learning communities improve first-year engineering student academic performance and retention at a small private university

Living-Learning Communities (LLCs), in which students share a residence, one or more classes, and extracurricular activities, have been shown to improve first-year student engagement, academic performance, and retention in non-engineering fields. Research on Engineering LLCs has focused primarily on student engagement. Two studies to examine performance and retention found that LLCs had little effect on first-semester grades but increased first-year retention in engineering by 2 to 12%. Unfortunately, one of these studies did not control for differences in incoming student characteristics, and another used a comparison group that differed little from the LLC group, possibly causing them to understate the LLC’s true effects. To improve our understanding, this paper examines performance and retention in the inaugural Engineering LLCs at a small, private non-profit, regional university in the northeastern United States. Results indicate that 82% of the Engineering LLC participants were retained within the engineering program, compared to 66% of first-year engineering students who chose not to participate. More strikingly, the average first-semester GPA of the LLC participants was 0.31 points (nearly a third of a letter grade) higher than that of the non-participants. To address the possibility that these improvements were caused by differences in incoming student characteristics, linear and logistic regression analyses were performed to control for gender, race/ethnicity, SAT scores, and other factors. These analyses suggest that LLC participation increased GPA by 0.35 points compared to first-year engineering students from prior years, while non-participation lowered GPA by 0.07 points. LLC participation increased the odds of retention in the major by 2.3 times compared to first-year students from prior years, while nonparticipation lowered the odds of retention by 1.35 times

Factors Influencing girls\u27 choice of Information Technology careers

Many western nations have experienced declining numbers of women in the information technology (IT) workforce (Trauth, Nielsen, & von Hellens, 2003). Between 1996 and 2002, women in the U.S. IT workforce declined from 41% to 34.9% (ITAA, 2003). This can hamper diversity and reduce the talent pool that can address needs of diverse end-users (Florida & Gates, 2002). Why do women choose IT careers or reject them? Multidisciplinary research on career genderization reveals gender imbalance (Trauth, Nielsen, & von Hellens, 2003). Career decisions against math, science, and technology (MST) are often made as early as age 11 without understanding long-term implications (AAUW, 2000). We examine influences on girls’ choice of IT careers, modeling social, structural, and personal variables that affect IT career choice. Using Ahuja’s (2002) classification of social and structural influences on women’s IT careers, Beise, Myers, VanBrackle, and Chevli-Saroq’s (2003) model of women’s career decisions, and individual differences suggested by Trauth (2002), we extend literature to children and adolescents’ career choices. Social influences bias internal and external gender perceptions and stereotyping, role models, peers, media, and family. Institutional support such as teachers and counselors, access to technology, and same-sex versus coeducational schools are structural influences. While both can influence career decisions, social factors have the most influence on children’s early perceptions. Both factors can introduce gender-stereotyping effects on career choices. Gender stereotyping explains how girls perceive their role in society based on subtle societal cues. It can limit opportunities for both sexes. We also examine personality traits and external influences that make children unique. Their individual differences draw them to activities and content areas such as problem solving and interaction with people. Finally, ethnic culture can exert an influence on social and structural variables. Figure 1 from Adya and Kaiser (2005) presents our career choice model that is discussed in the next section

Predicting time to graduation at a large enrollment American university

The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto's Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).Comment: 28 pages, 11 figure