During the biennial budget creation process in 2015, the Wisconsin governor and state legislature mandated many fundamental changes to the state statutes involving the University of Wisconsin System. These changes included altering the role of faculty governance at the campus level and the removal of faculty tenure protections from state law. The Board of Regents of the University of Wisconsin reacted to the state statue changes by modifying Regent policies to maintain some stability. This did involve some fundamental changes for faculty. For example, the Regents placed tenure protection into Regent policy, but also added in Regent policy an additional avenue for removing tenured faculty through a post-tenure review process. Such changes in tenure were seen by many faculty as weakening the protections to which faculty had grown accustomed. Furthermore, the changes in the faculty role in governance opened the possibility for significant reductions in the control faculty had over the mission and operations of the individual campuses. At the same time, the legislature enacted a $100 million a year reduction in the base budget of the UW System, an included an additional $25 million lapse for the first year of the 2015-17 budget. This cut was accompanied by the continuation of a resident tuition freeze which began in 2013 and continues today. This budget cut and the tuition freeze resulted in most of the UW campuses needing to undergo significant cost savings measures, which have impacted programs, faculty and students. In this paper, the impacts of these 2015 policy actions on UW System faculty will be explored. Data from 2012-2019 will be examined to determine the change in faculty size across the UW System. The paper will also study the impacts on the number of engineering faculty at the three campuses that have had sizeable engineering programs over the last decade: UW-Madison, UW-Milwaukee, and UW-Platteville. Finally, the impact on engineering class sizes will be analyzed, as reductions in the number of courses offered has been one of the impacts of reduced budgets. From this data, we will reach conclusions on the impacts of these legislative actions on faculty and students in engineering programs in the UW System.

A state university received an NSF S-STEM grant to provide scholarship funds and enhanced programmatic activities for engineering and computer science students. Some of the enhanced activities available to the scholarship recipients are faculty mentoring, meetings of the cohort students, the ability to attend professional workshops and participate in STEM outreach activities, and the opportunity to attend a Emerging Researchers National (ERN) conference in Washington, D.C. Some of these activities are similar to what other schools have in their S-STEM programs. While the effectiveness of the different program activities are often studied at institutions, it is often less clear how students view the usefulness of various program activities. In this paper, we will describe in more detail the scholarship program at XXX and provide explanations of the different programmatic activities available to the students in the program. We will then provide the results of a survey of the students in the program, where they were asked to provide their impressions of the program activities. The results of the survey can be beneficial to other schools developing S-STEM programs, as it sheds light on how receptive students may be to possible common program elements. The paper will also contain some discussion on the importance of the group activities in a virtual environment during the COVID-19 pandemic. Finally, we will provide some suggestions based on our experiences on how to improve the program activities to make them more beneficial to the students.

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract EVALUATION OF FACTORS AFFECTING THE SUCCESS OF IMPROVING MATH COURSE PLACEMENT FOR INCOMING FRESHMEN IN A SUMMER BRIDGE PROGRAM Abstract A summer bridge program for incoming engineering and computer science freshmen has been used at the University of Wisconsin-Milwaukee from 2007-09. The primary purpose of this program has been to improve the mathematics course placement for incoming students who initially place into a course below Calculus I on our math placement examination. The students retake the math place examination after completing the bridge program to determine if they then place into a higher-level mathematics course. If the students improve their math placement, the program is considered successful for that student. The math portion of the bridge program centers on using the ALEKS software package for targeted, self-guided learning. In the 2007 and 2008 versions of the program, both an on-line version and an on-campus version with additional instruction were offered. In 2009, the program was exclusively in an on-campus format, and also featured a required residential component and additional engineering activities for the students. From the results of these three programs, we are able to evaluate the success of the program in its different formats, and are able to judge the utility of the enhancements that have been added to the program. In addition, data has been collected and analyzed regarding the impact of other factors on the program’s success. The factors include student preparation before the beginning of the program (as measured by math ACT scores) and the amount of time the student spent working on the material during the program. Not surprisingly, better math preparation and the amount of time spent on the program are good indicators of success. Furthermore, the on campus version of the program is more effective than the on-line version. Introduction In the United States today, there is great interest in the education and graduation of more students in the Science, Technology, Engineering, and Mathematics (STEM) disciplines.1,2 There are two primary tasks that are needed for this goal to be accomplished. First, more students need to be attracted to pursue college-level studies in the STEM fields. Second, once those students are attracted to a STEM field, the colleges and universities must provide an attractive, nurturing environment designed to allow a wide range of students to succeed, while still providing a rigorous education. The College of Engineering and Applied Science (CEAS) at the University of Wisconsin- Milwaukee (UWM) has generally been able to attract as many students into its engineering and computer science programs as for whom it can provide quality educations. But the graduation rates have been lower than desired. While the number fluctuates a bit each year, based on incoming classes from 2003-2005, the graduation rate of incoming freshmen in CEAS is about 35%. Recognizing that this is an undesirably low graduation rate in that it does not advance

While ethanol use as a vehicle fuel has been promoted as a renewable alternative to fossil fuels, current production methods of ethanol from corn feedstock rely heavily on the combustion of nonrenewable fuels such as natural gas. Solar thermal systems can provide a renewable energy source for supplying some of the heat required ethanol production. In this paper, a model to analyze the feasibility of using solar thermal energy to reduce natural gas consumption in ethanol production is described and applied. Sites of current ethanol production facilities are used to provide a realistic analysis of the economic feasibility of using solar thermal energy in the ethanol production process. The results show that it is not reasonable to expect to replace all of the natural gas consumption in the heating processes in ethanol production but that application of solar thermal energy can be applied to a specific subsystem such as the preheating of boiler makeup water. Profitability of systems for replacing a fraction of the natural gas is analyzed. It is found that both location and local natural gas prices are important in determining whether to pursue such a project and that solar thermal systems should have long-term profitability.

A summer bridge program was developed in an engineering program to advance the preparation of incoming freshmen students, particularly with respect to their math course placement. The program was intended to raise the initial math course placement of students who otherwise would begin their engineering studies in courses below Calculus I. One reason given for low retention rates in this particular engineering program was that students needed to spend too much time taking math courses in college just to be ready to take the Calculus I course expected of incoming freshmen in the program; this extended their total time in college and delayed their ability to take the engineering courses that interested them. The program was successful at meeting its immediate goal of raising the math course placement of these students. However, the program’s success with regards to improving math course placement did not lead to significantly improved odds of the students being retained in engineering or graduating from engineering in comparison to students of similar abilities who did not participate in this bridge program.

Many interventions have been proposed to improve the retention and graduation rates of engineering students. One such intervention is to use study groups for first-year college students; such groups provide a structured environment in which the students can learn course material from each other outside of class and can provide the students with a sense of community. In this paper, we report on the impacts fostered by study groups in first-year mathematics courses on the odds of retaining and graduating engineering students. Students who participated in the study groups are compared to students of similar academic preparation who did not participate in such groups. It is found that student participation in study groups is significantly associated with the higher odds of being retained in engineering studies through the first 3 years of college. The results reported here are not as certain for the effect of study group participation on 5-year graduation odds for engineering students and some possible reasons for this are discussed.

Defining a Successful Undergraduate Research Experience in EngineeringAbstractIn recent years, there has been interest in broadening the participation of students inundergraduate research experiences in engineering disciplines. While there has beenconsiderable study and analysis of the benefits achieved by high-achieving undergraduatestudents engaged in research activities, relatively little consideration has been given tothe impact and benefits of research experiences on engineering students who are betterdescribed as “average”. Yet, these are the students to whom undergraduate researchopportunities need to be provided in order to achieve broader participation. Therefore, itis beneficial to understand how these experiences actually impact “average” students soas to not design programs that will not meet the students’ expectations or needs.The primary purpose of this NSF-sponsored work is to provide definitions of whatconstitutes a successful undergraduate research experience for a wide range of students.Particular attention is devoted to students whose academic background and performanceis solid, but not outstanding. For such students, some of the benefits seen in high-achieving students, such as increased likelihood of graduate school attendance, may notbe appropriate measures of a successful experience. Through surveys and interviews ofstudents who have engaged in undergraduate research experiences in engineering, as wellas surveys and interviews with engineering faculty, we have developed preliminarydefinitions of a successful research experience. These results will be presented in thepaper. For example, a larger percentage of these students found that the undergraduateresearch experience increased their confidence in their abilities to be a successfulpracticing engineer than those who found the experience increased their interest ingraduate school. The implication of this is that for these “average” students, a successfulresearch experience should be defined in terms of improving the skills needed to bepracticing engineers with B.S. degrees rather than inspiring them to immediately pursuegraduate studies.In addition to these preliminary definitions of a successful undergraduate researchexperience, the paper will also present insights gained from the study with regards towhat makes the experience more positive for the students. Finally, while the diversity ofthe students studied to date is not large, some observations of the overall impact andeffectiveness of the research experiences for different demographic groups will bediscussed.

As part of an NSF-supported project, a summer bridge program for incoming engineering and computer science freshmen was conducted each summer between 2009 and 2012. The primary purpose of this program was to improve the mathematics course placement for incoming students whose initial placement as determined by a math placement examination was below Calculus I. The students retake the university's math placement examination at the end of the bridge program to determine if they may enroll into a more advanced mathematics course. The immediate goal of the program is to improve the math placement of the students. However, it is just as important in evaluating the success of the program to consider the performance of the students in their Fall semester math courses.The mathematics portion of the bridge program centers on using the ALEKS software package for targeted, self-guided learning. The program took place exclusively in an on-campus format, and also featured a required residential component and additional engineering activities for the students. The program's duration was 4 weeks, and students were expected to improve their math placement by at least one semester. It is expected that improving their math placement will reduce the student's time-to-graduation, which should in turn improve retention rates and eventually graduation rates. Data from the four cohorts have been collected and analyzed to judge the effectiveness of the program with respect to both improving the students' math placement and the students' performance in future math courses. A lower percentage of students (69%) improved their math course placement in the 2009 cohort, but all categories of bridge program students performed as well as the class average in the Fall 2009 semester. For the 2010-2012 cohorts, students succeeded at improving their math placement at a higher rate (83%-88%). Students who have placed into Calculus I through the bridge program have successfully completed Calculus I at a rate similar to all students in the course in the Fall semester. However, the results for students who placed into College Algebra after the bridge program are more mixed. As a result, while the bridge program is clearly beneficial to many students, it is likely that additional interventions are needed to further help students who do not place into Calculus I even with a bridge program.

In the United States, the industrial sector is the driving engine of economic development, and the energy consumption in this sector may be considered as the fuel for this engine. In order to keep this sector sustainable (diverse and productive over time), energy planning should be carried out comprehensively and precisely. In this study, an ANN model was applied to forecast the industrial energy demand and perform future projections for the period 2013-2030. Among all effective independent parameters on energy demand in the industrial sector, energy costs and GDP growth have been considered in this study based on correlation coefficient analysis. For the future trend of GDP, a second order polynomial equation is fitted to the GDP growth curve. For the other independent variables, we define three scenarios for potential future changes: Constant Price Scenario, Ascending Price Scenario, and Descending Price Scenario. The Constant Price and Descending Price scenarios show increases in energy demand, while results show that along with an increase in energy prices, the demand may decrease slightly. For comparison purposes, the results of the three scenarios are presented along with the predictions from the EIA presented in the Annual Energy Outlook 2013.

In 2009, the transportation sector was the second largest consumer of primary energy in the United States, following the electric power sector and followed by the industrial, residential, and commercial sectors. The pattern of energy use varies by sector. For example, petroleum provides 96% of the energy used for transportation but its share is much less in other sectors. While the United States consumes vast quantities of energy, it has also pledged to cut its greenhouse gas emissions by 2050. In order to assist in planning for future energy needs, the purpose of this study is to develop a model for transport energy demand that incorporates past trends.This paper describes the development of two types of transportation energy models which are able to predict the United States' future transportation energy-demand. One model uses an artificial neural network technique (a feed-forward multilayer perceptron neural network coupled with back-propagation technique), and the other model uses a multiple linear regression technique. Various independent variables (including GDP, population, oil price, and number of vehicles) are tested.The future transport energy demand can then be forecast based on the application of the growth rate of effective parameters on the models. The future trends of independent variables have been predicted based on the historical data from 1980 using a regression method. Using the forecast of independent variables, the energy demand has been forecasted for period of 2010 to 2030.In terms of the forecasts generated, the models show two different trends despite their performances being at the same level during the model-test period. Although, the results from the regression models show a uniform increase with different slopes corresponding to different models for energy demand in the near future, the results from ANN express no significant change in demand in same time frame. Increased sensitivity of the ANN models to the recent fluctuations caused by the economic recession may be the reason for the differences with the regression models which predict based on the total long-term trends.Although a small increase in the energy demand in the transportation sector of the United States has been predicted by the models, additional factors need to be considered regarding future energy policy. For example, the United States may choose to reduce energy consumption in order to reduce CO2 emissions and meet its national and international commitments, or large increases in fuel efficiency may reduce petroleum demand.

Peer-led Team Learning (PLTL) is an instructional method reported to increase student learning in STEM courses. As mathematics is a significant hurdle for many freshmen engineering students, a PLTL program was implemented for students to attempt to improve their course performance. Here, an analysis of PLTL for freshmen engineering students in mathematics courses over three years is presented. The particular issue of concern is if a student's performance in their mathematics courses improves significantly with frequent participation in PLTL groups.Student performance in their mathematics course was evaluated through course grades. The level of participation by the students in their PLTL groups was determined through weekly attendance reports, with mentors assuring that all students participated fully while present. Grade comparisons were made both between participants who attended different numbers of group sessions and between participants and non-participants in their courses.Analysis of the students in the program suggests that increased participation in the PLTL groups correlates to better course performance. Data indicate that statistically significant subject mastery is achieved by PLTL participants in Calculus I courses. However, while Pre-Calculus level students show some improvement, the results are not consistently statistically significant. In general, it is found that greater participation in PLTL groups is beneficial for many students. PLTL groups offer educational benefits to many students, but participation does not guarantee improvements for all students.Keywords: peer-led team learning, freshmen engineering, college algebra, Calculus, engineering math1. IntroductionPeer-led Team Learning (PLTL) is an educational technique developed ini- tially for Chemistry courses that is designed to enhance student learning of the subject matter by fostering interaction between students in the course as they help each other to learn (Gosser 2011). The technique has subsequently been demonstrated to be successful in a variety of STEM disciplines courses (Baez- Galib et al. 2005; Hockings et al. 2008; Horwitz et al. 2009; Lewis and Lewis 2005; Lewis 2011; Lyle and Robinson 2003; Lyon and Lagowski 2008; Preszler 2009; Tien et al. 2002; Wamser 2006). While there are more reported results available for science courses, researchers have shown that the technique can be successful in mathematics and engineering courses as well. For example, Liou- Mark et al. (2010) applied PLTL methods to PreCalculus courses, and found that considerable improvement in the performance of participating students was gained. Loui and Robbins (2008) and Loui et al. (2009) used PLTL in fresh- men electrical engineering courses, and found that students with regular at- tendance at the sessions performed much better on their final exams in some, but not all, semesters. Furthermore, qualitative responses indicated additional benefits for the students, such as fostering a greater sense of belonging among the students. Overall, Gosser (2011) found that, across a number of studies, the average percentage of students receiving a C or better in their courses was 15 percent higher for students participating in PLTL groups versus students not participating in the groups.With this evidence in mind, PLTL groups were initiated at a large urban research university in the Midwest region of the United States in the fall 2009 semester. These groups were designed for incoming freshmen students in engineering and computer science. They were primarily organized around the students'mathematics courses. Secondarily, students in the same intended field of study were placed together in the groups when possible. For reference purposes, the courses for which study groups were used are listed in Table 1.The general content of the courses is as follows. Math 105 covers algebraic techniques involving such topics as polynomials, exponential and logarithmic functions, conic sections, and systems of linear equations. …

In the United States, the industrial sector is the driving engine of economic development, and energy consumption in this sector may be considered as the fuel for this engine. In order to keep this sector sustainable (diverse and productive over the time), energy planning should be carried out comprehensively and precisely. This paper describes the development of two types of numerical energy models which are able to predict the United States' future industrial energy-demand. One model uses an ANN (artificial neural network) technique, and the other model uses a MLR (multiple linear regression) technique. Various independent variables (GDP, price of energy carriers) are tested. The future industrial energy demand can then be forecasted based on a defined scenario.The ANN model anticipates a 16% increase in energy demand from 2012 by 2030. In this forecast, the model assumes that the effective independent parameters remain constant during this period and only GDP grows with a second-order polynomial trend. The forecast result, which shows consistency with published predictions, may be considered as an indication of the need for development of new and low-cost energy sources.This study suggests that the ANN technique is a reliable and powerful technique which can effectively perform input/output mapping. In order to validate the performance of the models, the results of the ANN model is compared to the projections from the Energy Information Administration of the U.S. Department of Energy. (C) 2014 Elsevier Ltd. All rights reserved.

While it is easy to recognize that mechanical engineers can lend their expertise to public policy makers as they create public policy related to science and technology, it is not as clear as to how to introduce mechanical engineering students to public policy activities. The undergraduate curricula in most mechanical engineering programs are considered full, and there are always additional topics that people wish to add. Educators are likely to hesitate before removing material from their programs in order to add material on public policy. Yet, there are techniques that can be used to incorporate aspects of public policy into a standard mechanical engineering curriculum without the removal of much, if any, current content. In this paper, several techniques for introducing mechanical engineering students to the process of public policy creation will be discussed. While these methods will not make the students experts in policy, they can introduce students to the tools that they need to influence the public policy creation process. These techniques include a comprehensive semester-long project in a technical elective course, a short policy analysis paper for development in a required or elective course, incorporation of public policy considerations in a capstone design project, policy discussions or debates in relevant courses, and a focus on public policy development in extracurricular activities. In their education, students should not only become technically proficient, but also learn how to track current events and trends, communicate their knowledge effectively, gain knowledge on applying proper engineering ethics, and be aware of the environmental and social context of their work. Through these knowledge areas and skills, students will gain the fundamental working knowledge that they need to influence public policy creation. It may be noted that these are also desirable outcomes for a student’s educational program as defined by ABET. Therefore, finding opportunities in a mechanical engineering program’s curriculum to address public policy creation activities also benefits the program by helping it more completely fulfill ABET accreditation requirements.

Construction of international sustainable engineering projects in developing communities by student organizations such as Engineers Without Borders involves a minimum of four basic stages: (1) project acquisition based on the communication of the need for the project; (2) travel to the project for site assessment; (3) project design, logistics, and communication of the design with the communities involved; and (4) travel to construct the project. In general, when construction projects are completed locally, there is an ease of communication and travel that shortens all four stages and inevitably the entire process. By comparison, distant locations, language and cultural differences, and exotic politics lengthen these processes, at times creating barriers that prevent the completion of much needed initiatives. An excellent counter to this situation is to utilize a native in-country coordinator who is privy to the local language and dialects and the cultural norms that stupefy outsiders and is available for site visits to assess progress and answer any design questions the foreign engineers may have. The in-country coordinator predicts the needs of the projects based on his or her background knowledge gained from being native to the area, thus preventing the engineering group from many misunderstandings and perhaps poor design constraints. The fundraising that is required to pay in-country coordinators to ease these processes poses a threat to the ability of student organizations to hire them; however, without their expertise and inherent ability to communicate easily with the community receiving the project, the completed construction and sustainability of the venture is put at risk.

Many engineering courses, such as Thermodynamics, have topics which build upon the material previously learned in the course. For example, students will have difficulty learning the Second Law of Thermodynamics if they have not mastered the First Law. Unfortunately, many students delay studying material in courses until an exam is drawing near. This can be a particular problem in a course which does not inherently interest a student, such as a non-Mechanical Engineering student required to take Thermodynamics as a course outside their major. As a result, they may find themselves well behind in a course and struggling with the material currently being taught because they had not spent enough time learning earlier material while it was being covered in class.One technique which has been used to motivate students to learn the course material promptly is to test students more frequently, rather than waiting a month or more to do so. The author used this more-frequent-testing technique for many years, using shorter (30-45 minute) quizzes every 2 to 3 weeks in a Basic Thermodynamics course. Before using this method, the author had used a more traditional approach of giving the students 2 mid-term exams during the semester. While the frequent-quiz technique generally received positive feedback from the students and appeared to aid in their learning of the material, the two techniques had not been directly compared to quantifiably measure their relative impact.In the Fall 2011 semester, the author taught two sections of Basic Thermodynamics, and used the frequent-quiz technique in one and the 2 mid-term exam technique in the other. Other than the testing frequency, the two sections were kept as similar as possible. The lecture content and homework assignments were identical. Results of the final exam in the course were used to judge which technique was more successful in aiding the students' learning. Yet to be determined is the impact of each technique on student retention of the material in a second Thermodynamics course.In this paper, a thorough discussion of the study methodology and results is presented. A discussion of the benefits and detriments of both techniques is provided, and recommendations for teachers on testing frequency in Thermodynamics courses are made.

As part of an NSF-sponsored STEP grant, formal peer-led team learning (PLTL) groups were created for first-year engineering and computer science students. The groups were organized around the math course taken by the students so that all students in a particular group were taking the same math course. In both the 2010-11 and 2011-12 academic years, these groups were offered as a formal class, with students receiving a grade based upon participation. This was done to stress the importance of the groups to the students, and increase the level of participation by the students. Work with the groups in previous years showed that increased levels of participation led to greater impacts on student grades.Approximately 73% of the first-year students in engineering and computer science participated in these PLTL groups in 2010-11, with most students attending most of the weekly sessions. This participation rate increased to 82% in 2011-12. The impact of the PLTL groups on students in Calculus-level classes (Calculus I and II) was strong. When compared to all students in the Calculus courses who did not participate in the PLTL groups, the grades of the students who participated in the PLTL groups were generally 0.4-0.7 points (on a 4-point scale) higher. However, the results at the Pre-Calculus level (College Algebra and Trigonometry) were not as impressive. Students in the PLTL groups in College Algebra only had average grades 0.2 points higher than non-participants, while the Trigonometry students demonstrated little impact from the PLTL groups. This difference may be a result of the students' self-perceived need for the PLTL groups, with Calculus-level students seeing a greater need for the groups.In this paper, the format of the PLTL groups is described in detail, and a detailed analysis of the impact of the PLTL groups on the student grades is presented.

This paper describes the development of energy-demand models which are able to predict the future energy demand in the residential sector of the United States. One set of models use an artificial neural network (ANN) technique, and the other set of models use a multiple linear regression (MLR) technique. The models are used to forecast future household energy demand considering different scenarios for the growth rates of the effective factors in the models. The household sector includes all energy-consuming activities in residential units (both apartments and houses) including space and water heating, cooling, lighting and the use of appliances. In order to understand the evolution of household energy use, a set of indicators has been developed. For instance, several factors affect energy consumption for space heating as a share of households' energy demand. These factors include, dwelling size, number of occupants, the efficiency of heating equipment and the useful energy intensity. The paper also analyzes the trend of energy consumption in the residential sector of the United States. Moreover, the effects of important indicators on the energy consumption are discussed. The analysis performed in this paper is done for each census region, where possible, to elucidate the effects of different indicators in each region. (c) 2013 Elsevier Ltd. All rights reserved.

Resorcinol was utilized as the sole carbon and energy source by Enterobacter cloacae (identification by 16S rDNA nucleotide sequencing Genbank Accession Number JN093148). The different concentration of resorcinol utilized by the bacterial isolate ranged between 55 and 220 mg l-1 at 30°C and pH of 7.0. It was observed that the batch experimental results were best fitted for Michaelis-Menten and Monod models (for 220 mg l-1 resorcinol) with time under defined conditions. The kinetics constants for the Michaelis-Menten equation (enzyme kinetics) were Km = 11.00 mM and Vmax = 0.03 mM min-1 and for the Monod equation (growth kinetics) was μmax = 0.0371 h-1 in the inhibitory region and KS = 22.09 mg l-1. It was assumed that enzyme reactions limit biomass production (Monod kinetics) during resorcinol degradation by E. cloacae. The enzyme kinetic model (Michaelis-Menten) used was fit to the resorcinol degradation profiles with a set of model parameters such as using pre-induced E. cloacae cells on 220 mg l-1 resorcinol.

Formal peer-led study groups were created for first-year engineering and computer science students. The groups were organized around the math course taken by the students so that all students in the study group were taking the same math course, although students did not necessarily come from the same course section. In the 2010-11 academic year, these groups were organized as a formal class, and students received a grade based upon their participation. This was done to increase participation rates over past years during which the study groups were offered in a less formal setting. Analysis of previous years’ groups had indicated that greater participation in the study groups correlated with higher grades in the associated math courses. Study groups featured 6-12 students, and were directed by an upper-level engineering or computer science student. The student peer mentor would pose math problems to the students in the class. These problems came from homework assigned in the math classes, additional nonassigned problems from the math books, and outside sources. The students then worked on the problems together, until a solution was found. The student mentor would provide guidance if the students were unable to solve a problem without assistance, but would not completely solve the problems for the students. In the second year of the study, approximately 70% of the first-year students in engineering and computer science attended at least one session of the study groups, with nearly all students attending 9 or more of the weekly sessions. Grades of the students who participated in the study groups were generally 0.3-0.7 points (on a 4-point scale) higher than the average course grades of all students in the courses. In this paper, the format of the study groups will be described in detail, and the analysis of the impact of the study groups on the student grades will be presented.

Formal peer-led study groups were created for first-year engineering and computer science students. The groups were organized around the math course taken by the students so that all students in the study group were taking the same math course, although students did not necessarily come from the same course section. In the 2010-11 academic year, these groups were organized as a formal class, and students received a grade based upon their participation. This was done to increase participation rates over past years during which the study groups were offered in a less formal setting. Analysis of previous years' groups had indicated that greater participation in the study groups correlated with higher grades in the associated math courses.Study groups featured 6-12 students, and were directed by an upper-level engineering or computer science student. The student peer mentor would pose math problems to the students in the class. These problems came from homework assigned in the math classes, additional non-assigned problems from the math books, and outside sources. The students then worked on the problems together, until a solution was found. The student mentor would provide guidance if the students were unable to solve a problem without assistance, but would not completely solve the problems for the students.In the second year of the study, approximately 70% of the first-year students in engineering and computer science attended at least one session of the study groups, with nearly all students attending 9 or more of the weekly sessions. Grades of the students who participated in the study groups were generally 0.3-0.7 points (on a 4-point scale) higher than the average course grades of all students in the courses.In this paper, the format of the study groups will be described in detail, and the analysis of the impact of the study groups on the student grades will be presented.