Evaluation of a K-8 LEGO Robotics Program

Session T1D

Evaluation of a K-8 LEGO Robotics Program Tanja Karp and Andreas Schneider Texas Tech University, [email protected], [email protected] Abstract – In this paper we describe the evaluation results from an annual LEGO robotics competition for students in elementary and middle schools held at Lubbock, Texas, that aims at increasing interest in science, technology, engineering, and math. In a beforeafter questionnaire we collected information on participants’ attitudes toward the scientific method of investigation, robotics in general and LEGO robotics in particular, and problem solving in groups. We also collected demographic data of the participants to see in how far our program is able to attract females, minorities, and socio-economically disadvantaged students. Index Terms – K-12 Engineering Education, LEGO NXT Robotics, Program Evaluation, STEM Education. INTRODUCTION Since 2006 faculty of the Electrical and Computer Engineering Department at Texas Tech University have conducted the annual Get Excited About Robotics (GEAR) LEGO robotics competition for elementary school and middle school students [1] in Lubbock, Texas, and schools in Educational Service Center (ESC) Region 17 and more recently also 18 [2]. Over the years the program has seen significant growth and Texas Tech University now serves as a competition hub for about 500 participants from over 30 schools. The purpose of the GEAR program is "to foster interest among school aged (mainly primary schools) students in the fields of engineering and science [...] to show our youth that these fields can be a fun and rewarding career option." [1]. Students build an automated robot from the LEGO NXT MINDSTORMS kit [3] that performs tasks specified in the annual challenge. In addition to bricks and beams, the kit consists of a programmable brain, motors, and a variety of sensors. This age-appropriate engineering task is conducted in teams at the schools and is supervised by teachers. While GEAR is similar to FIRST LEGO League [4] it offers more flexibility in the implementation at the local hubs and is offered at a significantly lower cost to the schools. The Lubbock GEAR challenge annually kicks off during Engineering Week in February and runs through middle of April, thus being over when most of the state-wide testing is performed at the schools. During the kickoff event, the annual challenge is revealed and the individual robot tasks and the scoring rules are explained. Challenges in previous years covered the areas of space exploration, life at a science station in Antarctica, maintenance tasks inside a particle

accelerator, and a robot sports competition. At the end of the kickoff event participating schools are provided with game rules, game boards, game pieces, and up to six LEGO MINDSTORMS kits per school at no cost. Teams from all schools meet again at Texas Tech University at the end of March for a practice run and middle of April for Game Day. Each school decides independently about the number of students involved in the program, the recruiting process, and the time periods dedicated to it. While most schools run GEAR as an after-school enrichment activity, some incorporate it into their curriculum. School teams in the vicinity of Lubbock are also mentored by undergraduate engineering students [5], [6]. The survey we conducted in 2007 demonstrated that participation in GEAR positively influenced the students’ attitude toward STEM disciplines [6], [7]. Given the limitations of that survey, we used a different evaluation instrument [8] during the 2010 GEAR challenge. EVALUATION INSTRUMENT AND ADMINISTRATION As an evaluation instrument to measure the changes in attitudes toward Science, Technology, Engineering, and Math (STEM) through our program, we chose a subset of a questionnaire designed, tested, and used for the evaluation of a LEGO robotics program in Nebraska involving students of the same age [8]. This is a pre-test post-test design where students receive the same questionnaire during the first week of the GEAR program and again during the last session after six to eight weeks. The questionnaire consisted of 25 questions which are listed in Appendix A, evaluating the following three concepts: 1. 2. 3.

attitudes towards the scientific method of investigation (questions 1, 3-7,11-14, 16-18) attitudes toward robotics (questions 2, 8-10, 15) and LEGO robotics in particular (questions 19 - 21) attitudes towards working in groups (questions 22 - 25)

All questions were coded on a Likert scale ranging from 'strongly agree' (5) to 'strongly disagree' (1). We also collected demographic data of the participants consisting of grade level, gender, and ethnicity. A question asking if students received free school lunches was used as a rough measure of their socio-economic status. Since students have the opportunity to participate in GEAR robotics several times, we also asked a question about their familiarity with LEGO robotics (no experience, less than 6 months, more than 6 months).

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Session T1D The questionnaires were distributed to the teachers during GEAR 2010 kickoff event. They were administered by the teachers at the schools and participation was voluntary. Students filled out the questionnaire either at home or during the robotics program and gave it to their teachers who returned the completed questionnaires and parental consent forms to the authors during GEAR Game Day. The names of participants were written on both questionnaires to match the pre-test and post-test participant. Once the pre-test and post-test questionnaires were matched, the participant names were removed. The questionnaire and its administration were approved by the Institutional Research Board at Texas Tech University. DATA SAMPLE AND METHODS As mentioned earlier, the selection of GEAR participants was organized by the teachers at the schools and varied largely from school to school. Participation criteria reported by teachers were:            

students interested in robotics students not in math or reading intervention volunteers with good academic standing teacher selection and try-outs students volunteered to participate application and essay teacher selection of students interested students signed up and were drawn randomly gifted and talented (GT) students + their peers students applied with reference letters, good citizenship students in STEM robotics class all students in grade levels 6-8 in Accelerated Curriculum for Excellence (ACE) classes

Of the approximately 400 elementary and middle school participants in GEAR 2010, 300 students from 19 schools participated in the pre-test and 225 students (75%) from 16 schools also completed the post-test. At three schools with a total of 30 pre-test participants the teachers did not administer the post-test. The self-reported sociodemographic information of the 225 participants can be summarized as follows:     

Grade level distribution: 1.8% in 3rd, 14.7% in 4th, 24% in 5th, 26.7% in 6th, 17.8% in 7th, 14.2% in 8th, and 0.9% provided no information. Gender: 43.6% females, 55.6% males, 0.9% provided no information or contradicting information. Ethnicity: 47.6% White, 36.4% Hispanic, 7.1% African American, 1.8% Asian, 0.9% American Indian, and 6.2% of other or mixed race or provided no information. Free lunch: 37.3% yes, 53.3% no, 9.3% provided no or contradicting information Prior robotics experience: 41.8% no, 27.1% with less than 6 months experience, 28% with more than 6

months experience, and contradicting information.

3.1%

provided

no

or

Questions that students answered incorrectly (e.g. circling two answers) or skipped were left blank. While the ratings of the 25 questions were designed to range from 1 to 5, we observed a strong ceiling effect in the responses, being reflected in mean ratings ranging from 3.77 to 4.58. Such an effect does not necessarily speak against the validity of questionnaire, but might be due to the nature of our investigation. Teacher and course evaluations, for example, often show such ceiling effects, too [9]. Thus, it is not surprising that program evaluations, especially of voluntary programs, share a similar distribution. To analyze the over-time impact of our robotics program and the explanation power of the questionnaire, we conducted a logistic regression in which pre-test (0) and post-test (1) were the dichotomous binary dependent variables, and questions 1 to 25 served as independent variables. We chose this model since, unlike multiple regression analysis, factor analytical designs, or discriminant analysis, it does not require assumptions about the distribution of the predictor variables and is also applicable if the criterion variable (before-after) is nonlinear, [10], [11]. First, a logistic regression model including all questions was used. The ceiling effect drastically limited the variation within the sample. For that reason, very little variation in the answers to the questions was expected to be explained by the data. Still, using all 25 questions as independent variables, a Nagelkerke R square [11] indicates that 11.2% of the variation in the variables is explained by the questionnaire which actually speaks for the overall explanation power of the questions used. Next, we reduced the number of variables to two core questions that best resembled each of our three concepts. The questions were determined by their ability to differentiate pre-test and post-test using a paired sample ttest, i.e. they have less of a ceiling effect and show higher variation than their alternatives. For the first concept, the scientific method of investigation, we kept questions 7 and 11, for the second concept, expertise with LEGO robotics, we kept questions 20 and 21, and for the third concept, willingness to participate in group work, we kept questions 23 and 25. The selected questions are shown in bold in Table I and in Appendix A. While the chi-square goodnessof-fit test [10] was highly significant (0.005) and indicated a good model, using the selected six variables naturally diminished the explanation power. However, the Nagelkerke R square still indicates 4.8% of variation to be explained by these six questions, i.e. 43% of the variation explained by the whole questionnaire is captured in 24% of the questions. The paired sample t-test was chosen since it has much more statistical power when the difference between groups is small relative to the variation within groups.

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Session T1D IMPACT OF THE GEAR PROGRAM Table I shows the result from the paired sample t-test from all participants for all 25 questions. In all the tables of this paper, 'Pair n' the refers to the paired variables 'question n' from the post-test minus 'question n' from the pre-test. The second column refers to the concept evaluated by the question. The mean and standard deviation (std) of the paired variables, the obtained t value, the degrees of freedom (df), which is one less than the sample size for that variable, and the level of significance (signif.), 2 tailed, are reported. A positive value for the mean describes a higher average scoring in the post-test than in the pre-test, i.e. an improvement in attitude for our set of questions. TABLE I PAIRED SAMPLE TEST OF ALL VARIABLES FROM ALL PARTICIPANTS variable concept Pair 1 1 Pair 2 1 Pair 3 3 Pair 4 1 Pair 5 1 Pair 6 1 Pair 7 1 Pair 8 2 Pair 9 2 Pair 10 2 Pair 11 1 Pair 12 1 Pair 13 1 Pair 14 1 Pair 15 2 Pair 16 1 Pair 17 1 Pair 18 1 Pair 19 2 Pair 20 2 Pair 21 2 Pair 22 3 Pair 23 3 Pair 24 3 Pair 25 3

mean .076 .041 -.005 -.112 .036 -.064 .145 -.036 -.009 .000 .188 -.014 .045 .061 .055 .112 .014 -.087 .041 .241 .197 .019 -.114 -.009 -.009

std. .932 .853 .890 .909 .852 .820 .872 .972 .984 .790 1.158 1.049 1.028 1.088 .944 .911 1.106 1.128 .851 1.003 1.133 1.072 .993 .935 .991

t 1.219 .709 -.075 -1.838 .632 -1.152 2.468 -.549 -.136 .000 2.335 -.193 .656 .817 .857 1.838 .183 -1.138 .713 3.563 2.571 .254 -1.697 -.143 -.136

df 223 221 221 223 220 219 220 224 223 221 207 219 219 213 219 222 220 218 219 219 217 214 219 222 220

signif. .224 .479 .940 .067 .528 .251 .014 .584 .892 1.000 .021 .847 .513 .415 .393 .067 .855 .256 .477 .000 .011 .799 .091 .886 .892

Looking at the results in Table I and using a significance level of 0.05, a significant change in attitude between the pre-test and post-test can only be reported for the following questions: 7

It is important for me to collect and interpret data to verify a prediction or hypothesis. 11 I like using scientific methods to solve problems. 20 I am certain I can fix the software program for a robot that does not behave as expected. 21 I am confident that I can program a LEGO robot to follow a black line using a light sensor

relate to LEGO NXT programming and students were expected to perform better after 6-8 weeks of programming experience. Given the broad range of participants from different schools, varying in age-group, ethnicity, socio-economic standing, and prior experience with LEGO robotics, we next looked at different groups of participants to determine if the GEAR program varied in impact for those populations. I. Gender The demographic section of our questionnaire revealed that the GEAR robotics program was successful in attracting girls to it who formed 44% of all participants. Tables II and III show the results for girls and boys, resp., for the six identified questions. TABLE II PAIRED SAMPLE T-TEST. GIRLS ONLY variable Pair 7 Pair 11 Pair 20 Pair 21 Pair 23 Pair 25

concept 1 1 2 2 3 3

mean .124 .247 .337 .237 .031 -.071

std .869 1.070 1.015 1.018 .951 .906

t 1.402 2.229 3.285 2.293 .320 -.776

df 96 92 97 96 96 98

signif. .164 .028 .001 .024 .750 .439

df 118 110 116 115 115 116

signif. .060 .320 .098 .181 .053 .795

TABLE III PAIRED SAMPLE T-TEST. BOYS ONLY variable Pair 7 Pair 11 Pair 20 Pair 21 Pair 23 Pair 25

concept 1 1 2 2 3 3

mean .151 .117 .154 .155 -.190 .026

std .870 1.234 .997 1.241 1.046 1.062

t 1.897 1.000 1.670 1.346 -1.953 .261

For girls the experience of participating in the GEAR program has a significant impact on their confidence fixing software problems and programming robots. The mean increases by twice as much as for the boys. There is a higher mean score also for boys, but no significant impact. While there is a significant improvement on the liking of using scientific methods (question 11) for the girls, this is not found among the boys. This result is in accordance with the often observed behavior among engineering students when performing experiments in laboratory courses: while the men are using trial and error and do not hesitate to hit arbitrary buttons in order to figure out the functionalities of the provided equipment, females tend to be more organized and are hesitant to start the experiment unless they have understood the instructions and have laid out a step-by-step plan for the experiment. While there is some indication that after participating in the program boys and girls show a slight dislike for group work, the support is mixed and statistically insignificant for both groups.

The highly significant increase in confidence with respect to questions 20 and 21 is not surprising since they directly 978-1-61284-469-5/11/$26.00 ©2011 IEEE October 12 - 15, 2011, Rapid City, SD 41st ASEE/IEEE Frontiers in Education Conference T1D-3

Session T1D II. First Time Participation

III. Extracurricular Robotics Clubs versus In-Class Activity

Some students have participated in the GEAR program more than once or had gained prior LEGO robotics experience through other programs. Given that the overall effects were weak, we wondered if the effects on first time participants were more pronounced. Particularly in voluntary, afterschool programs a pre-selection of students liking LEGO robotics could be expected for the second group and thus a weaker impact of the GEAR program. Tables IV and V show the results for the identified 6 questions as well as question 2 for first-time participants and participants with prior LEGO robotics experience, resp., as well as question 2 for participants with prior experience.

Voluntary extracurricular programs tend to have a strong self-selection of students. It is quite likely that only students with an interest in robotics and science in the first place will choose to participate in the program. This contributes to the aforementioned ceiling effect in the scores. We had one middle school, however, that implemented the LEGO robotics as an in-class activity. Albeit the participation in our survey was voluntary, the program participation was not. While we see weak and only partly significant differences in the appreciation of the scientific method of investigation and the perceived technological competence for students that opted for the GEAR program as an extracurricular activity, the impact on the program was substantial when being implemented into the curriculum. The GEAR program provided a substantial and even with only 60-68 valid cases significant change in the students' perceived competence to engage in technological tasks. At the same time, the dislike of group work was much higher than among students participating in after-school robotics clubs. This was an unanticipated dramatic finding that also explained that the limitations we experienced in our investigation through the ceiling effect was systematic and related to the problem we address in our investigation.

TABLE IV PAIRED SAMPLE T-TEST. FIRST TIME PARTICIPANTS variable Pair 7 Pair 11 Pair 20 Pair 21 Pair 23 Pair 25

concept 1 1 2 2 3 3

mean .130 .314 .289 .144 -.187 -.078

std. .867 1.201 1.063 1.241 1.115 1.030

t 1.443 2.425 2.579 1.104 -1.599 -.716

df 91 85 89 89 90 89

signif. .153 .017 .012 .273 .113 .476

TABLE V PAIRED SAMPLE T-TEST. PARTICIPANTS WITH PRIOR EXPERIENCE variable Pair 2 Pair 7 Pair 11 Pair 20 Pair 21 Pair 23 Pair 25

concept 1 1 1 2 2 3 3

mean .206 .143 -.071 .242 .177 -.065 .079

std .765 .877 1.024 1.035 1.064 .807 .747

t 2.140 1.293 -.522 1.840 1.313 -.629 .843

df 62 62 55 61 61 61 62

signif. .036 .201 .604 .071 .194 .531 .402

Indeed, the overall effect of the program on the attitudes towards the scientific methods and the perception of increased competence of handling technological tasks is mainly explained by first time participants, for which the GEAR program shows the strongest impact. For both groups, the program had no statistically significant effect on the liking of group work although its average decrease is twice as high among first time participants as those with prior experience. The main impact the GEAR program showed on repeated participant was their curiosity and interest in robots. The main influence of the GEAR program on repeat participants was the increased impression that "it is important to me to learn about robotics." We did not encounter this substantial and significant difference in any of our other comparisons. This result confirms what we had already observed in our prior survey during GEAR 2007 [6] where an increased improvement of attitudes toward science dependent on the amount of prior experience with robotics programs was shown.

TABLE VI PAIRED SAMPLE T-TEST. EXTRACURRICULAR ACTIVITY variable Pair 7 Pair 11 Pair 20 Pair 21 Pair 23 Pair 25

concept 1 1 2 2 3 3

mean .117 .189 .208 .093 -.026 .052

std .832 1.151 1.001 1.122 .969 1.050

t 1.743 2.000 2.576 1.016 -.335 .616

df 153 147 153 150 151 152

signif. .083 .047 .011 .311 .738 .539

TABLE VII PAIRED SAMPLE T-TEST. IN-CLASS ASSIGNMENT variable Pair 7 Pair 11 Pair 20 Pair 21 Pair 23 Pair 25

concept 1 1 2 2 3 3

mean .209 .183 .318 .433 -.309 -.147

std .962 1.186 1.010 1.131 1.026 .833

t 1.778 1.197 2.559 3.132 -2.482 -1.455

df 66 59 65 66 67 67

signif. .080 .236 .013 .003 .016 .150

The GEAR program has the strongest impact if it reaches a wide audience of students and not self-selected portions of students who already have a positive attitude towards science and technology. While it is certainly suited to maintain and substantiate the existing positive attitudes, the GEAR program is much more beneficial for students that are not likely to choose science and technology as part of their extracurricular activity. IV. Ethnicity With 47% White, 36% Hispanic, 7% African American, 2% Asian and 1% American Indian, and 13 % of participants of

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Session T1D mixed race or others, our sample closely represented the ethnic makeup of the West Texan population. In our investigation of the impact on ethnicity we focus on Hispanics and Whites, for which results are shown in Tables VIII and IX, resp., simply because of data limitations. TABLE VIII PAIRED SAMPLE T-TEST. HISPANICS ONLY variable Pair 7 Pair 11 Pair 17 Pair 20 Pair 21 Pair 23 Pair 25

concept 1 1 1 2 2 3 3

mean .183 .132 .229 .317 .293 -.086 .024

std .848 .900 0.967 .784 1.048 .977 .780

t 1.953 1.275 2.157 3.663 2.529 -.796 .281

df 81 75 82 81 81 80 82

signif. .054 .206 0.34 .000 .013 .428 .779

TABLE IX PAIRED SAMPLE T-TEST. WHITES ONLY variable Pair 7 Pair 11 Pair 20 Pair 21 Pair 23 Pair 25

concept 1 1 2 2 3 3

mean .049 .141 .196 .100 -.206 -.118

std .867 1.294 1.090 1.259 1.037 1.120

t .568 1.088 1.816 .794 -2.005 -1.061

df 102 98 101 99 101 101

signif. .571 .279 .072 .429 .048 .291

Only for Hispanics we can observe a significant increase of reported confidence in working with the LEGO robots. they also show a significant increase in carefully analyzing a problem before beginning to develop a solution (question 17). For White students we find weak support in one measurement variable for group work that they experienced an increased dislike in it. V. Socio-Economic Standing The participation in the free lunch program [12] was our indirect indicator for family income and thus socioeconomic standing suitable for the age range of our subjects. Taking this criterion, we classify 37% of our subjects a coming from a background of low socio-economic standing. Tables X and XI show the results for students qualifying and not qualifying for free school lunch, resp., with question 17 being added for socio-economically disadvantaged participants. TABLE X PAIRED SAMPLE T-TEST. FREE LUNCH variable Pair 7 Pair 11 Pair 17 Pair 20 Pair 21 Pair 23 Pair 25

concept 1 1 1 2 2 3 3

mean 0.198 0.280 .239 .218 .287 -.149 .068

std 0.823 1.158 1.039 1.050 1.011 .959 .944

t 2.226 2.194 2.154 1.940 2.652 -1.454 .677

df 85 81 87 86 86 86 87

signif. 0.029 0.031 0.034 0.56 0.01 .150 .500

variable Pair 7 Pair 11 Pair 20 Pair 21 Pair 23 Pair 25

TABLE XI PAIRED SAMPLE T-TEST. NO FREE LUNCH concept mean std t df 1 .078 .893 .990 127 1 .125 1.164 1.177 119 2 .278 .977 3.192 125 2 .121 1.234 1.092 123 3 -.119 1.001 -1.335 125 3 -.048 .962 -.556 125

signif. .324 .242 .002 .277 .184 .580

When comparing results in Tables X and XI, it is evident that socio-economically disadvantaged students significantly improve their attitude towards scientific methods of investigation whereas a slight improvement is also visible among other students but not significant. Both groups build up their confidence in knowing how to program robots. There is a slight dislike toward working in groups but it is not significant. CONCLUSIONS The evaluation of our GEAR robotics program showed that the program has the ability to attract a high percentage of girls, ethnic minorities, and socio-economically disadvantaged students into it. The effect of the program on all participants is weak and showed significant changes in attitudes toward scientific methods of investigation only in two questions (7 & 11) as well as an expected higher level in confidence toward the participants capabilities with respect to robotics (questions 20 & 21). When looking at different groups of participants with respect to gender, experience with LEGO robotics, ethnicity, and socio-economic standard, we showed that the outcomes for these groups are different. A significantly higher appreciation for scientific methods was observed among girls, first-time participants, and particularly strongly for socio-economically disadvantaged participants. All students reported an average improved their ability to solve robotics problems, however a significant change of questions 20 & 21 was only observed among girls, Hispanics, and students participating in curricular robotics classes. We were most surprised by the increased dislike of working in groups at the end of the program. This effect was most clearly seen among White students and students for whom robotics was part of the curriculum. This outcome was however confirmed by essays that participants submitted for the 'Young Engineer Award' in which they described the initial struggle of working in a team at length. Our analysis does not take correlations between different groups into consideration. First evaluations showed that the dislike of group work, e.g., is particularly high among first time participating girls, and that there is a large overlap in the population of low-income and Hispanic students. Evaluating these populations will be the focus of future investigations.

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Session T1D ACKNOWLEDGMENT The authors would like to thank Dr. Gwen Nugent from the Nebraska Center for Research on Children, Youth, Families and Schools at the University of Nebraska-Lincoln for providing them with the questions of the questionnaire. They would also like to thanks National Instruments, Alpha Industries, Texas Tech's Office for Institutional Diversity, the Whitacre College of Engineering and its Dean's Council for their generous support of the GEAR competition. APPENDIX A The described questionnaire consisted of the following questions. All answers were coded on a Likert scale ranging from strongly agree (5) to strongly disagree (1). 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

It is important for me to learn how to conduct a scientific investigation. It is important for me to learn about robotics. It is important for me to learn how to use appropriate tools and techniques to gather, analyze, and interpret data. It is important for me to learn how to use mathematical formulas to help solve practical problems It is important for me to learn how to make accurate measurements to help solve mathematical problems. It is important for me to be able to record measurements and calculations. It is important for me to collect and interpret data to verify a prediction or hypothesis. It is important for me to understand basic engineering concepts (e.g. design tradeoffs, speed, torque) behind building a robot. It is important for me to learn how to program a robot to carry out commands. I like learning new technologies like robotics. I like using scientific methods to solve problems. I like using mathematical formulas and calculations to solve problems. I use a step by step process to solve problems. I make a plan before I start to solve a problem. I am confident that I can program a robot to move forward two wheel rotations and then stop. I try new methods to solve a problem when one does not work. I carefully analyze a problem before I begin to develop a solution. In order to solve a complex program, I break it down into smaller steps. I am certain that I can build a LEGO robot by following design instructions. I am certain I can fix the software program for a robot that does not behave as expected. I am confident that I can program a LEGO robot to follow a black line using a light sensor. I like listening to others when trying to decide how to approach a task or problem.

23. I like to be being part of a team that is trying to solve a problem. 24. When working in teams, I ask my teammates for help when I run into a problem or I don’t understand something. 25. I like to work with others to complete projects. REFERENCES [1]

Get Excited About Robotics. [Online]. http://www.gearrobotics.org

[2]

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LEGO Mindstorms. [Online]. http://mindstorms.lego.com/

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FIRST LEGO League. [Online]. http://www.usfirst.org/roboticsprograms/fll/

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Karp, T., “Teaching a Service Learning Introductory Engineering Course - Lessons Learned and Improvements Made” . Abstract accepted for submission to IEEE ASEE Frontiers in Education Conference, Rapid Falls, SD, USA, October 2011.

[6]

Karp, T., Gale, R., Lowe, L., Medina, V. and Beutlich, E., “Generation NXT: Building Young Engineers with LEGO's”, IEEE Transactions on Education, Special Issue on Outreach to Prospective Electrical, Electronic, and Computer Engineering Students, vol. 53, issue 1, pp. 80-87, February 2010.

[7]

Gale, R., Karp, T., Lowe, L. and Medina, V., “Generation NXT”, IEEE Meeting the Growing Demand for Engineers and Their Educators 2010-2020 International Summit, 13 pages, Munich, Germany, November 2007.

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Nugent, G., Barker, B., Grandgenett, N., and Adamchuk, V., “The Use of Digital Manipulatives in K-12: Robotics, GPS/GIS and Programming”, IEEE/ASEE Frontiers in Education Conference, San Antonio, TX, 2009, pp. M2H1-6.

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Hays, W. L., Statistics. Fifth Edition. New York: Harcourt Brace College Publishers, 1994.

[10] Mertler, G. A. and Vannatta, R. A., Advanced and Multivariate Statistical Methods. Fourth Edition. Glendale CA: Pyreczak Publishing, 2010. [11] Norusis, M., SPSS 16.0 Advanced Statistical Procedures Companion. Chicago: Prentice Hall, 2010. [12] U.S. Department of Agriculture. National School Lunch Program. [Online]. http://www.fns.usda.gov/cnd/lunch/

AUTHOR INFORMATION Tanja Karp, Ph.D., is an Associate Professor of Electrical and Computer Engineering at the Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409-3102. She has organized the GEAR robotics competition at Texas Tech University since 2006. Andreas Schneider, Ph.D., is an Associate Professor of Sociology at the Department of Sociology, Anthropology, and Social Work, Texas Tech University, Lubbock, TX, 79409-1012.

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