{"id":570,"date":"2011-01-01T01:11:00","date_gmt":"2011-01-01T01:11:00","guid":{"rendered":"http:\/\/localhost:8888\/cite\/2016\/02\/09\/prime-the-pipeline-project-p3-putting-knowledge-to-work\/"},"modified":"2016-06-04T02:20:08","modified_gmt":"2016-06-04T02:20:08","slug":"prime-the-pipeline-project-p3-putting-knowledge-to-work","status":"publish","type":"post","link":"https:\/\/citejournal.org\/volume-11\/issue-1-11\/mathematics\/prime-the-pipeline-project-p3-putting-knowledge-to-work","title":{"rendered":"Prime the Pipeline Project (P3): Putting Knowledge to Work"},"content":{"rendered":"

 <\/p>\n

The need for more experts and innovators in the fields of science, technology, engineering and mathematics (STEM) in the U.S. has become a paramount issue for the success of our nation (Bray, 2010; Couto, Mani, Lewin, & Peeters, 2007; National Science Board, 2010).\u00a0While this need is increasing dramatically, the number of students pursuing and completing degrees in these fields is decreasing (Kendall, Pollack, Schwols, & Snyder, 2007; National Academies of Science, 2007; National Science Board, 2010).<\/p>\n

Three factors contributing to this problem may be (a) students\u2019 poor preparation and lack of interest and success with mathematics and science in high school (Arizona Department of Education, 2009; Mullis, Martin, & Foy, 2008; Organization for Economic Cooperation and Development, 2007);\u00a0 (b) teachers underprepared to engage students in the application of mathematics and science concepts and new technologies to the solution of problems that both mirror those faced by the workforce and demonstrate a usefulness for that knowledge (Boyd, Grossman, Lankford, Loeb, & Wyckoff, 2009; Hill, Rowan, & Ball, 2005; National Mathematics Panel, 2007; Stigler & Hiebert, 1999);\u00a0 and (c) student, teacher, and family lack of knowledge about new STEM careers and the academic preparation in high school and college necessary for those careers (Arrington, 2000; Hughes, Bailey, & Karp, 2002).<\/p>\n

Putting Knowledge to Work: Goals and Objectives<\/p>\n

The overarching goal of P3, a 3-year project funded by the National Science Foundation (ITEST) that began in 2008, is to design, implement, and evaluate a scientific village strategy to enhance the science, technology, engineering, mathematics, and business (STEM-B) pipeline from high school to college by increasing the number of students who enroll in and pursue college majors in preparation for STEM-B careers. The scientific village strategy is designed to<\/p>\n

    \n
  1. Increase students\u2019 interest in and success with the study of mathematics and science in high school;<\/li>\n
  2. Integrate workplace technologies, communication, collaboration skills, and critical thinking and risk-taking behaviors into the P3 learning environment;<\/li>\n
  3. Increase student awareness of STEM-B\u00a0 careers, university preparatory programs for these careers, and their own talents as related to these fields;<\/li>\n
  4. Update secondary school teachers in content (concepts and skills) in their own and related fields, technology, pedagogy, and STEM-B career opportunities;<\/li>\n
  5. Increase parents\u2019 knowledge of STEM-B careers, the preparation needed for them, and their children\u2019s talents as related to these careers.<\/li>\n<\/ol>\n

    The core element of the P3 strategy is engagement of students and teachers in scientific villages (communities) whose members work collaboratively on long-term projects\/problems that are of high interest and require application of STEM-B concepts and skills. Village members include high school students (class of 2011), secondary-certified teachers of STEM-B, undergraduate STEM-B majors who apply for and are trained to assist village leaders and to mentor participants, and scientists from Arizona State University (ASU), other colleges, and industry, who design and lead the villages.<\/p>\n

    P3 just began the third year of its 3-year funding at the time of this writing, but progress toward meeting two goals of the project are encouraging and compelling and provide the focus for this paper: Does the P3 scientific village strategy increase students\u2019 interest in and success with the study of mathematics and science in high school, and does it change teachers\u2019 instructional strategies and expectations for student performance? The rationale for the Pipeline project follows, along with a description of the instructional approach and program components, requirements for participation in P3, and the evaluation study, including participants\u2019 (intervention and control) demographics, \u00a0assessment instruments, data collection and\u00a0analyses methods, results,\u00a0 and conclusions.<\/p>\n

    Putting Knowledge to Work: Project Rationale<\/p>\n

    Rationale for the P3 approach comes from research on (a) integrated projects for enhancing the acquisition and application of mathematics and science concepts and skills and various technology tools (Corcoran & Silander, 2009; Darling-Hammond, et al., 2008; Duschl, 2008; Markham, Larmer, & Ravitz, 2003); (b) motivation to learn (Allen, Bonous-Hammarth, & Suh, 2005; Allen, Bonous-Mammarth, Yang, Gonz\u00e1lez, & DuCros, 2004; Sedlacek, 2004; Sedlacek & Sheu, 2004);\u00a0 and (c) skill requirements for the 21st-century workforce (Meeder, 2008; Secretary\u2019s Commission on Achieving Necessary Skills, U.S. Department of Labor, 1991).<\/p>\n

    Integrated Projects<\/p>\n

    Poor performance in mathematics and science and the lack of interest and persistence in STEM subjects in high school and college may occur as a result of a siloized approach to education and minimal time for true exploration and learning. In that approach, academic subjects are taught separately with few opportunities for students to apply what they have learned to solving interesting and compelling problems (Stone, Alfed, & Pearson, 2008). Students are not presented with situations in which to put their knowledge to work (Geier et al., 2008; Jerald, 2009; Kazis, 2005; Smith, Sheppard, Johnson, & Johnson, 2005).<\/p>\n

    In most high schools, mathematics has been taught as a collection of separate topics (e.g., algebra, geometry, statistics), often in 45- to 50-minute time blocks, with the primary goal of having students master computational algorithms (Stigler & Hiebert, 1999; Stone et al., 2008). More elaborate problems are generally avoided, with claims that students cannot solve them because they do not understand the context domains (e.g., economics, physics) or there is insufficient class time for students to wrestle with challenging problems.<\/p>\n

    Despite the fact that project based learning (PBL) is gaining momentum, it rarely involves problems typical of those solved by workers in STEM-B fields (Bronson, 2007; Clark & Ernst, 2007; Meeder, 2008). This situation has been the case since the seminal paper from the U.S. Department of Labor (Secretary\u2019s Commission on Achieving Necessary Skills, U.S. Department of Labor, 1991) that addressed the needs of the workplace and stressed the importance of better preparing students for work on project types of problems that require application of problem solving, collaboration, and communication skills.<\/p>\n

    Core to the P3 scientific village strategy are the integrated projects, developed by scientist leaders not only to engage villagers in STEM-B explorations, but also to promote collaboration, communication, multiple uses of technology, and the joy of problem solving. Based on literature on motivation cited later, projects are designed to<\/p>\n

      \n
    1. Encourage investigation, application, and the development of more robust understanding of key concepts and skills typically developed in high school mathematics and the sciences;<\/li>\n
    2. Require the use of workplace technology tools including databases, simulations, numerical techniques, search, sorting, and graphing strategies;<\/li>\n
    3. Examine problems of importance to society (e.g., alternative energy);<\/li>\n
    4. Require team collaboration to handle multidimensional projects;<\/li>\n
    5. Develop habits of mind such that students appreciate challenging problems, persevere to reach solutions, monitor their thinking and actions, and improve their performance by seeking more elegant solutions;<\/li>\n
    6. Promote communication through technical writing documentations of their work and oral and multimedia presentations.<\/li>\n<\/ol>\n

      Motivation<\/p>\n

      The theory for getting high school students interested in STEM-B disciplines and careers evolves from several areas of research:<\/p>\n

      Teachers.<\/em> Students are influenced by their teachers\u2019 expertise, innovative teaching styles, enthusiasm, and high expectations for performance (Boyd et al., 2009; Haladyna, Olsen, & Shaughnessy, 1982; Jones, 2005).<\/p>\n

      Experts.<\/em> Student access to a network of experts provides exposure to powerful transforming experiences that enhance understanding and enthusiasm for STEM-B careers (Allen et al., 2005; Sedlacek & Sheu, 2004).<\/p>\n

      Peers.<\/strong><\/em> Peer influence is a major determinant of college choice and academic preparation (Griffin, Allen, Kimura-Walsh, & Yamamura, 2007). Team approaches, in which students collaborate to study core subjects, are particularly effective for preparing students for college (Fernandez-Santander, 2008; Seymour, 1995) and the workplace (Kalman, 2007; Meeder, 2008). The goal of collaboration is to build a community that is supportive of academic achievement (Sedlacek & Sheu, 2004).<\/p>\n

      Parents.<\/em> The major influence on student preparation for and attendance at college is the family (Griffin et al., 2007). Parents need to be well educated about career opportunities for their children and the preparation necessary for them.<\/p>\n

      Interests.<\/em> Research shows that students\u2019 choices of careers can be influenced by capitalizing on their STEM-B interests, such as technology (Sedlacek, 2004).\u00a0 Students today are immersed in technology. They use cell phones and computers, create podcasts, play video games, write blogs, download and upload to their social communication networks, and they know how to search for information online. The greatest use of the internet is by youth in the age range of 12 to 18 (Gewertz, 2007). Students exhibit excellent learning behaviors when using these various technologies. They take risks and experiment with new ideas and they persevere (Gewertz, 2007). P3 is designed to stimulate those same learning behaviors while students are working with new ideas in mathematics, science, and other content areas.<\/p>\n

      Project-Based Learning.<\/em> With its \u201chands-on approach,\u201d relevance, and ability to accommodate various learning styles, talents, and the diversity of social transactions, PBL has been shown to correlate positively with student motivation (ChanLin, 2008; Helle, Tynjala, Olkinuora, & Lonka, 2007).<\/p>\n

      Time-on-Task<\/em>. Just as time-on-task is an important factor in learning, sustained time with the same project\/problem has been shown to increase achievement and interest (Aronson, Zimmerman, & Carlos, 1998).<\/p>\n

      Critical Workplace Skills<\/p>\n

      The economic success of the United States depends on students who are well educated\u00a0 to think critically, solve problems, demonstrate expertise with computers and other technology, communicate well, and assume greater responsibility for their futures (Partnership for 21st Century Skills, 2007; Public Works, 2006; State Educational Technology Directors Association, 2008; Secretary\u2019s Commission on Achieving Necessary Skills, U.S. Department of Labor, 1991). Students also need to improve their abilities to apply mathematics and technology to the solution of problems, locate information, read for information, observe, write, listen, judge, and make informed decisions. These are among the skills nurtured in the P3 project components and activities.<\/p>\n

      Prime the Pipeline: Approach, Components, and Activities<\/p>\n

      Scientific Instructional Approach<\/p>\n

      The approach used during project engagement reverses the lecture-and-then-apply method of instruction. Rather, villagers are urged to bring to bear what they already know to begin the solution process and get information or direction at point of need. Village leaders design projects that prompt learning of new concepts and ways to use technology by causing villagers to bump into \u201cobstacles.\u201d For example, these obstacles may include epistemological conflicts in which results of actions contradict expectations or prior knowledge, and villagers must wrestle with ideas to resolve the conflicts; insufficient knowledge to continue that requires a search for relevant information; and ambiguities that require making assumptions and considering the use of alternative heuristics, data, or data analyses procedures. Rarely do our village leaders lecture for more than 15 minutes and not usually at the start of an exploration.<\/p>\n

      Educating Students and Teachers Together<\/p>\n

      Typical teacher preparation and in-service courses are taught with a single content focus, that is, mathematics teachers receive training in mathematics content, pedagogy, and assessment, and science \u00a0teachers are trained in their specialty. Rarely are teachers of two or more subjects updated together in their respective and sister fields. Even rarer are programs in which teachers and students are educated side by side.<\/p>\n

      This approach is predicated on the following beliefs:<\/p>\n

        \n
      1. Mathematics teachers need to be well trained in other content areas (e.g., the sciences, economics)\u00a0 in order to help students recognize the usefulness of mathematics for modeling and analyzing problems in those areas (National Governors Association for Best Practices and the Council of Chief State School Officers, 2010).<\/li>\n
      2. Science teachers need to understand fully the value of mathematics as a tool to understand the sciences (American Association for the Advancement of Science, 2009), how formulas model phenomena, and how, for example, a change in one parameter of a formula affects a change in the model and vice versa.<\/li>\n
      3. Both mathematics and science teachers need to be updated in the use of workplace technologies.<\/li>\n
      4. Both mathematics and science teachers need to experience PBL themselves in order to better understand the elements of the problem solving\/scientific inquiry approach.<\/li>\n
      5. Both mathematics and science teachers profit from collaborating with students to solve complex challenging problems in terms of gaining greater insight into students\u2019 learning and problem-solving talents.<\/li>\n
      6. Students\u2019 talents with technology (they have grown up with it) and their risk-taking adventurous spirit while exploring new technologies will place them in the mode of assisting teachers, thereby providing greater insight into their own abilities.<\/li>\n<\/ol>\n

        Because each population has separate talents, they can learn new ideas together, provide support for one another in terms of their area(s) of expertise, and come to appreciate the knowledge each offers.<\/p>\n

        Components<\/p>\n

        P3 has three major components:\u00a0 academic year scientific villages, summer institutes, and summer connections courses for teachers. All village meetings take place in the computer and research laboratories on the Polytechnic campus of Arizona State University located in Mesa, Arizona. The end-of-semester and end-of-summer Showcase Open Houses provide venues for villagers to present their completed projects to their peers, families, and the community. The villages and the connections courses are supported by Blackboard, which enables communication among villagers during project explorations and provides village-specific information.\u00a0 The P3 website provides information about village topics, schedules of P3 events, student and teacher application forms, and links to information about STEM-B careers and college preparation for them.<\/p>\n

        P3 began in spring 2009.\u00a0 Since that time, four villages have been offered every semester and in the summer. Because lab facilities limit the number of participants to 24 and all villagers interested in some of the topics could not be accommodated in them, those villages were offered in a subsequent summer or academic year. Villagers continuing with P3 have first choice of village. Each village has a minimum of one scientist and one mentor.<\/p>\n

        Academic Year Villages<\/p>\n

        During the academic year, for 9 weeks each semester, villagers meet once a week on Tuesdays after school from 3:45 to 6 p.m. (20\u00bc contact hours per semester) to participate in scientific villages. Week 9 is the Showcase Open House for families and the community.<\/p>\n

        Summer Institutes<\/p>\n

        During the summer session, which begins shortly after the close of the previous academic year, villagers meet daily from 8 a.m. to 12 p.m. for 2 weeks (40 contact hours per summer). Each day begins with an assembly at 8 a.m. for announcements and village debriefings of work completed to date. Debriefings are presented by villagers\u2014students and teachers alternate\u2014to fellow participants and scientists. Day 10 is the Showcase Open House.<\/p>\n

        Connections Courses for Teachers<\/p>\n

        Held daily in tandem with the Summer Institute and led by project staff with guest presentations by experts in a variety of fields, teachers meet from noon to 2 p.m., Monday through Thursday, to gain greater insight into the big ideas in their content areas of expertise, in the sister subjects, and with various types of technology. They explore techniques for assessing students\u2019 depths of understanding through problem solving talk-alouds, flexible\/clinical interviews, and observation protocols. They explore better ways to counsel\/guide their students toward STEM careers. They develop proposals to fund various activities, supplies, and experts to assist with implementation of integrated content, and project learning in their classrooms.<\/p>\n

        They also connect\/network with each other to examine common difficulties and to learn about instructional programs offered by their peers. For example, two of our teachers described the math instruction they provide at a virtual online high school. Among the other special topics were Algebraic Reasoning: Development and Assessment, Assessing Teacher Success with the Integration of Mathematics and Science, Career Counseling and the Role of the Teacher, The Nature of College Preparation for STEM Careers, and Using White Boards to Enhance Group Collaboration and Documentation of Project Work.<\/p>\n

        P3 Website and Blackboard<\/p>\n

        The project website (http:\/\/primevillages.asu.edu<\/a>) is designed to enhance communication among project staff, mentors, teachers, and students.\u00a0 All villagers have ASU identification and email addresses that enable them to gain access to all project software, whether at home, school, or working at one of the open-access computing labs on ASU campuses. Each village has a Blackboard site where village leaders post homework assignments, resources for participants, and village updates. Villagers can chat with their own working groups on the Blackboard site.<\/p>\n

        Village Leader Orientation<\/p>\n

        Prior to the beginning of each semester and summer program, P3 staff meet with village leaders and mentors to orient them to the goals of the program, review their project goals, and distribute information about attendance and assessment requirements. Village topics are generally the pet projects of the village leaders and are most often tied to their research or teaching activities at their respective institutions.<\/p>\n

        Scientific Villages: Spring 2009 through Spring 2010<\/p>\n

        To gain greater insight into the nature of the Scientific Villages and the numbers of student and teacher villagers, see Table 1. The links in Table 1 provide connections to village specific information, including descriptions of project activities, assessments, photos of villagers at work, and in some cases, videos showing the villages in action. Some videos were created by participants in the film villages. Brief descriptions of the villages are found in Appendix A.<\/p>\n

        Table 1<\/strong>
        \nP3 Scientific Villages Spring 2009 through Spring 2010<\/em><\/p>\n

        \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
        \n

        Session Title<\/strong>[a]<\/p>\n<\/td>\n

        \n

        Number of Participants<\/strong><\/p>\n<\/td>\n

        \n

        Link to Additional Information<\/strong>[b]<\/p>\n<\/td>\n<\/tr>\n

        \n

        Students<\/strong><\/p>\n<\/td>\n

        \n

        Teachers<\/strong><\/p>\n<\/td>\n

        \n

        Total<\/strong><\/p>\n<\/td>\n<\/tr>\n

        1.\u00a0 Spring 2009<\/td>\n<\/tr>\n
        \n

        Cleanroom Science<\/p><\/blockquote>\n<\/td>\n

        \n

        9<\/p>\n<\/td>\n

        \n

        6<\/p>\n<\/td>\n

        \n

        15<\/p>\n<\/td>\n

        \n

        Cleanroom Science<\/a><\/p>\n<\/td>\n<\/tr>\n

        \n

        Cellular Communications and Network Design #1<\/p><\/blockquote>\n<\/td>\n

        \n

        17<\/p>\n<\/td>\n

        \n

        10<\/p>\n<\/td>\n

        \n

        27<\/p>\n<\/td>\n

        \n

        Cellular Communications and Network Design<\/a><\/p>\n<\/td>\n<\/tr>\n

        \n

        Cellular Communications and Network Design #2<\/p><\/blockquote>\n<\/td>\n

        \n

        18<\/p>\n<\/td>\n

        \n

        9<\/p>\n<\/td>\n

        \n

        27<\/p>\n<\/td>\n

        \n

        Cellular Communications and Network Design<\/a><\/p>\n<\/td>\n<\/tr>\n

        \n

        Film and Media Production<\/p><\/blockquote>\n<\/td>\n

        \n

        8<\/p>\n<\/td>\n

        \n

        7<\/p>\n<\/td>\n

        \n

        15<\/p>\n<\/td>\n

        \n

        Film and Media Production<\/a><\/p>\n<\/td>\n<\/tr>\n

        2.\u00a0 Summer 2009<\/td>\n<\/tr>\n
        \n

        Wind Energy<\/p><\/blockquote>\n<\/td>\n

        \n

        16<\/p>\n<\/td>\n

        \n

        9<\/p>\n<\/td>\n

        \n

        25<\/p>\n<\/td>\n

        \n

        Wind Energy<\/a><\/p>\n<\/td>\n<\/tr>\n

        \n

        Visual Programming and Gaming with Scratch<\/p><\/blockquote>\n<\/td>\n

        \n

        12<\/p>\n<\/td>\n

        \n

        6<\/p>\n<\/td>\n

        \n

        18<\/p>\n<\/td>\n

        \n

        Visual Programming and Gaming with Scratch<\/a><\/p>\n<\/td>\n<\/tr>\n

        \n

        3-D Virtual Modeling for Emergency Services<\/p><\/blockquote>\n<\/td>\n

        \n

        16<\/p>\n<\/td>\n

        \n

        7<\/p>\n<\/td>\n

        \n

        23<\/p>\n<\/td>\n

        \n

        3-D\u00a0Virtual Modeling for Emergency Services<\/a><\/p>\n<\/td>\n<\/tr>\n

        \n

        Film and Media Post-Production<\/p><\/blockquote>\n<\/td>\n

        \n

        11<\/p>\n<\/td>\n

        \n

        8<\/p>\n<\/td>\n

        \n

        19<\/p>\n<\/td>\n

        \n

        Film and Media Post-Production<\/a><\/p>\n<\/td>\n<\/tr>\n

        3.\u00a0 Fall 2009<\/td>\n<\/tr>\n
        \n

        Wind Energy<\/p><\/blockquote>\n<\/td>\n

        \n

        12<\/p>\n<\/td>\n

        \n

        9<\/p>\n<\/td>\n

        \n

        21<\/p>\n<\/td>\n

        \n

        Wind Energy<\/a><\/p>\n<\/td>\n<\/tr>\n

        \n

        Visual Programming and Gaming with Scratch\u00ae<\/p><\/blockquote>\n<\/td>\n

        \n

        10<\/p>\n<\/td>\n

        \n

        6<\/p>\n<\/td>\n

        \n

        16<\/p>\n<\/td>\n

        \n

        Visual Programming and Gaming with Scratch<\/a><\/p>\n<\/td>\n<\/tr>\n

        [a] See\u00a0Appendix A<\/a>\u00a0for descriptions of sessions.
        \n[b] See\u00a0
        Appendix B<\/a>\u00a0for website URLs.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

         <\/p>\n

        For the first P3 session in spring 2009, students and teachers were assigned to villages by the project staff. Subsequently, villagers indicated their village preference using a rank-ordering procedure. Efforts were made to honor their first or second choice. Initially, attempts were made to separate students and teachers from the same school. After one semester, students indicated that participating with their teachers was \u201cnot a problem\u201d and that they thought it was great that they could \u201chelp their teachers with the technology!\u201d Teachers agreed.<\/p>\n

        Participants and Their Communities<\/p>\n

        To participate in P3, students must have passing grades in Algebra I, membership in the graduating class of 2011, and interest in technology. Teachers are required to be state certified to teach secondary level (grades 7-12) STEM-B courses. Primary recruitment mechanisms include direct presentations to students and teachers, distribution of informational flyers to schools, teachers, and students, and word of mouth.<\/p>\n

        P3 students hail primarily from six school districts in Arizona: Chandler, Gilbert, Higley, Mesa, Payson, and Superior. With the exception of Payson and Superior, all high schools are within a 15-mile radius of the ASU Polytechnic campus, the location for all village work. Payson High School is 88 miles from the Polytechnic campus; Superior High School is approximately 50 miles away.\u00a0 The communities in which the schools are located vary by size, ethnic diversity, and median household income.\u00a0 Demographic data, including population, median income, and racial and ethnic diversity for each of our primary school districts are presented in Table 2. Note that Higley High School is located in the city of Gilbert. Teachers come from 15 different Arizona school districts.<\/p>\n

        Table 2<\/strong>
        \nDemographic Data for P3 Primary School Districts (2009)\u00a0\u00a0\u00a0\u00a0<\/em><\/p>\n\n\n\n\n\n\n\n\n\n
        \u00a0<\/strong><\/td>\n\n

        Chandler\u00a0<\/strong><\/p>\n<\/td>\n

        \n

        Gilbert\/ Higley<\/strong><\/p>\n<\/td>\n

        \n

        Mesa\u00a0<\/strong><\/p>\n<\/td>\n

        \n

        Payson<\/strong><\/p>\n<\/td>\n

        \n

        Superior\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

        Population<\/td>\n\n

        255,230<\/p>\n<\/td>\n

        \n

        217,285<\/p>\n<\/td>\n

        \n

        462,823<\/p>\n<\/td>\n

        \n

        15,547<\/p>\n<\/td>\n

        \n

        3,335<\/p>\n<\/td>\n<\/tr>\n

        Median income (state average: $69,205)<\/td>\n\n

        $69,000<\/p>\n<\/td>\n

        \n

        $83,000<\/p>\n<\/td>\n

        \n

        $55,000<\/p>\n<\/td>\n

        \n

        $44,000<\/p>\n<\/td>\n

        \n

        $38,000<\/p>\n<\/td>\n<\/tr>\n

        Caucasian<\/td>\n\n

        65%<\/p>\n<\/td>\n

        \n

        85%<\/p>\n<\/td>\n

        \n

        76%<\/p>\n<\/td>\n

        \n

        92%<\/p>\n<\/td>\n

        \n

        29%<\/p>\n<\/td>\n<\/tr>\n

        Hispanic<\/td>\n\n

        22%<\/p>\n<\/td>\n

        \n

        15%<\/p>\n<\/td>\n

        \n

        27%<\/p>\n<\/td>\n

        \n

        6%<\/p>\n<\/td>\n

        \n

        70%<\/p>\n<\/td>\n<\/tr>\n

        Other<\/td>\n\n

        13%<\/p>\n<\/td>\n

        \n

        0%<\/p>\n<\/td>\n

        \n

        7%<\/p>\n<\/td>\n

        \n

        2%<\/p>\n<\/td>\n

        \n

        1%<\/p>\n<\/td>\n<\/tr>\n

        Note<\/em>: Ethnic diversity percentages and median household incomes are rounded up.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u00a0<\/strong><\/p>\n

        \n

         <\/p>\n

        Methods<\/p>\n

        Data reported in this study are based on 17 months of information collected from P3 project participants during this 3-year project. The ongoing evaluation plan for P3 includes both quantitative and qualitative measures to assess the degree of increase in students\u2019 interest in and success with the study of science and mathematics in high school, and changes in teachers\u2019 instructional practices and expectations for student accomplishments.<\/p>\n

        A quasi-experimental design was used to analyze student data and included multivariate analyses on several dependent measures over time, including high school GPA, number of STEM-B courses completed, GPA in STEM-B courses, and number of advanced courses completed (i.e., honors, advanced placement, or dual enrollment) . Students from the class of 2011 who participated in the P3 project compose the intervention group. Students who applied, were accepted into P3, and chose not to participate, were used as statistical controls. To reduce error associated with self-selection and other threats to validity, each participant was matched at baseline to a control subject with similar characteristics with respect to indicators known to influence student performance. These indicators, obtained during the first semester of the sophomore year, included (a) school district, (b) gender, (c) self-identified ethnicity, and (d) performance in Algebra I. If more than one student in the pool of nonparticipants was eligible to serve as a control, the match was conducted randomly, a situation which occurred in only two cases.<\/p>\n

        In addition to assessing outcome measures related to the goal, there was interest in whether participants had gained village-specific content knowledge. For this question, intervention students served as their own controls on village leader-designed pre- and postassessments. Analyses were conducted using a multivariate repeated-measures design. In addition, qualitative data from interviews and informal satisfaction surveys were conducted to gather information regarding students\u2019 perceptions of the village experiences and their impact on STEM-B coursework and plans for future postsecondary education.<\/p>\n

        Sample<\/p>\n

        Cohorts are defined by the semester or summer in which they started P3. In the first semester of P3, student applications exceeded the available slots, so applicants were randomly selected for Cohort 1. In the subsequent summer session, students not selected originally for Cohort 1 were invited to participate as replacements for the dropouts. Thereafter, recruitment continued to achieve a minimum cohort size of 40. Cohorts are defined by the semester or summer in which they started P3. Attempts were made to match all intervention students with controls (those that applied but chose not to attend) within their cohort to reduce variability due to length of participation; 84.8% of matches were made within cohort. Attempts were made to create matches within schools and were successful for all but two students. In these two cases, matchers were made within school district.<\/p>\n

        Table 3 shows the participation and retention of intervention students who were recruited to and who completed P3 villages over the first 2 years of the project. As can be seen in the table, of the 52 students who completed the pilot (first semester, Cohort I), 50% were still participating at the end of Year 2. For all cohorts combined, over 17 months of activities, the retention rate was 45.2%.\u00a0 Factors affecting retention will be considered in the conclusion section.<\/p>\n

        Table 3<\/strong>
        \nParticipation and Retention Rates for Years 1 and 2<\/em><\/p>\n

        \u00a0<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
        \u00a0<\/strong><\/td>\n\n

        Year 1
        \nSpring 2009<\/strong><\/p>\n<\/td>\n

        \n

        Year 1
        \nSummer 2009<\/strong><\/p>\n<\/td>\n

        \n

        Year 2
        \nFall
        \n2009<\/strong><\/p>\n<\/td>\n

        \n

        Year 2
        \nSpring 2010<\/strong><\/p>\n<\/td>\n

        \n

        Retention<\/strong><\/p>\n<\/td>\n<\/tr>\n

        \u00a0<\/em><\/strong><\/td>\n\n

        E<\/p>\n<\/td>\n

        \n

        C<\/p>\n<\/td>\n

        \n

        E<\/p>\n<\/td>\n

        \n

        C<\/p>\n<\/td>\n

        \n

        E<\/p>\n<\/td>\n

        \n

        C<\/p>\n<\/td>\n

        \n

        E<\/p>\n<\/td>\n

        \n

        C<\/p>\n<\/td>\n

        \n

        \u00a0<\/em><\/p>\n<\/td>\n<\/tr>\n

        Cohort I<\/td>\n\n

        73<\/p>\n<\/td>\n

        \n

        52<\/p>\n<\/td>\n

        \n

        33<\/p>\n<\/td>\n

        \n

        29<\/p>\n<\/td>\n

        \n

        36*<\/p>\n<\/td>\n

        \n

        26<\/p>\n<\/td>\n

        \n

        32<\/p>\n<\/td>\n

        \n

        26<\/p>\n<\/td>\n

        \n

        50.0%<\/p>\n<\/td>\n<\/tr>\n

        Cohort II<\/td>\n\n

        \n<\/td>\n

        \n

        \n<\/td>\n

        \n

        24<\/p>\n<\/td>\n

        \n

        23<\/p>\n<\/td>\n

        \n

        15<\/p>\n<\/td>\n

        \n

        7<\/p>\n<\/td>\n

        \n

        10<\/p>\n<\/td>\n

        \n

        6<\/p>\n<\/td>\n

        \n

        26.1%<\/p>\n<\/td>\n<\/tr>\n

        Cohort III<\/td>\n\n

        \n<\/td>\n

        \n

        \n<\/td>\n

        \n

        \n<\/td>\n

        \n

        \n<\/td>\n

        \n

        13<\/p>\n<\/td>\n

        \n

        13<\/p>\n<\/td>\n

        \n

        7<\/p>\n<\/td>\n

        \n

        5<\/p>\n<\/td>\n

        \n

        38.5%<\/p>\n<\/td>\n<\/tr>\n

        Cohort IV<\/td>\n\n

        \n<\/td>\n

        \n

        \n<\/td>\n

        \n

        \n<\/td>\n

        \n

        \n<\/td>\n

        \n

        \n<\/td>\n

        \n

        \n<\/td>\n

        \n

        5<\/p>\n<\/td>\n

        \n

        5<\/p>\n<\/td>\n

        \n

        100.0%<\/p>\n<\/td>\n<\/tr>\n

        \n

        \u00a0<\/em><\/p>\n<\/td>\n

        \n

        73<\/p>\n<\/td>\n

        \n

        52<\/p>\n<\/td>\n

        \n

        57<\/p>\n<\/td>\n

        \n

        52<\/p>\n<\/td>\n

        \n

        64<\/p>\n<\/td>\n

        \n

        46<\/p>\n<\/td>\n

        \n

        54<\/p>\n<\/td>\n

        \n

        42<\/p>\n<\/td>\n

        \n

        45.2%<\/p>\n<\/td>\n<\/tr>\n

        Note<\/em>: Includes 5 students who participated in spring 2009, did not participate in summer 2009, and returned for fall 2009 P3<\/sup>\u00a0sessions.\u00a0 E = Enrolled, C = Completed.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u00a0<\/strong><\/p>\n

        \n

         <\/p>\n

        Although 42 students completed the fourth session in spring 2010, 9 of those students moved, and their year-end outcome data were unavailable. Thus, data analyses were conducted only for the remaining 33 students. No significant differences were noted for those who dropped out versus those who remained in the sample dataset (p<\/em> > 0.05) with regard to grades attained in Algebra I or Biology I as covariates of concern.\u00a0 Table 4 shows participant type by gender and ethnicity.<\/p>\n

        Table 4<\/strong>
        \nParticipant Type by Gender and Ethnicity<\/em><\/p>\n

         <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n
        \n

        Characteristics<\/strong><\/p>\n<\/td>\n

        \n

        Completers
        \n<\/em>(n<\/em>\u00a0= 42)<\/strong><\/p>\n<\/td>\n

        \n

        Intervention
        \n(N<\/em>i = 33)<\/strong><\/p>\n<\/td>\n

        \n

        Control
        \n(N<\/em>c = 33)<\/strong><\/p>\n<\/td>\n<\/tr>\n

        Female<\/td>\n\n

        13
        \n(30.9%)<\/p>\n<\/td>\n

        \n

        10
        \n(30.3%)<\/p>\n<\/td>\n

        \n

        13
        \n(39.4%)<\/p>\n<\/td>\n<\/tr>\n

        Male<\/td>\n\n

        29
        \n(69.1%)<\/p>\n<\/td>\n

        \n

        23
        \n(69.7%)<\/p>\n<\/td>\n

        \n

        20
        \n(60.6%)<\/p>\n<\/td>\n<\/tr>\n

        African American<\/td>\n\n

        2
        \n(4.7%)<\/p>\n<\/td>\n

        \n

        2
        \n(6.1%)<\/p>\n<\/td>\n

        \n

        0
        \n(0.0%)<\/p>\n<\/td>\n<\/tr>\n

        Asian\/ Pacific Islander<\/td>\n\n

        5
        \n(11.9%)<\/p>\n<\/td>\n

        \n

        4
        \n(12.1%)<\/p>\n<\/td>\n

        \n

        0
        \n(0.0%)<\/p>\n<\/td>\n<\/tr>\n

        Caucasian<\/td>\n\n

        31
        \n(73.8%)<\/p>\n<\/td>\n

        \n

        27
        \n(81.8%)<\/p>\n<\/td>\n

        \n

        32
        \n(97.0%)<\/p>\n<\/td>\n<\/tr>\n

        Native American<\/td>\n\n

        0
        \n(0.0%)<\/p>\n<\/td>\n

        \n

        0
        \n(0.0%)<\/p>\n<\/td>\n

        \n

        0
        \n(0.0%)<\/p>\n<\/td>\n<\/tr>\n

        Other<\/td>\n\n

        0
        \n(0.0%)<\/p>\n<\/td>\n

        \n

        0
        \n(0.0%)<\/p>\n<\/td>\n

        \n

        1
        \n(3.0%)<\/p>\n<\/td>\n<\/tr>\n

        Hispanic<\/td>\n\n

        9
        \n(21.4%)<\/p>\n<\/td>\n

        \n

        7
        \n(21.2%)<\/p>\n<\/td>\n

        \n

        7
        \n(21.2%)<\/p>\n<\/td>\n<\/tr>\n

        Non-Hispanic<\/td>\n\n

        29
        \n(78.6%)<\/p>\n<\/td>\n

        \n

        26
        \n(78.8%)<\/p>\n<\/td>\n

        \n

        26
        \n(78.8%)<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \n

        Data Collection Methods<\/p>\n

        All P3 student applicants to the program must gain parental approval to participate; complete the student application, including demographic descriptors and a short survey about their use of and interest in technologies; and agree to allow project staff to contact their school to access their transcripts and cumulative records. Application forms can be found on the project website (http:\/\/primevillages.asu.edu<\/a>). Upon completion of the application process, students were notified of their placement in one of the four concurrent villages being conducted.<\/p>\n

        Additional data, both quantitative and qualitative, have been gathered throughout the P3 experience. The types of data collected include the following:<\/p>\n

        Pre and post Village-Specific Knowledge Assessments focusing on village content were created by the village leaders and contained from 7 to 25 questions. They assessed basic content knowledge developed through village activities.\u00a0 All participants in P3 completed pre- and postassessments in their villages. Villagers were given the same assessment at the beginning and at the completion of their village exploration to measure knowledge gained during village participation and, thus, function as their own controls in the repeated measures design. The control group did not complete the village assessments.<\/p>\n

        Updated Academic Performance Measures were collected annually from the school districts by project staff for both intervention students and their matched controls following the spring semester. The measures obtained from transcript review included current GPA, GPA specific to STEM-B courses, and results from state standardized tests (Arizona\u2019s Instrument to Measure Standards, or AIMS) in reading, mathematics, writing, and science. Grades are based on a 5-point scale with an A grade in an Advanced Placement or honors classes awarded 5.0 points, a B awarded 4.0 points and so on. Grades in regular courses are based on the conventional system of A awarded 4.0 points, B awarded 3.0 points, and so on.\u00a0 Plans for collection of SAT and ACT college admission exam scores, along with final class rank will be conducted at the conclusion of students\u2019 senior year (spring 2011) and the conclusion of\u00a0 P3.<\/p>\n

        Progress in completing advanced coursework data were collected annually for intervention students and their matched controls and included the number and types of STEM-B honors, advanced placement, and dual enrollment college courses students completed.<\/p>\n

        The Student Plans for Postsecondary Options Survey requests information about students\u2019 plans for after high school graduation, their intended college major if they plan to go to college, the job they want to do when entering the work force, and talents they believe will make them successful in their chosen careers.<\/p>\n

        One-to-one interviews were conducted in summer 2010 by the P3 evaluator with 8 students randomly selected from among students who began P3 in spring 2009 (Cohort I). Eight questions were posed, including the following:<\/p>\n