Mobilize: Mobilizing for Innovative Computer Science Teaching and Learning
What is Mobilize?
Mobilizing for Innovative Computer Science Teaching and Learning is a targeted National Science Foundation Math Science Partnership funded for 2010-2015. The core partners are: UCLA Graduate School of Education and Information Studies (Center X), the Los Angeles Unified School District (LAUSD), the UCLA Center for Embedded Networked Sensing (CENS), and the Computer Science Teachers Association (CSTA).
Mobilize builds upon teenagers’ enchantment, fascination, and engagement with mobile technology. At the heart of Mobilize is the CENS Participatory Sensing system – an innovative method of data collection and analysis in which individuals use mobile phones to systematically collect and interpret data about issues important to them and their communities.
Mobilize will create Participatory Sensing hands-on, inquiry-based curricular units and teacher professional development for computer science, mathematics, and science high school classes. Mobilize projects bring together STEM and computational thinking with students’ sense of civic involvement in their own communities.
Mobilize is especially committed to assuring access for innovative instruction, especially in schools with high numbers of African Americans and Latino/a students. Mobilize projects will first be introduced in the Los Angeles Unified School District schools. In LAUSD, interdisciplinary teams of Exploring Computer Science, mathematics, life and physical science, as well as social science students and teachers will work on Mobilize projects. As computer science is now an integral part of innovation across all fields, a goal of Mobilize is to strengthen computer science instruction throughout our educational system.
Mobilize sits at the crux of several critical issues facing our country: How can we foster innovation and inventiveness, improving STEM education for students and teachers, and how do we guarantee quality and rigorous education for all students? What we learn about increasing opportunities for inquiry-based, rigorous learning of computer science and about innovative teacher professional development, especially in large urban school district, will be critically important for the entire country across multiple disciplines, communities, and institutions. Mobilize addresses the centrality of information technologies in our students’ lives, for whom a critical view of computing will be an increasingly important component as they enter the work force and engage as members and leaders of their communities.
Design a High School Curriculum
Data are everywhere: students will learn about spatial analysis, temporal analysis, text interpretation, through their own data collection and analysis project. Mobilize curriculum units will map onto California state standards for mathematics the science, and other disciplines on campus
Develop Participatory Sensing Software
Flexible, open, tools for authoring, implementing, and analyzing participatory sensing data campaigns using mobile phones and web services will be created.
Build Multidisciplinary STEM teacher teams and Professional Development
Inquiry based investigations on topics from mathematics and the sciences will be the focus of Professional Development for 300 teachers across LAUSD with the goal of increasing student learning.
Enhance Student Learning and Engagement
Through Mobilize units, students will engage with and learn about:[starlist]
- The nature of data – its representation, formats, and protocols for sharing.
- Computational thinking (the problem-solving, logical thinking at the heart of CS).
- Algorithms (the rules governing data collection and strategies for analysis).
- Statistics (visualization, basic inference, etc.).
- Intersection of mathematics, science and other disciplines with civic engagement.
- Problem-solving skills.
- Innovation, inventiveness, and interdisciplinary collaboration. [/starlist]
Support the following LAUSD Initiatives[starlist]
- Response to instruction and intervention.
- Personalization of the learning experience for all students based on student needs and talents.
- College preparedness and career readiness.
- Mathematics and science student achievement [/starlist]Campaign for New Educational Policy
In addition to the instructional and professional development focus of Mobilize, educational policy change is also addressed and includes:[starlist]
- Collaborating with other national and state organizations to change state and national educational policy with the goal of improving the quality of high school computer science instruction, assuring equal access and quality instruction in underserved schools with high numbers of African American and Latino/a students.
- Changing the academic status of computer science from vocational to academic and establishing a CS teaching credential pathway and methods course.
Build National Dissemination and Impact
Mobilize curriculum units and our model of professional development for teachers will be made available for national distribution through other school districts and informal education networks, including school clubs and community organizations.
Conduct Research, Evaluation, and Assessment of Student Learning
Mobilize at its core is focused on student learning, and for this teachers are key. Research will investigate students’ identity as doers of computer science, and teachers’ participation, engagement, and adoption of inquiry-based teaching methods. An extensive evaluation program will assess Mobilize impact on student learning, and design new and needed assessment tools and methods.
As “Mathematical Thinking” draws from fundamental ideas in Mathematics (as a discipline), and “Statistical Thinking” relates to the core of Statistics (again, as a discipline), so “Computational Thinking” involves basic notions of Computer Science. Computational Thinking teaches the use of abstraction and decomposition when solving complex problems; it presents a framework for understanding algorithms; and it describes essential concepts in dealing with data and code and in expressing the limits of modern computing machinery. That said, Computational Thinking is a relatively recent proposition; we use the term to refer to learning related to computer science that transcends the purely functional or vocational (as is the case with even the more mature disciplinary “thinking” movements), and provide students with important critical thinking skills. This approach allows us to present Computer Science as a living discipline, built around a set of core concepts, with technological byproducts that touch both our personal and professional lives.
Computer Science, and perhaps more broadly information technologies, have reshaped nearly every disciplinary practice, and we therefore believe that Computational Thinking, as a pedagogical device, has a role to play in every STEM program. Students in math and science, for example, need more than simple programming exercises. They should be able to apply basic strategies in problem solving, understand the character of a solution or algorithm, and have a sense of the ways in which computerization and digitization have changed how research is conducted.
Therefore, we use “Computational Thinking” as a major framing device to present core ideas in Computer Science. In this way, we move beyond the traditional functional approach and introduce broader themes. With Participatory Sensing we create a context for student-directed learning. Participatory Sensing is a bridge for developing hands-on, inquiry-based projects with impacts in Math and Science, as well as Computer Science.
Given the above background we can summarize the core intent of this project as being: to develop methods for educating and engaging students in Computational Thinking. We will do so by developing and applying our proposed Participatory Sensing methods, tools and systems infrastructure. Such a platform for embodied interaction and visualization is, at best, minimally available in current instructional tools. In this context, we will develop and support standards-based units for both Computer Science and traditional STEM Science and Mathematics disciplines.
Educational Goals and Outcomes related to Students
It is our hypothesis that by engaging in the Participatory Sensing units described in this proposal, students will develop beliefs about themselves as STEM doers and will develop attitudes towards STEM disciplines that contribute to their learning, achievement and further engagement with these disciplines.
Student Attitudes and Beliefs
At its heart, Participatory Sensing is about hands-on, student-centered inquiry and participation. The data collection and interpretation: the type of information collected, how it is organized, and how it is ultimately used is determined by the participants (the students) themselves. In addition to the large body of research that indicates that participation and relevancy are critical motivators for learning (cf. Shernoff et al, 2003; Pintrich, 2003), this project builds on the experiences of CENS faculty and staff who have participated in the center’s high school programs. Summer@CENS, for example, has completed its fourth year and demonstrates the effectiveness of the learning approach proposed in Mobilize. While this summer program attracts students who are already interested in math and science, creating the opportunity to be immersed in a university atmosphere, the follow-up evaluation (2009 Summer@ CENS evaluation report) points to the impact of the Participatory Sensing context on learning. Mobilize will build on this model, adapting and expanding its lessons to LAUSD students.
The evaluation of the both 2009 and 2010 Summer@CENS High School programs attests to the potential of Participatory Sensing contexts to influence student beliefs and attitudes regarding STEM disciplines. The evaluation found that:[starlist]
- The hands-on research is one of the most satisfying aspect of the program for students;
- Belief in the relevance of engineering to society was enhanced by participating in these projects;
- By the end of the program, most of the participants plan to pursue grad school in science or engineering;
- All of the students agreed with the statement “I feel more strongly now that my career lies in science and engineering fields” and
- By the end of the program, 6 of 7 female students indicated they would likely pursue a PhD in one of the core STEM fields included in this proposal (computer science, statistics, mathematics, or one of the targeted domain sciences). [/starlist]
The Participatory Sensing units described in our proposal incorporate a variety of skills and knowledge that characterizes a “doer” of math and science. These included: problem formulation, computational strategies and algorithmic design, computer implementation, software sharing, teamwork, and basic research, analytical and technical skills. We believe this portrayal of a scientist and mathematician as someone who is engaged in multiple aspects of doing math and science is important to attract and support diverse students. This is reflected in a female student’s description of how participation in the Participatory Sensing project impacted her sense of being a doer of science:
You’re involved in every part of the scientific process, it’s not like you’re just doing data collection or you’re just doing analysis or like we got to go through every step from like planning and making a proposal to writing procedures and stuff and then going out: it really makes you feel like you’re an actual scientist an actual researcher.
The evaluation team also found that “For the women, the enjoyment of working with computers increased greatly by the end of the program. With only half of the women reporting that they “Agreed Some what” or “Strongly Agreed” with the statement “I enjoy using computers”, at the beginning of the program, by the end of the program this number was 100%. Although the men rated themselves very high at 78% for this same question at the beginning of the program, they too increased their enjoyment of using computers to 100% by the end of the program.”
Just as much of the prior work at CENS with high school students has focused on addressing disparities in gender as evidenced by the evaluations above, the Education partners (Center X and LAUSD) bring a long history of research, practice and focus on addressing disparities in access to learning based on race and socioeconomics. Mobilize is committed to implementing participatory sensing learning in Los Angeles schools with high numbers of African American and Latino/as students.
Measurement of Outcomes
The schools participating in our Mobilize partnership have diverse student populations, providing a unique opportunity to study the “trajectories” of student’s identities by examining the qualitative impact our Participatory Sensing units have on their views of Computer Science and other STEM disciplines (Boaler & Greeno, 2000; Nasir, 2002; Cobb et al., 2009). In addition, we will measure student outcomes with quantitative indicators such as course enrollment and performance on class assignments and standardized tests.
Student Achievement in Math & Science Content
The core lessons in these units will be framed around principles of Computational Thinking. Specifically, through Participatory Sensing campaigns, students will identify phenomena to study, consider “designs” for how data are to be collected, and rally the computational resources to execute their plans. Along the way they will grapple with questions related to the nature of data (its representation, formats and protocols for sharing) and of algorithms (rules governing data collection, strategies for analysis). Students will learn about classical themes in Computer Science, from databases to networking, as well as lessons in Statistics, from visualization to basic inference. These thinking skills map onto California math and science achievement standards and goals. The study of mathematics and science in California is structured around state adopted standards. The mathematics standards for grades 6 through high school include statistics and mathematical reasoning, “ Data collection, representation, analysis and inference, decision-making and problem solving.” California state standards in science also include a cluster on Investigation and Experimentation, “Select and use appropriate tools and technology to perform tests, collect data, analyze relationships and display data (ESIE1.a) and “Evaluate the accuracy and reproducibility of data” (8SIE9.b). Through Participatory Sensing and our framework of Computational Thinking, students will develop skills in each of these areas, ultimately becoming, we believe, better mathematical and scientific “thinkers.” For all these reasons, we see a direct link between our Mobilize Participatory Sensing units and broad STEM achievement.
As we have mentioned above, Computer Science has changed research practices in the Sciences. Through our framework of Computational Thinking, we will be able to present the broad concepts that students need throughout STEM education. Participatory Sensing is our bridge to other curricula, providing engaging lessons in Biology, Mathematics, and Physical Sciences.
As Science covers a very broad area, the following two examples help to illustrate this point more concretely. There are many other diverse examples we could have used and the point of MOBILIZE is to make it practical to be able to use participatory sensing to engage students in this breadth of Science learning.[starlist]
- Consider the teaching of ecosystem dynamics in a biological science curriculum. Participatory Sensing can be used to support both hands on experimental design, data collection, and analysis around the distribution of species within a local ecosystem, subject to specific environmental conditions and variations. Through the use of these tools to collect systematic, authentic, and verifiable data in their “own backyards” students can translate and internalize book and laboratory based investigations.
- Similarly, consider the teaching of diffusion in a physical science curriculum. The students can collect their own travel patterns using participatory sensing based location-activity-traces, and analyze their exposure to air particulates as a way to explore the data and models of air pollution sources and distribution in an urban environment. [/starlist]
In both of these examples students not only experience the topical focus (ecosystems, diffusion…), they also engage in the role that modeling, data, and Computational Thinking generally play in these sciences.
Mathematics and Statistics
The National Council of Teachers of Mathematics Curriculum and Evaluation Standards for School Mathematics recommend that all students apply the mathematical modeling process in all courses at every level. In a 2004 Mathematics Association of America (MAA) report the recommendation was made to “replace traditional algebra courses with courses stressing problem solving, mathematical modeling, descriptive statistics, and applications in the appropriate technical areas” and “emphasize intricate algebraic manipulations.” The MAA suggests students completing courses focused on math modeling will develop stronger mathematical abilities, have computational skills equal or better than those of students who complete traditional courses, and will be more successful in subsequent courses.
Modeling is about the mathematics of change with classroom applications from algebra to calculus. Topics of study include iteration, recursive sequences, dynamical systems, data analysis, fractals, and chaos. A mathematical model usually describes a system by a set of variables and a set of equations that establish relationships between the variables with an emphasis on real-world applications. The functions needed for math modeling are linear, quadratic, polynomial, logarithmic, exponential, and trigonometric. Statistical models are built from many of the same ingredients, but begin with data and incorporate some notion of uncertainty.
Building a mathematical or a statistical model is usually a multi-stage process: you write down the equations, use them to predict what will happen, see if your predictions agree with experiments, modify your equations if necessary, make new predictions, and so on. These models may use one of more of these kinds of mathematics: linear algebra (systems of simultaneous equations, as solved in high school algebra), calculus (which deals with predicting a function’s values from knowing the rate at which the function is changing), and probability.
Through the Participatory Sensing units of Mobilize, students will learn about mathematics and statistics, in part, through modeling. Basic visualizations will guide the process, and in advanced portions of the curriculum, notions of inference will also be introduced.
Measurement of Outcomes
Student learning outcomes will be measured and compared to control groups on the following indicators:
1. California Standards Test strands on content standards listed above.
2. School and district assessments on content standards listed above.
We therefore represent the connection between our student learning goals, activities, and outcomes in the following way:
Goal 1: Student as Computational Thinker: To create challenging, cutting edge projects, tools and materials for teaching computational thinking in high school CS and in standards-based mathematics and science classes, with the aim of increasing student achievement in math, computer science, and the other sciences and student interest in STEM subjects.
|Participatory Sensing Units||Improved student Computer Science and Computational Thinking knowledge||1. Statistically significant increase pre to post CENS participation on CENS embedded assessment of computer knowledge and computational thinking|
|Participatory Sensing Units||Improved students STEM attitudes/beliefs, including STEM self-efficacy, careers identity||1. Statistically significant increase in number of STEM & CS courses taken by CENS participant students, relative to controls, as tracked by district data.
2. Statistically significant increase pre to post CENS participation on relevant student attitudes survey subscales.
|Participatory Sensing Units||Increased student achievement in mathematics and the sciences||1. CENS participant students’ statistically significant improvement on CST scores in relevant math and science strands relative to comparison students.
2. CENS participant students score significantly higher on CAHSEE relative to comparison students
The high schools participating in MOBILIZE are all large urban high schools with nearly all students of color. What we learn from this project will be about bringing Computational Thinking concepts to a diverse population, from informal education to a large urban school district curriculum.
Example topics for PS projects for high school students in an urban environment.
The number of authentic topics for Participatory Sensing projects is probably as large as the number of potential participants! We list here several illustrative examples, some of which are more environmentally focused and some more personal in nature. In all cases the computational thinking needed to frame the problem, design the experiment, develop the software, and analyze the results provides very rich learning opportunities in the context of a math, science, or computer science course. Neither this list nor the supporting categories are meant to be in any way exhaustive or complete.[starlist]
- Transportation: Students map their travel patterns during the course of days/weeks, analyze them for options reducing their carbon footprint, select a goal and, as a class, try to measurably reduce their transportation impact. Data: Smart phone application captures activity-decorated location and time series.
- Recycling: Students document the availability and actual usage of recycling bins around campus or in their neighborhoods, analyze what options exist to increase efficacy, and estimate possible savings if the change were implemented. Data: Focused, systematic geocoded and tagged image capture.
- Water: Students conduct systematic inventories of public sprinkler systems and water fountains and measure their conformance with water conservation regulations or best practices. Data: Focused, systematic geocoded and tagged image capture.
- Safety: Students document the safety and comfort of transportation options, including ratings of busstops, particular buslines at particular times of day, walking paths (safe paths to school), etc. Data: Focused, systematic geocoded and tagged image capture.
- Asthma/Allergy: Students, document the tree species and distribution throughout their neighborhoods, estimate where Asthma/Allergy triggers might occur in spring and calculate allergy-optimized running path maps for the neighborhood. Data: Focused, systematic geocoded and tagged image capture.
- Neighborhood: Stress/Chill Maps: Students document their campuses and neighborhoods by creating StressChill Maps in which they note particular context (a time and place and situation) that causes them stress or pleasure. Students analyze their data in small groups to explore similarities and differences. Data: Focused, systematic geocoded and tagged image capture.
- Daily habits: Students select a personal behavior that they want to better manage, and use prompted queries on the phone to report on their behavior several times a day (whether in regards to sleeping, eating, angry outbursts, biting nails, or procrastinating). They then use statistical techniques to look for correlations between these behaviors and other factors in their lives. They might also attempt a behavior change and see if they are able to affect measurable change. Data: A time and geo-coded series of self-reported observations. [/starlist]
Educational Goals and Outcomes Related to Teachers
Annual cohorts of 80 LAUSD high school math, science, and computer science teachers teachers will participate in hands-on inquiry sessions as they take part in a sensing project and learn to use the PS methods, tools, and materials to deepen their knowledge of CS concepts and to support student CS, math, and science learning. Teachers will develop plans for integrating what they learn into their curriculum. Follow-up sessions will also be held, focusing on using PS lessons and assessment strategies in the classroom (teaching for inquiry and analyzing and assessing student work for conceptual understanding), continuing teacher content knowledge learning, and developing capacity to support other teachers at the school sites. An in-classroom coaching program for teachers will also be able to lend support to the interdisciplinary teams.
Many of our country’s political, scientific, business, and education leaders have prioritized fostering innovation and inventiveness and improving STEM education while guaranteeing equal access for all students as among our country’s most pressing challenges. Mobilize sits at the crux of these issues. What we will learn about increasing opportunities for inquiry-based, rigorous learning of computer science and about innovative professional development in computer science, especially in large urban school districts, will be useful for the entire country across multiple disciplines and institutions. Through Mobilize we will have the opportunity to develop learning assessment measures for computer science, mathematics, science, problem solving and inquiry. Through the window of CS learning and inquiry, Mobilize will provide a model of what must be done on the school, district, state, and national levels to improve access to engaging learning and achievement for all students.
For more information, contact:
Center-X: Jane Margolis (firstname.lastname@example.org), Jody Priselac (email@example.com)
CENS: Deborah Estrin (firstname.lastname@example.org ), Mark Hansen (email@example.com)
LAUSD: Todd Ullah (firstname.lastname@example.org)
CSTA: Chris Stephenson (email@example.com)