Evaluation Plan and Instruments   To manage and analyze the data, this evaluation utilizes a mixed methods approach. Quantitative data will be collected using surveys. In order to increase confidence in the quantitative data collected, and to develop a rich understanding of why quantitatively verified outcomes emerge, a variety of qualitative triangulation collection strategies will also be used: formal and informal interviews of teachers, administrators, tech coordinators, parents, and students; classroom observations; and document analysis. "Signal Strength" Surveys To quickly gather information from teachers, school leaders, and students on the strengths and challenges of your learning with laptop initiative, the Signal Strength Surveys can be used.  Survey items are  based on the 2 critical components and 4 necessary supporting components outlined in the McMEL Model for Successful 1-to-1 Learning with Laptop Initiatives.  The surveys use the metaphor of the wireless signal strenght icon, to have participants rate various aspects of their program and then answer a couple brief follow up questions for each item. Student Signal Strength Survey Teacher Signal Strength Survey School Leader (Princ., Tech Coord., Tech Integrator, etc.) Signal Strength Survey Observations Classroom observations will be focused with an observation guide, and will provide investigators a glimpse into teachers' classrooms. The analysis of classroom observations will help contextualize and validate what is learned from the surveys and interviews. We will use the constant comparative method (Glaser & Strauss, 1967; Strauss, 1987) to analyze the data from the various sources, with special attention given to the impact of the pilot on the teachers' teaching and students' learning. Classroom Observation Form Directions for using the Classroom Observation Form A simple rubric of 3 criteria for teaching with technology   Interviews Consistent with the use of qualitative methods, interviews are intended to provide an inside view into the participants' beliefs (Erikson, 1986, Glesne & Peshkin, 1992; and Bogdan & Biklen, 1998), in this case, about teaching and learning with technology. Teachers and students will be both randomly and theoretically sampled to be interviewed. The interview guide approach (Patten, 1990; Glesne & Peshkin, 1992; and Bogdan & p, 1998) will be used in conducting interviews. Teacher Sample Interview Questions Technology Sample Team Interview Questions Administrator Sample Interview Questions   Document Analysis Additionally, the local schools and project staff will collect data and documents that will help tell the story of their initiative. These include, but are not limited to the following: student attendance data; behavior referral data; minutes from planning meetings; agendas and evaluations from professional development opportunities; maintenance, breakage, and loss records; student grades and achievement test scores. Online Surveys To gather more in depth information from participants, longer questionnaire sets are available online.  Questions are asked based on the 2 critical components and 4 necessary supporting components outlined in the McMEL Model for Successful 1-to-1 Learning with Laptop Initiatives. Teachers will be surveyed to gather data on teachers' stages of technology adoption; technology proficiency; attitudes toward computers; support needs; reactions to professional development, leadership, and tech support; teacher efficacy and self-efficacy; and pedagogical strategies. Teacher Questionnaire Sets Surveys will be used with students to gather data on their technology proficiency; attitudes toward computers and school; learning preferences; efficacy and self-efficacy; and level of engagement in their lessons and schoolwork. Student Questionnaire Sets Some questionnaires in each survey set have been created by MLLS for this purpose. Others have been developed and validated over the past ten years by researchers associated with the Institute for the Integration of Technology into Teaching and Learning (Knezek, Christensen, Miyashita, & Ropp, 2000). Others come from other research and evaluation projects. Data mining techniques (Hastie, Tibshirani, & Friedman, 2001) such as factor analysis, discriminant function analysis, and hierarchical cluster analysis will be used to identify attributes associated with greater academic success at the schools being evaluated. Further, formative data will be shared with the leadership team to help inform initiative needs. Scaling methods (Dunn-Rankin, Knezek, Wallace, & Zhang, 2003) will be employed to reduce a wide assortment of school, home, and student-specific attributes to a manageable set, which can be studied in greater detail in a hypothesis-testing context.

The 1-to-1 Advocate 1-to-1 Evaluation MLLS Home MLTI MLLS Research Briefs         The Maine Learning with Laptop Studies is a project of the Maine Center for Meaningful Engaged Learning in collaboration with The Institute for the Integration of Technology Into Teaching and Learning   Maine Center for Meaningful Engaged Learning University of Maine at Farmington 252 Main St. Farmington, ME 04938 http://www.mcmel.org Mike Muir, Director mmuir@maine.edu 207-778-7179 Inservice Available   The Institute for the Integration of Technology Into Teaching and Learning University of North Texas Matthews Hall Rm. 316 1300 Highland Ave. Denton, TX 76203 http://www.iittl.unt.edu/ Gerald Knezek, Director gknezek@gmail.com 940-565-2057 Rhonda Christensen, Associate Director rhonda.christensen@gmail.com Created by Mike Muir Last updated: May 3, 2006