Between 2010 and 2014 the project, Studying Topography, Orographic Rainfall, and Ecosystems with Geospatial Information Technology (STORE) explored the strategies that stimulate teacher commitment to the project’s driving innovation: having students use geospatial information technology (GIT) to find out about weather, climate, and ecosystems. The GIT future was a mixture of freely available place-based geospatial data sets and visualization tools. The goal was to structure the innovation and accompanying teacher professional development strategy in order that participating teachers would find it appealing, plan for and enact effective instruction with it, achieve optimal impacts on student learning and engagement, and stick with it enough to hold out two implementations.
A professional development strategy for bringing about these ends evolved iteratively. The article describes how STORE addressed the challenges of getting teachers to stick with the innovation and to use it skillfully. It culminated within the positing of a replacement model—Conceptualization, Iteration, Adoption, and Adaptation (CIAA)—for developing scientific data-centered instructional resources and accompanying teacher professional development.
CIAA emerged because the pathway to high teacher commitment to the project as codevelopers and users. Applying principles guiding the event of grounded theory (Glaser & Strauss, 1967), the project team developed the model as a product of their analyses of the teacher attitudes and behaviors, which they gathered in meetings and interviews with the teachers, observations of their classes, and studies of the curricula they implemented in those classes.
Sustaining teacher commitment to a curricular innovation is problematic, and rates of attrition from innovative practices are high (Kubitskey, Johnson, Mawyer, Fishman, & Edelson, 2012). Reasons include lack of your time to find out and pursue the innovation, lack of administrator support, lack of incentives, and absence of knowledgeable learning community (DuFour, 2004; Dunne, Nave, & Lewis, 2000; Louis & Marks, 1998; Strahan, 2003). Another factor could also be mismatch between the expectations of the project for teacher involvement and therefore the extent to which the actual participating teachers can meet those expectations (Rogers, 1962; Schoenfeld, 2011).
Commitment to innovation doesn’t necessarily cause enactment of top quality implementation (Darling-Hammond et. al., 2009). Reasons cited within the literature include lack of teacher skill in enacting high-quality instruction, especially when the instruction involves student-centered discussion, argumentation, and deep reasoning.
These challenges may arise even when the teacher may be a good planner and developer of high-quality lesson plans and assessments (Grossman, Hammerness, & McDonald, 2009). Teachers may lack sufficient content knowledge or background within the technologies needed for successful implementation. within the science classroom especially , these inadequacies could also be manifest in overly didactic and superficial treatment of content and, when there’s hands-on technology involved, inordinate attention to procedure at the expense of scientific analysis and communication (Penuel et al., 2006). Hence, two sorts of barriers confront teacher success in implementing innovations: barriers to their acquiring the talents they have for successful planning and implementation and barriers to their persistence. this text describes how the shop project addressed these barriers and what were the results. The results are a mixture of attitudes expressed on the teacher survey, the record of products that the teachers developed, and therefore the diversity of implementations that occurred.
The STORE project team compiled geospatial data sets from publicly available scientific portals and made them useable in ArcGIS Explorer Desktop and Google Earth. These GIT applications are freely available, and anyone can download onto their computers from the online . Free accessibility and downloading capability were the most reasons behind the project decision to use those two applications to host the info , albeit they’re less sophisticated than another GIT applications, like ArcMap, that cost money or require schools to get grants to get .
To guide classroom use of the GIT applications and data sets, the project research team, with the assistance of six design partner teachers, developed within the first project year the primary iterations of six thematically connected, hands-on exemplar lessons that provide students with the chance to ascertain focal enduring understandings played call at “study area” regions of mid-California and therefore the western a part of ny State. These enduring understandings include orographic impacts on weather and climate, sustainment of plant species and ecosystems, and global climate change . within the process, students were to find out about the character of geospatial data, including how the info are collected and visually rendered in layers on maps.
The lessons make use of parallel data sets about the 2 study areas. the info provide recent multidecadal averages of temperature, precipitation, and predominating land cover, also as global climate change model-based projections for temperature, precipitation, and land cover in 2050 and 2099. The model is that the A2 scenario from the Intergovernmental Panel on global climate change (Nakicenovic & Swart, 2000). This scenario assumes continued global growth of CO2 levels within the atmosphere like the continuation of current levels of increase .
The STORE data tell a comparatively clear narrative about orographic rainfall and the way it influences the climate and predominating land cover. The California orographic rainfall is influenced by winter weather systems coming off of the Pacific , going west over the Coastal Range, over the Central Valley, and up the slopes of the Sierra Nevada Mountains. Occasional summer storms coming north from the Gulf of California and northwest from the Gulf of Mexico have a rather different path. Yet, like the winter storms, they carry strong orographic rainfall effects.
The Western ny orographic rainfall is more subtle, influenced by less dramatic topographic variances. Also, Lake Ontario and Lake Erie exert a robust influence on the origins, magnitudes, and directions of storms. Hence, these data sets are especially reinforcing of learnings about the water cycle, the characteristics of populations and ecosystems, heredity, differences between weather and climate, principles of meteorology, and projected temperature increases on precipitation and land cover.
Lesson 1 introduces basic meteorological concepts about the connection between weather systems and topography. Lessons 2 and three specialise in recent climatological and land cover data (30-year averages). Lesson 4 brings in Excel because the technology for graphing relationships between temperatures at weather stations within the study areas and elevation. Lesson 4 reinforces learnings about temperature lapse rates, an idea first introduced in Lesson 1. The last two lessons specialise in model-based global climate change projections in reference to the possible fates of various regional species of vegetation. Answer keys are provided for the varied constructed response questions in each lesson.
Teachers have additionally access to geospatial data files that display some storm systems that moved over California and ny between the winter of 2011 and summer of 2012. These storm data sets are especially useful for exploring the similarities and differences between weather and climate. Students can study relationships between the storm behavior, the topography of the study areas, and therefore the extent to which the storms mimic the geospatial distributions of the 30-year climatology.
Figure 1 shows a picture of how a number of the shop data layers appear in Google Earth. during this image, precipitation accumulations from one particular day during a particular storm that skilled the California study area appear on top of a layer showing 30-year precipitation averages. Below the bottom map may be a transect that crosses weather stations from which the precipitation data were collected for the 30-year averages. This image is rich in conceptual learning. It exemplifies relationships between precipitation and elevation and lends itself to comparing rainfall accumulations from the storm to work out the extent to which the weather thereon day was according to the various topographically influenced microclimates within the study area. All the info for producing images like this one are available from the project website