The Impact of Professional Development in Natural Resource Investigations Using Geospatial Technologies

Abstract

As use of geospatial technologies has increased in the workplace, so has interest in using these technologies in the K–12 classroom. Prior research has identified several reasons for using geospatial technologies in the classroom, such as developing spatial thinking, supporting local investigations, analyzing changes in the environment, and interesting students in technology and geography. The National Research Council (NRC) advocates spatial thinking instruction across the K–12 curriculum and instruction in geospatial technologies, such as geographic information systems (GIS), is one way to increase understanding in spatial thinking. Many educators agree that GIS can be a useful tool for student learning; however, if GIS is going to be successfully integrated into the classroom, many issues need to be addressed, including those related to professional development. Many of the characteristics of effective professional development apply to professional development in geospatial technologies but researchers continue to identify best practices. The professional development objectives for the NSF ITEST (Innovative Technology Experiences for Students and Teachers) program at the University of Kentucky were threefold: (1) to increase knowledge of geospatial technologies, including GIS, GPS, and remote sensing; (2) to develop spatial thinking; and (3) to apply that knowledge to community‐based natural resource investigations, a localized form of project‐based learning (PBL). The UK team hypothesized that the unique components of this professional development program would be an effective way to increase teachers’ knowledge of new technologies and spatial thinking and to instruct teachers how to apply that knowledge to community‐based investigations.

A Systematic Review of the Emergence and Utilisation of Agricultural Technologies into the Classroom

“…Two studies used methods of data collection after an intervention, such as PD, which had already occurred [17,27]. Another two studies utilised both pre-and post-intervention surveys and an additional post-intervention data collection method [26,28]. Post-intervention methods were the only studies to include data collection by methods other than surveys (n = 2), by focus group [17] or interview [26].…”

“…Another two studies utilised both pre-and post-intervention surveys and an additional post-intervention data collection method [26,28]. Post-intervention methods were the only studies to include data collection by methods other than surveys (n = 2), by focus group [17] or interview [26]. Of those studies that utilised surveys (n = 13), there was a relatively even distribution of online (n = 4) or paper delivery (n = 3).…”

“…One study utilised both hard-copy and online forms [24]. The format of surveys for five studies was unknown [26][27][28][29][30]. The question styles utilised in the reviewed studies are shown in Figure 2b.…”

“…Aim b: Evaluate the Effectiveness of Teacher PD Four studies aimed to evaluate the effectiveness of teacher PD to increase teacher knowledge and self-efficacy to integrate AgTech into the classroom. Hanley et al [26] studied the effectiveness of PD developed for multidisciplinary teachers using geospatial technologies to increase teacher knowledge of this field and evaluate their ability to apply this knowledge into classroom lessons. Whannell and Tobias [27] evaluated PD for high school teachers to help develop an understanding of the role of maths, science and information technology in future farming to encourage the implementation of digital technologies in classrooms.…”

“…In a similar study, Hanley, Davis and Davey [26] engaged teachers in two weeks of PD involving geospatial technologies, followed by applied learning in community projects over the following months. Based on the PD, teachers demonstrated an improvement in knowledge in 17 of 21 areas, including general Geographic Information System (GIS) skills (e.g., topology models), spatial data (e.g., vector data and storage formats), analysis (e.g., buffering) and database skills (e.g., queries, cardinality, joining external tables).…”