Summer thunderstorm activity is often discontinuous in space (e.g., linear, isolated, clustered, scattered, widespread), sporadic in time (diurnal versus any time of day), and difficult to predict precisely in most areas of the United States (e.g., a “30% chance” forecast). This thunderstorm activity (or moist convection) in New Jersey is important to a variety of interests due to its panorama of effects which may include: cloud cover, lightning, heavy rainfall, flash flooding, hail, gusty winds, and on occasion pulse severity (i.e., causing property damage or be life threatening). In order to better cope with and be prepared for these impacts, the project “Research and Examination of Convection Operationally with Real-time Data by Students for Prediction in New Jersey (RECORDS for Prediction in NJ)” was established at Kean University to examine convective initiation in New Jersey during the summer as a function of sea breeze and other local circulations and factors as compared with synoptic (i.e., large scale) forcing. In addition to the research effort, in which students were involved in data collection, analysis, and assessment throughout the project and the pursuant academic year, other aspects of data archival, coordination with National Weather Service staff (and other partners) and use of the Department's operational WRF model (courtesy of Dr. Yoh) were also considered throughout the project. These represented important professional development opportunities for the students. Students also made direct use of both content and skills learned in a variety of coursework including synoptic meteorology, GIS, hydrology, and meteorological instrumentation. Work required skills development with regard to web interfaces, spreadsheet and statistical analysis of data, and the complexity of a project. Meetings were collective, for peer support, and individual so as to assign, discuss, and review any tasks assigned and the outcomes delivered by each student. Students were encouraged to use a team approach and to learn and practice positive interdependence (i.e., problem-based learning) when completing tasks and interpreting their findings.