Brock University Digital Repository
Brock University's Digital Repository is an online archive showcasing and preserving the Brock community's scholarly output as well as items from the Library's Archives & Special Collections. Researchers can disseminate their work by depositing it in this Open Access repository, which provides free, immediate access to users while also allowing Brock scholars to track downloads and views of their scholarship. The Digital Repository is also the home of the Brock University E-Thesis Portal.
For more information, see the repository's policies and procedures.
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Recent Submissions
The Press, Volume 24, Issue 15, January 13, 1988
(1988-01-13) Pavelka, Rita (Editor); Kelly, Brian (News); Nesbitt, Mark (Arts); Gerber, Matt (Sports); Woodward, Paula (Ad Manager); Wilson, Taylor (Circulation Manager); Arnold, Moira (Production Manager); Shaw, John K. (Illustrator); Haun, Richard (Typesetter); Carr, Sonja (Typesetter); Aggelonitis, Sophia (Writer); Mackenzie, Michael (Writer); Mathers, R. (Writer)
The Press, Volume 24, Issue 15 includes: TA’s are furious for being kicked out of their offices on the 10th floor to the Schmon Tower basement; Liberal party leader John Turner fights against free trade agreement; New BUSU minister of Academic Affairs quested for lack of duties; BUSU fails to advertise State of the Union meeting; no show;
Robert Nicholson PC MP Niagara Falls argues Canada should remain in NATO; Meech Lake Accord pros and cons; Greenpeace activists gives speech to Brock OPIRG to “leave uranium in the ground”; Student apathy plagues BUSU election.
The Press, Volume 24, Issue 14, December 9, 1987
(1987-12-09) Pavelka, Rita (Editor); Kelly, Brian (News); Gerber, Matt (Sports); Pellow, John (Photos); Woodward, Paula (Ad Manager); Wilson, Taylor (Circulation Manager); Arnold, Moira (Production Manager); Shaw, John K. (Illustrator); Haun, Richard (Typesetter); Carr, Sonja (Typesetter)
The Press, Volume 24, Issue 14 includes: Christmas edition; Christmas card photo of all current Press staff.
Community Connected Experiential Learning: Change in the K-12 Classroom
(Brock University) Pirosko, Jennifer; Kitchen, Julian; Department of Graduate and Undergraduate Studies in Education
Emerging from lived experience in education and student success administration, the researcher offers a critique of experiential learning in Ontario and moves to identify universal problems in policy and system implementation of community connected experiential learning. A qualitative study with K–12 educators from a school board in Ontario and grounded theory methodology is used to analyze, code, and identify several theoretical cornerstones. The prominent themes of teacher pedagogy and community connections emerge and offer implications for theory and practice. The resulting implementation framework—viewed from the school/district/state tri level lens—will guide teachers, administrators, and policy makers on the integration of community connected experiential learning with traditional learning methods and curriculum to provide K–12 students with rich, authentic, and engaging learning opportunities.
Performance Assessment of Bayesian Networks for Soil Moisture Prediction in Agricultural Water Management
(Brock University) Asgari, Kamran; Ramazi, Pouria; Department of Mathematics
Estimating soil moisture using easily measurable parameters can significantly improve irrigation management efficiency while reducing time and cost. This study evaluated the performance of LASSO, Decision Tree, and Random Forest models for predicting soil moisture. In addition, Bayesian networks were used to model the complex interactions between soil, weather, and topographical factors at the Schäfertal site in Germany. To achieve this, seven structure learning algorithms, including PC, Grow-Shrink, Incremental Association, Fast Incremental Association, Hill Climbing, Max-Min Hill-Climbing, and Tabu Search, were used. Subsequently, soil moisture was predicted using all these algorithms to determine the best performance among them. The results showed that Bayesian networks had the best
performance for prediction, while LASSO, Decision Tree and Random Forest did not perform as well, likely due to the complex relationships between variables. Bayesian networks, however, effectively identified key factors like soil texture, depth, and evapotranspiration using measured variables across all search algorithms. Bayesian networks showed better performance than the other three models. More importantly, they predicted soil moisture using only a subset of variables, with performance close to that achieved using the full set of variables. This result demonstrates improvements in predictions and handling missing data in practical applications such as precision agriculture and water management
The Press, Volume 24, Issue 13, December 2, 1987
(1987-12-02) Pavelka, Rita (Editor); Kelly, Brian (News); Nesbitt, Mark (Arts); Gerber, Matt (Sports); Pellow, John (Photos); Woodward, Paula (Ad Manager); Wilson, Taylor (Circulation Manager); Arnold, Moira (Production Manager); Shaw, John K. (Illustrator)
The Press, Volume 24, Issue 13 includes: Professors argue about the best use of Brock computer system; major accessibility problems; Brock’s architecture is crumbling; SWAP work abroad program in Australia feature; Brock student takes pilgrimage to Israel story; BUSU members’ pet peeves.