This study quantitatively synthesized the empirical research on the effects of social context (i.e., small group versus individual learning) when students learn using computer technology. In total, 486 independent findings were extracted from 122 studies involving 11,317 learners. The results indicate that, on average, small group learning had significantly more positive effects than individual learning on student individual achievement (mean ES = +0.15), group task performance (mean ES = +0.31), and several process and affective outcomes. However, findings on both individual achievement and group task performance were significantly heterogeneous. Through weighted least squares univariate and multiple regression analyses, we found that variability in each of the two cognitive outcomes could be accounted for by a few technology, task, grouping, and learner characteristics in the studies.

Small Group and Individual Learning with Technology: A Meta-Analysis:

The status of pesticide pollution in surface waters (rivers and lakes) of Greece. Part I. Review on occurrence and levels.

Application of constructed wetlands for wastewater treatment in developing countries--a review of recent developments (2000-2013).

Of particular importance to the study of large-scale phenomena in physical geography is the modifi able areal unit problem (MAUP). While often viewed as only a problem in human geography (particularly demographic studies), the MAUP is an issue for all quantitative studies in geography of spatial phenomena (Openshaw and Taylor, 1979). Increasingly, remote sensing and Geographic Information Systems (GIS) are being used to assess the distribution of phenomena from a large scale. These phenomena are modelled using areal units that can take any shape or size resulting in complications with statistical analysis related to both the scale and method used to create the areal units. In this paper, we defi ne the modifi able areal unit problem, present examples of when it is a problem in physical geography studies, and review some potential solutions to the problem. Our aim is to increase awareness of this complicated issue and to promote further discussion and interest in this topic.

http://wiki.dpi.inpe.br/lib/exe/fetch.php?media=artigo_giovanni.pdf

The modifiable areal unit problem (MAUP) in physical geography

Network design for water quality monitoring of surface freshwaters: A review

[1] An optimization algorithm linked with a nonpoint source (NPS) pollution model can be used to optimize NPS pollution control strategies on a field-by-field basis in a watershed by maximizing NPS pollution reduction and net monetary return. In this paper a methodology is described which integrated a genetic algorithm (GA) (an optimization algorithm) with a continuous simulation, watershed-scale, NPS pollution model, Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) to optimize the selection of best management practices (BMP) on a field-by-field basis for an entire watershed. To test the methodology, optimization analysis was performed for a U.S. Department of Agriculture experimental watershed in Pennsylvania to identify BMPs that minimized long-term (over a 4-year period) water quality degradation and maximized net farm return on an annual basis. Results indicate that the GA was able to identify BMP schemes that reduced pollutant load by as much as 56% and increased net annual return by 109%.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2001WR000365

Watershed optimization of best management practices using AnnAGNPS and a genetic algorithm

The principal instrument to temporally and spatially manage water resources is a water quality monitoring network. However, to date in most cases, there is a clear absence of a concise strategy or methodology for designing monitoring networks, especially when deciding upon the placement of sampling stations. Since water quality monitoring networks can be quite costly, it is very important to properly design the monitoring network so that maximum information extraction can be accomplished, which in turn is vital when informing decision-makers. This paper presents the development of a methodology for identifying the critical sampling locations within a watershed. Hence, it embodies the spatial component in the design of a water quality monitoring network by designating the critical stream locations that should ideally be sampled. For illustration purposes, the methodology focuses on a single contaminant, namely total phosphorus, and is applicable to small, upland, predominantly agricultural-forested watersheds. It takes a number of hydrologic, topographic, soils, vegetative, and land use factors into account. In addition, it includes an economic as well as logistical component in order to approximate the number of sampling points required for a given budget and to only consider the logistically accessible stream reaches in the analysis, respectively. The methodology utilizes a geographic information system (GIS), hydrologic simulation model, and fuzzy logic.

A water quality monitoring network design methodology for the selection of critical sampling points: Part I.

A comprehensive, GIS-based modeling approach was developed to enable accurate prediction of nutrient loads in watersheds throughout the state of Pennsylvania; particularly those watersheds for which stream monitoring data do not exist. This approach relies on the use of statewide GIS data sets for deriving reasonably good estimates for various critical model parameters that exhibit considerable spatial variability within the state. Data manipulation and subsequent simulation modeling is managed via an interface (called AVGWLF) between a popular desktop GIS software package (ArcView) and the Generalized Watershed Loading Function (GWLF) model. The modeling approach was tested in thirty-two (32) watersheds throughout Pennsylvania, and a statistical evaluation of the accuracy of the load predictions was made. Nash-Sutcliffe coefficients of correlation derived for the calibration and verification watersheds ranged in value from 0.92 to 0.97 for both nitrogen and phosphorus when considering mean annual loads. The median N-S values for nitrogen varied between 0.64 to 0.70 for monthly, seasonal, and year-to-year load estimates; and for phosphorus they varied between 0.61 and 0.72.

Paul D. Robillard

Papers

Network design for water quality monitoring of surface freshwaters: A review

Watershed optimization of best management practices using AnnAGNPS and a genetic algorithm

A water quality monitoring network design methodology for the selection of critical sampling points: Part I.

A Comprehensive GIS-based Modeling Approach for Predicting Nutrient Loads in Watersheds

Manure management with conservation tillage.