Machine-supported decision-making to improve agricultural training participation and gender inclusivity.

Women comprise a significant portion of the agricultural workforce in developing countries but are often less likely to attend government sponsored training events. The objective of this study was to assess the feasibility of using machine-supported decision-making to increase overall training turnout while enhancing gender inclusivity. Using data obtained from 1,067 agricultural extension training events in Bangladesh (130,690 farmers), models were created to assess gender-based training patterns (e.g., preferences and availability for training). Using these models, simulations were performed to predict the top (most attended) training events for increasing total attendance (male and female combined) and female attendance, based on gender of the trainer, and when and where training took place. By selecting a mixture of the top training events for total attendance and female attendance, simulations indicate that total and female attendance can be concurrently increased. However, strongly emphasizing female participation can have negative consequences by reducing overall turnout, thus creating an ethical dilemma for policy makers. In addition to balancing the need for increasing overall training turnout with increased female representation, a balance between model performance and machine learning is needed. Model performance can be enhanced by reducing training variety to a few of the top training events. But given that models are early in development, more training variety is recommended to provide a larger solution space to find more optimal solutions that will lead to better future performance. Simulations show that selecting the top 25 training events for total attendance and the top 25 training events for female attendance can increase female participation by over 82% while at the same time increasing total turnout by 14%. In conclusion, this study supports the use of machine-supported decision-making when developing gender inclusivity policies in agriculture extension services and lays the foundation for future applications of machine learning in this area.

Breaking out: the turning point in learning using mobile technology.

Despite considerable research on YouTube as a digital media platform, little research to date has quantified the device-type used to access that online media. Analyzing access-device data for videos on one YouTube video channel-Scientific Animations Without Borders (SAWBO), which produces educational content specifically accessible to low- or non-literate, poor, or geographically isolated learners in less developed areas of the world-the results identify the historical moments between 2015 and 2017 when mobile/smartphones, both globally and by region, crossed a tipping point to surpass all other ICT devices (including desktop PCs, laptops, and other Internet-accessing technologies) as the primary device-type for accessing SAWBO videos. Specifically, data from January 2013 to June 2018 obtained for SAWBO's YouTube channel were sampled to capture and distinguish the access device-type used and then summarized in broad global and regional categories. The tipping point, as the date where the percentage of views from mobile phones was equivalent to the percentage of views from computers, were also calculated globally and by region. Besides documenting this critical global-historical moment, the results also have implications for mass digital-messaging generally and mobile-based public service learning specifically.