Reflection #5

On a one to ten, I feel my confidence on my article is at 7. I feel settled in analyzing and presenting the data from archival research but I believe that I need to find a better way to merge my collected data with past data. In my genre the examples or evidence that appears is mostly statistical data with examples comparing from past research data. My genre is formatted in a way such that the problem is stated first and then statistical data is shown with solutions to tackle the odd sections of the data. My analysis continues this by showing data I have collected while comparing it with past archival data to show possible patterns for reasons productivity is not that high in certain game elements. My articles appearance will show that it is an informative paper showing a collection of results for a more solid conclusion than given by past researchers.

To answer my research question of how gamification of learning affects productivity of a students learning, I have taken past archival data to find game elements that helped affect motivation in a positive way and have collected other past data to elements that may have negatively impacted a student and compared them to find where each students motivation is increasing in comparison to other data about the increase in literacy due to gamified games. I have also conducted a survey to see how different students find their opinions are regarding how well they feel gamified learning apps work which I would use in addition to the past data to draw my conclusions.

Although research has been conducted on how literacy is affected by students and how students get motivated and what parts of gamification of learning doesn't really help students, finding the connection between them and how productivity is affected has not really been looked into by many researchers, hence I aim to bridge that gap hence answering "so what" we get from my research. For further research, I believe that a larger data set and a more updated statistics over motivation and literacy is required for more accurate generalizable results.