Research Question: Is there a relationship between a student's skill in SBMM (skill based matchmaking) video games and their skill in higher class mathematics?
a. Does a student's skill in SBMM video games have a positive or negative correlation with mathematics skill?
Lily Hoopes
Class of 2023
Many researchers have already covered similar topics, involving the effects of math-based video games on a students math skills, or simply how video games impact a students learning education. The gap that I am addressing focuses on the skill of video game players and how this may correlate with math skill. My research question has become more specific so I better address my gap. Although my gap is smaller than others, I’m still addressing a new aspect between video games and mathematics.
My research will be conducted using a correlational research design. As a descriptive branch of research, correlational research identifies the extent to which variables are associated with other variables. In my research, I am looking for connections between video game skill and math skill. The data I plan on collecting is quantitative, which will be through rating the skills of players using scales, and their skills in math, using standard tests. I’m assessing two separate variables in order to determine if and how they are related. Scatter plots are highly effective in this method of research, once I have all my data, I plan on using this as a tool to analyze it. This branch of research is classified as explain, which must include variables, and a cause-effect relationship, just as video games may affect math skill.
There is a lack of knowledge among students and teenagers regarding video games’ relationship with mathematics. Despite a great deal of research on how video games itself affects a students math skill, there are still students who play video games and aren’t successful in math, and students who don’t play video games yet are great at math. There’s a missing piece somewhere that must explain these differences and patterns within the research. The Pew Research Center reports that, “...97% of teens ages 12-17 play computer, web, portable, or console games.”, and a large group of researchers argue that video games improve math ability, including researchers at Stanford, who find that “third-graders who played a novel video math game for 30 minutes a week measurably improved their ability to reason through open-ended math problems”. (Geiger, 2020); (Andrews, 2016). However, the relationship between teenagers who play video games and their math skill do not completely line up. Most research up to this point only covers how video games itself impacts math, and the general trend in many arguments is that video games boost mathematical thinking and skill. The issue here is that students who are good at video games, yet lacking in math, are left without answers as to why this is true. Same goes for students who are better at math, and lesser at video games. Perhaps a study which investigates the relationship between video game skill and mathematical skill by a correlational study could remedy this situation.
I hypothesize that a higher skill in video games will correlate with a higher success rate in mathematics, and a lower skill in video games will correlate with a lower success rate in mathematics. Some video games emphasize skills in problem-solving, strategy, and critical thinking, which overlap with skills in mathematics. If a student practices these skills in video games, their skills in math are likely to have a similar status.
I hypothesize that a higher skill in video games will correlate with a higher success rate in mathematics, and a lower skill in video games will correlate with a lower success rate in mathematics. Some video games emphasize skills in problem-solving, strategy, and critical thinking, which overlap with skills in mathematics. If a student practices these skills in video games, their skills in math are likely to have a similar status.
Skill Based Video Games: Online games in which the outcome of the match is determined by the skill of the player, including logic abilities, strategic thinking, knowledge of game mechanics, planning, etc.
Skill Based Matchmaking (SBMM): A matchmaking process in video games that groups together players of similar skill level when generating online servers or lobbies.
Skill: The ability to do something effectively.
Video Game Skill (V): The representation of a student’s abilities in various video games. (V) is determined by compiling a student’s individual ratings per question, then averaging the values. The Video Game Skill Survey always rated 10 as higher skills and 1 reflecting lower skills, thus, averaging these individual ratings provided a representation of a student’s video game skill.
Math Skill (M): Represents a student’s ability to demonstrate problem solving and critical thinking skills, in addition to accuracy and speed.
Accuracy Factor (a): The number of correctly answered questions. More questions answered correctly results in a higher math skill rating. (10 of 35 questions)
Time Factor (t): The time a student takes to complete the test. Shorter times result in a higher skill rating, and vice versa. To calculate (t), the time a student took to complete the test was subtracted from the total time given for the test, 12 minutes. Higher (t) result in higher (M) values. (25 of 35 questions)
As correlational research methods collect quantitative data, I concluded that a Likert scale survey would be ideal to collect data on a student’s video game and math skill. My cohort involved high school students, who had taken math classes up to Algebra 2 and are active in video game genres including Puzzles, Racing games, FPS, BR, MMO, RPG, RTS, and MOBA genres.
Video Game Skill Survey: In order to effectively assess students’ video game skill, I developed a Likert Scale perception survey in which students are asked their perception of their video game skill in various genres. Each genre was broken down into a handful of questions, asking students to rate a specific aspect of their skill from 1 to 10.
Informational Math Test: With permission, questions were borrowed from Khan Academy, specifically from Algebra 1 and Algebra 2 courses and end-of-exam tests. The 35-question math test was broken into two sections, the first consisting of word problems, requiring critical thinking and problem solving skills to solve, while the second section consisting of simpler arithmetic questions, testing speed and accuracy. A google timer was installed on the math survey, tracking how long each student took to complete the math test.
Each google form was linked to its corresponding spreadsheet.
The Video Game Skill Survey collected the ratings each student would mark per question. The first page of this sheet recorded the responses to each question, and the second page anonymized each student's data. All the responses per student were then averaged, resulting in (V).
The second google form, labeled Informational Math Test, collected students’ names and emails, a timestamp, form timer, their overall math score, and individual answers to each of the questions. The form timer tracked, in minutes, how much time a student had remaining to submit their google form, from a 12 minute timer. On the second page of this sheet, student’s were labeled A-Z, corresponding to the labels created in the Video Game Skill Survey sheet. (t) was calculated, then (t) and (a) were imputed here. Finally, the average was taken between (t) and (a) to calculate (M).
Each students’ (V) was paired to their (M) to create an individualized data point. These data points were transferred to a final, third spreadsheet for graphic analysis. Using the google charts graphing application, these data points were graphed on a scatter plot.
From here, the resulting scatter plot and data were analyzed according to both correlational and scatter plot analysis rules and concepts. Aside from visual representations of the correlation, such as a trend line, calculations were made to numerically represent the correlation. I used online tools to calculate these values, including the correlation coefficient (Pearson’s R), the significance level (alpha (a)), p-value, and the degrees of freedom. These values and the visual representation provided the needed results to draw final conclusions on the study.
My results, shown in the graph, reflect a small postivie correlation between video game skill and math skill. The graph shows a positive correlation between the variables. Although technically the case, the relationship between a student’s video game skill and their math skill is weak.
The trendline follows a basic linear pattern in the positive direction, with weak association. Of the data collected, no outliers were present. To describe what a ‘weak’ association represents, Pearson’s r, the correlation coefficient was calculated.
r = .2375 or .24
N (degrees of freedom) = 13
α = .05
p-value = .434617
r(13) = .24, p = .434617
Although my research question and hypothesis were proven to be true, the results must come with limitations. The most apparent limitation was time, as data was collected for only two weeks. Due to time limitations, the sample size was much smaller, however I still got useful results because visible trends were still present to be analyzed. In this study, only 13 valid responses were recorded, all from the same school, therefore, the trends generated by this study cannot be generalized or applied on a larger scale. These limitations did impact this study, however, with more time and resources available, this research can be developed further to apply more generally.
Future Adjustments and Recommendations
Considering the current limitations of this study, I suggest allowing more time to collect data. To complement this, I would also develop more effective ways of advertising the research study to increase participants. This may be done through a larger incentive, posting on more social media platforms, or hanging posters in multiple locations through the school district, which includes multiple schools, offices, and colleges. The method selected for this study worked very effectively in reflecting possible relationships. I set aside extra time before data collection to focus on developing the ideal method and research design to best fit my research goals. In the end my method and design worked noticeably well with no issues.
Based upon the results and analysis in the previous sections, there is a correlation between video game skill and math skill of a student. My hypothesis, video game skill will share a positive correlation with math skill, was proven to be true, however, although true, the correlation is quite weak. Knowing that this correlation does exist answers one question, yet also creates more questions directed towards a deeper understanding on the topic overall. These include, “Is there a specific genre that impacts math skill the most?”, or “Which branch of math is most impacted by video game skill?”. With further development of this research, teachers, educators, and trainers may apply this knowledge towards creating more effective educational tools for students learning math. The next step in this research is to replicate the research with a larger cohort. With a larger cohort, the findings can be applied more generally, which then allows for new research to begin based on the findings. Although this research is only a small start, it serves as a starting point for other researchers. This research aided in filling knowledge gaps between video games and math relationships. This adds to the knowledge of human and technology interaction, along with connections between technology and human thinking. This knowledge becomes much more important because of technology’s rapid advancements. Understanding as much as possible about the correlations and relationships between humans and technology will better prepare us for the challenges and future ahead.
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Retrieved December 21, 2022, from http://cecs.louisville.edu/ry/Game.pdf