The purpose of this paper is to evaluate the differences between at-risk students who utilizes SI and at-risk students who do not. All of these students started on the same path, but this experiment reveals that any student can succeed with a little bit of guidance and help. A) There are many risk factors that lead students in college these days to a high risk of failing their courses or dropping them, but the two biggest risk factors in college learning and graduation rate are the student’s family income and standardized test scores. Students who have low income families tend to have many challenges in their daily lives. Many have money issues, which can overwhelm and cause stress that can lead to a student to not achieve the needs for learning and getting a passing grade. The tuition and loans can also overwhelm them, and force many to take up jobs which might be demanding and diminishing on the students’ performance in college. Another big risk factor in college students dropping out or failing, is standardized test scores. Students that come from low income families tend to also have low SAT scores, due to that fact that they don’t have the resources needed to practice and prepare for the test. With lower SAT scores these students seem to struggle more in college and seem to be the most at risk of failing or dropping out. 1B) SAT scores don’t predict college graduation, but instead the student’s family’s income is what determines if the student will graduate or not. Students that are born into a rich family, tend to have a higher chance of graduating in 4 years, than compared to students who are born into a poor or low income family. Even though when comparing a poor student with high SAT score to a wealthy student with a low SAT score, academic success and graduation rate tends to favor the families who are rich. 1C) One intervention colleges are now providing is extra classes and programs to better improve the grades of students and understand those students who are at risk of failing classes, so that they can help prevent them from dropping out or failing their courses. As shown in “Who Gets to Graduate” by Paul Tough, one intervention technique they used at universities to address college failure and dropout for at-risk students was a new scholarship program. This program allowed at-risk students to improve their skills, and help students that have unmet financial need, by giving these students who were in the program $5,000 every year. 1D) The hypothesis being addressed in the study that I am designing is that at-risk students who are receiving supplemental instructions, extra help, and extra classes will see an increase in their grades, while students that do not receive these benefits will have a very low chance of improving their grades and will continue to struggle.
There are many ways to plan your research, but in all researches it is important to have a consistent idea of what you are trying to learn from the research. 2AI) The participants would be the students from Dr. Steph introductory psychology class because Dr. Steph is worried about the many students who are at-risk of failing her introductory psychology course at City College. The target population would be the students that are at-risk of failure, since Dr. Steph wants them to pass her course. Since a passing grade in CCNY is considered from a range of C to A+, participants are selected and recruited for the study by grades ranging from F to C-, as students who receive these grades are considered failing. I would target the population randomly in order to make sure that there is no bias to this experiment and to give each student an equal opportunity. 2AII) I plan to address Dr. Steph’s concerns about the reluctance of at-risk students to participate by making announcements in class, through sending them emails, notifications on social media and notifications on blackboard. I will also inform them to join me after class for a short amount of time to talk about the SI and how it can benefit them and will also give them extra credit. To ensure that at- risk students participate in going to SI, SI attendance will count as extra credit, and make it mandatory for students below a C grade. I will use three groups for my research, two of which will be experimental and one of which is the control group. In both experimental groups the students will have SI but, in one experimental group the students have work (jobs) and in the other experimental group the students who do not work. The control group will just have students who do not get to have the SI sessions. I will randomly select about fifty at- risk students from each group. I will use random assignment by letting a computer program randomly pick at-risks students by their student ID code for each test group. For example, if there are 200 at-risk students in the experimental group with SI who don’t work, I will randomly pick 50 out of 200. This will allow the experiment to be bias free and provide a fair treatment to the students. 2BI) The SI intervention for the experimental group would include reviews of the lesson, homework help, test prep, essay rewrites and tutoring. For the control group there would be only independent studying and limited online resources included. To address Dr. Steph’s concern about intentionally withholding treatment from the participants in the control group, the students in the control group will be provided limited online resources, which will feature articles and videos. By doing so, the students will subconsciously believe that they are receiving the amount of knowledge to succeed the course, and thus not making them feel like they are being suppressed. 2BII) The three following variables, which are TA visits, outside work, and inconsistency of attendance in SI sessions can be controlled by having the student make TA classes mandatory and having a penalty for missing one class, which would be dropping the student from the course overall. By doing so this would prioritize the students outside work and plan out strategically their workload. This will probably cause them to be cautious and make a schedule of their daily activities, so it won’t interfere with the SI session. Also if the student has inconsistency of attendance, for example if a student is late they would stay 20 minutes and review what he/she missed and take a short quiz referring to the topic taught that day. Two other variables that need to be controlled in the study is the amount of homework turned in each week and the amount of extra credit provided. 2C) I can operationally define the dependent variable by the grades the at-risk student receives in quizzes, homework and exams. Three possible beneficial measures that can happen to an at-risk student is that they will have a better understanding of the material. Another benefit can be that the at-risk student gets higher scores on quizzes and exams because of SI. The last benefit can be that the at-risk student develops better studying and learning habits, such as involuntarily visiting their TA more often or start visiting if they had not before. 2D) The independent variables is whether the student will take SI or not. The control variables are TA visits, outside work, inconsistency of attendance in SI sessions and the reluctance of at-risk students.
When we find ourselves with the results of the research, we often stumble upon the meaning of it. So we turn to mathematics to reveal to us the magnitude of what we have discovered. 3A) A statistical test can be used to measure the test scores between the students who attended SI sessions and the students who didn’t. For example, we can find the difference by calculating the range of quizzes, homework and test scores from the students at-risk of who attended and the students at-risk of who did not. 3B) The variation between groups can be calculated through standard deviation. The first thing we need to do is to find the difference, which is calculated by taking each difference of all three groups, and then squaring the average results. This has to be done twice since there are two experimental groups. 3C) Statistical significance is the probability that the results of statistical test are not by chance. Statistical significance can be either weak or strong, for instance if the statistical significance is low, that the means it is least likely because of chance. I would conclude that the difference between groups is statistically significant if the difference of the averages of the groups is bigger than two. If the at-risk students who attend SI sessions receive grades with B’s or higher compared to students who did not attend the SI sessions, who still receive C’s and below would lead me to believe that the statistical significance is strong.
At the end the data has been collected the discoveries has been proved, the only thing left to do is to improve and learn from our findings. 4A) Dr. Steph’s conclusion is incorrect because the students who visited her were not out of a random sample from at-risk students. This clearly shows bias and also we do not know for sure the other variables that might come into play, such as outside work, class schedule, free time, and disabilities. 4B) It is possible that the at-risk pilot participants actually have much better course performance than the comparison pilot participants because there were only two volunteers compared to the size of the population of the comparison group. We also do not know what treatment the two groups got, perhaps the two students probably had more personal learning from the pilot study. Hence, this shows that this study does not represent the population because of the lack of descent amount of participants and lack of variable control. 4C) The SI had nothing to do with the difference between groups in the pilot study because the real source of this difference could be that the pilot experimental group could have already been doing better than the pilot control group, the pilot experimental group could have received outside help/resources or the control group could not have received any form of SI. 4D) I would recommend that Dr. Steph to gather more students through random sample for her pilot study. Overall Dr. Steph needs to provide a larger sample size and give both groups same level of treatment as much as possible. For instance, if you have a large experimental group, you should have a large control group as well.