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) The SAT scores of students don’t determine the students performance in school, but the students family income does. Rich students have the resources and less struggles than lower income students so they succeeded more often than lower income students.
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. There are many programs that colleges are providing to at-risk students, so they can improve their skills, and help students that have financial problems, by giving these students who were in the program $5,000 every year. 1D) My hypothesis is that at-risk students who are receive extra help like tutoring, will be able to improve on their skills, but students who don’t gets this benefit will either stay on their level they are at or fail. 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 of this experiment will be from a psychology class because Dr. Steph’s experiment reals on at-risk students who are taking this speafic course. The target population would be the students that are at-risk of failing the course or dropping out.
Since a passing grade in CCNY is considered from a range of C to A+, the experiment will have students that their grades rang from F to C-, since this this is considered failing. I will not target the population randomly because the experiment reals on at-risk students, we don’t care for regular students. 2AII) To address the concerns about the responsibility of at-risk students to participate by making giving them a lecture of free help, and insist that they take interest in this help.
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. The way I will make the at- risk students to participate in SI, is that SI attendance will count as extra credit toward their final grade, this will encourage many as they would get a higher 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, but other resources instead. 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 experimental group will include and TA discussion sessions. For the control group there would be only independent studying and limited online resources included.
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 variables, which are TA sessions, students jobs, and attendance in SI sessions can be controlled by having the student make TA sessions count more towards their final grade.
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 work given to the students and the amount of information given to them. 2C) The dependent variable is the grades the at-risk student receives in tests. Three possible things that can happen to an at-risk student is that they will better understand and learn the topics in class. Another benefit is that these students get high tests scores than ever before. The last benefit can be that the at-risk student develops better studying and learning habits and that they are inspired to go class. 2D) The independent variables is whether the student will take SI or not.
The control variables are TA sessions, jobs, attendance and the amount of responsibility the students have.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 of the experimental group and the control and see if they have any difference. 3B) We use standard deviation to figure out the benefits of SI. 3C) Statistical significance just shows that the results are not by chance.
Statistical significance can be either weak or strong, for instance I would just take difference between the experimental and control group and see how large or the small the difference is, which will show if the experiment is justified.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 were not randomly selected, and they are not 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) The experiment may have better tests scores because the sample size was to small, so two students are not enough for justifying your results. 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) one counter claim may be that the SI didn’t really help, since the students already was improving, and that they had more help than the control group did.
4D) I would recommend that Dr. Steph to target the right groups so the experiment will be justified. 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.
