The and graduation rate tends to favor the families

 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.

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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.