Targeted Intervention – Changing study habits of low-performing students
Rebecca Prescott (with bb edits round 2)
1. Summary of Intervention
In many university programs, how a student performs on the first exam is a good predictor of
how s/he will perform throughout the semester (Jensen and Moore 2009; Deslauiers et al. 2012;
Jensen and Barron 2014). In addition, these first experiences at college often affect retention
(DeBerard et al. 2012). This may be particularly true for students who spend many hours studying yet do not receive high scores. In such cases, a targeted study habit intervention has been
shown to improve scores by as much as 32% (Deslauriers et al. 2012). This intervention is outlined below.
2. Requirements for the Intervention
A. Prior to course, instructor needs to:
A. Set learning goals and share them with students
B. Write exams based on those learning goals.
C. Obtain midterm & final exam data from previous courses taught without any
interventions for 3-5years. (These data will function as the control group and possibly provide
data for a permutation-based statistical test).
Instructors need to build learning goals built into the course to be able to discuss with students
how to study based on targeted study practices that focus on those learning goals. Without the
learning goals, a professor or instructor may have a hard time helping a student focus their study
practices for the exam, stream-lining their study time to better process and learn the information.
B. First mid-term exam – Instructor should
A. include the question “How many hours did you study for this exam?”
B. Once graded, select the lowest quartile of students, and look at their answer to
the above question.
C. Decide the minimum number of hours that (in the instructor's opinion) should result in a decent grade.
4. Send an email to the lowest quartile students who studied more than the minimum number of hours an email. Invite them to a meeting to help them improve their study habits, with the goal of improving their grade on future exams.
C. 15-30 minute face to face interventions:
A. Interventions take place RIGHT after first exam.
B. Students should be allowed to introduce themselves
C. Students asked how they spent study time - (common answer: tried to
memorize everything).
D. Review learning goals and study habits that target those learning goals
NOTE: can attending an intervention meeting provide extra credit points to students that attend?
One big problem in this paper is that they have extremely small sample sizes for the statistical
methods used, because students who were invited to intervention meetings did not come (about
half did not come). There is no way to make all the students come I think! But, you might be
able to increase that number with some incentive for coming.
2. Research Objective and hypothesis
Instructors will investigate if a brief, timely intervention focusing on specific study strategies
improves student performance in university environmental science and geology courses. The
instructors will compare the differences in the midterm and final test scores of students who received a face to face intervention meeting, focused on study habits (treatment group), and those
students who did not receive an intervention (control group).
3. Data that needs to be collected
A. How many hours study
B. Biogeographical information of students in courses including but not limited to:
- year in school
- ethnicity
- gender
C. mid-term and final exam scores from previous years
D. mid-term and final exam scores for the current year
E. Answer to question “how long did you study for this exam?”
F. If the student attended or did not attend an intervention meeting.
5. Statistical analysis
1) Students that fall into the lowest quartile (usually lowest 25-30%) should be selected for face
to face intervention.
2) Parametric analysis (data should be continuous variable and approx. normally distributed):
* For large courses (samples sizes of intervention students should be n ~ 30 or more), a
paired T-test could be used for courses with only a mid-term and final (or 2
measures on the student subject). However, this would be IF you did not categorize
students by gender, year in school, etc…
*For large courses with 3 or more exams (n = 30 or more), a repeated measures ANOVA
should be used if comparing more than 2 measures of the student subject.
3) Non-parametric analysis: (no assumption of normality and may not be continuous variable):
* For small courses (or the intervention group is less than 30 students), with only 2
exams (or 2 measures on a student subject) a Wilcoxon Signed Rank Sum Test should
be used. The Wilcoxon signed rank sum test is the non-parametric version of a paired
sample t-test.
* For small courses (or the intervention group is less than 30 students), and more than 2
exams (or more than 2 measures per student subject), a Friedman Test is the most
common equivalent.
NOTE: non-parametric tests based on permutation are preferable to rank, and may be possible
if the course has previous year datasets. The simplest way is a Wilcoxon Signed Rank Sum
Test (2 measures) or a Friedman Test (3 or more measures). Any analysis that used permuta-
tion versus Friedman or Wilcoxon Rank would greatly contribute to the literature on this subject
and would be publishable.
References
DeBerard, M. Scott, Glen I. Spielmans, and Deana C. Julka. "Predictors Of Academic Achievement And Retention Amongcollege Freshmen: A Longitudinal Study." College Student Journal
38.1 (2004): 66-80.
Deslauriers, Louis, Harris, Sara E., Lane, Erin, Wieman, Carl E. Transforming the lowest performing students: an intervention that worked. Journal of College Science Teaching. 41(6): 8088.
Jensen, P.A. & Moore, R. (2009). What do help sessions accomplish in introductory science
courses? Journal of College Science Teaching. 38(5): 60-64
Jensen, Philip A., & Barron, James A. (2014). Midterm and First-Exam Grades Predict Final
Grades in Biology Courses. Journal of College Science Teaching. November/December.