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عنوان فارسی مقاله:
تاثیر نمرات پیش بینی شده و واقعی در مورد اهداف مطالعه
عنوان انگلیسی مقاله:
Influence of anticipated and actual grades on studying intentions
سال انتشار : 2017
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بخشی از مقاله انگلیسی:
3. Method
3.1. Student population This study took place at a Canadian university accredited by the Association to Advance Collegiate Schools of Business. The university has about 1600 students enrolled in its 4-year undergraduate business degree programs. These students must take a 1-semester business principles course, normally during their first year. The course contains 5 assessment components that each account for 20% of the total course grade: exam #1, a writing proficiency test, exam #2, an essay assignment, and exam #3. Grades are recorded on a scale from 0 to 100: 0e49 is a failure or “F”, 50e59 is a “D,” 60e69 is a “C,” 70e79 is a “B,” and 80e100 is an “A.” The average overall course grade is typically about 60. Almost 20% of students fail the course, and another 20% withdraw before completion; most of those students retake the course in a later semester. 3.2. Data collection We ran the experiment during the fall semesters of 2012 and 2013, during which 721 students finished the course. Each semester we administered 2 student surveys; to encourage participation, we held 2 prize draws for either a free textbook or a bookstore gift certificate. We administered the first survey in class in the 2nd week of the 12-week semester. The paper survey form asked students for their high school average, the target grade they had in mind for the course, their gender, and whether they had attended a domestic or foreign high school. The survey also included 10 Likert-type questions about personal control (see appendix). We used the revised scale recommended by Paulhus and Van Selst (1990), as it is parsimonious, has established reliability, and has face validity in our study's context. To prevent order effects, we made 3 versions of the scale with the questions in different random sequences. Half of the questions were worded to be reverse-scored. We administered the second survey online in the 8th week of the semester, when the students knew their marks for exam #1, the writing test, and exam #2. The online survey asked students to input these 3 actual marks, and also the final course grades they subsequently expected to receive. The software then calculated and displayed their current course grade (i.e., the average of the 3 assessments), along with their minimum and maximum possible final grades. For half of the students (the “full feedback” group), the software also displayed a forecast of their final course grade. The other students (the “partial feedback” group) were not shown the forecast. This division allowed us to check whether students reacted to the forecast grade itself, or to the feedback exercise in general. After seeing the full or partial feedback, the students were asked whether they were planning to increase their studying (see appendix). The survey did not define “studying”, but instead left that term open for students to apply in their own contexts. We created the forecasting equations for the full-feedback group using linear regression on grades from a previous offering of the course, as in Armstrong (2013). This produced point and interval estimates of the final course grades. The best-fitting regression equation was Forecast ¼ 11.20 þ 0.334 Exam1 þ 0.217 Writing þ 0.278 Exam2.
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