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These six academic websites rate and review teachers and professors, so you can be better prepared for the school or college year ahead. Ranking of Top public high schools with the best teachers based on key teaching statistics and teacher ratings from students and.
Rating high school teachers.High School Teachers
With over million professors, 7, schools & 15 million ratings, Rate My Professors is the best professor ratings source based on student feedback. Find & rate your professors or . #2 Districts with the Best Teachers in ook High Schools District School District,GLENVIEW, IL,67 Niche users give it an average review of stars. Featured . Throughout the academic year, high school teachers typically work Monday through Friday during the hours students are in school. For Delaney, that’s a.m. to p.m.
– Rating high school teachers
Ratings of factors like clarity, organization, and overall quality influence promotion, pay raises and tenure in higher education. Thus, we asked: Do better teachers get better ratings? Therefore, our question is, do students give the highest ratings to the teachers from whom they learn the most? Given the ubiquity and importance of teacher ratings in higher education, we limited our review to research conducted with college students.
Figure 1 presents a framework for understanding teacher ratings. This framework is simply a way of organizing the possible relationships among what students experience in a course, the ratings they give their instructor, and how much they learn. While students also typically rate instructors on preparedness, content knowledge, enthusiasm, clarity of lectures, etc.
Framework for understanding possible influences on student evaluations of teaching. In the figure, educational experience is the broad term we are using to refer to everything students experience in connection with the course they are evaluating e. The first course is the one taught by the professor being evaluated.
Subsequent course performance means how those same students do in related, follow-up courses. Subsequent course performance is included because, for example, a good Calculus I teacher should have students who do relatively well in follow-up courses that rely on calculus knowledge, like Calculus II and engineering.
Our main interest was the relationship between how college students evaluate an instructor and how much they learn from that instructor, which is represented by the C and D links in Figure 1. Some links in Figure 1 have been researched more extensively than others.
They have identified an extensive list of student, instructor, and course characteristics that can influence ratings, including student gender, prior subject interest, and expectations for the course; instructor gender, ethnicity, attractiveness, charisma, rank, and experience; and course subject area, level, and workload for reviews, see Neath, ; Marsh and Roche, ; Wachtel, ; Kulik, ; Feldman, ; Pounder, ; Benton and Cashin, ; Spooren et al. This literature is difficult to succinctly review because the results are so mixed.
For many of the questions one can ask, it is possible to find two articles that arrive at opposite answers. For example, a recent randomized controlled experiment found that students gave online instructors who were supposedly male higher ratings than instructors who were supposedly female, regardless of their actual gender MacNell et al. One reason studies come to such different conclusions may be the fact that many studies do not exercise high levels of experimental control: They do not experimentally manipulate the variable of interest or do not control for other confounding variables.
But variable results may also be inherent in effects of variables like instructor gender, which might not be the same for all types of students, professors, subjects, and course levels. Finally, the mixed results in this literature may be due to variability in how different teacher evaluation surveys are designed e.
Our goal is not to review this literature in detail, but to discuss what it means for the question of whether better teachers get higher ratings. The educational experience variables that affect ratings can be classified into two categories: those that also affect learning and those that do not.
Presumably, instructor attractiveness and ethnicity should not be related to how much students learn. Instructor experience could be however. Instructors who have taught for a few years might give clearer lectures and assign homework that helps students learn more than instructors who have never taught before McPherson, ; Pounder, If teacher ratings are mostly affected by educational experience variables that are not related to learning, like instructor attractiveness and ethnicity presumably, then teacher ratings are not a fair way to identify the best teachers.
It is possible though that teacher ratings primarily reflect student learning, even if some variables like attractiveness and ethnicity also affect ratings, but to a much smaller degree. However, most of the studies covered in the reviews of the B link do not measure student learning objectively, if at all.
Therefore, the studies identify educational experience factors that affect ratings, but do not shed light on whether students give higher ratings to teachers from whom they learn the most.
Thus, they are not directly relevant to the present article. To answer our main question—whether teachers with higher ratings engender more learning i. These features describe what a randomized controlled experiment on the relationship between ratings and learning would look like in an educational setting.
TABLE 1. Ideal features of a study that measures the relationship between ratings and learning. The features in Table 1 are desirable for the following reasons. First, a lab study cannot simulate spending a semester with a professor.
Second, if the subsequent courses are not required, the interpretation of the results could be obscured by differential dropout rates. For example, a particular teacher would appear more effective if only his best students took follow-up courses. Third, random assignment is necessary or else preexisting student characteristics could differ across groups—for example, students with low GPAs might gravitate toward teachers with reputations for being easy.
Fourth, comparable or identical measures of student knowledge allow for a fair comparison of instructors. Course grades are not a valid measure of learning because teachers write their own exams and the exams differ from course to course. Next, we review the relationship between ratings and first course performance i.
Then we turn to newer literature on the relationship between teacher ratings and subsequent course performance. A wealth of research has examined the relationship between how much students learn in a course and the ratings they give their instructors i.
This research has been synthesized in numerous reviews Abrami et al. The studies included in these meta-analyses had the following basic design: Students took a course with multiple sections and multiple instructors.
Objective measures of knowledge e. Table 2 shows the mean correlation between an overall measure of teacher effectiveness and first course performance. Cohen reported the highest average correlation of 0. Clayson reported the lowest mean correlation of 0. A few recent studies have examined the relationship between ratings, first course performance, and crucially, subsequent course performance, which has been advocated as a measure of long-term learning Johnson, ; Yunker and Yunker, ; Clayson, ; Weinberg et al.
Subsequent-related course performance is arguably more important than first course performance because the long-term goal of education is for students to be able to make use of knowledge after a course is over. It is important to distinguish between student knowledge and teacher contribution to student knowledge. Students who do well in the first course will tend to do well in subsequent related courses e.
The studies we describe next used value-added measures to estimate teacher contribution to knowledge. Since there is typically a positive relationship between ratings and first course performance, we might also predict a positive relationship between ratings and subsequent performance.
Yet, three recent studies suggest that ratings do not predict subsequent course performance Johnson, ; Yunker and Yunker, ; Weinberg et al. These studies represent an important step forward, but they are open to subject-selection effects because students were not assigned to teachers randomly and follow-up courses were not required; additionally, only Yunker and Yunker used an objective measure of learning a common final exam.
Only two studies, conducted by Carrell and West and Braga et al. We review these studies next. Carrell and West examined data collected over a year period from over 10, students at the United States Air Force Academy.
This dataset has many virtues. There was an objective measure of learning because students enrolled in different sections of a course took the same exam. The professors could see the exams before they were administered. Lenient grading was not a factor because each professor graded test questions for every student enrolled in the course.
Students were randomly assigned to professors. Carrell and West used value-added scores to measure teacher effectiveness. The difference between the actual and predicted grade can be attributed to the effect of the teacher, since non-teacher variables were controlled for.
A single value-added score for was then computed for each teacher. This score was meant to capture the difference between the actual and predicted grades for all the students in their course section. A high value-added score indicates that overall, the teacher instilled more learning than the model predicted. The same non-teacher variables were used to predict grades in Calculus II and other follow-up courses, which were then compared to actual grades.
Subsequent course performance told a different story, however. The teachers who contributed more to learning as measured in follow-up courses had been given relatively low ratings in the first course.
These teachers were also generally the more experienced teachers. In other words, getting low ratings in Calculus I was a sign that a teacher had made a relatively small contribution to learning as measured in Calculus I but a relatively large contribution to learning as measured in subsequent courses requiring calculus Figure 2.
Summary of the relationship between teacher ratings, value-added to first course and value-added to subsequent courses. Braga et al. Teachers given higher ratings tended to have less experience.
Receiving low ratings at the end of course 1 predicted that a teacher had i made a relatively small contribution to learning as measured at the end of course 1 and ii made a relatively large contribution to learning as measured in subsequent courses Figure 2. There is one other key finding from Braga et al.
In one analysis, Carrell and West ranked teachers in terms of both contribution to course 1 and contribution to subsequent courses. It is important to remember that these claims have to do with teacher contribution to learning, not individual student aptitude. Students who did better in course one also did better in subsequent courses, but individual student aptitude was controlled for in the value-added models and by the fact that students were assigned to courses randomly.
It is difficult to interpret the strength of the correlations in Figure 2 because of the complexity of the value added models, but three things seem clear. First, there is evidence from Carrell and West , Braga et al. Our conclusion is that better teachers got lower rating in the studies conducted by Carrell and West and Braga et al.
In drawing, this conclusion we assume that the long-term goal of education is for knowledge to be accessible and useful after a course is over.
Therefore, we consider the better teachers to be the ones who contribute the most to learning in subsequent courses. Until this research has been done, we can only speculate about why better teachers got lower ratings in these two studies. For example, mixing different types of math problems on a problem set, rather than practicing one type of problem at a time, impairs performance on the problem set but enhances performance on a later test e. Most research on desirable difficulties has examined memory over a short period of time.
Short-term performance typically refers to a test a few minutes after studying and long-term learning is usually measured within a week, whereas course evaluations take a full semester into account. However, the benefits of desirable difficulties have also been observed over the course of a semester Rohrer et al. Multiple studies have shown that learners rate desirable difficulties as counterproductive because their short-term performance suffers e. A similar effect seems to occur with teacher ratings: Making information fluent and easy to process can create an illusion of knowledge Abrami et al.
It is not always clear which difficulties are desirable and which are not. Difficulties that have been shown to benefit classroom learning include frequent testing e.
Table 3 lists teacher behaviors that seem likely to increase course difficulty and deep learning, but simultaneously decrease ratings.
These behaviors are relevant even in situations where teaching to the test is not relevant, and their effects might be worth investigating in future research.
TABLE 3. The recent disappearance of the favorite site, Rate My Teacher, has left some students and staff wanting answers. For years, the anonymous review page was a hallmark of high school, allowing for students to give feedback on their favorite teachers or for some, to have a sense of passive aggressive closure with a horrible class. While Rate My Teacher was intended to give students a way to review their classroom experiences on a scale from 1 to 5 under categories like knowledge and helpfulness, the page known for its funny comments soon became anything but lighthearted for some teachers.
Complaints filed by teachers with the Better Business Bureau include reports of comments with racist, homophobic, sexist, and some downright mean content.
As comments such as these seemed to get more intense, school administrations across the country claimed RMT was promoting slander as well as a cyberbully culture amongst students which is likely why the company turned over as they feared a lawsuit. Great, but neither of those feelings is useful to the next person. The Internet is forever, and if you have a genuine personal conflict with a teacher, RMT is not the venue to air a grievance.
However, RMT has recently opened up to comments after popular demand. As for the site itself, you can find schools and courses by country. If you want to see more personal comments from students, try Rate My Professors.
Unlike the above entry, this website allows people to make personal comments about each professor. This acts as a double-edged sword, however. When done well, you can find good, constructive reviews of professors and how they work. A relatively unknown entry to the teacher-rating niche, Rate Your Lecturer is a UK-based middle ground between the above two.
It asks for six ratings for different aspects via a questionnaire style and allows the student to manually enter the pros and cons for the lecturer. What makes this site good is how it highlights the top professors in an institution. When you search for a specific school, the site will let you know who the top five highest-rated professors are. Yes, all the ratings sites are committed to creating a safe space for students to voice their concerns and are therefore anonymous.
Want to run a poll for your classroom and send out some questionnaires? Then see our guide on how to create polls and ask questions. Or if you just want to kick back with some pals digital, then check out our list of online board games with friends.
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