What does the assessment in a technical college depend on?
Hi Habr!
Once upon a time I wondered: how fair is the rating system in most of our technical colleges? And what generally affects the assessment received by the student?
After all, quite often a student who went the whole semester, wrote lectures and performed laboratory tests on time, gets “ud.” On the exam, and the lucky gouge snaps off the top five.
How much is all this random? No matter how you learn, you will get a rating “random”? Or not? And if you are a beautiful girl in a short miniskirt, what are your chances compared to guys? (Exclusively the figure of speech - no sexism)
Under the cut you will see the results of my research, in which I tried to answer these and some other questions. Several thousand students of my high school became experimental guinea pigs - MSTU. N.E. Bauman.
I apologize in advance if I chose the wrong hub, and perhaps I shouldn’t have to write this on Habré at all. But I wanted to share.
')
I will also immediately say that I pretend to be too scientific and I will be happy to point out all my inaccuracies, errors in the selection of criteria and other things, for statistics and sociology is not my profession, but rather a small hobby.
Here I will give only a part of the tested hypotheses in order not to inflate the article. Also in the course of the study, I built a regression model to determine the student's assessment, depending on the various parameters of the student, but I will not push it here either - and it came out so volumetrically. Maybe next time, if someone is interested.
So let's go.
The factors whose influence I studied are, of course, not comprehensive. And they do not pretend to cover the whole range of reasons why a student can get this or that grade. But why did I choose these factors? Because they could be obtained from official documents, without resorting to such labor-intensive methods as questioning, interviewing, etc. For this I did not have the time and resources.
So, a list of factors:
1) Student's floor
2) Faculty
3) Cathedral item or not
4) Semester
5) Attendance
For all factors except the last (attendance), I took information from the Electronic University system (hereinafter referred to as EI), which stores data on the results of all sessions since 2007. Thus, the sample coincided with the general population, and therefore, automatically representative.
With attendance everything is more complicated. In an amicable way, it should also be entered into the “EU”, but only in the first two courses. And after consulting with a friend of the deputy, it became completely clear that you shouldn’t even count on it - the data is either not entered, or entered as God’s per capita. What is sad.
However, attendance is such an important factor that it was impossible not to analyze it. I had to scoop information from attendance journals, which lead the headman, and at the end of the semester pass to the dean's office. Given that these journals do not exist electronically, we had to scan several hundred pages and then process them manually.
Having estimated how much time I will kill, if I process the data for the entire university, I decided to confine myself to only one (my own) faculty - “Computer Science and Control Systems” (hereinafter - “IU”). And only for the last academic year.
Of course, the data from the journals do not reflect the reality of 100%, because they are filled by the headmen themselves, and they can make mistakes and cover their comrades without noting any omissions. But the best is not given. At least, the proportions of attendance remain: the “bot” will still not have gaps in the magazine, and “dolt”, even if he is not given half of “enok”, will still have worse attendance.
The number of students of the faculty "IU" - 2931 people. The number of results is 1,550. According to Paniotto and Maksimenko , the sample is representative.
Of course, there are many specialized programs, however, since I am familiar with them only by hearsay, and there was no strong desire to get acquainted closer.
I wanted not only to calculate the coefficients and fix them, but also to enter the data on students in the database, so that later it was possible to supplement it, to keep history; test other hypotheses that were not originally included in my research; build beautiful graphics with the most flexible settings, etc.
In addition, I wanted to do something of my own, sharpened by 100% for my task.
Since I work in a 1C franchise company, I know 1C best of all from development tools. So, the choice was obvious (and from the means to create a database with a web interface, I was completely familiar with only 1C, so the choice was doubly obvious).
I downloaded the data from the EC as html pages, wrote a parser and uploaded it to my database.
Attendance data (from magazines) had to be manually hammered, although I honestly tried to first write a program to recognize data from log scans. But the quality of the images was not so hot, so that the written program did not accelerate the process.
Finally, having scored all the data and having implemented all the necessary algorithms for testing statistical hypotheses in my database, I, trembling with impatience, began to build graphs and count coefficients. And that's what happened ...
So, the most interesting. The results, which gave me my program.
I analyzed the results using the chi-square test, and the strength of the connection using the Spearman coefficient. But, probably, the plates with numbers are not very interesting to readers, so here I will give only visual graphs. If someone is very interesting, publish the numbers.
Wow, girls don't give a chance to guys! Probably because they try harder, attend classes better? And here is no:
But judging by the schedule, attendance is practically independent of the floor (this is confirmed by calculations). It turns out that girls get higher points, and attendance is the same as that of the guys.
Among techies, there is a widespread opinion that there are “simple” faculties, such as economics or humanities, and there are “complex” ones, such as engineering.
On the graph, I cited only two faculties - with the highest average score (for all courses) and the lowest. The Law Faculty is the Law Faculty (yes, there is one in Baumanka), the MA - optoelectronics. As can be seen from the graph, the differences are quite strong.
However, if you build a histogram of the average score for all faculties, the difference will not be so obvious.
For cathedral subjects (they are taught by the teachers of the same department where the student is studying), the student's reputation can play a role, which is hardly known to the teacher not from the department. It is believed that the attitude of the department is more loyal. There are almost no non-cathedral subjects at senior courses.
4) Semester.
As they say, the main thing is to survive the first two courses. That is, 4 semesters, which the schedule, in general, confirms.
Attendance measured on a scale from 0 to 1. Where 1 - 100% attend all lectures and seminars. 0 –– one attendance of lectures and seminars.
It seems everything is true - the better you go, the higher the score (that's just the failure of 20% of attendance - I can not explain, except as a fad of the sample). But now we should see if this connection is equally strong on all courses?
The communication strength was measured using the Cramer coefficient.
As we can see, to senior courses, the strength of communication decreases, while in junior courses it is maximum. I grouped data by pairs, because this is a more linear graph, and also because the training system for 1-2 courses is very different from 3-6 courses.
That is, the real significance, attendance of students is only in the first two courses. On which the so-called “modular system” is used, which, when grading, is more focused not on the exam, but on the progress and attendance of the student during the semester.
It is impossible not to draw attention to the strange overestimation of the girls with the same level of attendance as the guys. But perhaps this is not at all overpricing, but are girls just smarter? After all, I did not measure the mental abilities of the respondents (and is it really possible?).
Separate attention is also worthy of the fact that attendance greatly affects the assessment only in the first two courses.
A picture emerges confirming the widespread (at least in MSTU) opinion: starting with a course of 3-4, students begin to “hammer out” for study, getting jobs in various firms with a degree or not. Attendance is falling, at senior courses, more and more assessments are set “for free”, as the teachers are chairmen and acquaintances, and the quality of knowledge ... However, this is a topic for a separate study.
This is a normal picture, or not - everyone decides for himself. With my personal experience and observations, this coincides. And does not cause positive emotions, frankly.
I hope someone found this article useful or interesting for themselves. Thanks for attention.
Once upon a time I wondered: how fair is the rating system in most of our technical colleges? And what generally affects the assessment received by the student?
After all, quite often a student who went the whole semester, wrote lectures and performed laboratory tests on time, gets “ud.” On the exam, and the lucky gouge snaps off the top five.
How much is all this random? No matter how you learn, you will get a rating “random”? Or not? And if you are a beautiful girl in a short miniskirt, what are your chances compared to guys? (Exclusively the figure of speech - no sexism)
Under the cut you will see the results of my research, in which I tried to answer these and some other questions. Several thousand students of my high school became experimental guinea pigs - MSTU. N.E. Bauman.
I apologize in advance if I chose the wrong hub, and perhaps I shouldn’t have to write this on Habré at all. But I wanted to share.
')
I will also immediately say that I pretend to be too scientific and I will be happy to point out all my inaccuracies, errors in the selection of criteria and other things, for statistics and sociology is not my profession, but rather a small hobby.
Here I will give only a part of the tested hypotheses in order not to inflate the article. Also in the course of the study, I built a regression model to determine the student's assessment, depending on the various parameters of the student, but I will not push it here either - and it came out so volumetrically. Maybe next time, if someone is interested.
So let's go.
Factors
The factors whose influence I studied are, of course, not comprehensive. And they do not pretend to cover the whole range of reasons why a student can get this or that grade. But why did I choose these factors? Because they could be obtained from official documents, without resorting to such labor-intensive methods as questioning, interviewing, etc. For this I did not have the time and resources.
So, a list of factors:
1) Student's floor
2) Faculty
3) Cathedral item or not
4) Semester
5) Attendance
Sample
For all factors except the last (attendance), I took information from the Electronic University system (hereinafter referred to as EI), which stores data on the results of all sessions since 2007. Thus, the sample coincided with the general population, and therefore, automatically representative.
With attendance everything is more complicated. In an amicable way, it should also be entered into the “EU”, but only in the first two courses. And after consulting with a friend of the deputy, it became completely clear that you shouldn’t even count on it - the data is either not entered, or entered as God’s per capita. What is sad.
However, attendance is such an important factor that it was impossible not to analyze it. I had to scoop information from attendance journals, which lead the headman, and at the end of the semester pass to the dean's office. Given that these journals do not exist electronically, we had to scan several hundred pages and then process them manually.
Having estimated how much time I will kill, if I process the data for the entire university, I decided to confine myself to only one (my own) faculty - “Computer Science and Control Systems” (hereinafter - “IU”). And only for the last academic year.
Of course, the data from the journals do not reflect the reality of 100%, because they are filled by the headmen themselves, and they can make mistakes and cover their comrades without noting any omissions. But the best is not given. At least, the proportions of attendance remain: the “bot” will still not have gaps in the magazine, and “dolt”, even if he is not given half of “enok”, will still have worse attendance.
The number of students of the faculty "IU" - 2931 people. The number of results is 1,550. According to Paniotto and Maksimenko , the sample is representative.
Data processing
Of course, there are many specialized programs, however, since I am familiar with them only by hearsay, and there was no strong desire to get acquainted closer.
I wanted not only to calculate the coefficients and fix them, but also to enter the data on students in the database, so that later it was possible to supplement it, to keep history; test other hypotheses that were not originally included in my research; build beautiful graphics with the most flexible settings, etc.
In addition, I wanted to do something of my own, sharpened by 100% for my task.
Since I work in a 1C franchise company, I know 1C best of all from development tools. So, the choice was obvious (and from the means to create a database with a web interface, I was completely familiar with only 1C, so the choice was doubly obvious).
I downloaded the data from the EC as html pages, wrote a parser and uploaded it to my database.
Attendance data (from magazines) had to be manually hammered, although I honestly tried to first write a program to recognize data from log scans. But the quality of the images was not so hot, so that the written program did not accelerate the process.
Finally, having scored all the data and having implemented all the necessary algorithms for testing statistical hypotheses in my database, I, trembling with impatience, began to build graphs and count coefficients. And that's what happened ...
results
So, the most interesting. The results, which gave me my program.
I analyzed the results using the chi-square test, and the strength of the connection using the Spearman coefficient. But, probably, the plates with numbers are not very interesting to readers, so here I will give only visual graphs. If someone is very interesting, publish the numbers.
1) The first factor, as you remember - "Student's floor"
Wow, girls don't give a chance to guys! Probably because they try harder, attend classes better? And here is no:
But judging by the schedule, attendance is practically independent of the floor (this is confirmed by calculations). It turns out that girls get higher points, and attendance is the same as that of the guys.
2) Faculty
Among techies, there is a widespread opinion that there are “simple” faculties, such as economics or humanities, and there are “complex” ones, such as engineering.
On the graph, I cited only two faculties - with the highest average score (for all courses) and the lowest. The Law Faculty is the Law Faculty (yes, there is one in Baumanka), the MA - optoelectronics. As can be seen from the graph, the differences are quite strong.
However, if you build a histogram of the average score for all faculties, the difference will not be so obvious.
3) Cathedral item or not.
For cathedral subjects (they are taught by the teachers of the same department where the student is studying), the student's reputation can play a role, which is hardly known to the teacher not from the department. It is believed that the attitude of the department is more loyal. There are almost no non-cathedral subjects at senior courses.
4) Semester.
As they say, the main thing is to survive the first two courses. That is, 4 semesters, which the schedule, in general, confirms.
5) One of the most interesting factors: attendance
Attendance measured on a scale from 0 to 1. Where 1 - 100% attend all lectures and seminars. 0 –– one attendance of lectures and seminars.
It seems everything is true - the better you go, the higher the score (that's just the failure of 20% of attendance - I can not explain, except as a fad of the sample). But now we should see if this connection is equally strong on all courses?
The communication strength was measured using the Cramer coefficient.
As we can see, to senior courses, the strength of communication decreases, while in junior courses it is maximum. I grouped data by pairs, because this is a more linear graph, and also because the training system for 1-2 courses is very different from 3-6 courses.
That is, the real significance, attendance of students is only in the first two courses. On which the so-called “modular system” is used, which, when grading, is more focused not on the exam, but on the progress and attendance of the student during the semester.
findings
It is impossible not to draw attention to the strange overestimation of the girls with the same level of attendance as the guys. But perhaps this is not at all overpricing, but are girls just smarter? After all, I did not measure the mental abilities of the respondents (and is it really possible?).
Separate attention is also worthy of the fact that attendance greatly affects the assessment only in the first two courses.
A picture emerges confirming the widespread (at least in MSTU) opinion: starting with a course of 3-4, students begin to “hammer out” for study, getting jobs in various firms with a degree or not. Attendance is falling, at senior courses, more and more assessments are set “for free”, as the teachers are chairmen and acquaintances, and the quality of knowledge ... However, this is a topic for a separate study.
This is a normal picture, or not - everyone decides for himself. With my personal experience and observations, this coincides. And does not cause positive emotions, frankly.
I hope someone found this article useful or interesting for themselves. Thanks for attention.
Source: https://habr.com/ru/post/199286/
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