Not the quality of teaching, but the way it operates
Easier admission and graduation bar applied to online degrees
Studies show that higher quality attracts more passion from students

Although much of the prejudice against online education courses has disappeared during the COVID-19 period, there is still a strong prejudice that online education is of lower quality than offline education. This is what I feel while actually teaching, and although there is no significant difference in the content of the lecture itself between making a video lecture and giving a lecture in the field, there is a gap in communication with students, and unless a new video is created every time, it is difficult to convey past content. It seems like there could be a problem.

On the other hand, I often get the response that it is much better to have videos because they can listen to the lecture content repeatedly. Since the course I teach is an artificial intelligence course based on mathematics and statistics, I heard that students who forget or do not know mathematical terminology and statistical theory often play the video several times and look up related concepts through textbooks or Google searches. There is a strong prejudice that the level of online education is lower, but since it is online and can be played repeatedly, it can be seen as an advantage that advanced concepts can be taught more confidently in class.

Is online inferior to offline?

While running a degree program online, I have been wondering why there is a general prejudice about the gap between offline and online. The conclusion reached based on experience until recently is that although the lecture content is the same, the operating method is different. How on earth is it different?

The biggest difference is that, unlike offline universities, universities that run online degree programs do not establish a fierce competition system and often leave the door to admission widely open. There is a perception that online education is a supplementary course to a degree course, or a course that fills the required credits, but it is extremely rare to run a degree course that is so difficult that it is perceived as a course that requires a difficult challenge as a professional degree.

Another difference is that there is a big difference in the interactions between professors and students, and among students. While pursuing a graduate degree in a major overseas city such as London or Boston, having to spend a lot of time and money to stay there was a disadvantage, but the bond and intimacy with the students studying together during the degree program was built very densely. Such intimacy goes beyond simply knowing faces and becoming friends on social media accounts, as there was the common experience of sharing test questions and difficult content during a degree, and resolving frustrating issues while writing a thesis. You may have come to think that offline education is more valuable.

Domestic Open University and major overseas online universities are also trying to create a common point of contact between students by taking exams on-site instead of online or arranging study groups among students in order to solve the problem of bonding and intimacy between students. It takes a lot of effort.

The final conclusion I came to after looking at these cases was that the difficulty of admission, the difficulty of learning content, the effort to follow the learning progress, and the similar level of understanding among current students were not found in online universities so far, so we can compare offline and online universities. I came to the conclusion that there was a distinction between .

Would making up for the gap with an online degree make a difference?

First of all, I raised the level of education to a level not found in domestic universities. Most of the lecture content was based on what I had heard at prestigious global universities and what my friends around me had heard, and the exam questions were raised to a level that even students at prestigious global universities would find challenging. There were many cases where students from prestigious domestic universities and those with master's or doctoral degrees from domestic universities thought it was a light degree because it was an online university, but ran away in shock. There was even a community post asking if . Once it became known that it was an online university, there was quite a stir in the English-speaking community.

I have definitely gained the experience of realizing that if you raise the difficulty level of education, the aspects that you lightly think of as online largely disappear. So, can there be a significant difference between online and offline in terms of student achievement?

The table above is an excerpt from a study conducted to determine whether the test score gap between students who took classes online and students who took classes offline was significant. In the case of our school, we have never run offline lectures, but a similar conclusion has been drawn from the difference in grades between students who frequently visited offline and asked many questions.

First, in (1) – OLS analysis above, we can see that students who took online classes received grades that were about 4.91 points lower than students who took offline classes. Various conditions must be taken into consideration, such as the student's level may be different, the student may not have studied hard, etc. However, since it is a simple analysis that does not take into account any consideration, the accuracy is very low. In fact, if students who only take classes online do not go to school due to laziness, their lack of passion for learning may be directly reflected in their test scores, but this is an analysis value that is not reasonably reflected.

To solve this problem, in (2) – IV, the distance between the offline classroom and the students' residence was used as an instrumental variable that can eliminate the external factor of students' laziness. This is because the closer the distance is, the easier it will be to take offline classes. Even though external factors were removed using this variable, the test scores of online students were still 2.08 points lower. After looking at this, we can conclude that online education lowers students' academic achievement.

However, a question arose as to whether it would be possible to leverage students' passion for studying beyond simple distance. While looking for various variables, I thought that the number of library visits could be used as an appropriate indicator of passion, as it is expected that passionate students will visit the library more actively. The calculation transformed into (3) - IV showed that students who diligently attended the library received 0.91 points higher scores, and the decline in scores due to online education was reduced to only 0.56 points.

Another question that arises here is how close the library is to the students' residences. Just as the proximity to an offline classroom was used as a major variable, the proximity of the library is likely to have had an effect on the number of library visits.

So (4) – After confirming that students who were assigned a dormitory by random drawing using IV calculations did not have a direct effect on test scores by analyzing the correlation between distance from the classroom and test scores, we determined the frequency of library visits among students in that group. and recalculated the gap in test scores due to taking online courses.

(5) – As shown in IV, with the variable of distance completely removed, visiting the library helped increase the test score by 2.09 points, and taking online courses actually helped increase the test score by 6.09 points.

As can be seen in the above example, the basic simple analysis of (1) leads to a misleading conclusion that online lectures reduce students' academic achievement, while the calculation in (5) after readjusting the problem between variables shows that online lectures reduce students' academic achievement. Students who listened carefully to lectures achieved higher achievement levels.

This is consistent with actual educational experience: students who do not listen to video lectures just once, but take them repeatedly and continuously look up various materials, have higher academic achievement. In particular, students who repeated sections and paused dozens of times during video playback performed more than 1% better than students who watched the lecture mainly by skipping quickly. When removing the effects of variables such as cases where students were in a study group, the average score of fellow students in the study group, score distribution, and basic academic background before entering the degree program, the video lecture attendance pattern is simply at the level of 20 or 5 points. It was not a gap, but a difference large enough to determine pass or fail.

Not because it is online, but because of differences in students’ attitudes and school management

The conclusion that can be confidently drawn based on actual data and various studies is that there is no platform-based reason why online education should be undervalued compared to offline education. The reason for the difference is that universities are operating online education courses as lifelong education centers to make additional money, and because online education has been operated so lightly for the past several decades, students approach it with prejudice.

In fact, by providing high-quality education and organizing the program in a way that it was natural for students to fail if they did not study passionately, the gap with offline programs was greatly reduced, and the student's own passion emerged as the most important factor in determining academic achievement.

Nevertheless, completely non-face-to-face education does not help greatly in increasing the bond between professors and students, and makes it difficult for professors to predict students' academic achievement because they cannot make eye contact with individual students. In particular, in the case of Asian students, they rarely ask questions, so I have experienced that it is not easy to gauge whether students are really following along well when there are no questions.

A supplementary system would likely include periodic quizzes and careful grading of assignment results, and if the online lecture is being held live, calling students by name and asking them questions would also be a good idea.

Asian companies convert degrees into years of work experience
Without adding extra values to AI degree, it doesn't help much in salary
'Dummification' in variable change is required to avoid wrong conclusion

In every new group, I hide the fact that I have studied upto PhD, but there comes a moment when I have no choice but to make a professional remark. When I end up revealing that my bag strap is a little longer than others, I always get asked questions. They sense that I am an educated guy only through a brief conversation, but the question is whether the market actually values ​​it more highly.

When asked the same question, it seems that in Asia they are usually sold only for their 'name value', and the western hemisphere, they seem to go through a very thorough evaluation process to see if one has actually studied more and know more, and are therefore more capable of being used in corporate work.

Typical Asian companies

I've met many Asian companies, but hardly had I seen anyone with a reasonable internal validation standard to measure one's ability, except counting years of schooling as years of work experience. Given that for some degrees, it takes way more effort and skillsets than others, you may come to understand that Asian style is too rigid to yield misrepresentation of true ability.

In order for degree education to actually help increase wages, a decent evaluation model is required. Let's assume that we are creating a data-based model to determine whether the AI degree actually helps increase wages. For example, a new company has grown a bit and is now actively trying to recruit highly educated talent to the company. Although there is a vague perception that the salary level should be set at a different level from the personnel it has hired so far, there is actually a certain level of salary. This is a situation worth considering if you only have very superficial figures about whether you should give it.

Asian companies usually end up only looking for comparative information, such as how much salary large corporations in the same industry are paying. Rather than specifically judging what kind of study was done during the degree program and how helpful it is to the company, the 'salary' is determined through simple separation into Ph.D, Masters, or Bachelors. Since most Asian universities have lower standard in grad school, companies separate graduate degrees by US/Europe and Asia. They create a salary table for each group, and place employees into the table. That's how they set salaries.

The annual salary structure of large companies that I have seen in Asia sets the degree program to 2 years for a master's and 5 years for a doctoral degree, and applies the salary table based on the value equivalent to the number of years worked at the company. For example, if a student who entered the integrated master's and doctoral program at Harvard University immediately after graduating from an Asian university and graduated after 6 years of hard work gets a job at an Asian company, the human resources team applies 5 years to the doctoral degree program. The salary range is calculated at the same level as an employee with 5 years of experience. Of course, since you graduated from a prestigious university, you may expect higher salary through various bonuses, etc., but as the 'salary table' structure of Asian companies has remained unchanged for the past several decades, it is difficult to avoid differenciating an employee with 6 years of experience with a PhD holder from a prestigious university.

I get a lot of absurd questions about whether it would be possible to find out by simply gathering 100 people with bachelor, master, and doctoral degree, finding out their salaries, and performing 'artificial intelligence' analysis. If the above case is true, then no matter what calculation method is used, be it highly computer resouce consuming recent calculation method or simple linear regression, as long as salary is calculated based on the annualization, it will not be concluded that a degree program is helpful. There might be some PhD programs that require over 6 years of study, yet your salary in Asian companies will be just like employees with 5 years experience after a bachelor's.

Harmful effects of a simple salary calculation method

Let's imagine that there is a very smart person who knows this situation. If you are a talented person with exceptional capabilities, it is unlikely that you will settle for the salary determined by the salary table, so a situation may arise where you are not interested in the large company. Companies looking for talent with major technological industry capabilities such as artificial intelligence and semiconductors are bound to have deeper concerns about salary. This is because you may experience a personnel failure by hiring people who are not skilled but only have a degree.

In fact, the research lab run by some passionate professors at Seoul National University operates by the western style that students have to write a decent dissertation if to graduate, regardless of how many years it takes. This receives a lot of criticism from students who want to get jobs at Korean companies. You can find various criticisms of the passionate professors on websites such as Dr. Kim's Net, which compiles evaluations of domestic researchers. The simple annualization is preventing the growth of proper researchers.

In the end, due to the salary structure created for convenience due to Asian companies lacking the capacity to make complex decisions, the people they hire are mainly people who have completed a degree program in 2 or 5 years in line with the general perception, ignoring the quality of thesis.

Salary standard model where salary is calculated based on competency

Let's step away from frustrating Asian cases. So you get your degree by competency. Let's build a data analysis in accordance with the western standard, where the degree can be an absolute indicator of competency.

First, you can consider a dummy variable that determines whether or not you have a degree as an explanatory variable. Next, salary growth rate becomes another important variable. This is because salary growth rates may vary depending on the degree. Lastly, to include the correlation between the degree dummy variable and the salary growth rate variable as a variable, a variable that multiplies the two variables is also added. Adding this last variable allows us to distinguish between salary growth without a degree and salary growth with a degree. If you want to distinguish between master's and doctoral degrees, you can set two types of dummy variables and add the salary growth rate as a variable multiplied by the two variables.

What if you want to distinguish between those who have an AI-related degree and those who have not? Just add a dummy variable indicating that you have an AI-related degree, and add an additional variable multiplied by the salary growth rate in the same manner as above. Of course, it does not necessarily have to be limited to AI, and various possibilities can be changed and applied.

One question that arises here is that each school has a different reputation, and the actual abilities of its graduates are probably different, so is there a way to distinguish them? Just like adding the AI-related degree condition above, just add one more new dummy variable. For example, you can create dummy variables for things like whether you graduated from a top 5 university or whether your thesis was published in a high-quality journal.

If you use the ‘artificial intelligence calculation method’, isn’t there a need to create dummy variables?

The biggest reason why the above overseas standard salary model is difficult to apply in Asia is that it is extremely rare for the research methodology of advanced degree courses to actually be applied, and it is also very rare for the value to actually translate into company profits.

In the above example, when data analysis is performed by simply designating a categorical variable without creating a dummy variable, the computer code actually goes through the process of transforming the categories into dummy variables. In the machine learning field, this task is called ‘One-hot-encoding’. However, when 'Bachelor's - Master's - Doctoral' is changed to '1-2-3' or '0-1-2', the weight in calculating the annual salary of a doctoral degree holder is 1.5 times that of a master's degree holder (ratio of 2-3). , or an error occurs when calculating by 2 times (ratio of 1-2). In this case, the master's degree and doctoral degree must be classified as independent variables to separate the effect of each salary increase. If the wrong weight is entered, in the case of '0-1-2', it may be concluded that the salary increase rate for a doctoral degree falls to about half that of a master's degree, and in the case of '1-2-3', the same can be said for a master's degree. , an error is made in evaluating the salary increase rate of a doctoral degree by 50% or 67% lower than the actual effect.

Since 'artificial intelligence calculation methods' are essentially calculations that process statistical regression analysis in a non-linear manner, it is very rare to avoid data preprocessing, which is essential for distinguishing the effects of each variable in regression analysis. Data function sets (library) widely used in basic languages ​​such as Python, which are widely known, do not take all of these cases into consideration and provide conclusions at the level of non-majors according to the situation of each data.

Even if you do not point out specific media articles or the papers they refer to, you may have often seen expressions that a degree program does not significantly help increase salary. After reading such papers, I always go through the process of checking to see if there are any basic errors like the ones above. Unfortunately, it is not easy to find papers in Asia that pay such meticulous attention to variable selection and transformation.

Obtaining incorrect conclusions due to a lack of understanding of variable selection, separation, and purification does not only occur among Korean engineering graduates. While recruiting developers at Amazon, I once heard that the number of string lengths (bytes) of the code posted on Github, one of the platforms where developers often share code, was used as one of the variables. This is a good way to judge competency. Rather than saying it was a variable, I think it could be seen as a measure of how much more care was taken to present it well.

There are many cases where many engineering students claim that they simply copied and pasted code from similar cases they saw through Google searches and analyzed the data. However, there may be cases in the IT industry where there are no major problems if development is carried out in the same way. As in the case above, in areas where data transformation tailored to the research topic is essential, statistical knowledge at least at the undergraduate level is essential, so let's try to avoid cases where advanced data is collected and incorrect data analysis leads to incorrect conclusions.