Digital Culture by Elena Mikhalkova

This course is about digital culture. Here you will learn how to correctly collect, process and visualize data based on a specific task. The task is set by the teachers of the course “Russia and the World”. While doing that course, you will gather a lot of information. It needs to be presented to people who are interested in it, or it can even improve the world around them. In the 21st century, the best way to share information is via the Internet. This is where your digital culture becomes so valuable. This course will help you choose the optimal digital environment for your data and create a website, a game or another digital product that will be available to users 24/7.

Another important point: you will carry out the project as a team. If you already have a good command of information technologies, you can offer your team an unusual way of presenting the data. The main thing is to convince the team that they do it your way #humor. However, if you keep clear of technologies, this is a great chance to get out of your comfort zone and acquire new skills in one of the most widespread areas of human activity: IT and computer science.

My name is Elena Mikhalkova. I am an associate professor (Dozent) of the English language department at the Institute of Social Sciences and Humanities, Tyumen State University. In 2009 I received my PhD in Philology, and in 2015 I graduated with a Master's degree in Applied Informatics (in Economics).

For over 10 years I have been working in the field of computational linguistics. It is a discipline at the intersection of linguistics and computer science; it is also a part of artificial intelligence. Computational linguistics is also closely related to data analysis. In my profile on ResearchGate you can learn more about my scientific projects. I am currently developing three projects:

Computer analysis of the narrative in a literary text.
Automatic extraction of figurative speech (statements with a figurative meaning; ambiguous statements).
Determining the interests of users in social networks such as Vkontakte and Twitter.

You can use computational linguistics and data analysis in your projects, too. But this requires programming. Even if you don't have programming skills, I can help you master the most basic text and image processing algorithms in Google Colab. All you need is a Google account.

If you have people on your team who are fairly fluent in programming and want to use these skills in your project, then I can suggest more sophisticated tools. Together we can write a program that collects data from the Vkontakte social network, communicates with the user (a chat-bot), or recognizes some types of texts and images.

The course consists of six lectures, ten practical and ten laboratory lessons, the pre-defense and the final defense. Points for lessons are distributed as follows:

A lecture: 2 points (the total of 6 * 2 = 12 points).
A practical lesson (a seminar): 3 points (10 * 3 = 30 points).
A laboratory lesson (a lab): 4 points (10 * 4 = 40 points).
The pre-defense (when you submit your project for a preliminary assessment by the teacher in labs): 23 points.
The defense (exam): 15 points.

Please note that the pre-defense is one of the most important stages of the project, which is worth even more points than the exam. Usually, the pre-defense involves three steps:

The student submits the finished project to their laboratory teacher before the deadline. Most often, the last laboratory lesson is considered the deadline, but the teacher can extend the deadline. (up to 10 points)
The teacher evaluates the quality and readiness of the submitted project. (up to 8 points)
Self-assessment: You assess work of you teammates on the project. (up to 5 points)

At the pre-defense, each teacher can evaluate the success of the project in their own way, but there are a number of general criteria that are often applied in the assessment. These criteria are enlisted in my checklist. Save it to your device or print out. Go through this checklist when you approach the pre-defense and defense. NB! Each project is unique, so some of the criteria might not apply.

If the document does not show, click on the link.

Check-list_UTMNDC

At the exam, projects are graded based on the following features:

Consistency and clarity of the presentation: from -2 to +2 points.
Product readiness: from +1 to +3 points.
Feasibility of using IT tools: from +1 to +3 points.
Usability: from +1 to +3 points.
Compliance with copyright: from -1 to +1 points.
Variation of data presentation means: from +1 to +3 points.

The exam is "double", i.e. it is held simultaneously for two courses: "Russia and the World" and "Digital Culture". The examination committee consists of three teachers of R&W and two teachers of DC.

The exam is conducted in the form of a project presentation. Typically, students have ten minutes to speak about their project and five minutes to answer the questions. You can get 15 points maximum for your defense. These points are awarded to each member of the team, but for those who waived the project.

The final grade is based on the amount of points that you scored for the course:

91 points and above: "Excellent".
From 76 to 90 points: "Good".
From 61 to 75 points: "Satisfactory".
Less than 61 points: "Unsatisfactory".

If you did not show up for the exam, you have no valid reason for absence and you did not warn the office about it in advance (at least before the start of the exam, if it is force majeure), then "Did not show up" is put on the exam list. It doesn't matter how many points you got before the exam. If you “did not show up,” you can only retake the course in the next semester. The same applies to those who got "Unsatisfactory".

If you were not present in class, you cannot make up for it outside class or receive an additional assignment at the end of the semester.

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