Academic Portfolio

I Pedagogical Activity

Teaching Experience

Supervision

20 master's and 17 bachelor's theses have been defended under my supervision since 2007. In addition, I have been a supervisor for 16 research papers in the Core Study. Currently I am supervising one doctoral student; the topic of her thesis is "Economic Growth and Financial System Development in Consolidating and Globalizing Environment".

Teaching Philosophy

I have met many business people who have told me that they would never use statistics because they consider statistics as complicated calculations. In my opinion teaching statistics should motivate people to use statistics in their work and everyday life without any terrifying complexity.

My personal teaching philosophy includes the following statements:
• The best learning method is “learning by doing”. It is not enough to read a textbook or to attend lectures, students should analyse data independently.
• Examples and problems from the real life increase students’ motivation. Therefore, every new topic should be started with some examples.
• The aim of my courses is not only to give knowledge about methods and skills to use them, but also to improve students’ analytical and logical thinking.
• Each student is an individual who has different ways of learning. Therefore, I use several techniques to communicate important information clearly and in varied ways.
• Lecturers should continuously enhance their professional skills to improve their knowledge and develop the teaching methods.

Teaching Methodologies

Learning statistics is impossible without practical data analysis. However, until this year there was no statistics textbook with downloadable data files written and published in Estonian. Nowadays it is not a reader-friendly approach to publish printed data tables so that one must independently enter the data to calculate examples and to solve data analysis problems. This was the main reason to publish my statistics textbook (Sauga, A., Statistika õpik majanduseriala üliõpilastele, 2017, TTÜ Kirjastus, 766 lk). The textbook was published under the programme of the Estonian language textbooks for higher education institutions, funded by the Ministry of Education and Research. The programme supports publishing Estonian study and research literature and the main goal is to develop the scientific terminology of the Estonian language.

The textbook introduces basic statistical methods and their application in solving various business and economic problems. Guidance for the statistical data analysis in Excel and LibreOffice Calc is provided. There are nearly 200 examples in the textbook, most of which can be found in the spreadsheet files attached to the textbook. Example files include calculations and explanatory comments. There are over 400 exercises to be solved independently, about half of which need to be solved in the spreadsheet, using the data from the attached files. All exercises are equipped with answers. The vast majority of the data used in examples and exercises comes from the real life. The sources of data include online databases, scientific publications, published surveys, supervised student works, etc.

I have been using the possibilities of Internet to support my courses since the end of the 90's. At first I made all teaching and learning materials (lecture notes, spreadsheet files) downloadable from my homepage. Later I included some screen videos, made with software Wink. As the feedback about performance and self-assessment are important in the learning process, I learned HTML language and Javascript to design and build quizzes including multiple-choice and true-false questions.

Nowadays there are many e-learning environments, which carry out this kind of tasks. In TUT we use an open-source learning platform Moodle. All my lecture courses are supported by the corresponding e-course in Moodle. There are lecture slides, data files, self-tests, forums and links. For example, the question bank of the Statistics course contains 230 questions in various formats, grouped by the topic. These questions are used to build self-assessment quizzes to help students quickly see what they know and do not know. Wrong answers are provided with comments. There is a quiz after each lecture and students should complete it before the next lecture. This motivates students to study consistently during the semester.

Data Analysis Software

Statistics and Econometrics courses both have computer labs in the schedule, but it is important that students have a possibility to deal with practical data analysis independently. Thus, they should have the opportunity to use the suitable software.

In Statistics course we use MS Excel in the computer lab. All TUT students have free access to download and install MS Office in their own computer and thus they can use Excel outside the computer lab too. Their second option is to use freeware LibreOffice. Instructions for the statistical data analysis in Excel and LibreOffice Calc are presented in my textbook.

In Econometrics course for masters students we use econometrics software Gretl (http://gretl.sourceforge.net/). It is free, open-source software and students can download and install it in their own computer. Its main features are: easy intuitive interface, a wide variety of estimators, time series methods, models with limited dependent variables and panel-data estimators. To support students I have written the online user guide for Gretl in Estonian (http://www.sauga.pri.ee/gretl/).

In doctoral courses we use econometrics software Stata and EViews.

Assessment Methods

• it is impossible to become a good practitioner without knowing the theory;
• complicated calculations can be left to the computers, a human should interpret the results.

1. A test paper: performing statistical calculations by calculator. Topics are: statistical averages and measures of variation, probability, index numbers.
2. Students' activities during the semester: participation in computer labs and activity in the Virtual Learning Environment Moodle.
3. Examination consists of two parts.
   • Part 1. A quiz in the Moodle (terms, concepts).
   • Part 2. Data analysis in Excel and interpretation of results; four problems with different complexity.

The test paper accounts for 20%, student's activity 10% and exam 70% of the final grade.

1. A test paper: a quiz (terms, concepts) and a practical problem (evaluation of model parameters, testing, interpretation of results).
2. Home assignments where students are expected to use some data to set an econometric model, evaluate the parameters, test the model and write a report.
3. An examination consists of two parts.
   • Part 1. A quiz in the Moodle (terms, concepts).
   • Part 2. Four different practical data analysis problems: evaluating of models' parameters, testing models, interpreting results.

After completing the test paper and the exam all students receive an individual feedback in Moodle.

The test paper accounts for 20%, home assignment 25% and exam 55% of the final grade.

II Academic Excellence

Teaching is very important for me and I put a lot of time, energy and emotion in it. I am greatly interested in how effective my efforts have been and therefore I observe the students' feedback every semester, which I find very useful. The average score for the Statistics course (Spring 2016) was 4.58 and the average of Econometrics course (Fall 2016) was 4.9 according to the students' feedback.

• “Throughout the whole course it was seen that the lecturer is an indisputable expert in his field. As regards the working methods and the availability of materials, each TUT lecturer should take the lead. Excellent!”
• "The lecturer has been very thoroughly preparing the materials, self-tests, exercises to enable the subject to be acquired. He uses many Moodle's technical options for teaching and he should be the role model for all other lecturers."
• “The lecturer responded quickly to messages, was helpful and supporting for students. We solved many problems in the computer labs that provided a good overview of how to work on the subject.”

• Self-analysis. After every lecture, I analyse the students’ response: whether the examples were interesting and understandable or I must change something. Then I write down the memos to use them next academic year.
• Analysing the quiz results statistics in Moodle. This allows a teacher to see quickly which questions are causing problems.
• Communication with colleagues. It is useful to communicate with the lecturers of the other courses (Economics, Finance) to get information, which topics I have to include in my Statistics and Econometrics courses or what is less important.
• Supervision and reviewing students’ theses and participation in the defence committees gives also useful information, how students apply statistical and econometric methods and what is needed to be improved.
• Reading methodological articles and Internet posts of other statistics lecturers gives ideas to implement new teaching methods. In addition, at TUT there are seminars “From a lecturer to a lecturer”.

Individual Development

Future plans:
• to develop my academic English;
• to improve using statistical software R;
• to familiarize myself with Big Data for the purpose of understanding which ideas in applied statistics are relevant for Big Data analysis.

III Management in the Field of Education

IV Other

Learning Materials for High School

From 2011 to 2013 I participated in the project which goal was to create teaching and learning materials for the high school mathematics course "Foundations of Mathematical Economics" (project number K103). Our team prepared the materials for 35 academic hours: a textbook, slides and methodical material for teachers, e-course in Moodle.

In 2013 our team won the public procurement to create teaching and learning materials for the other high school mathematics course "Mathematical applications, study of real processes". We prepared the materials for 35 academic hours: textbook, computer based tasks, interactive demonstrations in CDF format, slides and methodical material for teachers and e-course in Moodle.

Invitations

In summer 2013, I was invited to give a presentation on the Math Teachers' Days. I presented two new sets of learning materials, which were prepared by our team.

In 2011, I was proposed to write some articles for the journal "Raamatupidamise Praktik" ("Accounting Practitioner"). As a result, six articles about the statistical averages, percentage calculation, logarithms, index numbers, present and future value of money and annuity were published.

In April 2007, I received an invitation to lead a workshop in economic analysis for the employees of the Statistics Estonia.

In Nov, 2006 we performed two radio broadcasts with journalist and editor Priit Ennet on the topic "Can noise be useful?".

Teaching Awards

V Appendices

References to online materials.

Syllabuses of Statistics, Econometrics and Time Series Econometrics

• Lecture slides "Sampling and estimation with confidence intervals, I" (in Estonian)
• Data files of my statistics textbook (in Estonian)

• Lecture slides "Stationary time series" (in Estonian)
• Exercises "ARIMA modelling" (in Estonian)
• An example of students' homework, econometrics project (in Estonian)

• Extended syllabus
• Lecture slides "Modelling volatility"
• Exercises "Modelling volatility"
• Final examination data analysis problems

Interactive demos in Statistics and Econometrics in CDF format.

• How to add the Excel Data Analysis Toolpak, screen video (in Estonian).
• How to use the Excel FREQUENCY function, screen video (in Estonian).
• How to calculate weighted average in Excel, screen video (in Estonian).
• How to calculate mean, median and mode in the case of frequency table in Excel, screen video (in Estonian).
• How to calculate the range, standard deviation, coefficient of variation and inter-quartile range in Excel, screen video (in Estonian).
• Gretl online manual (in Estonian)

Average marks of student surveys.