Aihearkisto: Articles in English

Social media as a marketing channel for FMCG brands in Russia

In modern world, social media plays an important role and have changed the way people communicate, search information about products or services, find media content. This development has changed the way businesses are promoted. This article is going to provide a basic understanding of social media concept, evaluate the role of social media in promotion of a FMCG (fast-moving consumer goods) brand in the Russian market utilizing the case of Company X.

Authors: Tamara Vladimirskaia and Marja Viljanen

Social media as a marketing tool

Social media are online tools and platforms that allow internet users to collaborate on content, share insights and experiences, and connect for business or pleasure (Strauss & Frost 2009, 329). Social media includes social networks, blogs, microblogs, photo, and video host services, forums blogs. Most of them have a unique, recognizable interface and work on a set of different technologies that make a certain website a unique communicational tool. (Neyaskin 2010, 7). There are several significant issues that distinct social media from traditional ones: the ability to share information immediately, less censored content, ability to edit and publish any kind of the material. Hence, many companies nowadays have shifted to social media instead of the use of television, newspapers, radio and magazines as marketing channels.

Social media marketing (SMM) is the complex use of social media websites and social networks to promote a company’s products and services. SMM is used to increase sales, brand awareness, attract new customers and partners, analyze the market and search for new opportunities for a business. The main channel in SMM promotion is promotion via social networks. Social networks are websites or apps that unify different people and provides an opportunity to communicate, share media, discuss opinions and search for information. Different social networks could be utilized for different purposes of the company.

Social networks and social media marketing in Russia

In Russia, social networks are popular among people of different ages, nationalities and social status. According to the latest statistics provided by State Statistics Service of Russian Federation (2018), 71.5% of the population in Russia aged 15-72 are active Internet users. Currently, 45% of citizens of the Russian Federation over 18 years old are currently using at least one of the social networks almost every day, 62% – at least once a week. Therefore, there are unlimited opportunities for different companies to use social media as a marketing tool. This is applicable to different types of business, including fast-moving consumer goods brands (FMCG). In the Russian market, the most popular social networks are YouTube, VKontakte, Odnoklassniki, Instagram and Facebook. VKontakte and Odnoklassniki are Russian-based social networks, launched by Russian web-developers in accordance with customer needs and preferences in the specific market.

Currently, social media are used by different size companies for their promotion. This usage is applied to convey the market position, become more customer-friendly and customer-oriented. (Saravanakumar & SuganthaLakshmi 2012). For an FMCG brand, such as Company X, social media practices are an excellent opportunity not only to attract new customers and increase sales but to create a channel to receive feedback, respond to comments and communicate directly with the consumer. For an FMCG brand, the recommendations for SMM strategies are the following:

  • Strategy planning for social media campaigns, clearly stated goals and prepared plan for tools utilization;
  • Content planning with proper media content that shows values of the company and specially designed for each publication;
  • Involving users of social networks and customers of a brand in a content creation to increase loyalty for a brand;
  • The diversity of the content used in promotional campaigns;
  • Budget planning for digital marketing activities in accordance with the needs of the company.
Research and findings

The empirical research done for the case Company has shown several significant issues that could be utilized by an FMCG brand. The empirical research was based on the analysis of the case company social media marketing activities, the survey of the customers and the interview with personnel. Even thought the research was conducted especially for the case company, the results are applicable to other companies in Russia in order to improve SMM activities.

The first key finding was a demand to improve the strategic planning. This includes the need to plan and schedule publications, the budget planning for tools and resources used and the general concept for the promotional plan, content plan development. Second, the content planning requires more attention. There are variety of media types that could be utilized to diversify the content in social networks. This may attract new customers and help to avoid user fatigue from brand content in social networks. Third, due to the specifics of Russian market the focus should be shifted to the social networks that are the most popular in this country. These social networks are YouTube, VKontakte, Odnoklassniki, Instagram and Facebook. All these networks have their own features, different audiences and user interfaces. Therefore, different groups of the target audience might be covered via diverse marketing channels. Furthermore, the implementation of contests and giveaways on a regular basis might stimulate users to create user-generated content and positively influence on the brand image. Finally, the utilization of different SMM tools, such as target advertising, advertising via influence agents, hashtags implementation, execution of pixels of the social network to the website, attracting traffic from the communities bring significant results.

The research results showed that the strategic planning, content planning, different social networks utilization, use of the diverse SMM tools could work in compilation for the stable and ongoing results. Social media is a great tool for promotion, attracting new customers and maintaining existing one. The Russian market with its own specific characteristics is a perspective for FMCG brands further development.


Neyaskin G. 2010. Social media influence on business communication. Dialog communication in business. Moscow: High School of Economics.

Saravanakumar M., SuganthaLakshmi T. 2012. Social Media Marketing. Life Science Journal 9(4):4444-4451. [Cited 23 Nov 2018]. Available at:

State Statistics Service of Russian Federation. 2018. Use of the Internet by Population of Russian Federation. Tables 3.1, 3.11. [Cited 25 Nov 2018]. Available at:

Strauss J. & Frost R. 2009. E-marketing. 5th edition. USA, New Jersey: Pearson Prentice Hall.

Vladimirskaia, T. 2018.  The Role of Social Media in Promotion of the Fast-Moving Consumer Good Brand in The Russian Market. Case: Company X.  Bachelor’s thesis. Lahti University of Applied Sciences, Faculty of Business and Hospitality Management. Lahti. [Cited 11 Dec 2018]. Available at:


Tamara Vladimirskaia has studied Business and Administration at Faculty of Business and Hospitality Management at Lahti University of Applied Sciences and has graduated and received a BBA degree in December 2018.

Marja Viljanen works as a Senior Lecturer at the Faculty of Business and Hospitality Management, Lahti University of Applied Sciences.

Illustration:  (geralt/CC0)

Published: 12.12.2018

Reference to this publication

Vladimirskaia, T. & Viljanen, M. 2018. Social media as a marketing channel for FMCG brands in Russia. LAMK Pro. [Cited and date of citation]. Available at:


Automation – Make the machine do the work

As information technologies evolve in a rapid pace, human race has always been trying to find the best use of computers, from complicated mathematical calculation to deep machine learning. One of the most common uses amongst those is automation. By manipulating the machine with programs using codes and scripts, humans can describe any repetitive tasks in a form for a machine to understand. This makes it possible for a machine to execute the tasks without any human’s need for supervision. In software development, automation comes in handy in product testing and deployment phase. In fact, humans can effectively replace the boring manual testing with automation testing, reduce time consumption and increase the product quality.

Authors: Quan Dao and Sariseelia Sore

Automation testing and its benefits

Manual testing refers to the concept of performing test cases using human interaction (Itkonen & al. 2009, 494). Traditionally, manual testing is accounted for a great part of the quality assurance process. However, it is noticeable that automation testing is slowly replacing manual testing, due to its ability of cross-browser testing with deep penetration based on the predefined test suite. By utilizing machine or software in automation testing, developers can execute a certain amount of test cases based on the test script written in a particular programming language without human obstruction. Later on, the results are compared with the expected outcome. (Dustin et al. 2009, 4.) Compared to the manual testing where the cost of time, money and effort remain a tremendous burden, automation testing appears to be a better solution for regression and time efficiency. (Nguyen et al. 2006, 28.)

 It is a good practice to evaluate and plan the testing method before any development. This can prevent big errors in further stage of the development as well as preserve time and resources. Test automation is a promising candidate when it comes to regression testing (re-test the preexisted product and functions that are being changed or developed). Regression testing helps to ensure that the newly introduced changes work with the old program version. Furthermore, any repetitive tests, which are not likely to change throughout the development, sprint or cycle, should also be optimized to be automated. Generally, these tests are high data-driven, which means that the same function can be entered with large different data or input. Those data can be imported from a database to the test case to run in a different environment. (Fernandes & Fonzo 2018.)

 How to control the website interface in automation testing?

Selenium is an open source software testing framework developed in 2004 by Jason Huggins under Apache 2.0 (Selenium 2018). It serves for the purpose of mimicking the end-users’ interaction with a web application by controlling a browser’s web driver. Selenium supports almost every popular web browser nowadays, and the test suite can be written with many languages (Selenium 2018).

Selenium WebDriver is the most popular tool in test automation for web applications. Figure 1 indicates that Selenium WebDriver enables developers to compile and run an end-to-end test suite with its API. Based on different test cases, the tool passes the Selenese test script to the Selenium Core and controls the browser without the need to connect to a remote server unless it is not the native machine (Avasarala 2014, 13).

Figure 1. Interaction of WebDriver (Avasarala 2014, 13)

Each individual element on the web page can be located using UI locators that include various techniques to locate the desired elements on the webpage for the machine to interact with. After being queried, the elements can be manipulated with different methods, such as: clicking on the elements, moving the cursor, typing and input in textbox, etc.  (Selenium 2018.)

The stack requirements for installing Selenium WebDriver using Java programming language are listed in the Table 1. These components are critical in order to run and perform any automation tasks on browsers.

Table 1. Components requirement

An example: Signing up unlimited Facebook Accounts on Chrome browser

Figure 2 below displays the content of a java page that the author has created to sign up new accounts for Facebook using automation. The aim of this task is to open Chrome browser, get to Facebook page and sign up new accounts using random emails that are generated with random numerical suffix. At the beginning, the machine will import necessary components to run the script, for example ChromeDriver, junit, and Selenium WebDriver.

The script should be chosen to be run with junit as junit is defined to be the test runner. Junit executes the script starting from the @Test anchor. In the figure 2, each line of code ends with comments explaining the purpose of the step.

Figure 2. Script to automate Facebook account signing-up

This example shows how developers can automate the signing up process on webpages with programming. As there is no assertions to evaluate the outcome to any expected results, this is not considered testing. However, for further development with automation testing, developers can add assertions to assess whether or not the outcome is desired. For instance, the value of the textbox can be stored and compared to certain input value to decide if it meets the requirements. In fact, the machine can check if the button is clicked and if any further notifications follows. These practices are possible because they are happening on the webpage itself, and the Selenium WebDriver can detect and interact with them.

The Figure 3 illustrates clearly that the driver is controlling the browser with the notification “Chrome is being controlled by automated test software”. In this example, the driver enters all the needed information on the sign-up form, and then clicks on the submit button. Additionally, all text fields can be parsed as parameters, or the data may be imported from a database. This example test script can be run as many times as required with different, randomly generated email address every time. With this implementation, unlimited number of Facebook accounts can be created by running a simple script. It took approximately 10 seconds for the script to sign up for one account. Whereas, for a human, to sign-up for Facebook, a normal process will take up to several minutes. Moreover, the result is at maximum accuracy compared to human results with repetitive tasks.

Figure 3. Facebook sign-up automated page

Prevent your website from malicious automation attack

It does not require much to set up and run an automation task on a web browser. That is why it is necessary for any website to have some protection method against potential automation attack. Using captcha for verifying humans and computers apart is by far the most effective technique for protecting a site against automation (Robert 2018). Captcha generates elements on the site with non-static ID, which makes it impossible for the pre-determined script to locate, thus get access to.


For automation, it is imperative to ensure the reliability of the scripts, and frequent maintenance to keep track with changes of the automated object. Website automation tasks depend heavily on the web elements, and sometimes they are slightly changed on different web browsers and web resolutions, which results in the failure of the test case or automation task.

Overall, automation has been widely adopted, and implemented in every aspect of technology. The resources and time to set up an automation system may be demanding at first, however, in the long term, the effectiveness of the result is significant. For example, by running an automation test suite for a web application in a start-up company, the development team can witness a 71.46% of time reduction compared to manual testing of all the required functionalities. In fact, the number is only concluded within a two-month development timeframe. The use of automation testing will most likely appear even more efficient with time. (Dao 2018, 52.)


Avasarala, S. 2014. Selenium WebDriver Practical Guide. Birmingham B3 2PB, UK: Packt Publishing.

Dao, Q. 2018. Implementing test automation with Selenium WebDriver. Case study: Bachelor’s thesis. Lahti University of Applied Sciences, Faculty of Business and Hospitality Management. Lahti.  [Cited 1 Nov 2018]. Available at:

Dustin, E., Garrett, T. & Gauf, B. 2009. Implementing Automated Software Testing: How to Save Time and Lower Costs While Raising Quality. New York: Pearson Education Inc.

Fernandes, J. & Fonzo, A.D. 2018. When to Automate your Testing (and when not to). Oracle. [Cited 1 Oct 2018]. Available at:

Itkonen, J., Mäntylä, M.V. & Lassenius, C. 2009. How do testers do it? An exploratory study on manual testing practices. In: 2009 3rd International Symposium on Empirical Software Engineering and Measurement. Lake Buena Vista, FL, USA. 15-16 Oct. 2009. 494-497. [Cited 1 Nov 2018]. Available at:

Nguyen, H.Q., Hackett, M. & Whitlock, B.K. 2006. Global Software Test Automation: A Discussion of Software Testing for Executives. Cuperrtino: Happy About.

Robert, E. 2018. 9 Recommendations to Prevent Bad Bots on Your Website. [Cited 2 Nov 2018]. Available at:

Selenium. 2018. SeleniumHQ. Browser automation. [Cited 2 Oct 2018]. Available at:


Quan Dao is a recent graduate at Lahti University of Applied Sciences, major in Business Information Technology. He finds joy in ensuring the quality of the product in the development process, with an aim to optimize the development and operation pipeline. He is interested in new opportunities, preferably in Quality Assurance and DevOps.

Sariseelia Sore works as Senior Lecturer in Business Information Technology Degree Programme at Lahti University of Applied Sciences, Faculty of Business and Hospitality Management.

Illustration: (Ilya Pavlov/CC0)

Published 11.12.2018

Reference to this publication

Dao, Q. & Sore. S. 2018. Automation – Make the machine do the work. LAMK Pro. [Cited and date of citation]. Available at:

Bioeconomy Revolution in Europe

The world is facing many challenges. The growing population demands more resources, including a safe food supply. At the same time, climate change, pollution, and biodiversity loss limit the availability. A disruptive and comprehensive solution can be offered by a sustainable and circular bioeconomy as it bridges environment, society and economy. The future success of Europe lies on how the bioeconomy is embraced by the EU citizens as a solution for improving our environment, providing us with renewable resources and healthy food, and offer jobs where people live, both in urban and rural areas.

Author:  Katerina Medkova

Sustainable & Circular Bioeconomy – the European Way

On October 22, 2018, the Bioeconomy (BE) conference was organised under the Austrian presidency by the European Commission and by the Austrian Federal Ministry for Sustainability and Tourism in the Charlemagne building, in Brussels, Belgium. The full title of the conference was Sustainable & circular bioeconomy, the European way. The reason for organizing this high-level event was to turn the spotlight on the update of the 2012 EU Bioeconomy Strategy, which was announced on October 11, 2018. (European Commission 2017; 2018)

Figure 1. Bioeconomy conference in Brussels gathered many participants.

The strategy defines BE as ”the production of renewable biological resources and the conversion of these resources and waste streams into value-added products, such as food, feed, bio-based products as well as bio-energy”. Bioeconomy is worth of € 2.2 trillion in turnover and employs over 18 million people in the EU. The sustainable and circular BE can generate up to 1 000 000 new jobs by 2030. BE can help us to reach the renewable energy targets of 27% by 2020 and 32% by 2030. (European Commission 2017; 2018)

According to John Bell, Director Bioeconomy, not one BE exists but many diverse bioeconomies, which Europe should harvest. BE pulls together all the aspects of the challenges Europe is facing, such as climate change, protection and restoration of natural resources, biodiversity and food security. Bell also stated that Europe is moving from an aspiration to the realisation of sustainable bioeconomy. Sustainability is not a concept; Bell explained it as a place the Europeans chose to live, work and nurture nature.

Carlos Moedas, Commissioner of Research, Science and Innovation, compared the BE potential to the introduction of lightbulb 139 years ago by Thomas Alva Edison. In the beginning, people were afraid of the revolutionary technology, so most of the population did not switch to the electric bulb until later on. Likewise, the BE potential is there but people cannot see it. This can be changed only if communication is done in a way that people understand, using storytelling and examples. Phil Hogan, Commissioner of Agriculture & Rural Development, referred to BE as not being “a pie in the sky”, as many successful examples already exist in Europe. BIOREGIO project also aims at promoting and sharing good examples and practices related to the bio-based circular economy. Concrete examples from different European regions are available from the project website, under the section Good Practices. (Interreg Europe 2018)

Another interesting comparison of BE is that of Anthony Luzzatto Gardner, the U.S. Ambassador to the European Union, who once stated: “The EU is like a window, you only realise that is there when there is a crack or dirt, but it always protects from the wind and the cold.”

During one of the panel discussions, Sveinn Margeirsson, CEO of Matis, pointed out the presence and use of plastic and glass water bottles during the event. He emphasized the need of courage to make a decision of no plastic bottles use and demonstrated it by throwing the plastic bottle away as better alternatives already exist.

Bio-based Products

BE is highly complex and touches many areas of our lives; the food we eat, products and energy we use, or clothes we wear. Therefore, bioeconomy and bio-based products are the tools to fight the climate change and dependency on fossil fuels. The challenge here is to let people see that these products are as good as or even better than the traditional ones. During the event, a bioeconomy corner was organised to showcase bio-based products, including compostable 3D printed cups and plates, cosmetics using insect protein, alternatives to plastic foil using beeswax wraps as well as clothes from coffee grounds.

Figures 2 and 3. Bio-based products displayed at the bioeconomy corner.


BE is the next economy the Europeans will have in the future; the European way as the title of the conference says. The updated strategy and the action plan can accelerate the BE development and a transition towards a circular and low-carbon economy. The huge BE potential can be unlocked only if everyone is on board and understand its benefits. BE touches everybody, everything and every day. Effective and clear communication is the key to nourish the collaborative spirit and together build the European sustainable future.


European Commission. 2017. Review of the 2012 European Bioeconomy Strategy. European Commission Directorate-General for Research and Innovation, Directorate F – Bioeconomy. Unit F.1 – Strategy. Luxembourg: Publication Office of the European Union. [Cited 05 Nov 2018]. Available at:

European Commission. 2018. Sustainable and circular Bioeconomy, the European way. European Commission. Bioeconomy Conference, 22 October 2018, Brussel, Belgium. Available at:

Interreg Europe. 2018. BIOREGIO Regional circular economy models and best available technologies for biological streams. [Cited 25 Nov 2018]. Available at:


Katerina Medkova works as a Communication Manager of the BIOREGIO project at LAMK.

All photos by Katerina Medkova.

Published 3.12.2018

Reference to this publication

Medkova, K. 2018. Bioeconomy Revolution in Europe. LAMK Pro. [Cited and date of citation]. Available at:

An insight on Circular economy in South Korea

After the industrial revolution, our economy and our environment has undergone tremendous changes. Consequently, we are facing challenges that drive us towards rethinking our ways of action. In this, developing circular economy provides a solution. This article presents a view on how South Korea is developing towards circular economy.

Authors: Seongreal Yun and Susanna Vanhamäki

Definition of circular economy

For a long time, our economy has followed a linear economy model, which means producing a product and throwing it away after use. Linear economy is also referred to as “take-make-use-dispose” (Lieder 2016). The last step is disposal, in other words, the product becomes waste. One of the traditional ways of handling waste has been landfilling. Landfilling has a direct impact on the environment as it contaminates the soil and e.g. toxic materials from the waste are absorbed to the ground water and pollute it.

Circular economy is a regenerative system were resource input and waste, as well as emissions and energy leakage are minimized by aiming towards closed material loops  (Yuan et al. 2006). Therefore, circular economy produces remarkably less waste than linear economy. The products can be restorative or regenerative thus the whole economy is able to be regenerative (Lieder 2016). If simplified, the “dispose” phase in linear economy would be replaced by “take” in circular economy. Eventually circular economy aims to remove non-recyclable waste and recycle the waste as a raw material. To develop circular economy long-lasting design, maintenance, reuse and recycling should be the basis (Ghisellini et al. 2016).

Circular economy in South Korea

Many countries are aiming to develop circular economy. According to OECD, South Korea had the second highest recycling rate country in OECD countries in 2013 (McCarthy 2016). Furthermore, the Korean ministry of environment (2017) has announced that it would enact the principle of resource circulation from 1.1.2018. According to the ministry, the resource circulation law includes parts like ‘circulation resource recognition’, ‘resource circulation performance management’, ‘cycle availability evaluation’ and ‘waste disposal fee’. It contains policies to reduce waste in all process like production, distribution, consumption and disposal of products and to promote recycling.

‘Resource circulation performance management’ will be enforced to plants that discharge designated waste over 100 tons or discharge other waste over 1,000 tons. It sets a customized goal for each plant for evaluating the circulation of materials and how much the plant needs to decrease waste. If a producer makes products which is hard to be recycled, ‘cycle availability evaluation’ would be applied to the situation. The policy requires the producer to reduce a material which cannot be recycled or make the product easier to recycle. An evaluation plan has to be made every three years continuously. If a plant is not making any improvement the situation will be revealed on internet. If a municipality or a producer landfill/incinerate waste, they have to pay ‘waste disposal fee’ which is 0.01-0.02 €/kg depending on the type of waste. Waste which is not harmful to the environment and is traded for a fee will be affirmed as a ‘circulation resource recognition’ if it satisfies standard regulations. Affirmed plants will be examined constantly if they fulfil the standard or not. (Korea ministry of environment 2017)

A Korean good practice in recycling

Repaper is a Korean company, which design and develop paper cups (Repaper 2018). Repaper invented a new paper cup which can be recycled in 2018. According to the Repaper website (2018), generally a paper cup isn’t biodegradable because it is coated with polyethylene which is plastic. Therefore, it can’t be recycled as paper or cardboard, nor composted. Paper cups cause a huge amount of non-recyclable waste. Repaper has developed a technology to coat the cup without plastic. This new paper cup has received several patents in e.g. South Korea, U.S.A and Europe. It is the world’s first food container made of paper to get a UL ECVP 2485, which is an Environmental Claim Validation Procedure for Recyclability of Paper-Based Products. It also has got a certification of DIN CERTCO which means it is compostable. In addition to the cup, the technology can be applied to the other disposable products. (Repaper 2018)

The innovative material in the cup is acrylate. Acrylate has hydrophobicity in normal circumstances, which can prevent water absorption to the paper. The coating can entirely dissociate and it has been proved that the cup can be recycled. The advantage of this cup is that it is also able to be used in microwave or oven. The cup can be recycled either as a paper or as a compostable product. (Lee 2018)


Even if the recycling rate in South Korea is high, it doesn’t mean that the Koreans live in a circular economy. To develop towards circular economy, South Korea should consider the concept in a holistic way. This includes developing circular design of products e.g. reducing the use of plastic and use more bio-degradable and bio-based products. Consumers are more and more interested in the types of materials than before and they are trying to find new eco-friendly products to use. The biodegradable paper cup of Repaper is a good example on the development. New similar innovations can be made to reach more circular solutions.


Ghisellini, P & Cialani, C & Ulgiati, S. 2016. A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems. Journal of Cleaner Production. Vol.114(15), 11-32. [Cited 30 Oct 2018]. Available at:

Korea ministry of environment. 2017. Enforcement of the resource recycling law. [Cited 12 Oct 2018]. In Korean. Available at:

Lee, O.S. 2018. Repaper got credit for recyclable paper cup in Europe. Hankyung. [Cited 31 Oct 2018]. In Korean. Available at:

Lieder, M. 2016. Towards circular economy implementation: a comprehensive review in context of manufacturing industry. Cleaner Production. Vol.115, 36-51. [Cited 6 Nov 2018]. Available at:

McCarthy, N. 2016. The Countries Winning the Recycling Race. Forbes. [Cited 7 Nov 2018]. Available at:

Repaper. 2018. rePAPER. [Cited 31 Oct 2018]. Available at:

Yuan, Z., Bi, J. & Moriguichi, Y. 2006. The circular economy: a new development strategy in China. Journal of Industrial Ecology. Vol.10 (1–2), 4-8. [Cited 6 Nov 2018]. Available at:


Seongreal Yun is an exchange student in environmental technology at LAMK.

Susanna Vanhamäki is working as an RDI Specialist and is the Project Manager in the BIOREGIO project at LAMK.

Illustration: (CC0)

Published 30.11.2018

 Reference to this publication

Yun, S. & Vanhamäki, S. 2018. An insight on Circular economy in South Korea. LAMK Pro. [Cited and date of citation]. Available at:

Improving the Economy of Universities of Applied Sciences using Artificial Intelligence

Finnish economy has been suffering for almost a decade now, and the field of education has also had to participate in the required savings of public resources. Focusing the reduced resources efficiently on targets and activities that best improve the measured results has become essential. Applications of artificial intelligence could help in identifying these targets and activities.

Author: Antti Salopuro

Battlefield of Universities of Applied Sciences

For the Finnish universities of applied sciences (UAS), the prolonged depression has caused a reduction of public funding by 22% (Arene 2018). In the current UAS funding model, 85% of the total funding depends on the UAS performance in educational measures such as number of degrees granted and progress of the studies (Opetushallitus 2014). The UASs have thus had to focus on making the students proceed faster with their studies and eventually graduate in higher numbers. As the motivation and capabilities of a student are important in enabling a successful study path on a personal level, a successful assessment of these measures and using them as criteria in student selection could be an asset for any UAS. Equally, identifying the students that most probably will have difficulties with their studies at an early stage could help in targeting supportive activities to correct individuals.

Improving selection of students

A successful match of students with different study programs has always had an impact, not only on UASs, but also on the success of the whole nation, and, therefore, it has been a topic of research. Rantanen (2001) identified this being a difficult problem in general, and specifically for Finnish UASs. The power of the entrance test in forecasting study success was found to be much poorer than previously assumed. In his follow-up research, Rantanen (2004) created stepwise regression models to explain success in studies with all information available at the time of applying to a UAS. In general, the average grade of the high school final certificate was found to be the most significant single factor (r2 = 0.18) in explaining the success. Also, matriculation exam grades were found to correlate to success in studies more significantly than the entrance exam scores. One impact of this can be seen in the methodology of the future application process of UASs. Starting in 2020, the principle of student selection will mainly be based on the grades of the certificate of the applicants from the previous education (Opintopolku 2018). The admission score of an individual applicant will be calculated as a weighted sum of a set of grades, which is both a practical and a transparent method. The weights applied are the same in almost all programs with only a few exceptions.

This new approach of student selection is thus even more straightforward and simpler than the current one and no longer allows individual UASs or their programs to decide on the criteria independently. But could a better solution for building such scoring models be found by applying artificial intelligence (AI) and by, again, acknowledging the requirements of the study program? Kabakchieva (2013) experimented with six different classifiers to model the university performance (output) of a set of Bulgarian students using some pre-university data (input). In addition to the secondary school final grades and entrance test scores , some background information such as the place and profile of the pre-university education was also used as input data. In general, the applied classifiers were able to predict with an accuracy of 60% or more only on three levels (1, 3 and 4) of the five-level scale. The classification of the students falling in either ‘average’ (2) or ‘excellent’ (5) classes were identified poorly, with accuracies of less than 10%. On the other hand, the ability to identify students that would finally perform the weakest were identified with an accuracy of 80%. It is exactly this kind of ability that would be helpful for a UAS to filter out the worst performing students from the group of applicants.

Identifying students in need of help

While the renewal of the entrance criteria, again, will most likely not be possible in the near future, another question could be made if there were any ways to make it easier for a UAS to identify the students that managed their ways into a program but have a high risk of being left behind or even drop out of the studies? This would allow, already in an early stage, focusing the limited student counselor resources on students for those it would be the most helpful. Huang and Fang (2013) studied the ability of four different methods, each considered to be an elementary part of the standard AI toolkit, to predict the student performance in the final exam of an engineering dynamics course. Six combinations of a set of input variables, consisting of the total sum of grade points collected so far and grades of seven previously studied courses, were applied. The results were promising: for the 24 different models (combinations of the methods and the variables) assessed, the authors report a minimum average prediction accuracy of 86.5%. This implies that even with a simple method (e.g. linear regression) and a single predictor (e.g. grade of single previous course) it is possible to foresee student success in a future exam accurately.

A natural follow up to the works referenced above would be to combine the prediction power of the pre-university grades with that of the university course grades in seeking for a better prediction of the university graduation performance. Asif, Merceron and Pathan (2015) have done exactly this: in addition to the pre-university grades (as in Rantanen 2001 and 2004), some marks from the first two years in the university were also included in the set of predictor variables as they predicted the overall success of students in the university level studies. The study applied two separate datasets, both consisting of data from two subsequent academic years (four full years covered), thus simulating a real life situation where the data of the previous year is used to build a model to forecast the performance of the following year. Splitting the data (N = 347) into four subsets has, however, resulted in relatively small sizes of both the predictor and the test data sets. This has, very likely, reduced the predictive power of the models created. Despite this, the paper reports some sufficiently high precision values, especially with respect to the use case of identifying the students that would best benefit from specific supportive activities. The students whose final grade would most likely be one of the two lowest (D or E) are, at best, identified with an accuracy of 89.5%. The best performances are achieved with models applying a decision tree with different hyperparameters. The study concludes that including both pre- and past-admission marks improves the prediction power of the graduation level grades significantly compared to cases where only one of these is used as a predictor.


From these few referenced examples it is possible to conclude that applying statistical models and artificial intelligence has potential in helping to recognize the applicants that most probably do not have the capabilities required for graduation and especially the students with the highest need for an intervention by a study counselor. Not selecting applicants that have a high risk of not ever finishing the degree would -even if it meant groups of program students being smaller- benefit all stakeholders. It would leave more resources to be targeted on students with good enough acquirements which would serve both students and teaching personnel. Accurate identification of students in most need of help would allow focusing the supportive resources such that it optimally improves the graduation rate and thus the most important measured result of UASs. Therefore, both these applications of AI would further help the Finnish UASs to manage themselves in the world with less public funding.


Arene.  2018. Osaaminen ja työllisyys kasvu-uralle – Amk-rahoitus kuntoon. [Cited 20 Oct 2018]. Available at:

Asif, R., Merceron, A. & Pathan, M. K. 2015. Predicting Student Academic Performance at Degree Level: A Case Study. I.J. Intelligent Systems and Applications.  Vol. 7 (1), 49 – 61. [Cited 20 Oct 2018]. Available at:

Huang, S. & Fang, N. 2013. Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computer and Education. Vol. 61, 133-145. [Cited 20 Oct 2018]. Available at:

Kabakchieva, D. 2013. Predicting Student Performance by Using Data Mining. Cybernetics and Information Technologies. Vol. 13 (1), 61 – 72. [Cited 20 Oct 2018]. Available at:

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Rantanen, P. 2001. Valintakoe vai ei? Ammatillisen Koulutuksen ja ammattikorkeakoulujen opiskelijavalinnan tarkastelua. Helsinki: Opetusministeriö. Koulutus ja tiedepolitiikan osaston julkaisusarja, 83.

Rantanen, P. 2004. Valinnasta työelämään. Ammatillisen koulutuksen ja ammattikorkeakoulujen opiskelijavalinnan tarkastelua. Helsinki: Opetusministeriö. Opetusministeriön julkaisuja 2004:19.[Cited 20 Oct 2018]. Available at:


Antti Salopuro is Senior Lecturer of Business Information Technology at Lahti University of Applied Sciences, private practitioner at Tietokutomo and student of Data Sciences at University of Helsinki. He believes that using natural wisdom could sometimes protect against problems that can be solved using artificial intelligence.

Illustration: (CC0)

Published 22.10.2018

Reference to this publication

Salopuro, A. 2018. Improving the Economy of Universities of Applied Sciences using Artificial Intelligence. LAMK Pro. [Cited and date of citation]. Available at: