Poproszono nas o wypowiedź do artykułu BRANŻA EDTECH W 2019 ROKU: PROGNOZY EKSPERTÓW I ZAŁOŻYCIELI FIRM Z CAŁEGO ŚWIATA, który ukazał się niedawno w serwisie news.elearninginside.com. Oto nasza część tej publikacji:
Cyfrowe narzędzia oceny dostarczają nam już ram i danych, które, przekazane do systemów kształcenia, ułatwiają zdobywanie wiedzy i jej ocenę w szkołach, domach i centrach edukacyjnych.
Michał Korpys, Dyrektor Produktu w NUADU
Prognoza NUADU dla branży EdTech na rok 2019
Michał Korpys jest współzałożycielem NUADU, opartej na sztucznej inteligencji platformy, która zmienia sposób, w jaki uczniowie osiągają cele edukacyjne, a nauczyciele przydzielają i oceniają zadania. W 2019 roku Korpys spodziewa się rosnącego wpływu obiektywnych danych w adaptywnej edukacji – i to według niego będzie ewolucyjne rozszerzenie procesu cyfrowej oceny ucznia. Liczy, że dzięki tej zmianie nauczyciele będą mogli poświęcać więcej czasu na to, co potrafią najlepiej: na wsparcie uczniów w osiąganiu celów edukacyjnych.
Prognoza NUADU dla branży EdTech na rok 2019
– Cyfrowe narzędzia oceny dostarczają nam już ram i danych, które, przekazane do systemów kształcenia, ułatwiają przyswajanie wiedzy i jej ocenę w szkołach, domach i centrach edukacyjnych – mówi Korpys. – Dojdziemy w końcu do momentu, w którym proces edukacji zacznie przebiegać płynnie. Nauczyciele będą dobrze wykorzystywać czas spędzany z uczniami, a uczniowie będą dobrze wykorzystywać czas poświęcany na zdobywanie wiedzy w najskuteczniejszy możliwy sposób. Systemy, które za tym wszystkim stoją, też będą działać optymalnie, zapewniając cyfrowe wsparcie edukacji adaptywnej i administracyjnej papierkowej roboty.
Prognoza NUADU dla branży EdTech na rok 2019
Nie są to jednak jedyne prognozy Michała Korpysa na 2019 rok.
– W 2019 roku ludzie będą też bardziej otwarci na spersonalizowane ścieżki nauczania wykorzystujące sztuczną inteligencję – mówi Korpys. – Jeśli chodzi o samodzielną naukę, nic nie przebije dobrego nauczyciela albo prostego cyfrowego narzędzia, które będzie nam towarzyszyć, przewidywać naszą ścieżkę nauczania i dopasowywać ją w czasie rzeczywistym do naszych celów edukacyjnych.
Korpys podkreśla, że aby tak się stało, interakcje na linii człowiek-komputer muszą rozwijać się i stawać coraz bardziej złożone. Wierzy, że to właśnie nastąpi w nadchodzącym roku.
Prognoza NUADU dla branży EdTech na rok 2019
Na koniec, Michał Korpys ma nadzieję, że w 2019 roku będziemy poświęcać coraz więcej uwagi umiejętnościom miękkim:
– Świat bardzo tego potrzebuje, a my musimy zacząć tworzyć nową kulturę, której podstawą będzie dążenie do doskonałości w tej dziedzinie. Zrównoważony rozwój: ekologia, recykling, walka z plastikiem – to wszystko jest częścią powstającego paradygmatu, w którym rozwiązania złożonych problemów wynikają z empatii, są oparte o analizę danych i powstają dzięki innowacjom.
We were asked to provide the input for the article EDTECH IN 2019: EXPERTS AND FOUNDERS AROUND THE WORLD SHARE PREDICTIONS, recently published by news.elearninginside.com. Here is our part:
Digital assessment tools already provide us with the data and framework to feed it back to learning systems to facilitate knowledge acquisition and evaluation in schools, homes, and learning centers.
“Digital assessment tools already provide us with the data and framework to feed it back to learning systems to facilitate knowledge acquisition and evaluation in schools, homes, and learning centers,” observes Korpys, “Eventually we’ll get to the point of ‘smooth education,’ where teachers have quality time teaching students, students have quality time learning in the most efficient way, and the systems running underneath have quality time as well; performing their tasks, deep within within the digital realm of adaptive learning support and dungeons of paperwork administration.” But these are not Korpys’s only predictions for the 2019.
“In 2019,” says Korpys, “I also see people growing more open to AI-led personalized learning paths. When it comes to self-study, nothing can beat a good teacher, or a simple digital companion that is able to predict your learning path and adjust it to your learning goals in real-time.” But for this to work, Korpys emphasizes, the complexity of human-machine interactions needs to keep growing. He’s optimistic this will continue to happen in 2019.
Finally, Korpys is hopeful that in 2019, we will also increasingly turn our attention to soft skills: “The world needs it badly and we need to build a new culture based on excellence in that. Sustainable development: ecology, recycling, plastic war–all that is part of the emerging paradigm where solutions to complex problems are animated by empathy, based on big data analysis, and solved by innovations.”
The Five Senses NUADU team took part in the English Teachers Association NSW conference #LetsCreate. Dozens of English teachers from Australia offered valuable feedback and insights on how the Australian school system can benefit from implementing the NUADU platform. The main topic was how to make formative and personalised learning available to everyone. #Australia #NUADU #FiveSenses #EdTech #Learning
Adaptive learning and machine learning are not techniques that have emerged over the last few years with the spreading of computers and smartphones. The beginnings date back to the 1960s and Arthur Samuel from IBM, who developed the first chess training program.
But if we take a look at the wider definition of adaptive learning with the technology section excluded, adaptive learning being an educational system that analyzes students’ performance in real time and modifies teaching methods based on these data, we will notice that there is nothing radically new about it. In fact, it describes the very kind of work that a teacher performs or should perform when working with students. In such a system, the teachers apply their intuition, professional experience and knowledge of the students. The pedagogue’s experience will always have a huge impact on the child’s educational success; however, on the one hand it will be only based on the teacher’s personal abilities, and, on the other hand, is time-consuming and difficult to implement in the classical (school-paper-based) learning model. Only the use of technology may enable its wider use.
At the moment, we can find many products that offer adaptations to the individual abilities of the student. The most-used solution is tailored to their abilities and skills by proposing individual learning paths for students. We can group them into several types:
Students create paths manually
In products of this type, a student who wants to master the subject they is interested in has ready access to all materials and selects relevant fragments relying on their own knowledge of what they wants to learn, thus creating a personalized learning path on their very own. This solution allows the student full personalization of the learning paths because they choose only those things which seem important and necessary.
The obvious disadvantage of this solution is that it only offers partial knowledge if a given student works individually, without consulting a teacher or a tutor. In addition, not many students are fully aware of their educational needs and how to compose their own learning path. Therefore, an alternative solution is to engage the teacher.
Teachers create paths manually
In this product type, the teacher manually creates individualized paths for individual students or groups of students, deciding which exercises they should do as well as the level of difficulty or what presentation materials they should familiarize themselves with, and in what order.
This method, despite being very beneficial from a didactic point of view, nonetheless implies a huge amount of work and time on behalf of the teacher. Although grouping students with specific abilities and skills together allows to save a certain amount of time, such grouping is once again based on the teacher’s experience and in-depth knowledge of students, which in practice is not possible for teachers with dozens of students to take care of. Therefore, another idea for the student and teacher is to rely more on technology to work and prepare ready-made paths that both teachers and students can use.
Predefined graph of dependencies
Solutions of this type offer a system that proposes to students various educational paths that they can follow while learning. Typically, they are organized as predefined sets of sequentially arranged exercises and knowledge presentation materials. The system draws paths for a student using the rule-based method with a decision tree, where decisions are made depending on the results of a given pupil. Thus, a student gets access to the very materials that should fit them and ensure a learning process that corresponds to their skills and abilities.
However, this method is far from flawless as well. The main disadvantage of this type of solution is the incomplete personalization of learning paths and the fact that the links between fragments of material are predefined — in other words, developed by a specific person based on their personal knowledge, and said paths do not change even if they are ineffective for a particular student or group of students.
Additionally, such solutions are usually limited only to mathematics or chemistry, for which the creation of connections between fragments of material comes somewhat easier. In some e-learning systems, the decision to choose a specific path and a set of content for a given student is based on the results of the initial diagnostic test performed by the student after entering the system. This is a good method to test a student’s level of knowledge; however, it is often limited to detecting said student’s knowledge gaps and offering material to eliminate them without providing opportunities to develop other skills that a student could improve.
AI learning paths
All the above-mentioned methods have two basic disadvantages, namely that they either fail to offer exactly matched learning paths, or that they require a lot of work. These problems can be prevented thanks to a system which, by collecting data from a given student and other users and processing them with the help of sophisticated algorithms based on mathematical models, probability and statistical reasoning, will suggest to the student appropriate materials at the appropriate time and in just the right order. Scientific research on teaching methodology, nervous system physiology, and pedagogical methods provides solid data on the learning process that should be implemented in such a system — after all, it is used to teach human beings, not machines.
On the other hand, it is very important to be able to see the results of the students’ interaction with the system and modify the rules in real time. This is possible due to the huge number of online learners, based on whose performance the system can determine what works and what does not work thanks to the power of big data and analytics. It can also learn what behaviour patterns are the most effective. The use of AI also allows a holistic approach to learning covering aspects such as diagnosis, gaining theoretical knowledge and practical skills through exercises, as well as maintaining motivation and regularity. In addition, a virtual AI-based tutor is never tired, has no prejudices, and does not build student feedback based on a personal attitude towards a given student. Such a teacher is always available ready to respond to any change in a student’s behaviour, and capable of anticipating possible risks.
In NUADU, by creating an adaptive learning system with AI support, we endeavour to take into account all the mentioned advantages and disadvantages so as to create a perfect system that will be a great support for students and teachers alike. Therefore, the whole solution is planned to be reliant on something other than modern technology per se — namely. a plethora of scientific discoveries made in such fields as didactics, neurobiology, and psychology. We want to use what is most valuable in the most important theories of learning, such as Behaviourism, Cognitivism, and Constructivism. Our idea is to support the complex learning/teaching process via employing educational diagnosis, creating individual learning plans, gaining theoretical knowledge, and building practical skills. We also want to give students the opportunity to fill in the gaps in knowledge and skills and consolidate gained knowledge effectively.
Summary
A system offering individualized learning paths for each student matching what they should learn at any given moment could be an ideal solution supporting learning and teaching, but what is the reality now? The truth is that such solutions are at an early stage of development, and though our knowledge about learning is growing, it is still in its infancy. Despite the fact that the future looks really promising, we still have to wait for it to come — most likely, for such a system to be created by NUADU.
Wojciech Wiśniowski is a biotechnologist — with an emphasis on “technologist”. A tech geek, always happy to spend a fortune on state-of-the-art gear to stay ahead of the curve. A devoted product manager and product owner with over 10 years of professional experience in discovering the impact made by modern technology and the possibilities it offers in enhancing and facilitating the learning and teaching process. And he is always delighted to share his passion with others.
We are delighted to inform that on October 12, NUADU signed a contract with the city of Oleśnica.
The agreement made our solution available for the students and local schools for 6 years, along with the educational content – 50.000 exercises, games, tests, images, and films from all subjects covering Polish national curriculum. The tender NUADU successfully participated in was a part of the implementation of the “e-Learning II” (“e-Oświata II”) project.
Tego lata zorganizowaliśmy serię przemiłych spotkań z uczniami, nauczycielami, dyrektorami szkół, naczelnikami oświaty oraz włodarzami miast, którzy korzystają z NUADU lub którzy swoimi decyzjami przyczynili się do popularyzacji naszego rozwiązania.
Pragniemy im wszystkim serdecznie podziękować za słowa uznania, entuzjazm i udział w naszym przedsięwzięciu. Wasze uwagi – szczególnie te krytyczne 😉 – mają bezpośredni wpływ na kształt #NUADU. Tworzymy NUADU dla Was. Dziękujemy! #NowaSzkola
Over the course of the last summer we have organized a series of amazing encounters with students, teachers, headmasters, and city authority representatives who use NUADU, or who have contributed to the popularization of our project.
We would like to thank all of them for their appreciation, enthusiasm and participation in our undertaking. Your comments—the critical ones in particular—have a direct impact on how #NUADU develops. NUADU has been created for your benefit. Thank you!
Encounters with Teachers in Poland, Singapore, and Jakarta
October 2018 was a very fruitful month for NUADU. We had a series of meetings with school teachers, directors and heads of municipalities, mayors of cities. We visited the 5th ICT Congress in Education in Toruń and the VII Forum of Directors of Education Institutions in Katowice. We conducted interviews and surveys with users of our platform in Gdynia, Singapore, and Jakarta to take a closer look at their everyday work, learn about problems and needs. We will present our findings in the research report in Q1 2019! Watch this space or subscribe to our newsletter on www.nuadu.com!
