Back to search

NAERINGSPH-Nærings-phd

Enabling personalized education through the use of machine learning and learning analytics

Alternative title: Muliggjøring av personalisert læring ved hjelp av maskinlæring og kunstig intelligens

Awarded: NOK 1.6 mill.

Project Manager:

Project Number:

333381

Application Type:

Project Period:

2021 - 2027

Funding received from:

Organisation:

This project will use machine learning to support student learning. In order to improve the student's learning journey, we will develop algorithms that create student profiles based on the student's digital activities and algorithms for encoding educational resources into a uniform and comparable format. We will build the student profiles as a natural part of the learning process instead of relying on standardized tests. Initially, we will focus on filling the profiles with the reading and writing skills of the students, and from there, we aim to expand into explainable insights into how the student learns best. As machine learning models generally require input in a standarized format, these encodings will provide a powerful and straightforward starting point for many AI applications for the education sector. Our algorithms for encoding educational resources will be able to handle any data modality combination (pdf-files, videos, educational games, etc. ). A big problem with research in this field is that theories and frameworks lack real-world testing. We will address this issue by implementing the Ph.D. candidate's prototypes into the Neddy platform and testing them with our partner schools.

This project plans to employ machine learning to support student learning. The project's core is student profiling, enabled by AI and interpretable by humans to assist teachers, parents, and students in understanding how the student learns best. We plan to create a machine learning framework and a fully functional prototype that will seamlessly build profiles while the student works integrated into the learning experience without explicit testing. The framework will also aid teachers in creating content for their class, optimizing the learning activities to account for the profiles of the students in class. Lastly, we will focus on the individual student's learning process and investigate if AI can improve the students' learning strategies and techniques. The project will utilize and advance the field of multimodal neural networks by first applying such networks to dyslexia detection and on the remaining objectives. We will build on the recent development for the NorBERT model by using it as an information encoder of Norwegian classroom content. The core of student profiling will fill address an open research question of skill assessment for adaptive learning systems. We will also create a novel dataset by cooperating with Norwegian schools to advance research endeavors in AI in educational technologies. We plan the following academic papers: Paper 1: Dyslexia detection with deep neural networks Paper 2: Dyslexia detection with multimodal networks Paper 3: Big data and ANN techniques for content recommendation and adaption. Paper 4: Optimizing classroom content based on student profiles Paper 5: Optimizing study techniques based on student profiles Paper 6: Evaluation of AI optimization for real-world Norwegian classrooms During the project phase, the candidate will stay a minimum of one year at the company (where the candidate has a specified desk during the entire period), and a minimum of one year at UiA (the candidate has available desk at UiA).

Funding scheme:

NAERINGSPH-Nærings-phd