Adaptive Study Management Platform and Recommendation using Artificial Intelligence
Spahr, Bryan (2019)
2019
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2019052812454
https://urn.fi/URN:NBN:fi:amk-2019052812454
Tiivistelmä
This paper has been written as part of my studies in Business Information Technology at the Haaga-Helia University of Applied Sciences. The following research corresponds to my bachelor thesis. The goal of this paper is to present and comment the development of an adaptive study management platform that uses different subfields of artificial intelligence.
In collaboration and with Dr. Amir Dirin, we decided to resume the original idea and work of Dr. Laine and himself “Towards an Adaptive Study Management Platform: Freedom Through Personalization”. We put the theory into practice and developed a solution for both teachers and students regarding study in general.
The project we developed is a full stack application composed of several layers. Its architecture is a three-tier architecture that includes a presentation tier, a logic tier and a data tier. The project includes a mobile application for students to study, an adaptive website for teachers to manage their courses, a back-end server with an API and a NoSQL database to save and store the data.
The objective of this solution is to provide students a modern and new way to study and teachers an adaptive and proactive way to teach. The end goal is to apply artificial intelligence subfields such as data mining and machine learning to provide students a personalized study path recommendation.
The following paper covers an introduction part, research questions and methodology, discussions about related research, a design and implementation part, results to some study and test cases alongside with a general discussion and recommendations.
In collaboration and with Dr. Amir Dirin, we decided to resume the original idea and work of Dr. Laine and himself “Towards an Adaptive Study Management Platform: Freedom Through Personalization”. We put the theory into practice and developed a solution for both teachers and students regarding study in general.
The project we developed is a full stack application composed of several layers. Its architecture is a three-tier architecture that includes a presentation tier, a logic tier and a data tier. The project includes a mobile application for students to study, an adaptive website for teachers to manage their courses, a back-end server with an API and a NoSQL database to save and store the data.
The objective of this solution is to provide students a modern and new way to study and teachers an adaptive and proactive way to teach. The end goal is to apply artificial intelligence subfields such as data mining and machine learning to provide students a personalized study path recommendation.
The following paper covers an introduction part, research questions and methodology, discussions about related research, a design and implementation part, results to some study and test cases alongside with a general discussion and recommendations.