Artificial Intelligence-based Personalized Learning

AIPerLA is a software package for facilitating personalized learning implementation. The purpose is to allow students to have a personalized learning plan through their history of interactions with the student information system and learning management system, and for the instructors to get a recommendation of teaching intervention to ensure a quality teaching delivery and curriculum. AIPerLA consists of five prototypes namely ISPerL, CLADS, STILA, AcaPerforma and VLearnAI. Each prototype addresses the requirements different purposes as below:

1. ISPerL is an intelligent assistant for instructors to get input for conducting personalized learning interventions by getting triggers about students at risk, their performance, and participation. Students could also get an overview of their performance compared to their peers. ISPerL is high fidelity and low functional system, with a focus on the user interface design for an intelligent assistant. The ISPerL system for the web consists of 7 pages for instructors, and 12 for students. The design is based on research on the analytics needed by instructors and students to get personalized learning. 

2. CLADS is a dashboard that is built using learning analytics and rules based technologies for monitoring courses and students engagement into CASSMiLe, an online learning platform for IEEE Circuit and Signal System Society (CASS). CLADS has several features to support CASS curriculum and platform managers to strategize for their micro-credentials programs as follows:

• identification of learning accomplishment for insights about learners support, 

• classification of learner types (based on visual, social, challenger, explorer, all-rounder, inactive) for personalizing micro lesson, 

• impact analysis based on content preference to identify the strengths and weaknesses of the micro lesson

3. AcaPerforma is a predictive analytics based system that uses machine learning algorithms for early prediction of students final grade (as soon as week 2). AcaPerforma uses data from UPM’s eSMP and PutraBLAST where the prediction is based on students engagement in PutraBLAST and continuous assessment records 

4. VLearnAI is a personalized video recommender system based on artificial intelligence (AI) algorithm called content-based filtering that facilitates AI learning for a community of learners using videos. VLearnAI has the following functions:

• Searching of uploaded video (from YouTube)

• Recommendation of video based on viewing trends

• Recommendation of video based on past user’s history using content-based recommender system

• Analytics of viewed videos and user interests

5. STILA is a dashboard for instructors to obtain students’ achievement insights (course outcome achievement, classification of chronotype, and clustering of learners achievement) from online course logs that will support educators to personalize learning interventions based on the similarities and gaps between the learners and the peers, as well as performance metering.

We have published a playbook earlier on Technology for Future Learning Ecosystem

Flip the book on the right by clicking the arrow or view and download at https://anyflip.com/hxudq/bpxk/ or read below

Acknowledgment

The project has been developed based on the support of several sponsors and contributors:

1) Technology for Future Learning Ecosystem, Enhancing Education for Human Capital Development through Establishing Future Learning Ecosystem, Ministry of Higher Education, Malaysia, 2018-2022

2) Interactive Machine Learning based on Deep Reinforcement Learning and Generative Adversarial Network Hybrid for Digital Twin, Air Force Office of Scientific Research (AFOSR), United States, 2021-2024 

3) Deep Recurrent Q-Network Approach for Multi-Objective Markov Decision Process in Partially Observable Environment, United Air Force Office of Scientific Research (AFOSR), United States, 2018-2021

4) IEEE CASSMiLe https://ml.ieee-cas.org/  

5) National Task force for AI Roadmap Implementation 2021-2025

Special thanks to many people who have contributed to this project:

1) Team members of  the research grant entitled "Enhancing Education for Human Capital Development through Establishing Future Learning Ecosystem", founded by the Ministry of Higher Education, Malaysia

2) Student developers and research associates: Dr. Anahita Ghazvini, Mr Ee Yeo Keat, Mr Mohd Izzuddin Yusof, Ms Khor Jun Yi, Mrs. Nurfatin Afza

3) Data providers at UPM: Centre for Academic Development (CADe) and InfoComm Development Centre (IDEC)

For more information and potential collaborations, please contact nurfadhlina@upm.edu.my