The machine learning is an excellent technology to enhance teaching and learning in higher education. This presentation provides a brief introduction of how to achieve academic success through machine learning, specifically improve student learning performance and reduce the drop-out rate.

1. Learning
Machine
Michael
ZHANG

2. How to learn?
Learn from Experience
Computers Stop Squinting
and Open TheirEyes
Errorrates on a popularimage recognition
challenge.
Source: Bloomberg

3. Machine Learning
Algorithm
Data
Learning
Performance
C ourses
Demographi
c
Library Network Marks
 PredictStudentG rades
 SupportDecision-Making
 LowerDrop-O utRates
 PredictC ourse Success

4. 3. Data Presentation2. Data Analysis1. Data Acquisition
Reference: Learning Analytics in HigherEducation. A review ofUK and internationalpractice Fullreport. April201 6.
Student
APPStudent
Consent
Services
Alert &
Intervention
Staff Dashboard
Self-Declared Data
StudentInfo System
(ProjectTransform)
E-Learning (Moodle)
…
Librarycheck in/out
Learning Records
Warehouse Learning Analytics
Processor
Machine Learning for Student Learning Analytics

5. Case Studies
The UKis now starting to wake up to the possibilities that learning analytics
provides.
1.Traffic Lights and Interventions: Signals at Purdue University
2.Analysing use of the VLE at the University of Maryland, Baltimore County
3.Identifying at-risk students at New York Institute of Technology
4.Fine-grained analysis of student data at California State University
5.Transferring predictive models to other institutions from Marist College
6.Enhancing retention at Edith Cowan University
7.Early alert at the University of New England
8.Developing an ‘analytics mind-set’ at the Open University
9.Predictive analytics at Nottingham Trent University
10.Analysing social networks at the University of Wollongong
iLAVS

6. Case Studies
The UKis now starting to wake up to the possibilities that learning analytics
provides.
1.Traffic Lights and Interventions: Signals at Purdue University
2.Analysing use of the VLE at the University of Maryland, Baltimore County
3.Identifying at-risk students at New York Institute of Technology
4.Fine-grained analysis of student data at California State University
5.Transferring predictive models to other institutions from Marist College
6.Enhancing retention at Edith Cowan University
7.Early alert at the University of New England
8.Developing an ‘analytics mind-set’ at the Open University
9.Predictive analytics at Nottingham Trent University
10.Analysing social networks at the University of Wollongong
iLAVS