Learning analytics: Predicting student performance with classification trees (in R and SPSS)

Facilitators: Dennis Foung (ELC), Hilary Ng (EDC)
Date: 24 Oct 2018
Time: 12:30 PM - 2:00 PM
Venue: TU616

The classification tree is one of the most commonly used data mining techniques for predicting student performance. This technique is popular because it is easy to interpret and can provide students with specific suggestions for improvement. This session aims to help colleagues master the basic concepts of classification trees and develop classification trees for their teaching context. To achieve the most from the workshop, you are expected to (1) bring your laptop to the session; (2) install R and R studio; and (3) try to access SPSS before attending the session. More information and download links will be sent to registered participants later. Come and join us to experience learning analytics!

This activity is jointly organised by the CoP on Conducting Learning Analytics to Inform Teaching and Learning and EDC.

Home > Training > Learning analytics: Predicting student performance with classification trees (in R and SPSS)

Our Support

Training for Teachers
Teacher Support
FAQ for Teachers
Student Support
FAQ for Students

eLearning

Blackboard Mobile
BOLT
MOOC(HKPolyUx)
Building Blocks

Ask For Help

IT Online Service Desk
Contact Us

Share To

EDC eLearning WhatsApp Support (text only): (+852) 5717 7377 (Monday to Friday, 08.30-18.00)

The learning and teaching platforms of The Hong Kong Polytechnic University (‘PolyU”) are for the use of PolyU students to facilitate their learning. The student shall use the platforms and the materials available (including teaching sessions conducted by staff of PolyU) for their personal study only. Where a student needs to download or save the materials available on the platforms for the permitted purposes, the student shall take all necessary measures to prevent their access by other parties. The materials are copyright protected. Save for the permitted purposes, no copying, distribution, transmission or publication of the materials in whole or in part in any form is permitted.