A Matching Problem in Peer Assessment on E-learning Platforms

MPhil Thesis Defence


Title: "A Matching Problem in Peer Assessment on E-learning Platforms"

By

Mr. Chung Hin KWOK


Abstract

The advancement of technology has made learning possible regardless of temporal 
and spatial barriers through e-learning platforms. They host numerous MOOCs 
that deliver quality content to learners around the world. Most traditional 
materials used in classrooms such as lecture videos and readings can be scaled 
up to accommodate an unprecedentedly large size of students in a MOOC, but 
scaling up for evaluation and assessment becomes difficult, particularly when 
evaluation and assessment require human interpretation and judgement. To 
address this problem, instructors in MOOCs resort to peer assessment to tackle 
the enormous scale of learning evaluations. As peers have different 
understanding towards the concepts that appear in the assessment, how they are 
assigned to peer homework for assessment could affect the overall grading 
quality. In this thesis, we study an assignment problem in peer assessment. We 
aim at maximizing the learning concept coverage when homework is assigned to 
learners, which turns out to be an NP-hard problem. We propose a greedy 
algorithm and two variations for this problem. Finally, we conducted 
experiments on several datasets to verify the effectiveness of our algorithms.


Date:			Thursday, 23 August 2018

Time:			3:00pm - 5:00pm

Venue:			Room 3494
 			Lifts 25/26

Committee Members:	Dr. Raymond Wong (Supervisor)
 			Prof. Dit-Yan Yeung (Chairperson)
 			Prof. Andrew Horner


**** ALL are Welcome ****