Non-cognitive skills : the art of learning to learn well (part A)
Date of Issue2016
School of Electrical and Electronic Engineering
With the support of various digitization approaches, delivery methods and the Internet, students and educators use E-learning platforms mostly as a distant learning tool outside of the classroom. Therefore, in order to help monitor the students’ performance, it is essential to understand their learning behaviors and habits so as to allow the educators to gain insights into the students and also react to their learning patterns accordingly. In this project, the author focuses on students’ behaviors and their non-cognitive skills. To achieve the goal of understanding students’ actions, the author employed a sequence clustering model called CLUSEQ by utilizing the statistical properties associated with the sequences of students’ actions. This model made use of the probabilistic suffix tree (PST), a variation of the traditional suffix tree, to organize the conditional probability distribution (CPD) of the next item given the preceding segment in order to measure the similarity among sequences effectively and efficiently. The interesting sequential patterns in students’ actions when they interacted with the E-learning platform were clustered and identified. Sets of comprehensible labels were developed to describe students according to the profile groups they fall in. The interpretation of the clustering result pertaining to the students’ behaviors was discussed in details.
Final Year Project (FYP)
Nanyang Technological University