Pattern discovery, visualization and interaction data analytics in a process-aware multi-tabletop collaborative learning environment

Last modified: May 15, 2019
You are here:
Estimated reading time: 1 min
Project Title: Pattern discovery, visualization and interaction data analytics in a process-aware multi-tabletop collaborative learning environment
Author: Mr. Parham Porouhan
Advisor: Associate Professor Wichian Premchaiswadi, Ph.D., Professor James G. Williams, Ph.D.
Degree: Doctor of Philosophy (Information Technology)
Major: Information Technology in Business
Faculty: Graduate Schools
Academic year: 2017

การอ้างอิง/citation

Porouhan, Parham. (2017). Pattern discovery, visualization and interaction data analytics in a process-aware multi-tabletop collaborative learning environment. (Doctoral dissertation). Bangkok: Siam University.


Abstract

This dissertation builds on the intersection of educational process mining and the automatic
analysis of student’s collaborative interaction data previously collected from a networked multitabletop
learning environment. The main focus of the study was to analyze and interpret the data
using several process mining techniques in order to increase the instructor’s awareness about the
students’ collaboration process with respect to specific quantitative indicators as follows:
participation (consisting of participation density, participation rate and participation dynamics
metrics), interaction (consisting of interaction density and interaction dynamics metrics), time
performance (including the number of time intervals between the activities as well as the duration of
idle/inactive periods), similarity of tasks (symmetry of actions) and division of labor
(symmetry of roles).

The empirical findings showed that high performance groups exhibited increased tendencies
to perform tasks simultaneously (together) or alternatively (between group peers). Moreover, high
performance groups also showed increased tendencies to interact with objects created by their other
fellow group members. Although both groups showed long waiting times at the beginning of a task,
high performance groups were mostly brainstorming while low performance groups were playing an
idle role. High performance groups showed increased tendencies to work on the same range of actions
‘together’. Quite the opposite, low performance groups showed increased tendencies to work on a
dissimilar range of actions ‘individually’


Pattern discovery, visualization and interaction data analytics in a process-aware multi-tabletop collaborative learning environment

Doctor of Philosophy in Information Technology, Siam University, Bangkok, Thailand

Tags:
Was this article helpful?
Dislike 0
Views: 14