Exploring Computer-Based Learning Behaviour Using Lag Sequential Analysis Technique: A Tutorial
Nurbiha A Shukor
Universiti Teknologi Malaysia
In computer-based education, students’ learning behavior can be at macro and micro
level. At the macro level, students’ learning behavior can include posting comments, viewing learning materials, or answering quizzes. At the micro level, learning behavior can be viewed from the way students interact with peers and instructors such as sharing facts, elaborating ideas, or arguing. At the micro level, understanding these behaviours usually applied content analysis technique but little is known about how significant were these learning behavior. Identifying the significant sequence of behaviours can assist social science researchers to evaluate the learning pattern and strategize the most effective intervention to induce the desired learning behavior. This tutorial will demonstrate the data analysis process to evaluate learning behavior using lag sequential analysis technique. It will also explain how data are interpreted and how a report is written based on the data to provide a meaningful contribution to a social science study.
Participants will be introduced to the importance of understanding behavioural learning pattern for computer education research, introduction to lag sequential analysis and previous studies that used this technique through lecture and active learning activities. Hands-on activities through exploration of GSEQ software will also be carried out. Participants will be able to experience the data analysis process using GSEQ software and learn how to report findings based on the data analysis process.
MATERIALS FOR TUTORIAL
1) GSEQ software has to be downloaded from
2) Laptop that runs Windows.
Nurbiha A Shukor, is a Creative Multimedia and Learning Technology Manager at
Universiti Teknologi Malaysia and also an experienced Senior Lecturer with a
demonstrated history of working in the higher education industry. Skilled in E-Learning, Educational Technology, educational data mining and recently involved in projects related to STEM Education in Schools with Ministry of Education Malaysia as a Project Manager for STEMazing Project UTM. Manages and conducted training on open education resources development including OCW and MOOCs for University. She is also active in tutoring several projects including UNESCO Open Education Project (http://unesco.ijs.si/project/open-education-resources-oer-for-statistic-and-
probability-in-pacific/) . She graduated from Universiti Teknologi Malaysia and received postdoctoral training from Radboud University, The Netherlands for the project Students’ Learning Regulation in Online Mathematics Learning.
Application of Text Analytics to Enhance Teaching and Learning Experience
Prof Venky Shankararaman
Professor of Information Systems (Education)
Associate Dean (Education)
School of Information Systems, Singapore Management University
Prof Swapna Gottipati
Assistant Professor of Information Systems (Education)
Program Director (IS Major)
School of Information Systems, Singapore Management University
Education Domain has always had the power to generate a large amount of data. The data is either in the form of structured data such as grades, student profile, indexed library resources, and quantitative student feedback etc., or unstructured such as learning objectives, assessments, curriculum, course content, qualitative teaching evaluations, job and internship posting etc. The unstructured data can be in various forms including text, images, videos, audios etc. Mining such datasets provides valuable insights and enables the stakeholders to make informed decisions to improve the teaching and learning experience. Text analytics is a research area that studies a body of text to find meaningful patterns or insights. It is being applied in several business domains and is recently gaining popularity in education domain.
Traditionally, qualitative analysis has been mostly a manual process which is a tedious and painstaking work. The traditional approach of processing text information involves human actions in information gathering, analysis, and dissemination. However, the emergence of analytics techniques and tools that leverages vast amounts of data sets to gain insights, from both structured and unstructured data, has opened new possibilities. The main objective of text analytics in education is to use a data-driven approach to gain insights into the effectiveness of the education process and thus improve the overall teaching and learning experience. This new capability, analytics technology, plays a major role to automate many stages and tasks of the education process.
This tutorial will introduce and discuss the basics of text analytics, the different unstructured datasets in the education domain, and use cases in the education domain where text analytics can be applied. Additionally, we will look at emerging and likely future trends in this field that can give ideas to participants to discover use cases applicable in their institutions. The tutorial session will be highly interactive, discussion-oriented and will include examples and exercises to enhance participation.
Key Learning Outcomes
On completing this tutorial, you will be able to
- Understand the basics of text analytics and explore various unstructured datasets in the education domain.
- Gain insights into how text analytics can be applied to the datasets to enhance teaching and learning
- Identify potential use cases where text analytics can be applied in your institution
Venky Shankararaman is a Professor of Information Systems (Education) and Associate Dean (Education) at the School of Information Systems, Singapore Management University. He holds a PhD in Engineering from the University of Strathclyde, Glasgow, UK. His current areas of specialization include business process management and analytics, enterprise systems architecture and integration, and education pedagogy. He has over 25 years of experience in the IT industry in various capacities as a researcher, academic faculty member, IT professional and industry consultant. Venky has designed and delivered professional courses for government and industry in areas such as business process management and analytics, enterprise architecture, technical architecture, and enterprise integration. He has published over 65 papers in academic journals and conferences.
Swapna Gottipati is an Assistant Professor of Information Systems (Education) at the
School of Information Systems, Singapore Management University. Her research interests include text analytics, natural language processing, information extraction, opinion mining, machine learning and social networking. Her main focus is to enhance data mining models while she applies her research findings to software, education, security and mobile applications. Prior to joining SMU, she worked as a consultant for banking, financial, health and mobile projects, where she designed, developed and supported various software systems.