Text analytics approach to extract course improvement suggestions from students’ feedback

Singapore National Research Foundation under International Research Centre @ Singapore Funding Initiative; Ministry of Education, Singapore under its Academic Research Funding Tier

Latent Dirichlet Allocation for textual student feedback analysis

Ministry of Education, Singapore under its Academic Research Funding Tier 2; National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Cluster Analysis in Online Learning Communities: A Text Mining Approach

This paper presents a theory-informed blueprint for mining unstructured text data using mixed- and multi-methods to improve understanding of collaboration in asynchronous online discussions (AOD). Grounded in a community of inquiry theoretical framework to systematically combine established research techniques, we investigated how AOD topics and individual reflections on those topics affect formation of clusters or groups in a community. The data for the investigation came from 54 participants and 470 messages. Data analysis combined the analytical efficiency and scalability of topic modeling, social network analysis, and cluster analysis with qualitative content analysis. The cluster analysis found three clusters and that members of the intermediate cluster (i.e., middle of three clusters) played a pivotal role in this community by expressing uncertainty statements, which facilitated a collective sense-making process to resolve misunderstandings. Furthermore, we found that participants’ selected discussion topics and how they discussed those topics influenced cluster formations. Theoretical, practical, and methodological implications are discussed in depth

Machine Learning applications to e-learning courses

The Ph.D. thesis project is aimed at improving the quality and the effectiveness of on-line teaching in scientific degree courses at the University Level that required the use of E-learning platform, based on the Moodle Content Management System. The aim of this research project is to assist the teacher, through the development of new tools based on Artificial Intelligence, to design innovative successful e-learning courses to give to the students the opportunity to improve their learning outcomes. These originals tools overcome the limitations of the standard Moodle activities applying machine learning techniques by analysing large amount of students’ data extracted by Moodle log data. Recently many e-learning resources have been developed for university students, are available on the Web. The increase of LMS (Learning Management System) as Moodle and their ease of use led many teachers to realize e-learning paths for their students, often supporting them with some frontal activities, giving to them the advantages of on-line learning. The aim was to deepen the topics discussed in class through the consultation of additional materials, video recordings of lessons, and other activities to exploiting the potentials of on-line courses

Teaching analytics and teacher dashboards to visualise SET data: Implication to theory and practice

Teaching Analytics (TA) is an emergent theoretical approach that combines teaching expertise, visual analytics, and design-based research to support teachers' diagnostic pedagogical ability to use data as evidence to improve teaching quality. The thesis is focused on designing dashboards to help teachers visualise Student Evaluation of Teaching (SET) data as a form of TA for improving the quality of teaching. The research examined the role of TA by deploying customisable dashboards to support teachers in using data to design and facilitate learning. The researcher carried out an integrated literature review to explore the notion of TA and SET data. Moreover, a Data Science Life Cycle model was proposed to guide teachers and researchers using SET data to improve learning and teaching quality. The research comprised several phases. In phase I, a simulated data technique was used to generate SET scores that informed the development of a preliminary teacher dashboard. Phase II surveyed teachers' use of SET data. The survey results indicated that more than half of the participants used SET for improving teaching practice. The research also showed that participants valued the free-text qualitative comments in SET data. Hence, phase III collected real free-text qualitative comments in SET data on students' perceptions of a previously tutored course. The survey results further indicated that although teachers were unaware of a dashboard's value in presenting data, they wanted to visualise SET data using dashboards. Phase IV redesigned the preliminary dashboards to present the real SET data and the simulated SET scores. Finally, phase V carried out usability testing to evaluate teachers' perceptions of usability and usefulness of the teacher's dashboards. Overall, the result of the usability study indicated the perceived value of the teacher's dashboards