The document discusses the application of text analytics techniques to analyze student comments from course experience questionnaires in the science and technology field. It utilizes tools like KH Coder for linguistic processing, emphasizing the significance of practical work in students' education. The analysis involves methods such as multi-dimensional scaling and co-occurrence networks to visualize relationships between themes in the feedback.

1. Stuart Palmer(@s_palm ) & Malcolm Campbell
Deakin University
Text analytics visualisation of
Course Experience Questionnaire
student comment data in science
and technology

2. “… a set of linguistic, statistical, and machine
learning techniques that model and structure
the information content of textual sources for
business intelligence, exploratory data analysis,
research, or investigation.” (Hu & Liu, 2012, p.
388)
Text analytics

3. Uses KH Coder – http://khc.sourceforge.net/en/
Stop word removal
Stemming for root word consolidation
Text unit of analysis is paragraph
Multi-dimensional Scaling
Co-occurrence Network
Comment pre-processing

4. Faculty level

5. Faculty level

6. Faculty level

7. KWIC concordancepractical work was very affective and applicable
the practical work
practical work was excellent
practicals and lab work.
practical work
able to apply knowledge acquired from theory and practical work to real life situations and able to…
…meeting new people and lecturers. practical work was good too.
use of online technology , interesting practical work ability to choose electives…
practical applications of field work
field, camps and practical work.
good practical field work
practical field work was thoroughly enjoyable…
practical work but the course could use some more
practical work and professional practice placement
the practical work that i did.
practical work
units where the practical and tutorial classes involved working…

8. Inter-School level

9. Inter-School level

10. Method for visualisation of CEQ comments
MDS – relationship between words/themes
CON – key word phrases/clusters
KWIC – interrogates underlying comment text
Still a manual task to interpret the charts
Use with other sources evaluation data
Conclusions

11. Thank you for your time
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