A1 Journal article (refereed), original research

Palaute: An online text mining tool for analyzing written student course feedback

Open Access publication

Publication Details

Authors: Grönberg Niku, Knutas Antti, Hynninen Timo, Hujala Maija

Publisher: Institute of Electrical and Electronics Engineers (IEEE): OAJ / IEEE

Publication year: 2021

Language: English

Related journal or series: IEEE Access

eISSN: 2169-3536

JUFO level of this publication: 2

Digital Object Identifier (DOI): http://dx.doi.org/10.1109/ACCESS.2021.3116425

Open Access: Open Access publication


Collecting student feedback is commonplace in universities. These
surveys usually include both openended questions and Likert-type scale
questions but the answers to open questions tend not to be analysed
further than simply reading them. Recent research has shown that text
mining and machine learning methods can be utilized to extract useful
topics from masses of open student feedback. However, to our knowledge,
not many off-the-shelf applications exist for processing open-ended
student feedback automatically. Additionally, the use of text mining
tools may not be available to all educators, as they require in-depth
knowledge of text-mining, data-analysis, or programming tools. To
address this gap the current study presents a tool (Palaute) for
analyzing written student feedback using topic modeling and emotion
analysis. The utility of this tool is demonstrated with two real life
use cases: First we analyze student feedback data collected from courses
in a software engineering degree programme, and then feedback from all
courses organized in a university. In our experiments, the analysis of
open-ended feedback revealed that on certain software engineering course
modules the workload is perceived as heavy, and on some programming
courses the automatic code grader could be improved. The university-wide
analysis produced indicators of good teaching quality, such as
interesting courses, but also some concrete improvement points like the
time given to complete course assignments. Therefore, the use of the
tool resulted in actionable improvement points, which could not have
been identified using only numeric feedback metrics. Based on the
demonstrated utility, this paper describes the design and implementation
of our open-source tool.

Last updated on 2021-08-11 at 09:18