Using students' participation data to understand their impact on students' course outcomes : a thesis presented in partial fulfilment of the requirements for the MPhil degree at Massey University, Albany, New Zealand, Master of Philosophy degree in Information Technology
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Date
2016
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Open Access Location
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Massey University
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Abstract
Many students with diverse needs are enrolled in university courses. Not all these students are able to
be successful in completing their courses. Faculty members are keen to identify these students who
have the risk of failing their courses early enough to help them by providing timely feedback so
that students can meet the requirements of their courses. There are many studies using educational
data mining algorithms which aim to identify at risk students by predicting students’ course outcomes,
for example, from their forum activities, content requests, and time spent online. This study addresses
this issue by clustering the students’ course outcomes using students’ class participation data which
can be obtained from various online education technological solutions. Using data mining in
educational systems as an analytical tool offers researchers new opportunities to trace students’ digital
footprints in various course related activities and analyse students’ traced data to help the students in
their learning processes and teachers in their educational practices. In this study the focus is not only
on finding at risk students but also in using data for improving learning process and supporting
personalized learning. In‐class participation data was collected through audience participation tools,
the out‐of‐class participation data was collected from Stream and combined with the qualitative and
quantitative data from questionnaires. The participation data were collected from 5 different courses
in the mainstream university programs. Our first aim was to understand the perception of students
regarding the effect of participation and using the audience participation tools in class and their effects
on students’ learning processes. Moreover, we would like to identify to what extents their perceptions
match with their final course outcomes. Therefore, the tool has been used in different mainstream
courses from different departments. The results of our study show that students who participated
more and thought that the tool helped them to learn, engaged and increased their interest in the
course more, and eventually achieved highest scores. This finding supports the view that inclass
participation is critical to learning and academic success.
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Keywords
Data mining, College dropouts, Educational statistics, Data processing, Academic achievement, Research Subject Categories::TECHNOLOGY::Information technology