What is it about?

This work divides the moodle data sets from six different sections of an annual postgraduate program at the Hellenic Open University in six periods for each section, due to the number of written assignments. Then it implements data mining techniques to analyze the activity, polarity and emotions of tutors and students in order to predict students’ grades. The results indicate the algorithm with the highest precision in each prediction. In addition, the research concludes that polarity and emotions as independent variables provide better performance in comparative models. Moreover, tutors' variables are highlighted as an important factor for more accurate predictions of student grades. Finally, a comparison of actual and predicted grades indicates which students have used a third party to fulfill their assignments.

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Why is it important?

The main effort of this work is the implementation of Big Data methodologies to provide timely, personalized and as accurately as possible the scores of eight target classes. These are the six written assignments, the total average grades and the final grades of the students' assessment. The data origins are six different sections of an annual postgraduate program at the Hellenic Open University (HOU). By applying ML and developing regression algorithms, paper studies model behavior in six different periods of an academic year, adding variables from structured and unstructured LMS data. In addition to student interactions with the platform contents, we also look at the predictive value of students' raw data in forum interactions with their classmates, tutors and vice versa. To this end, a sentiment analysis is applied to characterize the polarity of students’ and tutors’ texts, to reveal their emotions and finally examine the involvement of the feeling in the precision of the models. As the state of emotions is not stable, it is examined in six different periods during the academic year. Polarity is represented as three categorical approaches and feelings as eight dimensional approaches. Furthermore, this paper studies the tutor's activity both in structured and raw data, analyzes their emotional state and examines their predictive contribution to the eight categories of student grade objectives. Moreover, a comparison of actual and predicted grades indicates the number of students, which of them and in what section a third party is used to fulfill their assignments.

Perspectives

Following the global trend in tertiary education, the current project implements ML and compares four models to predict each student's grades. Taking into account the current literature, this paper looks at three additional perspectives. In terms of the first, investigates the tutors’ activity through their authentic academic activity in the LMS as predictive factors in our models. In terms of the second, apart from students, the tutor's sentiments are also studied. Forum texts are used to describe polarity and emotions in three and eight characteristics respectively for both concerned. The analysis of tutors' polarity and emotions is not based on students' views on academic work or tutor support, as some are not objective due to emotional load but are derived from DM methodology [40]. As far as the last perspective is concerned, tutors' and students' moodle activity blend with their polarity and emotional states as prognostic variables for 8 different target classes. The methodology of the project also leads to a comparison of predictive and actual grades, indicating students who were most likely addressed to third parties to complete their written assignments. Timely prediction of student grades in different periods during the academic year, taking into account, the LMS typical activity and the changing emotional state of platform’s daily users, promotes tutors’ self-evaluation and focused interventions, learning content evolution but mainly, students’ retention in the learning process.

Dr Andreas Fotios Gontzis
University of Patras

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This page is a summary of: Polarity, emotions and online activity of students and tutors as features in predicting grades, Intelligent Decision Technologies, September 2020, IOS Press, DOI: 10.3233/idt-190137.
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