Sequence Modelling For Analysing Student Interaction with Educational Systems

The analysis of log data generated by online educational systems is an important task for improving the systems, and furthering our knowledge of how students learn. This paper uses previously unseen log data from Edulab, the largest provider of digital learning for mathematics in Denmark, to analyse the sessions of its users, where 1.08 million student sessions are extracted from a subset of their data. We propose to model students as a distribution of different underlying student behaviours, where the sequence of actions from each session belongs to an underlying student behaviour. We model student behaviour as Markov chains, such that a student is modelled as a distribution of Markov chains, which are estimated using a modified k-means clustering algorithm. The resulting Markov chains are readily interpretable, and in a qualitative analysis around 125,000 student sessions are identified as exhibiting unproductive student behaviour. Based on our results this student representation is promising, especially for educational systems offering many different learning usages, and offers an alternative to common approaches like modelling student behaviour as a single Markov chain often done in the literature.Comment: The 10th International Conference on Educational Data Mining 201

Trends in student behavior in online courses

Learning management systems provide an easy and effective means of access to learning materials. Students’ access to course material is logged and the amount of interaction is assumed to be a measure of student engagement within the course. In previous research, typically frequencies of student activities have been used, but this disregards any temporal information. Here, we analyze the amount of student activity over time during courses. Based on activity data over 11 online courses, we cluster students who show similar behavior over time. This results in three different groups: a large group of students who are mostly inactive; another group of students who are very active throughout the course; and a group of students who start out being active, but their activity diminishes throughout the course. These groups of students show different performance. Overall, more active students yield better results. In addition to these general trends, we identified courses in which alternative trends can be found, such as a group of students who become more active during the course. This shows that student behavior is more complex than can be identified from an individual course and more research into patterns of learning activities in multiple courses is essential

Using Data Mining in Educational Administration - A Case Study on Improving School Attendance

open access articlePupil absenteeism remains a significant problem for schools across the globe with its negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96\%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, show that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools

Using Data Mining in Educational Administration: A Case Study on Improving School Attendance

The authors would like to thank the leadership and staff of Willen Primary School for permitting us to use their data and for their efforts in supporting this study, in particular, Ms Emma Warner (attendance officer), Ms Carrie Matthews (headteacher), and Ms Sarah Orr (deputy headteacher).Pupil absenteeism remains a significant problem for schools across the globe with negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, showed that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools

Improving Hybrid Brainstorming Outcomes with Scripting and Group Awareness Support

Previous research has shown that hybrid brainstorming, which combines individual and group methods, generates more ideas than either approach alone. However, the quality of these ideas remains similar across different methods. This study, guided by the dual-pathway to creativity model, tested two computer-supported scaffolds – scripting and group awareness support – for enhancing idea quality in hybrid brainstorming. 94 higher education students,grouped into triads, were tasked with generating ideas in three conditions. The Control condition used standard hybrid brainstorming without extra support. In the Experimental 1 condition, students received scripting support during individual brainstorming, and students in the Experimental 2 condition were provided with group awareness support during the group phase in addition. While the quantity of ideas was similar across all conditions, the Experimental 2 condition produced ideas of higher quality, and the Experimental 1 condition also showed improved idea quality in the individual phase compared to the Control condition

Temporally Coherent Clustering of Student Data

The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show low performance for data that inherently suffer from noise, resulting in temporally inconsistent clusters. We propose an evolutionary clustering pipeline that can be applied to learning data, aiming at improving cluster stability over multiple training sessions in the presence of noise. Our model selection is designed such that relevant cluster evolution effects can be captured. The pipeline can be used as a black box for any intelligent tutoring system (ITS). We show that our method outperforms previous work regarding clustering performance and stability on synthetic data. Using log data from two ITS, we demonstrate that the proposed pipeline is able to detect interesting student behavior and properties of learning environments