11 research outputs found
Clickstream-based outcome prediction in short video MOOCs
In this paper, we present a data mining approach for analysing students’ clickstream data logged by an e-learning platform and we propose a machine learning procedure to predict course completion of students. For this, we used data from a short MOOC course which was motivated by the teachers of elementary schools. We show that machine learning approaches can accurately predict the course outcome based on clickstream data and also highlight patterns in data which provide useful insights to MOOC developers
MOOC next week dropout prediction: weekly assessing time and learning patterns
Although Massive Open Online Course (MOOC) systems have become more prevalent in recent years, associated student attrition rates are still a major drawback. In the past decade, many researchers have sought to explore the reasons behind learner attrition or lack of interest. A growing body of literature recognises the importance of the early prediction of student attrition from MOOCs, since it can lead to timely interventions. Among them, most are concerned with identifying the best features for the entire course dropout prediction. This study focuses on innovations in predicting student dropout rates by examining their next-week-based learning activities and behaviours. The study is based on multiple MOOC platforms including 251,662 students from 7 courses with 29 runs spanning in 2013 to 2018. This study aims to build a generalised early predictive model for the weekly prediction of student completion using machine learning algorithms. In addition, this study is the first to use a ‘learner’s jumping behaviour’ as a feature, to obtain a high dropout prediction accuracy
Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of FutureLearn Courses
Big data and analytics for educational information systems, despite having gained researchers’ attention, are still in their infancy and will take years to mature. Massive open online courses (MOOCs), which record learner-computer interactions, bring unprecedented opportunities to analyse learner activities at a very fine granularity, using very large datasets. To date, studies have focused mainly on dropout and completion rates. This study explores learning activities in MOOCs against their demographic indicators. In particular, pre-course survey data and online learner interaction data collected from two MOOCs, delivered by the University of Warwick, in 2015, 2016, and 2017, are used, to explore how learnerdemographic indicatorsmay influence learner activities. Recommendations for educational information system development and instructional design, especially when a course attracts a diverse group of learners, are provided
Sentiment analysis in MOOCs: a case study
Proceeding of: 2018 IEEE Global Engineering Education Conference (EDUCON2018), 17-20 April, 2018, Santa Cruz de Tenerife, Canary Islands, Spain.Forum messages in MOOCs (Massive Open Online Courses) are the most important source of information about the social interactions happening in these courses. Forum messages can be analyzed to detect patterns and learners' behaviors. Particularly, sentiment analysis (e.g., classification in positive and negative messages) can be used as a first step for identifying complex emotions, such as excitement, frustration or boredom. The aim of this work is to compare different machine learning algorithms for sentiment analysis, using a real case study to check how the results can provide information about learners' emotions or patterns in the MOOC. Both supervised and unsupervised (lexicon-based) algorithms were used for the sentiment analysis. The best approaches found were Random Forest and one lexicon based method, which used dictionaries of words. The analysis of the case study also showed an evolution of the positivity over time with the best moment at the beginning of the course and the worst near the deadlines of peer-review assessments.This work has been co-funded by the Madrid Regional Government, through the eMadrid Excellence Network (S2013/ICE-2715), by the European Commission through Erasmus+ projects MOOC-Maker (561533-EPP-1-2015-1-ESEPPKA2-CBHE-JP), SHEILA (562080-EPP-1-2015-1-BEEPPKA3-PI-FORWARD), and LALA (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and by the Spanish Ministry of Economy and Competitiveness, projects SNOLA (TIN2015-71669-REDT), RESET (TIN2014-53199-C3-1-R) and Smartlet (TIN2017-85179-C3-1-R). The latter is financed by the State Research Agency in Spain (AEI) and the European Regional Development Fund (FEDER). It has also been supported by the Spanish Ministry of Education, Culture and Sport, under a FPU fellowship (FPU016/00526).Publicad
Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network
Student performance prediction is critical to online education. It can
benefit many downstream tasks on online learning platforms, such as estimating
dropout rates, facilitating strategic intervention, and enabling adaptive
online learning. Interactive online question pools provide students with
interesting interactive questions to practice their knowledge in online
education. However, little research has been done on student performance
prediction in interactive online question pools. Existing work on student
performance prediction targets at online learning platforms with predefined
course curriculum and accurate knowledge labels like MOOC platforms, but they
are not able to fully model knowledge evolution of students in interactive
online question pools. In this paper, we propose a novel approach using Graph
Neural Networks (GNNs) to achieve better student performance prediction in
interactive online question pools. Specifically, we model the relationship
between students and questions using student interactions to construct the
student-interaction-question network and further present a new GNN model,
called R^2GCN, which intrinsically works for the heterogeneous networks, to
achieve generalizable student performance prediction in interactive online
question pools. We evaluate the effectiveness of our approach on a real-world
dataset consisting of 104,113 mouse trajectories generated in the
problem-solving process of over 4000 students on 1631 questions. The experiment
results show that our approach can achieve a much higher accuracy of student
performance prediction than both traditional machine learning approaches and
GNN models.Comment: 8 pages, 8 figures. Accepted at CIKM 202
Recognizing Multidimensional Engagement of E-learners Based on Multi-channel Data in E-learning Environment
Despite recent advances in MOOC, the current e-learning systems have
advantages of alleviating barriers by time differences, and geographically
spatial separation between teachers and students. However, there has been a
'lack of supervision' problem that e-learner's learning unit state(LUS) can't
be supervised automatically. In this paper, we present a fusion framework
considering three channel data sources: 1) videos/images from a camera, 2) eye
movement information tracked by a low solution eye tracker and 3) mouse
movement. Based on these data modalities, we propose a novel approach of
multi-channel data fusion to explore the learning unit state recognition. We
also propose a method to build a learning state recognition model to avoid
manually labeling image data. The experiments were carried on our designed
online learning prototype system, and we choose CART, Random Forest and GBDT
regression model to predict e-learner's learning state. The results show that
multi-channel data fusion model have a better recognition performance in
comparison with single channel model. In addition, a best recognition
performance can be reached when image, eye movement and mouse movement features
are fused.Comment: 4 pages, 4 figures, 2 table
Multimodal data as a means to understand the learning experience
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