Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment

Abstract

In higher education, predicting the academic performance of students is associated with formulating optimal educational policies that vehemently impact economic and financial development. In online educational platforms, the captured clickstream information of students can be exploited in ascertaining their performance. In the current study, the time-series sequential classification problem of students’ performance prediction is explored by deploying a deep long short-term memory (LSTM) model using the freely accessible Open University Learning Analytics dataset. In the pass/fail classification job, the deployed LSTM model outperformed the state-of-the-art approaches with 93.46% precision and 75.79% recall. Encouragingly, our model superseded the baseline logistic regression and artificial neural networks by 18.48% and 12.31%, respectively, with 95.23% learning accuracy. We demonstrated that the clickstream data generated due to the students’ interaction with the online learning platforms can be evaluated at a week-wise granularity to improve the early prediction of at-risk students. Interestingly, our model can predict pass/fail class with around 90% accuracy within the first 10 weeks of student interaction in a virtual learning environment (VLE). A contribution of our research is an informed approach to advanced higher education decision-making towards sustainable education. It is a bold effort for student-centric policies, promoting the trust and the loyalty of students in courses and programs.

Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques

“…These achievements are important for the design of personalized education proposals. Likewise, personalization goes hand in hand with profitability and sustainability of resource development and use [33,34].…”

“…The application of resources such as eye tracking also facilitates the use of data mining techniques. These offer different forms to predict the students at risk or to classify different needs of students [34], all of which allow the teacher to make a more precise and adjusted method of teaching and therefore more sustainable.…”

DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era

“…From a more general perspective, DeepLMS aligns with the previous efforts that incorporate LSTM-based predictions in the context of online education, yet not at the exact same specific problem settings as in DeepLMS. Hence, the latter is well-positioned with the approaches related to: i) cross-domains analysis, e.g., MOOCs impact in different contexts 57 , as DeepLMS could be easily adapted to a micro analysis of the QoI per discipline/course and transfer learning from one discipline to another at the same course (or courses with comparable content), as shown here with the application of DeepLMS to DB1-DB3, in a similar manner that was applied in MOOCs from different domains 57 ; ii) combination of learning patterns in the context domain with the temporal nature of the clickstream data 58 , and identification of students at risk 59 , as DeepLMS could be combined with an autoencoder to capture both the underlying behavioral patterns and the temporal nature of the interaction data at various levels of the predicted QoI (e.g., low (<0.5) QoI (at risk level)); iii) predicting learning gains by incorporating skills discovery 60,61 , as DeepLMS could provide the predicted QoI as an additional source of the user profile to his/her skills and learning gains; iv) user learning states and learning activities prediction from wearable devices 62 , as DeepLMS could easily be embedded in the expanded space of affective (a-) learning, and inform a more extended predictive model that would incorporate the learning state with the estimated QoI; v) increasing the communication of the instructional staff to learners based on individual predictions of their engagement during MOOCs 63,64 , as DeepLMS could facilitate the coordination of the instructor with the learner based on the informed predicted QoI; and vi) predicting the learning paths/performance 65 and the teaching paths 66 , as the DeepLMS could be extended in the context of affecting the learning/teaching path by the predicted QoI.…”

Peer Interaction Teaching-Learning Approaches for Effective Engagement of Students in Virtual Classroom

“…The session plan should include course/program/module title, Session objective, Methodology, Duration, and Breaks. It is very important to include activities to make the participants engage and think [18][19][20]. Some of the suggested activities by the participants to make the virtual learning interesting and effective are: The finding of the study is based on the collection of data which shows that the respondents are ready to adapt the virtual learning methodologies.…”