3 research outputs found
Analysis of Student Behaviour in Habitable Worlds Using Continuous Representation Visualization
We introduce a novel approach to visualizing temporal clickstream behaviour
in the context of a degree-satisfying online course, Habitable Worlds, offered
through Arizona State University. The current practice for visualizing
behaviour within a digital learning environment has been to generate plots
based on hand engineered or coded features using domain knowledge. While this
approach has been effective in relating behaviour to known phenomena, features
crafted from domain knowledge are not likely well suited to make unfamiliar
phenomena salient and thus can preclude discovery. We introduce a methodology
for organically surfacing behavioural regularities from clickstream data,
conducting an expert in-the-loop hyperparameter search, and identifying
anticipated as well as newly discovered patterns of behaviour. While these
visualization techniques have been used before in the broader machine learning
community to better understand neural networks and relationships between word
vectors, we apply them to online behavioural learner data and go a step
further; exploring the impact of the parameters of the model on producing
tangible, non-trivial observations of behaviour that are suggestive of
pedagogical improvement to the course designers and instructors. The
methodology introduced in this paper led to an improved understanding of
passing and non-passing student behaviour in the course and is widely
applicable to other datasets of clickstream activity where investigators and
stakeholders wish to organically surface principal patterns of behaviour
ECO D2.5 Learning analytics requirements and metrics report
In MOOCs, learning analytics have to be addressed to the various types of learners that participate. This deliverable describes indicators that enable both teachers and learner to monitor the progress and performance as well as identify whether there are learners at risk of dropping out. How these indicators should be computed and displayed to end users by means of dashboards is also explained. Furthermore a proposal based on xAPI statements for storing relevant data and events is provided.Part of the work carried out has been funded with support from the European Commission, under the ICT Policy Support Programme, as part of the Competitiveness and Innovation Framework Programme (CIP) in the ECO project under grant agreement n° 21127
Elearning, Communication and Open-data: Massive Mobile, Ubiquitous and Open Learning
ABSTRACT: In MOOCs, learning analytics have to be addressed to the various types of learners that participate. This deliverable describes indicators that enable both teachers and learner to monitor the progress and performance as well as identify whether there are learners at risk of dropping out. How these indicators should be computed and displayed to end users by means of dashboards is also explained. Furthermore a proposal based on xAPI statements for storing relevant data and events is provided