Analysis of Student Behaviour in Habitable Worlds Using Continuous Representation Visualization

• Published in: Journal of Learning Analytics Vol. 6; no. 1; pp. 1 - 15
• Main Authors: Pardos, Zachary A, Horodyskyj, Lev
• Format: Journal Article
• Language: English
• Published: Society for Learning Analytics Research 01.01.2019
• Subjects: Academic Achievement; Academic Failure; Behavior Patterns; Case Studies; Computer Mediated Communication; Course Descriptions; Data Analysis; Graphs; Group Discussion; Instructional Improvement; Introductory Courses; Online Courses; Recordkeeping; Science Instruction; Student Behavior; Teaching Methods; Undergraduate Students; Visualization; Arizona
• ISSN: 1929-7750; 1929-7750
• DOI: 10.18608/jla.2019.61.1

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 is 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 making 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 suggest 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 applicable to other datasets of clickstream activity where investigators and stakeholders wish to organically surface principal patterns of behaviour.