Name
Abstract | ||
|---|---|---|
Data solutions in the teaching and learning space are in need of pro-active innovations in data management, to ensure that systems for learning analytics can scale up to match the size of datasets now available. Here, we illustrate the scale at which a Learning Management System (LMS) accumulates data, and discuss the barriers to using this data for in-depth analyses. We illustrate the exponential growth of our LMS data to represent a single example dataset, and highlight the broader need for taking a pro-active approach to dimensional modelling in learning analytics, anticipating that common learning analytics questions will be computationally expensive, and that the most useful data structures for learning analytics will not necessarily follow those of the source dataset. |
Year | Venue | Field |
|---|---|---|
2018 | LAK | Data science,Data structure,Learning analytics,SCALE-UP,Learning Management,Data retention,Computer science,Dimensional modeling,Big data,Data management |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarah Taylor | 1 | 0 | 0.34 |
| Pablo Munguia | 2 | 1 | 1.02 |