Name
Title | ||
|---|---|---|
Contextualizable Learning Analytics Design: A Generic Model and Writing Analytics Evaluations |
Abstract | ||
|---|---|---|
A major promise of learning analytics is that through the collection of large amounts of data we can derive insights from authentic learning environments, and impact many learners at scale. However, the context in which the learning occurs is important for educational innovations to impact student learning. In particular, for student-facing learning analytics systems like feedback tools to work effectively, they have to be integrated with pedagogical approaches and the learning design. This paper proposes a conceptual model to strike a balance between the concepts of generalizable scalable support and contextualized specific support by clarifying key elements that help to contextualize student-facing learning analytics tools. We demonstrate an implementation of the model using a writing analytics example, where the features, feedback and learning activities around the automated writing feedback tool are tuned for the pedagogical context and the assessment regime in hand, by co-designing them with the subject experts. The model can be employed for learning analytics to move from generalized support to meaningful contextualized support for enhancing learning.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3303772.3303785 | Proceedings of the 9th International Conference on Learning Analytics & Knowledge |
Keywords | Field | DocType |
CLAD, conceptual model, contextualizable learning analytics, learning design, writing analytics | Data science,Conceptual model,Learning analytics,Computer science,Authentic learning,Analytics,Learning design,Student learning,Scalability | Conference |
ISBN | Citations | PageRank |
978-1-4503-6256-6 | 1 | 0.35 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonette Shibani | 1 | 7 | 3.50 |
| Simon Knight | 2 | 41 | 10.75 |
| Simon Buckingham Shum | 3 | 1415 | 161.39 |