| 71423 |
Creator |
715d1c3cf4a6414e4b3c21063064203c |
| 71423 |
Creator |
c9d52ef4a11ad1277436a89c20d0a5d4 |
| 71423 |
Creator |
480e8a34e82efe72669859ad8f018ff8 |
| 71423 |
Creator |
5d3d843d207672ff3d3bdab509f9c6dd |
| 71423 |
Creator |
d4e9466eb17c22823bfd1270bb6f4de4 |
| 71423 |
Date |
2020-07-14 |
| 71423 |
Is Part Of |
repository |
| 71423 |
abstract |
In the last twenty years a range of approaches have been adopted to facilitate Assessment
of Learning as well as Assessment for Learning. With the increased interest in measuring
learning gains using assessment data, it is important to recognise the potential limitations
of using grades as proxies for learning. If there is a lack of alignment in terms
of grade descriptors between modules within a qualification, students might perform
really well on one module, and may underperform in a module that has relatively “harsh”
grading policies. Using principles of Big Data, we explored whether students’ grade
trajectories followed a consistent pattern over time based upon their abilities, efforts,
and engagement in two distinct studies. In Study 1, we explored a relatively large
dataset of 13,966 students using multi-level modelling, while in a more fine-grained
Study 2 we focussed on the pathways of students choosing their first two modules in
six large qualifications. The findings indicated substantial misalignments in how
students progressed over time in 12 large qualifications in Study 1. In Study 2, our
analyses provided further evidence that students’ grades did not seem to be well aligned.
In all qualifications we found a highly significant effect of change over time depending
on the achievement group. Based upon these findings, we provide clear recommendations
how institutions might use similar insights into big data, and how they may improve
the longitudinal alignment of grading trajectories by using consistent grading policies. |
| 71423 |
authorList |
authors |
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editorList |
editors |
| 71423 |
status |
peerReviewed |
| 71423 |
uri |
http://data.open.ac.uk/oro/document/1184007 |
| 71423 |
uri |
http://data.open.ac.uk/oro/document/1184021 |
| 71423 |
uri |
http://data.open.ac.uk/oro/document/1184026 |
| 71423 |
uri |
http://data.open.ac.uk/oro/document/1184027 |
| 71423 |
uri |
http://data.open.ac.uk/oro/document/1184028 |
| 71423 |
uri |
http://data.open.ac.uk/oro/document/1184029 |
| 71423 |
uri |
http://data.open.ac.uk/oro/document/1186397 |
| 71423 |
volume |
7 |
| 71423 |
type |
Article |
| 71423 |
type |
BookSection |
| 71423 |
label |
Rogaten, Jekaterina ; Clow, Doug ; Edwards, Chris ; Gaved, Mark and Rienties, Bart
(2020). Are Assessment Practices Well Aligned Over Time? A Big Data Exploration. In:
Bearman, M; Dawson, P; Ajjawi, R.; Tai, J. and Boud, D. eds. Re-imagining University
Assessment in a Digital World. The Enabling Power of Assessment, 7. Cham: Springer,
pp. 147–164. |
| 71423 |
Publisher |
ext-1c5ddec173ca8cdfba8b274309638579 |
| 71423 |
Title |
Are Assessment Practices Well Aligned Over Time? A Big Data Exploration |
| 71423 |
in dataset |
oro |