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Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rule-based models

Overview of attention for article published in International Journal of Educational Technology in Higher Education, September 2022
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Mentioned by

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1 X user

Citations

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Readers on

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83 Mendeley
Title
Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rule-based models
Published in
International Journal of Educational Technology in Higher Education, September 2022
DOI 10.1186/s41239-022-00354-6
Authors

Balqis Albreiki, Tetiana Habuza, Nazar Zaki

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 83 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 7 8%
Student > Ph. D. Student 6 7%
Student > Master 6 7%
Student > Doctoral Student 5 6%
Unspecified 4 5%
Other 13 16%
Unknown 42 51%
Readers by discipline Count As %
Computer Science 20 24%
Engineering 4 5%
Social Sciences 3 4%
Unspecified 2 2%
Economics, Econometrics and Finance 1 1%
Other 9 11%
Unknown 44 53%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 September 2022.
All research outputs
#19,911,682
of 24,469,913 outputs
Outputs from International Journal of Educational Technology in Higher Education
#410
of 411 outputs
Outputs of similar age
#311,246
of 425,385 outputs
Outputs of similar age from International Journal of Educational Technology in Higher Education
#15
of 16 outputs
Altmetric has tracked 24,469,913 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 411 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 425,385 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.