Multi-faceted data-driven assessment analytics approach for workplace learning¶
Research Method · All research methods
Evidence · 3 claims (3 for) · 1 study (1 associational),q2· 0 of 1 report an effect size · 3 claims rest on one study
Description¶
A computational framework combining three analytical channels applied to workplace-based assessment data: "(i) a set of features for analysing clinical skills WBA data, (ii) analysis of the temporal aspects of that data using process mining, and (iii) utilising text analytics to compare student reflections on WBA". It is aimed at programme design and evaluation, helping instructors compare cohorts and assess the effectiveness of clinical settings, in contrast to student-centred analytics. The approach treats engagement with formative workplace assessment as a proxy for aspects of self-regulated learning.
Accounts¶
- Multi-faceted data-driven assessment analytics approach for workplace learning: A computational framework combining three analytical channels applied to workplace-based assessment data: "(i) a set of features for analysing clinical skills WBA data, (ii) analysis of the temporal aspects of that data using process mining, and (iii) utilising text analytics to compare student reflections on WBA". It is aimed at programme design and evaluation, helping instructors compare cohorts and assess the effectiveness of clinical settings, in contrast to student-centred analytics. The approach treats engagement with formative workplace assessment as a proxy for aspects of self-regulated learning. (Alicja Piotrkowicz et al. (2021))
Claims¶
- Year 1 medical students complete more WBA assessments in secondary care placements, especially when secondary care is the first placement block [+M]
- Process mining of WBA logs shows students typically start with Handwashing, then Pulse and History, with frequent looping back to basic clinical skills [+M]
- Student reflections share common frequent words across placements but differ between primary and secondary care and across year groups [+M]
Related Research Methods¶
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Key Sources¶
- Alicja Piotrkowicz, Kaiwen Wang, Jennifer Hallam, Vania Dimitrova. (2021). Data-driven Exploration of Engagement with Workplace-based Assessment in the Clinical Skills Domain. International Journal of Artificial Intelligence in Education 31:1022–1052. https://doi.org/10.1007/s40593-021-00264-0
Page metadata
| Field | Value |
|---|---|
type |
research-method |
id |
multi-faceted-data-driven-assessment-analytics-approach-for-workplace-learning |
title |
Multi-faceted data-driven assessment analytics approach for workplace learning |
description |
A computational framework combining three analytical channels applied to workplace-based assessment data: "(i) a set of features for analysing clinical skills WBA data, (ii) analysis of the temporal aspects of that data using process mining, and (iii) utilising text analytics to compare student refl |
status |
review |
generated |
by process:settle-candidates on 2026-10-27 |
trust |
unverified — no one has confirmed this page's content |
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