Toward Automatic Interpretation of Narrative Feedback in Competency-Based Portfolios

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3ATMPTW9SZ" target="_blank" >RIV/00216208:11320/22:TMPTW9SZ - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/TLT.2022.3159334" target="_blank" >https://doi.org/10.1109/TLT.2022.3159334</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TLT.2022.3159334" target="_blank" >10.1109/TLT.2022.3159334</a>

Result language

angličtina

Original language name

Toward Automatic Interpretation of Narrative Feedback in Competency-Based Portfolios

Original language description

Self-directed learning is generally considered a key competence in higher education. To enable self-directed learning, assessment practices increasingly embrace assessment for learning rather than the assessment of learning, shifting the focus from grades and scores to provision of rich, narrative, and personalized feedback. Students are expected to collect, interpret, and give meaning to this feedback, in order to self-assess their progress and to formulate new, appropriate learning goals and strategies. However, interpretation of aggregated, longitudinal narrative feedback has been proven to be very challenging, cognitively demanding, and time consuming. In this article, we, therefore, explored the applicability of existing, proven text mining techniques to support feedback interpretation. More specifically, we investigated whether it is possible to automatically generate meaningful information about prevailing topics and the emotional load of feedback provided in medical students’ competence-based portfolios (N = 1500), taking into account the competence framework and the students’ various performance levels. Our findings indicate that the text-mining techniques topic modeling and sentiment analysis make it feasible to automatically unveil the two principal aspects of narrative feedback, namely the most relevant topics in the feedback and their sentiment. This article, therefore, takes a valuable first step toward the automatic, online support of students, who are tasked with meaningful interpretation of complex narrative data in their portfolio as they develop into self-directed life-long learners.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

—

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Transactions on Learning Technologies

ISSN

1939-1382

e-ISSN

1939-1382

Volume of the periodical

15

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

179-189

UT code for WoS article

000814628600005

EID of the result in the Scopus database

2-s2.0-85126513657