Generating timely individualized feedback to support student learning of conceptual knowledge in Writing-To-Learn activities

Document Type

Article

Publication Date

6-1-2024

Abstract

As a pedagogical strategy, Writing-to-Learn uses writing to improve students’ understanding of course content, but most existing writing feedback systems focus on improving students’ writing skills rather than their conceptual development. In this article, we propose an automatic approach to generate individualized feedback based on comparing knowledge representations extracted from lecture slides and individual students’ writing assignments. The novelty of our approach lies in the feedback generation: to help students assimilate new knowledge into their existing knowledge better, their current knowledge is modeled as a set of matching concepts, and suggested concepts and concept relationships for inclusion are generated as feedback by combing two factors: importance and relevance of feedback candidates to the matching concepts in the domain knowledge. A total of 88 students were recruited to participate in a repeated measures study. Results show that most participants felt the feedback they received was relevant (78.4%), easy to understand (82.9%), accurate (76.1%) and useful (79.5%); they also felt that the proposed system made it easier to study course concepts (80.7%) and was useful in learning course concepts (77.3%). Analyses of students’ submitted assignments reveal that more course concepts and concept relationships were included when they used the proposed system.

Identifier

85149303574 (Scopus)

Publication Title

Journal of Computers in Education

External Full Text Location

https://doi.org/10.1007/s40692-023-00261-3

e-ISSN

21979995

ISSN

21979987

First Page

367

Last Page

399

Issue

2

Volume

11

Grant

103599

Fund Ref

UK Research and Innovation

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