C-STARS: a collaborative online learning environment for situated translation teaching in higher education

Abstract

PurposeBefore COVID-19 pandemic, translation students in higher education attended courses on computer-assisted translation to acquire operational knowledge of professional software, without experiencing collaborative/interactive learning. In few European universities skills labs were introduced to offer fieldwork-like activities, but without shared modelling standards and mainly held in presence for few consecutive days. As the COVID-19 pandemic further deprived students of crucial educational experiences, the purpose of this paper is to present an online collaborative learning environment exploiting a customised combination of information and learning applications the students already experienced during the pandemic to smoothen their learning curve and increase the educational effectiveness.Design/methodology/approachA multi-framework methodology to build and operate a computer-supported online collaborative skills lab for translation students is proposed. Reference standards and guidelines are leveraged to provide participants with a common knowledge ground, to activate the computer-supported collaborative learning perspective, and to ensure an appropriate simulation fidelity.FindingsAn end-to-end implementation of the proposed approach along with educational effectiveness analysis performed via complementary assessment strategies is presented. Achieved outcomes highlight significant participation rate, consistent improvement in technical and soft skills at both individual and collective levels, and clear activation of collaborative learning dynamics.Practical implicationsEvery master degree in translation teaching can adopt this approach by complying with the proposed guidelines.Originality/valueThe proposed multi-framework approach is novel and applied to a teaching sector where it is highly needed to fill a pre-existing gap.

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