Luzhen Tang, Kejie Shen, Huixiao Le, Yuan Shen, Shufang Tan, Yueying Zhao, Torsten Juelich, Xinyu Li, Dragan Gašević, Yizhou Fan
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引用次数: 0
Abstract
Background
Learners' writing skills are critical to their academic and professional development. Previous studies have shown that learners' self-assessment during writing is essential for assessing their writing products and monitoring their writing processes. However, conducting practical self-assessments of writing remains challenging for learners without help, such as formative feedback.
Objectives
To facilitate learners' self-assessment in writing, we developed a writing analytics toolkit and used data visualisation and cutting-edge machine learning technology that provides real-time and formative feedback to learners.
Methods
To investigate whether our newly-developed tool affects the accuracy and process of learners' self-assessment, we conducted a lab study. We assigned 59 learners to complete writing (2 h) and revising (1 h) tasks. During the revision stage, we randomly assigned the learners to two groups: one group used the writing analytics toolkit while the second group was not granted access to the toolkit. Learners' self-assessment accuracy and process of self-assessment were compared between the two groups.
Results
In our study, we found the toolkit helped learners in the experimental group improve the self-assessment accuracy of their writing products compared to the learners in the control group. In addition, we also found that the affordances of the toolkit affected the learners' self-assessment process, and poor design affordances may have prevented the learners from reflecting by themselves.
Conclusions
Together, our empirical study shed light on the design of future writing analytics tools which aim at improving learners' self-assessment during formative writing processes.
期刊介绍:
The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope