NDLI: Introduction of learning visualisations and metacognitive support in a persuadable open learner model

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Introduction of learning visualisations and metacognitive support in a persuadable open learner model

Content Provider	ACM Digital Library
Author	Ginon, Blandine Johnson, Matthew Bull, Susan Boscolo, Clelia
Abstract	This paper describes open learner models as visualisations of learning for learners, with a particular focus on how information about their learning can be used to help them reflect on their skills, identify gaps in their skills, and plan their future learning. We offer an approach that, in addition to providing visualisations of their learning, allows learners to propose changes to their learner model. This aims to help improve the accuracy of the learner model by taking into account student viewpoints on their learning, while also promoting learner reflection on their learning as part of a discussion of the content of their learner model. This aligns well with recent calls for learning analytics for learners. Building on previous research showing that learners will use open learner models, we here investigate their initial reactions to open learner model features to identify the likelihood of uptake in contexts where an open learner model is offered on an optional basis. We focus on university students' perceptions of a range of visualisations and their stated preferences for a facility to view evidence for the learner model data and to propose changes to the values.
Starting Page	30
Ending Page	39
Page Count	10
File Format	PDF
ISBN	9781450341905
DOI	10.1145/2883851.2883853
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-04-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Learning analytics for learners Open learner models Visual learning analytics Persuading the learner model
Content Type	Text
Resource Type	Article

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