Open AccessProceedings Article
Comparing Student-Constructed Open Learner Model Presentations to the Domain
Andrew Mabbott,Susan Bull +1 more
- 08 Jun 2007
- pp 281-288
10
TL;DR: The results suggest learners have different preferences for the type of view they would like to create, and in many cases use informal or unstructured representational systems.
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Abstract: Increasingly, learning environments are opening the learner model to the user it represents. This paper describes a study in which students were able to create their own presentations of their learner model. We examine the nature of the learner model views created, and the degree of correspondence with the domain. Our results suggest learners have different preferences for the type of view they would like to create, and in many cases use informal or unstructured representational systems.
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Citations
Open Learner Models
Susan Bull,Judy Kay +1 more
- 01 Jan 2010
TL;DR: The range of purposes that Open Learner Models can serve are described, illustrating these with diverse examples of the ways that they have been made available in several research systems.
254
Fifteen years of constraint-based tutors: what we have achieved and where we are going
TL;DR: The current research focuses on building affect-sensitive and motivational tutors, which has progressed from a theoretical idea to a mature, reliable and effective methodology for developing effective tutors.
There are Open Learner Models About
TL;DR: This overview outlines key issues in learning with an open learner model (OLM) and how opening the model in an understandable form provides additional methods of prompting reflection, planning, and other metacognitive activities that are important in learning.
72
Open Learner Models in Smart Learning Environments
Angeliki Leonardou,Maria Rigou,John Garofalakis +2 more
- 01 Jan 2019
TL;DR: This chapter presents the evolution and current state of OLMs, summarizes related research in the field emphasizing on OLM types, locus of control between the system and the user and visualizations categorized on the basis of quantized/continuous and structured/unstructured representations.
11
Open Learner Models for Self-Regulated Learning: Exploring the Effects of Social Comparison and Granularity
Guerra Hollstein,Julio Daniel +1 more
- 24 Jan 2018
TL;DR: This thesis deepens the understanding of OLM and OSLM by a series of studies in which different versions of Mastery Grids are evaluated, incorporating features that were designed to match different motivational profiles, which are grounded in theories of Self-Regulated Learning and Learning Motivation.
4
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TL;DR: In this paper, the cognitive differences between reasoning with self-constructed external representations (ERs) and reasoning with presented representations (e.g., textbook diagrams) are discussed.
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Exploring Various Guidance Mechanisms to Support Interaction with Inspectable Learner Models
Diego Zapata-Rivera,Jim E. Greer +1 more
- 02 Jun 2002
TL;DR: This paper explores the interactions between learners and inspectable learner models using various guidance mechanisms (i.e. following a protocol, interacting with human peers and artificial guiding agents, interaction with the teacher, and exploring the model as part of a group).
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Representational Bias as Guidance for Learning Interactions: A Research Agenda.
Daniel D. Suthers
- 01 Jan 1999
TL;DR: In this paper, a theoretical analysis of the roles of constraint and saliency in the effect of representational bias on collaborative learning discourse is presented, and suggests a research agenda for systematic investigation of these claims.
26
20000 inspections of a domain-independent open learner model with individual and comparison views
Susan Bull,Andrew Mabbott +1 more
- 26 Jun 2006
TL;DR: A domain-independent open learners model with multiple simple views on individual learner model data is introduced to help learners identify their knowledge, difficulties and misconceptions; prompt reflection on their knowledge and learning; and facilitate planning.
Designing learner-controlled educational interactions based on learning/cognitive style and learner behaviour
TL;DR: It is suggested that while style information can be used to inform the design of learning environments that accommodate learners' individual differences, it would be wise to recommend interactions based on learners' behaviour.
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