Individual and peer comparison open learner model visualisations to identify what to work on next

Open learner models (OLM) can support self-regulated learning, collaborative interaction, and navigation in adaptive educational systems. Previous research has found that learners have a range of preferences for learner model visualisation. However, research has focused mainly on visualisations that are available in a single system, meaning that not all visualisations have been compared to each other. We present a study using screen shots of OLM visualisations for individuals and for comparing one's own learner model to the models of other individuals or the group, to define visualisations that students would be able to use to identify their next steps, across a wider range of options

Analysis and Comparison of Open Student Models

[EN] This article is focused on the study of Open Student Models, area that takes on the opening of Student Models¿ characteristics in Technology Based Learning Systems. In this work a review of the state of the art on Open Student Models is performed. Different approximations of the literature are compared against an opening guide that authors have defined. This guide is formulated on three main parts: learning domain, learning state and progress and student profile.Este trabajo está cofinanciado por la Universidad del País Vasco/Euskal Herriko Unibertsitatea (EHU09/09), el Ministerio de Ciencia y Tecnología a través del programa CICYT (TIN2009-14380) y el Gobierno Vasco (IT421-10).Rueda Molina, U.; Calvo Fabo, I.; Arruarte Lasa, A.; Elorriaga Arandia, JA. (2011). Análisis y Comparación de Modelos de Estudiante Abiertos. Rita -IEEE-. 6(1):19-27. http://hdl.handle.net/10251/30170S19276

Fine-Grained Open Learner Models: Complexity Versus Support

Open Learner Models (OLM) show the learner model to users to assist their self-regulated learning by, for example, helping prompt reflection, facilitating planning and supporting navigation. OLMs can show different levels of detail of the underlying learner model, and can also structure the information differently. As a result, a trade-off may exist between the potential for better support for learning and the complexity of the information shown. This paper investigates students' perceptions about whether offering more and richer information in an OLM will result in more effective support for their self-regulated learning. In a first study, questionnaire responses relating to designs for six visualisations of varying complexity led to the implementation of three variations on one of the designs. A second controlled study involved students interacting with these variations. The study revealed that the most useful variation for searching for suitable learning material was a visualisation combining a basic coloured grid, an extended bar chart-like visualisation indicating related concepts, and a learning gauge

Design of interactive visualization of models and students data

This document reports the design of the interactive visualizations of open student models that will be performed in GRAPPLE. The visualizations will be based on data stored in the domain model and student model, and aim at supporting learners to be more engaged in the learning process, and instructors in assisting the learners

An Open Learner Model Dashboard for Adaptive Learning

The thesis describes the design process of the independent OLM dashboard, MittFagkart, that visualizes student activity data across digital math tools used in Norwegian classrooms for teachers.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

MOOClm: Learner Modelling for MOOCs

Massively Open Online Learning systems, or MOOCs, generate enormous quantities of learning data. Analysis of this data has considerable potential benefits for learners, educators, teaching administrators and educational researchers. How to realise this potential is still an open question. This thesis explores use of such data to create a rich Open Learner Model (OLM). The OLM is designed to take account of the restrictions and goals of lifelong learner model usage. Towards this end, we structure the learner model around a standard curriculum-based ontology. Since such a learner model may be very large, we integrate a visualisation based on a highly scalable circular treemap representation. The visualisation allows the student to either drill down further into increasingly detailed views of the learner model, or filter the model down to a smaller, selected subset. We introduce the notion of a set of Reference learner models, such as an ideal student, a typical student, or a selected set of learning objectives within the curriculum. Introducing these provides a foundation for a learner to make a meaningful evaluation of their own model by comparing against a reference model. To validate the work, we created MOOClm to implement this framework, then used this in the context of a Small Private Online Course (SPOC) run at the University of Sydney. We also report a qualitative usability study to gain insights into the ways a learner can make use of the OLM. Our contribution is the design and validation of MOOClm, a framework that harnesses MOOC data to create a learner model with an OLM interface for student and educator usage

Supporting students’ confidence judgement through visualising alignment in open learner models

Supporting students’ knowledge monitoring skills, a component of metacognition, can help students regulate their own learning. This thesis investigates the alignment of learners’ confidence in their knowledge with a computer’s assessment of their knowledge, visualised using an Open Learner Model (OLM). The research explored students’ preferred method for visualising inconsistent data (e.g. misalignment) in an OLM, and the ways in which visualising alignment can influence student interaction with the computer. The thesis demonstrates that visualising alignment in Open Learner Models signifi-cantly increases students’ confidence compared to a control condition. In particular, visualising alignment benefited low-achieving students, in terms of knowledge monitoring and this was associated with improvements in their performance. Students showed a preference towards the visualisations that provides an overview of the in-formation (i.e. opacity) rather than ones, which provide detailed information. Graph-ical representation is shown to be more beneficial in motivating students to interact with the system than text-based representation of the same information in the con-text of representing the alignment within OLMs

Open Learner Models for Self-Regulated Learning: Exploring the Effects of Social Comparison and Granularity

Open Learner Models (OLM) show the learner the internal model that the computer-based adaptive or tutoring system maintains. In the context of Self-Regulated Learning, where the learner is able to make decisions about what to learn and how to learn, OLM bring a wide variety of supporting features, ranging from metacognitive support, to navigational support, to engagement with the learning content. In prior work using OLM which featured social comparison features (OSLM), I have discovered interesting effects from these systems, regarding engagement with the system, encompassing considerable variations across different studies. My thesis deepens the understanding of OLM and OSLM by a series of studies in which I evaluate different versions of Mastery Grids, incorporating features that were designed to match different motivational profiles, which are grounded in theories of Self-Regulated Learning and Learning Motivation. A large classroom study with more than 300 active students was conducted to deepen the exploration of the social comparison features in terms of engagement and navigation within the system. The results of this study confirmed the positive effects of the social comparison features and also brought insights into why certain students are influenced, based on their motivational orientations and prior-knowledge. A second large classroom study expanded the exploration by deploying the Rich-OLM, an extension of Mastery Grids featuring coarse- and fine-grained information about the learner model, which was designed to help students navigate the content contained in the system. Results showed that students exposed to the fine-grained components took comparatively less time navigating the interface with higher rates of attempting content that they had opened. Results also raised concerns about increasing the complexity of the interface by integrating fine-grained visualization and social comparison features. My work contributes to the understanding of the effects of Open Learner Models and additional features that provide social comparison and detailed information. It also contributes bringing learning motivation aspects into the understanding of Open Learner Models. Learning motivation in the context of self-regulated learning, provides a valuable theoretical basis to study how different students react and use learning tools