There are Open Learner Models About!

Bull, Susan

doi:10.1109/tlt.2020.2978473

Cited by 58 publications

(36 citation statements)

References 92 publications

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“…This is consistent with Expectancy-value Theory of Motivation ( Wigfield and Eccles, 2000 ) and corresponding value-based interventions (e.g., Hulleman et al, 2010a ), which suggest that increasing utility appraisals (and reducing cost appraisals) will enhance achievement. Second, PA scaffolding could be designed to enhance users’ sense of autonomy by allowing learners to select the frequency and type of feedback delivered, in line with an open learner model ( Bull, in press ). Mastery-approach learners, in particular, may also benefit from less frequent agent interaction or from fading scaffolds over time ( Belland, 2013 ).…”

Section: Motivation During Learning With Metatutormentioning

confidence: 99%

Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System

et al. 2022

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Self-regulated learning (SRL) is critical for learning across tasks, domains, and contexts. Despite its importance, research shows that not all learners are equally skilled at accurately and dynamically monitoring and regulating their self-regulatory processes. Therefore, learning technologies, such as intelligent tutoring systems (ITSs), have been designed to measure and foster SRL. This paper presents an overview of over 10 years of research on SRL with MetaTutor, a hypermedia-based ITS designed to scaffold college students’ SRL while they learn about the human circulatory system. MetaTutor’s architecture and instructional features are designed based on models of SRL, empirical evidence on human and computerized tutoring principles of multimedia learning, Artificial Intelligence (AI) in educational systems for metacognition and SRL, and research on SRL from our team and that of other researchers. We present MetaTutor followed by a synthesis of key research findings on the effectiveness of various versions of the system (e.g., adaptive scaffolding vs. no scaffolding of self-regulatory behavior) on learning outcomes. First, we focus on findings from self-reports, learning outcomes, and multimodal data (e.g., log files, eye tracking, facial expressions of emotion, screen recordings) and their contributions to our understanding of SRL with an ITS. Second, we elaborate on the role of embedded pedagogical agents (PAs) as external regulators designed to scaffold learners’ cognitive and metacognitive SRL strategy use. Third, we highlight and elaborate on the contributions of multimodal data in measuring and understanding the role of cognitive, affective, metacognitive, and motivational (CAMM) processes. Additionally, we unpack some of the challenges these data pose for designing real-time instructional interventions that scaffold SRL. Fourth, we present existing theoretical, methodological, and analytical challenges and briefly discuss lessons learned and open challenges.

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Section: Motivation During Learning With Metatutormentioning

confidence: 99%

Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System

et al. 2022

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“…Similar to LADs, OLMs can be designed to offer information to support learners’ monitoring, reflection and self‐regulation to improve their learning and academic achievement (Bodily et al, 2018; Bull, 2020; Bull & McKay, 2004; Conati et al, 2018; Hooshyar et al, 2020). A key difference, however, is that OLMs tools are based on models related to learners’ knowledge, interest, affect and other cognitive variables with applications in areas such as intelligent tutoring systems, artificial intelligence and adaptive hypermedia, which use learner modelling to automatically create personalised instructional interventions and recommendations to the learner, while LADs are data‐driven decision‐making tools that usually do not involve ‘learner modeling’ or ‘user modeling’ approaches (Bodily et al, 2018; Bull, 2020; Hooshyar et al, 2020). Another distinctive trend is that while OLM research systems are more likely to be interactive and use assessment data, learning analytics applications are usually based on trace data from learners’ activities such as behavioural metrics (Bodily et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…There is, however, a push for greater integration between OLMs and LADs based not only on their shared tradition as data visualisation tools, but also based on their progressive convergent developments: while some OLMs have been integrating behavioural metrics as part of their design approach (Bodily et al, 2018; Bull, 2020), some LADs have been using learner modelling (Baneres et al, 2019; Raza et al, 2019). For example, Baneres et al, 2019 have used an adaptive predictive model to identify students at‐risk in the development of an early warning system that also included visualisation dashboard and an e‐mail messaging system to send personalised recommendations based on student profile.…”

Section: Discussionmentioning

confidence: 99%

Staying on target: A systematic literature review on learner‐facing learning analytics dashboards

Valle

Antonenko

Dawson

et al. 2021

Brit J Educational Tech

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The advances in technology to capture and process unprecedented amounts of educational data has boosted the interest in Learning Analytics Dashboard (LAD) applications as a way to provide meaningful visual information to administrators, parents, teachers and learners. Despite the frequent argument that LADs are useful to support target users and their goals to monitor and act upon the information provided, little is known about LADs’ theoretical underpinnings and the alignment (or lack thereof) between LADs intended outcomes and the measures used to evaluate their implementation. However, this knowledge is necessary to illuminate more efficient approaches in the development and implementation of LAD tools. Guided by the self‐regulated learning perspective and using the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) framework, this systematic literature review addressed this gap by examining whether and how learner‐facing LAD’s target outcomes align with the domain measures used to evaluate their implementations. Out of the 1297 papers retrieved from 15 databases, 28 were included in the final quantitative and qualitative analysis. Results suggested an intriguing lack of alignment between LADs’ intended outcomes (mostly cognitive domain) and their evaluation (mostly affective measures). Based on these results and on the premise that LADs are designed to support learners, a critical recommendation from this study is that LADs’ target outcomes should guide the selection of measures used to evaluate the efficacy of these tools. This alignment is critical to enable the construction of more robust guidelines to inform future endeavours in the field. What is already known about this topic There has been an increased interest and investment in learning analytics dashboards to support learners as end‐users. Learner‐facing learning analytics dashboards are designed with different purposes, functionalities and types of data in an attempt to influence learners’ behaviour, achievement and skills. What this paper adds This paper reports trends and opportunities regarding the design of learner‐facing learning analytics dashboards, contexts of implementation, as well as types and features of learner‐facing learning analytics dashboard studies. The paper discusses how affect and motivation have been largely overlooked as target outcomes in learner‐facing learning analytics dashboards. Implications for practice and/or policy Based on the evidence gathered through the review, this paper makes recommendations for theory (eg, inclusion of motivation as an important target outcome). The paper makes recommendations related to the design, implementation and evaluation of learning analytics dashboards. The paper also highlights the need for further integration between learner‐facing learning analytics dashboards and open learner models.

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“…Following best practices outlined by the 'students as partners' approach [101], RiPPLE introduces mutually beneficial learning partnerships between/within learners and experts for providing high-quality learning at scale. Our approach has focused on utilising students' contributions and data towards the development of open learner models (OLMs) [23] that capture an abstract representation of a student's knowledge state. By and large, existing learner models are grounded in psychometrics and approximate a student's knowledge state solely based on their performance on assessment items.…”

Section: Adaptive Educational Systemsmentioning

confidence: 99%

Information Resilience: the nexus of responsible and agile approaches to information use

et al. 2022

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The appetite for effective use of information assets has been steadily rising in both public and private sector organisations. However, whether the information is used for social good or commercial gain, there is a growing recognition of the complex socio-technical challenges associated with balancing the diverse demands of regulatory compliance and data privacy, social expectations and ethical use, business process agility and value creation, and scarcity of data science talent. In this vision paper, we present a series of case studies that highlight these interconnected challenges, across a range of application areas. We use the insights from the case studies to introduce Information Resilience, as a scaffold within which the competing requirements of responsible and agile approaches to information use can be positioned. The aim of this paper is to develop and present a manifesto for Information Resilience that can serve as a reference for future research and development in relevant areas of responsible data management.

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There are Open Learner Models About!

Cited by 58 publications

References 92 publications

Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System

Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System

Staying on target: A systematic literature review on learner‐facing learning analytics dashboards

Information Resilience: the nexus of responsible and agile approaches to information use

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