Guiding Learners into Reengagement through the SCALE Environment: An Empirical Study

Verginis, Ilias; Gouli, Evaggelia; Gogoulou, Agoritsa; Grigoriadou, Maria

doi:10.1109/tlt.2011.20

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…In terms of supporting students' learning processes, ITSs seem to be most impactful on metacognitive strategies by prompting students to apply self-regulation skills and monitor their progress when learning (Bouchet et al, 2016 ). For example, Verginis et al ( 2011 ) showed that the use of an open-learner model guided previously disengaged online students toward re-engagement and, ultimately, to improved post-test performance. Similarly, Arroyo et al ( 2014 ) provided evidence of the positive impact that learning companions had on the improvement of low-achieving students' affective states and their motivation.…”

Section: State-of-the-art In Aied Researchmentioning

confidence: 99%

The potential of learning with (and not from) artificial intelligence in education

Chichekian

Benteux

2022

Front. Artif. Intell.

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AI-powered technologies are increasingly being developed for educational purposes to contribute to students' academic performance and overall better learning outcomes. This exploratory review uses the PRISMA approach to describe how the effectiveness of AI-driven technologies is being measured, as well as the roles attributed to teachers, and the theoretical and practical contributions derived from the interventions. Findings from 48 articles highlighted that learning outcomes were more aligned with the optimization of AI systems, mostly nested in a computer science perspective, and did not consider teachers in an active role in the research. Most studies proved to be atheoretical and practical contributions were limited to enhancing the design of the AI system. We discuss the importance of developing complementary research designs for AI-powered tools to be integrated optimally into education.

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Section: State-of-the-art In Aied Researchmentioning

confidence: 99%

The potential of learning with (and not from) artificial intelligence in education

Chichekian

Benteux

2022

Front. Artif. Intell.

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“…Although the original SMILI☺ paper is cited as indicated in Table 6, this has mostly been as a reference to the existence of the framework (e.g., Brusilovsky et al 2011;Paramythis et al 2010), as a detailed review and/or as giving examples of open learner models (e.g., Hsaio and Brusilovsky 2012;Kump et al 2012), or with reference to the elements and especially purposes of OLMs (e.g., Long and Aleven 2013;Martin et al 2009;Verginis et al 2011), rather than using the full framework to describe the OLMs presented by the authors. There could be several reasons for this, for example:…”

Section: How Has the Smili☺ Open Learner Modelling Framework Been Used?mentioning

confidence: 99%

“…While this had been a purpose of earlier negotiated OLMs (e.g., Bull and Pain 1995;Dimitrova 2003), this is an aspect we came to appreciate more fully for OLMs in general, since the original SMILI☺ paper (subsequently explored more extensively in: Kay 2008, 2013). These metacognitive purposes have been the most common drivers for creating many OLMs (e.g., Feyzi-Behnagh et al 2013;Long and Aleven 2013;Verginis et al 2011).…”

Section: The Revised Smili☺ Open Learner Modelling Frameworkmentioning

confidence: 99%

SMILI☺: a Framework for Interfaces to Learning Data in Open Learner Models, Learning Analytics and Related Fields

Bull

Kay

2016

Int J Artif Intell Educ

118

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The SMILI☺ (Student Models that Invite the Learner In) Open Learner Model Framework was created to provide a coherent picture of the many and diverse forms of Open Learner Models (OLMs). The aim was for SMILI☺ to provide researchers with a systematic way to describe, compare and critique OLMs. We expected it to highlight those areas where there had been considerable OLM work, as well as those that had been neglected. However, we observed that SMILI☺ was not used in these ways. We now reflect on the reasons for this, and conclude that it has actually served a broader role in defining the notion of OLM and informing OLM design. Since the initial SMILI☺ paper, much has changed in technology-enhanced learning. Notably, learning technology has become far more pervasive, both in formal and lifelong learning. This provides huge, and still growing amounts of learning data. The fields of Learning Analytics (LA), Learning at Scale (L@S), Educational Data Mining (EDM) and Quantified Self (QS) have emerged. This paper argues that there has also been an important shift in the nature and role of learner models even within Artificial Intelligence in Education and Intelligent Tutoring Systems research. In light of these trends, and reflecting on the use of SMILI☺, this paper presents a revised and simpler version of SMILI☺ alongside the original version. In both cases there are additional categories to encompass new trends, which can be applied, omitted or substituted as required. We now offer this as a guide for designers of interfaces for OLMs, learning analytics and related fields, and we highlight the areas where there is need for more research.

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“…We found substantial recent research on hints for ITS in CS/SE education. 14,19,22,23 Interestingly, there is recognition that the conditions for providing hints include the motivational state of the student because de-motivated students are inclined to request hints immediately rather than expend effort in problem solving. 22 The problem of automatically generating hints is quite specific to ITS that coach beginning programmers.…”

Section: Recent Trends and Themesmentioning

confidence: 99%

“…14,19,22,23 Interestingly, there is recognition that the conditions for providing hints include the motivational state of the student because de-motivated students are inclined to request hints immediately rather than expend effort in problem solving. 22 The problem of automatically generating hints is quite specific to ITS that coach beginning programmers. In generating hints for the BOTS environment, for example, researchers found that they were able to generate hints more easily by analyzing the state of the world resulting from the program that moved the blocks (i.e., the positions of blocks) than by analyzing the program itself.…”

Section: Recent Trends and Themesmentioning

confidence: 99%

Work in Progress: Intelligent Tutoring Systems in Computer Science and Software Engineering Education

Nesbit¹,

Liu²,

Liu³

et al.

2015 ASEE Annual Conference and Exposition Proceedings

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where he conducts research on argumentation and learning, multimedia learning, self-regulated learning and the effectiveness of intelligent tutoring systems. He has collaborated with colleagues to publish over 60 peerreviewed journal articles, book chapters and indexed conference papers. Li Liu, Simon Fraser UniversityLi Liu is a second year doctoral student in Educational Technology and Learning Design at Simon Fraser University. With an interdisciplinary background in interaction design, media arts and education, her passion lies in exploring how innovative technologies can be harnessed to promote teaching and learning. Qing Liu, Simon Fraser UniversityQing Liu is a doctoral student in Educational Technology and Learning Design at Simon Fraser University. Her research focuses on conceptual change, the potential of learning by arguing, the role of need for cognition in learning, the effectiveness of intelligent tutoring systems, and meta-analysis of empirical studies. Dr. Olusola O Adesope, Washington State University-PullmanDr. Olusola O. Adesope is an Assistant Professor of Educational Psychology at Washington State University, Pullman. His research is at the intersection of educational psychology, learning sciences, and instructional design and technology. His recent research focuses on the cognitive and pedagogical underpinnings of learning with computer-based multimedia resources; knowledge representation through interactive concept maps; meta-analysis of empirical research, and investigation of instructional principles and assessments in STEM. AbstractMany research reports have been published over the last 30 years on the use of intelligent tutoring systems in computer science and software engineering education, but no previous systematic review has been conducted to describe and assess the field as a whole. This project (in progress) searched for publications meeting defined inclusion criteria and identified 280 eligible reports. We are currently coding these works using 28 variables that will allow us to describe the research field in aggregate. The results will tell us: What research questions are being asked? What are the types of student modeling being used? What subject domains have ITS been designed for? What issues or themes are most evident in recent research? What are the gaps in research on intelligent tutoring systems in computer science and software engineering education. Finally, what technological and pedagogical innovations are needed to advance research in this field?

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Guiding Learners into Reengagement through the SCALE Environment: An Empirical Study

Cited by 10 publications

References 12 publications

The potential of learning with (and not from) artificial intelligence in education

The potential of learning with (and not from) artificial intelligence in education

SMILI☺: a Framework for Interfaces to Learning Data in Open Learner Models, Learning Analytics and Related Fields

Work in Progress: Intelligent Tutoring Systems in Computer Science and Software Engineering Education

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