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Supporting Self-Reflection in Personal Learning Environments Through User Feedback
2012
Self-regulated learning (SRL) is a crucial skill in the era when people need to learn during their whole lives. However, the traditional educational system, which is teacher-centered, does not cultivate this competence very well. In this paper we propose an approach to stimulate reflection of learners about their own learning processes, which is an important part of SRL. The approach is based on a mashup recommender that provides guidance in creating Personal Learning Environments and a widget supporting self-reflection of learners. They receive information on their usage of individual widgets and provide feedback assigning learning activities to these widgets. The aim is to raise awareness of the learners on their learning activities and how they cover the whole spectrum of SRL. We expect that this tool complemented by other ones, will support self-regulation of learners.
It is important that the educational system helps learners develop a general ability to get up to speed quickly in new domains. In order to do that students need to be able to manage their learning, for example, by setting goals, planning their learning, monitoring their progress, and responding appropriately to difficulties and errors. These general learning skills are often referred to as metacognition, or self-regulated learning (SRL). Bransford et al. [3] suggest focusing on metacognition as one of three principles that should be applied to educational research and design, as stated in the influential volume "How People Learn." A similar recommendation is given also in Clark and Mayer's [4] book about e-learning design principles. Azevedo and colleagues have found that students who regulate their learning in a hypermedia environment are more likely to acquire deep understanding of the target domain [2]. A key question is whether instructional technology can be as effective in fostering metacognitive skills as it is in teaching domain-specific skills and knowledge. Numerous learning environments include metacognitive support in order to improve domain-level learning (e.g., [5] and [1] support self-explanation in order to promote learning of Physics and Geometry, respectively.) However, only a few systems actually attempt to help students to acquire or improve the metacognitive skills themselves (and not only the domain-level knowledge). Some work suggests that improving metacognitive and SRL skills can be done using educational technologies. Examples include the Help Tutor [6], Betty's Brain [7] and MetaTutor [2]. However, a lot remains to be known about the fashion in which educational technologies can support the acquisition of metacognitive and SRL skills. The modeling, tutoring, and evaluation of metacognitive skills and knowledge poses a number of challenges:
Enhancing Self-Regulated Learning through Metacognitively-Aware Intelligent Tutoring Systems
This symposium identifies current trends and future directions in research on metacognition and Self-Regulated Learning (SRL) in educational technologies, and specifically, Intelligent Tutoring Systems (ITS). Each paper will elaborate on detection and assessment of metacognition/SRL, forms of support and scaffolding, and self-and co-regulation processes and authoring of environments that support ITS. The symposium will conclude with discussions that describe the manner in which metacognitive development can be promoted through strategies that support individual differences in multiple contexts. The alternative perspectives presented in this session will help advance our understanding of support for metacognition and SRL in ITS, as well as identify gaps that will influence future research pursuits.
Artificial Intelligence in Education, 2011
Helping students' improve their metacognitive and self-regulation skills holds the potential to improve students' ability to learn independently. Yet, to date, there are relatively few success stories of helping students enhance their metacognitive skills using interactive learning environments. In this paper we describe the Self-Assessment Tutor, an intelligent tutoring system for improving the accuracy of the judgments students make regarding their own knowledge. A classroom evaluation of the Self-Assessment Tutor with 84 students found ...
Improving metacognition through self-explication in a digital self-regulated learning tool
Educational technology research and development
Digital support during self-regulated learning can improve metacognitive knowledge and skills in learners. Previous research has predominantly focused on embedding metacognitive support in domain-specific content. In this study, we examine a detached approach where digital metacognitive support is offered in parallel to ongoing domain-specific training via a digital tool. The primary support mechanism was self-explication, where learners are prompted to make, otherwise implicit, metacognition concrete.In a controlled pre-test/post-test quasi-experiment, we compared domain-specific and domain-general support and assessed the effects, use, and learners' perceptions of the tool. The results showed that self-explication is an effective mechanism to support and improve metacognition during self-regulated learning. Furthermore, the results confirm the effectiveness of offering detached metacognitive support. While only domain-specific metacognitive support was found to be effective, q...
It’s all about metacognitive activities: computerized scaffolding of self-regulated learning
2011
Students in elementary education often learn in small groups in open learning environments, such as the Internet, e-learning environments and games. Students will be working and learning in small groups with computers throughout their lives. They therefore need to be able to regulate their learning in multiple settings to become successful life-long learners in the global knowledge society. However, practice and research have shown that many students lack the skills to adequately regulate their learning. This thesis describes a computerized scaffolding system that was developed to provide dynamic scaffolds that stimulate self-regulated learning. The goal of the scaffolding was to support small groups in complex computer-based learning environments to enhance their self-regulation and their learning. The findings show that scaffolding stimulated students’ metacognitive activities and enhanced their knowledge. Scaffolding also supported group performance but did not affect students’ d...
A Framework to Induce Self-Regulation Through a Metacognitive Tutor
Proceedings of the 2010 Association for the Advancement of Artificial Intelligence (AAAI) Fall Symposium, 2010
A new architectural framework for a metacognitive tutoring system is presented that is aimed to stimulate self-regulatory behavior in the learner. The new framework extends the cognitive architecture of TutorJ that has been already proposed by some of the authors. TutorJ relies mainly on dialogic interaction with the user, and makes use of a statistical dialogue planner implemented through a Partially Observable Markov Decision Process (POMDP). A suitable two-level structure has been designed for the statistical reasoner to ...
Frontiers in Psychology
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 effectiv...
Multidisciplinary innovations and technologies for facilitation of self-regulated learning
Computers in Human Behavior, 2019
Technology-enhanced learning environments provide ample opportunities for learners to self-regulate their learning processes and activities for achieving the intended learning outcomes in various disciplines from soft to hard sciences and from humanities to the natural and social sciences. This special issue discusses the emerging technological advancements and cutting-edge research on self-regulated learning dealing with different cognitive, motivational, emotional, and social processes of learning both at the individual and group levels. Specifically, it discusses how to optimally use advanced technologies to facilitate learners' self-regulated learning for achieving their own individual learning needs and goals. In this special issue, seven researchers/research teams from the fields of collaborative learning, computational thinking, educational psychology, and learning analytics presented contributions to self-regulated learning with the goal of stimulating cross-border discussion in the field.
Supporting Self-Regulated Learning
Self-regulated learning (SRL) competences are crucial for lifelong learning. Their cultivation requires the right balance between freedom and guidance during the learning processes. Current learning systems and approaches, such as personal learning environments, give overwhelming freedom, but also let weak learners alone. Other systems, such as learning management systems or adaptive systems, tend to institutionalise learners too much, which does not support the development of SRL competences. This chapter presents possibilities and approaches to support SRL by the use of technology. After discussing the theoretical background of SRL and related technologies, a formal framework is presented that describes the SRL process, related competences, and guidelines. Furthermore, a variety of methods is presented, how learners can be supported to learn in a self-regulated way.