Sharing Learners' Behavior to Enhance a Metacognition-oriented Intelligent Tutoring System

International audienceLiterature shows that Intelligent Tutoring Systems (ITS) are growing in acceptance and popularity because they increase performances of students, leverage cognitive development, but also significantly reduce time to acquire knowledge and competencies. Moreover, monitoring metacognitive skills enables learners to assess performance and select appropriate fix-up: individuals unable to ensure self-monitoring cannot detect errors and as a consequence, they process information less efficiently than skilled monitors. Thus, we present an ITS offering the opportunity of evaluating various metacognitive indicators and able to share this information with others learning tools. Our online tutor is based on an existing ITS authoring tool that we extended to support metacognition and share learners’ profiles and activities into a standardized, distributed and open tracking repository. This framework, validated by an experimentation, thus helps to correlate metadata experiences with real performanc

AI as a Methodology for Supporting Educational Praxis and Teacher Metacognition

Evidence-based practice (EBP) is of critical importance in education where emphasis is placed on the need to equip educators with an ability to independently generate and reflect on evidence of their practices in situ – a process also known as praxis. This paper examines existing research related to teachers’ metacognitive skills and, using two exemplar projects, it discusses the utility and relevance of AI methods of knowledge representation and knowledge elicitation as methodologies for supporting EBP. Research related to technology-enhanced communities of practice as a means for teachers to share and compare their knowledge with others is also examined. Suggestions for the key considerations in supporting teachers’ metacognition in praxis are made based on the review of literature and discussion of the specific projects, with the aim to highlight potential future research directions for AIEd. A proposal is made that a crucial part of AIEd’s future resides in its curating the role of AI as a methodology for supporting teacher training and continuous professional development, especially as relates to their developing metacognitive skills in relation to their practices

Scaffolding Reflection: Prompting Social Constructive Metacognitive Activity in Non-Formal Learning

The study explores the effects of three different types of non-adaptive, metacognitive scaffolding on social, constructive metacognitive activity and reflection in groups of non-formal learners. Six triads of non-formal learners were assigned randomly to one of the three scaffolding conditions: structuring, problematising or epistemological. The triads were then asked to collaboratively resolve an ill-structured problem and record their deliberations. Evidence from think-aloud protocols was analysed using conversational and discourse analysis. Findings indicate that epistemological scaffolds produced more social, constructive metacognitive activity than either of the two other scaffolding conditions in all metacognitive activities except for task orientation, as well as higher quality interactions during evaluation and reflection phases. However, participants appeared to be less aware of their activities as forming a strategic, self-regulatory response to the problem. This may indicate that for learning transfer, it may be necessary to employ an adaptive, facilitated reflection on learners' activities

Theoretical perspectives on mobile language learning diaries and noticing for learners,teachers and researchers

This paper considers the issue of 'noticing' in second language acquisition, and argues for the potential of handheld devices to: (i) support language learners in noticing and recording noticed features 'on the spot', to help them develop their second language system; (ii) help language teachers better understand the specific difficulties of individuals or those from a particular language background; and (iii) facilitate data collection by applied linguistics researchers, which can be fed back into educational applications for language learning. We consider: theoretical perspectives drawn from the second language acquisition literature, relating these to the practice of writing language learning diaries; and the potential for learner modelling to facilitate recording and prompting noticing in mobile assisted language learning contexts. We then offer guidelines for developers of mobile language learning solutions to support the development of language awareness in learners

An Investigation Of The Relationship Between The Use Of Modern Digital Technologies, Language Learning Strategies, And Development Of Second Language Skills

Like many other areas of human knowledge, the field of language learning has undergone changes as a consequence of the application of digital technologies. Extensive exposure and anytime and anywhere access availability to data in a second or foreign language (L2) bring almost unlimited learning opportunities for digital age students, which affects their learning behaviors also known as language learning strategies (LLS). The purpose of the present study is to define preferred LLS patterns of digitally native L2 learners and to establish relationships between types of existing digital technologies, learners’ demographic characteristics, and the use of learning strategies to support the development of specific language skills and aspects. The setting for this study was made up by a medium-sized university in the northern U.S., particularly, its undergraduate student population enrolled in foreign language courses in the Department of Modern and Classical Languages and Literatures during the 2021 fall semester. They were asked to complete a survey that contained the original validated version of the Strategy Inventory for Language Learning (SILL) instrument (Oxford, 1990) and three additional sections disclosing the participants’ demographics, technology use experience, and targeted language skills and aspects. Both descriptive and inferential quantitative methods of data analysis were used in the study to elucidate the research questions. A number of analytic procedures using SPSS® Statistics software were performed to find out detailed statistic values of the research variables. Frequencies and descriptive statistics, analysis of correlations, extreme groupings t-tests to explore the relationships between the subsets of categorical variables, and factor analysis of LLS domains were implemented to identify meaningful patterns of technology use in L2 learning. Data from this study provide a view of how the Digital Natives themselves see their technology use and approaches to learning. Research conclusions based on obtained self-reported evidence allow us to make broader recommendations for changes in the L2 teaching methodology. They may also prevent instructors from making unsupported assumptions about their students\u27 mastery of educational technology, and, thereby, from neglecting to teach students the skills they need for academic success. Keywords: digital native learner, digital technology categories, language learning strategies, L2 language skill

Artificial Intelligence in Education

Artificial Intelligence (AI) technologies have been researched in educational contexts for more than 30 years (Woolf 1988; Cumming and McDougall 2000; du Boulay 2016). More recently, commercial AI products have also entered the classroom. However, while many assume that Artificial Intelligence in Education (AIED) means students taught by robot teachers, the reality is more prosaic yet still has the potential to be transformative (Holmes et al. 2019). This chapter introduces AIED, an approach that has so far received little mainstream attention, both as a set of technologies and as a field of inquiry. It discusses AIED’s AI foundations, its use of models, its possible future, and the human context. It begins with some brief examples of AIED technologies

Supporting Adult Learners\u27 Metacognitive Development with a Sociotechnical System

Metacognition is defined as thinking about and reflecting on one\u27s cognitive processes. In learning contexts, strong metacognition leads to retention, academic success, and deep learning. While we know a lot about the metacognition of learners in grades K-12 and college, there are limited studies on adult learners\u27 (24 and older) metacognitive awareness, how to support it, or the role technology can play, particularly since e-learning is quickly becoming the central mode of learning for adult learners. Thus, I have the following motivating research question: How can we support adult learners\u27 metacognitive development in e-learning environments? To better understand adult learners\u27 needs, I conducted a content analysis of adults\u27 learning ePortfolios and surveyed a cross-section of adult learners to determine their metacognitive awareness. Based on those findings and the literature on designing learning technologies for adult learners, I iteratively designed and developed a web-based application with adult learning, social learning, and persuasive design elements. During two sections of an online course, a treatment group used the intervention and a control group did not. Both groups completed a pre-/post-self report of their metacognitive awareness, developed a learning portfolio that was rated by two raters for evidence of metacognition, and participated in interviews. This research shows that (a) adult learners are adept at planning and monitoring their learning but need more support in managing information and evaluating their learning; (b) a web-based intervention with social-persuasive design elements supports adult learners in metacognitive development; and (c) social and persuasive design elements, when aligned with adult learning principles, support adult learners\u27 narrative identity, which I argue is a key factor in supporting their metacognitive development. This research aims to provide designers, educators, and learners with a better understanding of adult learners needs and offers design principles and guidelines for development of sociotechnical systems that can promote their metacognitive development in e-learning environments

A student-facing dashboard for supporting sensemaking about the brainstorm process at a multi-surface space

© 2017 Association for Computing Machinery. All rights reserved. We developed a student-facing dashboard tuned to support posthoc sensemaking in terms of participation and group effects in the context of collocated brainstorming. Grounding on foundations of small-group collaboration, open learner modelling and brainstorming at large interactive displays, we designed a set of models from behavioural data that can be visually presented to students. We validated the effectiveness of our dashboard in provoking group reflection by addressing two questions: (1) What do group members gain from studying measures of egalitarian contribution? and (2) What do group members gain from modelling how they sparked ideas off each other? We report on outcomes from a study with higher education students performing brainstorming. We present evidence from i) descriptive quantitative usage patterns; and ii) qualitative experiential descriptions reported by the students. We conclude the paper with a discussion that can be useful for the community in the design of collective reflection systems

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing