Peer assessment and knowledge discovering in a community of learners

Thanks to the exponential growth of the Internet, Distance Education is becoming more and more strategic in many fields of daily life. Its main advantage is that students can learn through appropriate web platforms that allow them to take advantage of multimedia and interactive teaching materials, without constraints neither of time nor of space. Today, in fact, the Internet offers many platforms suitable for this purpose, such as Moodle, ATutor and others. Coursera is another example of a platform that offers different courses to thousands of enrolled students. This approach to learning is, however, posing new problems such as that of the assessment of the learning status of the learner in the case where there were thousands of students following a course, as is in Massive On-line Courses (MOOC). The Peer Assessment can therefore be a solution to this problem: evaluation takes place between peers, creating a dynamic in the community of learners that evolves autonomously. In this article, we present a first step towards this direction through a peer assessment mechanism led by the teacher who intervenes by evaluating a very small part of the students. Through a mechanism based on machine learning, and in particular on a modified form of K-NN, given the teacher’s grades, the system should converge towards an evaluation that is as similar as possible to the one that the teacher would have given. An experiment is presented with encouraging results

Assessing collaborative learning: big data, analytics and university futures

Traditionally, assessment in higher education has focused on the performance of individual students. This focus has been a practical as well as an epistemic one: methods of assessment are constrained by the technology of the day, and in the past they required the completion by individuals under controlled conditions, of set-piece academic exercises. Recent advances in learning analytics, drawing upon vast sets of digitally-stored student activity data, open new practical and epistemic possibilities for assessment and carry the potential to transform higher education. It is becoming practicable to assess the individual and collective performance of team members working on complex projects that closely simulate the professional contexts that graduates will encounter. In addition to academic knowledge this authentic assessment can include a diverse range of personal qualities and dispositions that are key to the computer-supported cooperative working of professionals in the knowledge economy. This paper explores the implications of such opportunities for the purpose and practices of assessment in higher education, as universities adapt their institutional missions to address 21st Century needs. The paper concludes with a strong recommendation for university leaders to deploy analytics to support and evaluate the collaborative learning of students working in realistic contexts

Advances in Technology Enhanced Learning

‘Advances in Technology Enhanced Learning’ presents a range of research projects which aim to explore how to make engagement in learning (and teaching) more passionate. This interactive and experimental resource discusses innovations which pave the way to open collaboration at scale. The book introduces methodological and technological breakthroughs via twelve chapters to learners, instructors, and decision-makers in schools, universities, and workplaces. The Open University's Knowledge Media Institute and the EU TELMap project have brought together the luminaries from the European research area to showcase their vision of the future of learning with technology via their recent research project work. The projects discussed range widely over the Technology Enhanced Learning area from: environments for responsive open learning, work-based reflection, work-based social creativity, serious games and many more

weSPOT: A personal and social approach to inquiry-based learning

weSPOT is a new European initiative proposing a novel approach for personal and social inquiry-based learning in secondary and higher education. weSPOT aims at enabling students to create their mash-ups out of cloud based tools and services in order to perform scientific investigations. Students will also be able to share their inquiry accomplishments in social networks and receive feedback from the learning environment and their peers. This paper presents the research framework of the weSPOT project, as well as the initial inquiry-based learning scenarios that will be piloted by the project in real-life educational settings

Adaptivity through self-directed learning to meet the challenges of our ever-changing world

Fostering adult learners’ competence to adapt appropriately to our ever-changing world is a primary concern of adult education. The purpose of the present article is novel and examines whether the consideration of modes of learning (instruction, performance, and inquiry) could assist in the design of adult education that facilitates self-directed learning and enables learners to think and perform adaptively. The concept of modes of learning originated from the typology of Houle. However, to date, no study has reached beyond this typology, especially concerning the potential of using modes of learning in the design of adult education. Specifically, an apparent oversight in adult learning theory is the foremost importance of the consideration of whether inquiry is included in the learning process: its inclusion potentially differentiates the purpose of instruction, the nature of learners’ performance, and the underlying epistemological positioning. To redress this concern, two models of modes of learning are proposed and contrasted. The reinforcing model of modes of learning (instruction, performance, without inquiry) promotes teacher-directed learning. A key consequence of employing this model in adult education is that learners may become accustomed to habitually reinforcing patterns of perceiving, thinking, judging, feeling, and acting—performance that may be rather inflexible and represented by a distinct lack of a perceived need to adapt to social contextual changes: a lack of motivation for self-directed learning. Rather, the adapting model of modes of learning (instruction, performance, with inquiry) may facilitate learners to be adaptive in their performance—by encouraging an enhanced learner sensitivity toward changing social contextual conditions: potentially enhancing learners’ motivation for self-directed learning

Personalized and adaptive learning: educational practice and technological impact

Education Technology advances many aspects of learning. More and more learning is taking place online. Learners’ learning behaviors, style, and performance can be easily profiled through learning analytics which collects their online learning footage. It enables and encourages educational research, learning software application development, and online education practices towards personalized and adaptive learning. As we continue to see personalized and adaptive learning progress, we must also pay attention to the negative impacts that feed into our research. In this paper, we will present our introspection of personalized and adaptive learning and argue that it is the social and moral responsibility of educators and institutions to apply personalized and adaptive learning wisely in their education practice. Educators and institutions should also recognize the realistic diversities of individual students’ learning styles and variable learning progress, contextually dependent learning accessibility, and their correspondent support needs for the fine-grained learning activities. We argue that the strategically balanced practices and innovated learning technology are crucial towards an optimized learning experience for the learners. A Tecnologia da Educação avança muitos aspectos da aprendizagem. Cada vez mais aprendizagem está ater lugar online. Os comportamentos de aprendizagem, estilo e desempenho dos aprendentes podem serfacilmente perfilados através de análises de aprendizagem que recolhem as suas filmagens de aprendizagemon-line. Permite e encoraja a investigação educacional, o desenvolvimento de aplicações de software deaprendizagem, e práticas de educação em linha para uma aprendizagem personalizada e adaptativa. À medidaque continuamos a ver progressos na aprendizagem personalizada e adaptativa, devemos também prestaratenção aos impactos negativos que alimentam a nossa investigação. Neste documento, apresentaremos anossa introspecção de aprendizagem personalizada e adaptativa e argumentaremos que é da responsabilidade social e moral dos educadores e instituições aplicar sabiamente a aprendizagem personalizada e adaptativa nasua prática educativa. Os educadores e as instituições devem também reconhecer as diversidades realistas dosestilos de aprendizagem dos estudantes individuais e o progresso variável da aprendizagem, a acessibilidade àaprendizagem contextualmente dependente, e as suas necessidades de apoio correspondente para as actividadesde aprendizagem de grão fino. Argumentamos que as práticas estrategicamente equilibradas e a tecnologiade aprendizagem inovadora são cruciais para uma experiência de aprendizagem optimizada para os alunos

Academic Self-Concept and Master Adaptive Learning in First Year Medical Students: A Validation and Scale Construction Study

Students’ academic self-concepts (ASC) and their orientation towards self-regulated learning are important elements of success. Despite this fact, little work has been conducted exploring these areas medical students. Given the shifting priorities of medical education toward competency-based education and self-directed learning, the goals of this study were to validate an existing measure of ASC and to improve our measurement capabilities for understanding the Master Adaptive Learner (MAL). Evidence for validity and scale reliability was collected for the ASCS with this novel population and a range of motivational and self-regulative variables (Goal orientation, academic emotion regulation, and lifelong learning) were analyzed and reduced to produce a single scale for MAL. Surveys were administered to 203 medical students at an urban, Mid-Atlantic medical school and students’ grades were linked to survey responses. Results of a confirmatory factor analysis indicated that the original factor structure was not a good fit to the data for the current data. An exploratory factor analysis (EFA) was conducted to identify which structure fit better, and while a three-factor structure was produced, only one factor met reliability standards. This factor, confidence, was merged with items from the other surveys, and reliability scores for a composite MAL scale were identified. Based on these findings and the result of an EFA, the total item pool was reduced from 83 to 25. These 25 items discriminated between two clusters of students: MALs and others. Students’ membership in the MAL cluster predicted greater performance on the first exam in medical school, but not on any other grade outcomes. These results provide early evidence for the continued study of MAL and motivation in medical school, which will help researchers and curriculum designers support the development of future physicians

Power to the Teachers:An Exploratory Review on Artificial Intelligence in Education

This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers’ roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models

A Literature Review on Intelligent Services Applied to Distance Learning

Distance learning has assumed a relevant role in the educational scenario. The use of Virtual Learning Environments contributes to obtaining a substantial amount of educational data. In this sense, the analyzed data generate knowledge used by institutions to assist managers and professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide variety of intelligent services for assisting in the learning process. This article presents a literature review in order to identify the intelligent services applied in distance learning. The research covers the period from January 2010 to May 2021. The initial search found 1316 articles, among which 51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the principal services offered are recommendation systems and learning systems. In these services, the analysis of student profiles stands out to identify patterns of behavior, detect low performance, and identify probabilities of dropouts from courses.info:eu-repo/semantics/publishedVersio