15,173 research outputs found
Rich environments for active learning: a definition
Rich Environments for Active Learning, or REALs, are comprehensive instructional systems that evolve from and are consistent with constructivist philosophies and theories. To embody a constructivist view of learning, REALs: promote study and investigation within authentic contexts; encourage the growth of student responsibility, initiative, decision making, and intentional learning; cultivate collaboration among students and teachers; utilize dynamic, interdisciplinary, generative learning activities that promote higher-order thinking processes to help students develop rich and complex knowledge structures; and assess student progress in content and learning-to-learn within authentic contexts using realistic tasks and performances. REALs provide learning activities that engage students in a continuous collaborative process of building and reshaping understanding as a natural consequence of their experiences and interactions within learning environments that authentically reflect the world around them. In this way, REALs are a response to educational practices that promote the development of inert knowledge, such as conventional teacher-to-student knowledge-transfer activities. In this article, we describe and organize the shared elements of REALs, including the theoretical foundations and instructional strategies to provide a common ground for discussion. We compare existing assumptions underlying education with new assumptions that promote problem-solving and higher-level thinking. Next, we examine the theoretical foundation that supports these new assumptions. Finally, we describe how REALs promote these new assumptions within a constructivist framework, defining each REAL attribute and providing supporting examples of REAL strategies in action
Harnessing Technology: new modes of technology-enhanced learning: opportunities and challenges
A report commissioned by Becta to explore the potential impact on education, staff and learners of new modes of technology enhanced learning, envisaged as becoming available in subsequent years. A generative framework, developed by the researchers is described, which was used as an analytical tool to relate the possibilities of the technology described to learning and teaching activities.
This report is part of the curriculum and pedagogy strand of Becta's programme of managed research in support of the development of Harnessing Technology: Next Generation Learning 2008-14. A system-wide strategy for technology in education and skills.
Between April 2008 and March 2009, the project carried out research, in three iterative phases, into the future of learning with technology. The research has drawn from, and aims to inform, all UK education sectors
Research questions and approaches for computational thinking curricula design
Teaching computational thinking (CT) is argued to be necessary but also admitted to be a very challenging task. The reasons for this, are: i) no general agreement on what computational thinking is; ii) no clear idea nor evidential support on how to teach CT in an effective way. Hence, there is a need to develop a common approach and a shared understanding of the scope of computational thinking and of effective means of teaching CT. Thus, the consequent ambition is to utilize the preliminary and further research outcomes on CT for the education of the prospective teachers of secondary, further and higher/adult education curricula
Catalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion and Diversity
Collaboration is key to STEM, where multidisciplinary team research can solve
complex problems. However, inequality in STEM fields hinders their full
potential, due to persistent psychological barriers in underrepresented
students' experience. This paper documents teamwork in STEM and explores the
transformative potential of computational modeling and generative AI in
promoting STEM-team diversity and inclusion. Leveraging generative AI, this
paper outlines two primary areas for advancing diversity, equity, and
inclusion. First, formalizing collaboration assessment with inclusive analytics
can capture fine-grained learner behavior. Second, adaptive, personalized AI
systems can support diversity and inclusion in STEM teams. Four policy
recommendations highlight AI's capacity: formalized collaborative skill
assessment, inclusive analytics, funding for socio-cognitive research, human-AI
teaming for inclusion training. Researchers, educators, policymakers can build
an equitable STEM ecosystem. This roadmap advances AI-enhanced collaboration,
offering a vision for the future of STEM where diverse voices are actively
encouraged and heard within collaborative scientific endeavors.Comment: 21 pages, 0 figure, to be published in Policy Insights from
Behavioral and Brain Science
Discriminative Transfer Learning for General Image Restoration
Recently, several discriminative learning approaches have been proposed for
effective image restoration, achieving convincing trade-off between image
quality and computational efficiency. However, these methods require separate
training for each restoration task (e.g., denoising, deblurring, demosaicing)
and problem condition (e.g., noise level of input images). This makes it
time-consuming and difficult to encompass all tasks and conditions during
training. In this paper, we propose a discriminative transfer learning method
that incorporates formal proximal optimization and discriminative learning for
general image restoration. The method requires a single-pass training and
allows for reuse across various problems and conditions while achieving an
efficiency comparable to previous discriminative approaches. Furthermore, after
being trained, our model can be easily transferred to new likelihood terms to
solve untrained tasks, or be combined with existing priors to further improve
image restoration quality
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