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What does this type of learning look like in practice? Educators across the country are using the deeper learning framework, developed by The William and Flora Hewlett Foundation, in their classrooms. Through design thinking challenges, project-based learning activities, Genius Hours, and more, teachers can ensure that students are engaged, motivated to persist, and developing key skills.

Deeper

Digital Promise recently released a report, "Developing a System of Micro-Credentials: Supporting Deeper Learning in the Classroom," that further explores the six categories above. The report also showcases 40 educator micro-credentials that Digital Promise designed to recognize educators who have developed competencies that support deeper learning. Micro-credentials recognize teachers for the skills and competencies they develop throughout their careers.

Through the deeper learning micro-credentials, educators identify and demonstrate key competencies they can apply in their classrooms to ensure that their students enter college and the workforce ready to tackle complex challenges. Micro-credentials allow these educators to share these competencies with their peers, building a stronger professional learning community. As teachers seek to strengthen their instruction, it is vital that we support them in developing these competencies and provide meaningful recognition for those who do.

What does deeper learning mean to you? How do you engage students with deeper learning experiences in your classroom and beyond? What competencies have supported your success? We'd love to hear what you think in the comments below.

Deeper section levels of endometrium were done in 41.2% at 4 levels and 11.8% at 1 level, 2 levels, 3 levels and 6 levels each while 5.9% had deeper sections at 5 levels and 12 levels each, respectively.

Designing for and implementing deeper learning across classrooms and schools that serve communities disadvantaged by the U.S. educational system is challenging. This paper illuminates this challenge by asking the question: What would designers of interventions at the classroom, school, and district levels have to take into consideration when they want to powerfully set their organizations on a developmental path towards deeper learning?

The thinking put forth in this paper is closely informed by the experiences of a number of change projects aimed at furthering deeper learning districtwide. The projects were funded by the William & Flora Hewlett Foundation and organized as research-practice partnerships (RPPs) in which improvement teams worked together to design interventions or change activities over a period of three to four years. The approaches taken by the projects differed widely. The purpose of this paper is to aid the thinking of deeper learning designers for future undertakings by putting forth a theory of improvement informed by prior research, offering a matrix of concrete design tasks, and exploring trade-offs of pursuing different approaches.

Deeper learning in the instructional core is characterized by students productively struggling with complex ideas that are important to them given their lived experiences. Students explore these ideas with voice, inquisitiveness, imaginativeness, and collaboration. Therefore, deeper learning in the interaction between students and teachers is the hoped-for outcome of the designed and implemented intervention.

Realizing deeper learning in classrooms, and with a clear and consistent focus on attenuating inequities, requires substantial shifts in the work of teaching. It is therefore imperative that any deeper learning initiative provide sustained, high-quality professional learning opportunities for teachers.

By analyzing their chosen set of activities for a given period of time in reference to the full spectrum of multi-level design tasks, designers can realistically assess what aspects of the deeper learning challenge are still unaddressed, what outcome are realistic to expect, and what elements should be phased in over time.

MEHTA: Deeper learning is the understanding of not just the surface features of a subject or discipline, but the underlying structures or ideas. If we were talking about a biological cell, shallow learning would be able to name the parts of the cell; deeper learning would be able to understand the functions of the cell and how they interrelate.

We also say in our book that deeper learning tends to emerge at the intersection of mastery, identity, and creativity. Mastery is developing significant knowledge and skill; identity is seeing yourself as connected to doing the work; and creativity is not just taking in knowledge but doing something in the field. When those three elements come together, it often yields deep learning.

Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semi-supervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex features and graph topology in the convolutional layers. Although the GCN model compares favorably with other state-of-the-art methods, its mechanisms are not clear and it still requires considerable amount of labeled data for validation and model selection. In this paper, we develop deeper insights into the GCN model and address its fundamental limits. First, we show that the graph convolution of the GCN model is actually a special form of Laplacian smoothing, which is the key reason why GCNs work, but it also brings potential concerns of over-smoothing with many convolutional layers. Second, to overcome the limits of the GCN model with shallow architectures, we propose both co-training and self-training approaches to train GCNs. Our approaches significantly improve GCNs in learning with very few labels, and exempt them from requiring additional labels for validation. Extensive experiments on benchmarks have verified our theory and proposals. 041b061a72