Deep(er) Learning

Designing Deeper Learning Experiences for Online Instruction

Deeper learning promotes students’ active engagement in learning environments, so they can continuously explore, reflect and produce information to build complex knowledge structures. Consequently, deeper learning has become a major focus of scholarly investigation and debate. Multiple studies have been conducted to describe the characteristics of deeper learning and to determine methods for in...

Student-Faculty Partnership for Deeper Learning

Epistemological sensitisation causes deeper elaboration during self-regulated learning

Previous research indicates that students’ adaptation to task complexity in the planning stages of self-regulated learning are related to their epistemological beliefs (Stahl, Pieschl, & Bromme, 2006), but it is an open issue if students enact similar strategies in subsequent stages. Based on the COPES-model (Winne & Hadwin, 1998) the impact of epistemological beliefs on learning is tested here...

Delving Deeper into Convolutional Networks for Learning Video Representations

We propose an approach to learn spatio-temporal features in videos from intermediate visual representations we call “percepts” using Gated-Recurrent-Unit Recurrent Networks (GRUs). Our method relies on percepts that are extracted from all levels of a deep convolutional network trained on the large ImageNet dataset. While high-level percepts contain highly discriminative information, they tend t...

Delving Deeper into Convolution Networks for Learning Video Representation

Video analysis and understanding represents a major challenge for computer vision and machine learning research. While previous work has traditionally relied on hand-crafted and task-specific representations, there is a growing interest in designing general video representations that could help solve tasks in video understanding such as human action recognition, video retrieval or video caption...