This research explored the possibility that a metacognitive control process (namely, the allocation of self-paced study time) might be affected by the output from metacognitive monitoring processes (i.e., ease-of-learning and/or feeling-of-knowing judgments). In three experiments, university undergraduates received instructions that emphasized either accuracy of learning or speed of learning. T...

Continual learning algorithms which keep the parameters of new tasks close to that previous tasks, are popular in preventing catastrophic forgetting sequential task settings. However, 1) performance for continual learner will be degraded without distinguishing contributions previously learned tasks; 2) computational cost greatly increased with number since most existing need regularize all when...

Multi-task learning is a paradigm, where multiple tasks are jointly learnt. Previous multi-task learning models usually treat all tasks and instances per task equally during learning. Inspired by the fact that humans often learn from easy concepts to hard ones in the cognitive process, in this paper, we propose a novel multi-task learning framework that attempts to learn the tasks by simultaneo...

In 2 experiments we investigated the efficacy of self-paced study in multitrial learning. In Experiment 1, native speakers of English studied lists of Dutch-English word pairs under 1 of 4 imposed fixed presentation rate conditions (24 × 1 s, 12 × 2 s, 6 × 4 s, or 3 × 8 s) and a self-paced study condition. Total study time per list was equated for all conditions. We found that self-paced study ...

As research-based, self-paced electronic learning tools become increasingly available, a critical issue educators encounter is implementing strategies to ensure that all students engage with them as intended. Here, we first discuss the effectiveness of electronic learning tutorials as self-paced learning tools in large enrollment brick and mortar introductory physics courses and then propose a ...

Convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks. In order to distinguish the reliable data from the noisy and confusing data, we improve CNNs with self-paced learning (SPL) for enhancing the learning robustness of CNNs. In the proposed self-paced convolutional network (SPCN), each sample is assigned to a weight to reflect the easines...