itselfteaching

Itselfteaching is a learning approach in which the learner relies primarily on its own outputs, reflections, or generated data to guide subsequent learning, rather than depending on direct instruction or externally labeled materials. The term can apply to human education, where individuals engage in self-directed learning activities such as setting goals, designing practice problems, self-testing, and reflecting on errors to adjust strategies. It also applies to artificial systems, where learning signals are produced by the system itself.

In human contexts, techniques associated with itselfteaching include retrieval practice, metacognitive assessment, journaling, and project-based exploration. The emphasis is on autonomy, goal orientation, and iterative improvement.

In computational contexts, it is often referred to as self-training, pseudo-labeling, or self-supervised learning. A model trained on a labeled dataset then labels a larger set of unlabeled data with its own predictions; high-confidence examples are added to the training set in successive rounds. Self-supervised methods derive supervision from intrinsic structure in the data, such as predicting missing parts or solving pretext tasks, enabling representation learning without explicit labels.

Benefits include reduced labeling costs, the ability to exploit vast unlabeled data, and potential performance gains from iterative refinement. Challenges include confirmation bias from reinforcing incorrect predictions, distribution mismatch between labeled and unlabeled data, and the need for careful calibration of confidence thresholds and evaluation.

Itselfteaching overlaps with related concepts such as self-directed learning in education, self-supervised learning in machine learning, and semi-supervised approaches that blend self-generated and external supervision. It emphasizes learner autonomy and the capacity to improve through reflection and self-generated data.