Semi-supervised learning is a class of supervised learning tasks and techniques that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data. Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy.

for more information please follow this link https://en.wikipedia.org/wiki/Semi-supervised_learning

The term may has a different meaning in the field of computation as shown in the common definition provided by wikipedia. In other fields such as education, training, extension and possibly other disciplines, some more definitions may be found. However, this kind of learning (semi-supervised) is not necessarily to occur in class but could also be undertaken in other forms of education such as agricultural extension through the use of the so-called expert system which might be termed as semi-supervised learning. Although specialized expert systems (learning modules) are supposed to stand alone as an automated package and for it is being "interactive", but users may seek the help of human expert from time to time to clarify some points or provide answer to some raising questions and/or give further clues and clarification to the learner even though from a distance using a telephone, email, social media or a drop-by visit etc.. I hope you'll find this contribution useful too. Regards.