Self paced learning

Self paced learning
See also
Internal training CAE Algorithm Training and development Advantages of simulation Budget manual Organization of administrative and office work Learning by doing Adaptive learning

Self paced learning is a recently proposed learning regime inspired by the learning process of humans and animals that gradually incorporates easy to more complex samples into training .

The philosophy under this learning paradigm is to simulate the learning principle of humans/animals, which generally starts by learning easier aspects of a learning task, and then gradually takes more complex examples into training .

As stated in How to Master Online Learning self paced learning or independent study, involves no interaction with instructors or other students. In these types of courses, students are directed to the course materials and are then expected to learn independently by following their own schedule. Most self paced courses do not have a start and end date. Instead, students begin the course and progress through the course materials at their own pace. These courses usually require the greatest amount of self-discipline because you are the only one to set your schedule for completing assignments and finishing the course .

Opportunities

As stated in Effective Learning Environments self paced learning opportunities are independent learning situations in which a learner progresses through activities at his or her own pace. Examples are tutorials and text-based, and computer-based training. One of the advantages of self-paced learning opportunities is the flexibility of use that results when these opportunities are modularized, self-contained, and technology based. Learners can access them whenever and wherever they want without the need to schedule a classroom delivery event .

Advantages

There are many advantages of self paced learning for an example :

• students learn more
• prepares individuals to become more self-directed
• individuals become more responsible in further work experiences.

Limitations

There are also some limitations to self-paced learning :

• requires careful planning for the development of self-study materials
• requires extra equipment, multiple copies of materials, and suitable facilities
• may require new habits for study to be formed by some students to overcome lack of self-discipline or procrastination
• may require qualified personnel available to assist students while studying
• requires variety in activities and resources so the same routine is not repeatedly followed in a lengthy training program that could become monotonous.

The deep active self-paced learning strategy

As stated in Deep Learning in Healthcare The Deep Active Self-paced Learning (DASL) strategy is a combination of Active Learning (AL) and Self-Paced Learning (SPL) that alleviates the lack of fully-annotated samples and make use of unannotated samples. Active Learning Strategy attempts to overcome the annotation bottleneck by querying the most confusing unannotated instances for further annotation. We utilize a straightforward strategy to select confusing samples during model training, different from, which applied a set of fully convolutional networks (FCN) for sample selection. The calculation of this sample uncertainty is defined as:

• Ud= 1 - max(Pd,1 - Pd),

where Ud denotes the uncertainty of the dth sample and Pd denotes the posterior probability of dth sample .

Examples of Self paced learning

• Self-paced learning can be seen in the classroom setting, where students are encouraged to progress at their own pace in acquiring knowledge and mastering skills. This type of learning is often used in online courses or tutorials where students can work through materials at their own speed.
• Self-paced learning can also be seen in the workplace, as employees are encouraged to take ownership of their training and development. This allows them to learn new skills and gain knowledge at their own pace, instead of being forced to keep up with a pre-defined timeline or pace.
• Self-paced learning can be used in the development of Artificial Intelligence (AI) models. In this case, AI algorithms such as Reinforcement Learning (RL) are used to slowly introduce new data into a model's training set. This allows the AI model to learn at its own pace and gradually become more accurate and precise with its predictions.

Other approaches related to Self paced learning

Self paced learning is an approach to machine learning that draws on the natural processes of human and animal learning. Other types of learning approaches related to self paced learning include:

• Active Learning: A machine learning technique in which the algorithm can interact with the environment and/or humans to acquire additional knowledge.
• Transfer Learning: A machine learning approach that utilizes knowledge from related tasks to improve the performance of another task.
• Reinforcement Learning: A machine learning technique that uses rewards and punishments to teach the algorithm how to perform a task.
• Meta-Learning: A machine learning technique that uses meta-data to identify patterns in the data and allow the algorithm to learn more efficiently.

In summary, self-paced learning is a recently proposed machine learning regime inspired by the learning process of humans and animals. Other related approaches to self-paced learning include active learning, transfer learning, reinforcement learning, and meta-learning.

References

• Chen Y. (2020)., Deep Learning in Healthcare Springer
• Jiang L., Meng D., Yu S., Lan Z., Shan S., Hauptmann A. (2014)., Self-Paced Learning with Diversity
• Kemp J., Cochern G. (1994)., Planning for Effective Technical Training: A Guide for Instructors and Trainers Educational Technology
• Menga D., Zhaoa Q., Lu Jiangb L. (2016)., What Objective Does Self-paced Learning Indeed Optimize? Journal of LaTeX Templates
• Morrison G., Ross S., Kalman H., Kemp J. (2012)., Designing Effective Instruction John Wiley & Sons
• Peterson's (2010)., How to Master Online Learning: Looking at Online Options: Part I of III
• Sisakhti R. (1998)., Effective Learning Environments: Creating a Successful Strategy for Your Organization American Society for Training and Development

Footnotes

Author: Paulina Wolnik