Teaching-Learning Based Optimization Algorithm

Some teachers have up to 30 plus students per class and up to 6 or more classes each day. This can pose a challenge with the teachers on how effectively they can work with students on a one-on-one level. Unfortunately, a situation can also arise in which the teacher does not necessarily bother with teaching students and are not bothered if they begin to fall behind.

The teacher will continuously carry out assessments and reviews taking into consideration the surroundings, the schedule, themselves and their learners in order that aims and objectives are met and

Students will receive the lesson in small groups. The highest ability group will receive the lesson first and be released to work on the independent practice alone while the teacher works with lower ability groups on completing the practice

This discussion then forms a mutually agreed individual learning goal that will enable them to achieve the required outcome. This then allows SMARTER objectives to be set.

And therefore utilizes six variables as an integral part of the teaching procedure that include Errorless teaching, Most-to-least Prompts, Variable Ratio of Reinforcement, Mix and Vary instructional Demands, Intersperse Easy and Hard Demands and lastly Fluency , using discrete trail teaching as well as Natural Environment Teaching for carrying out effective and efficient teaching ( Lesson 5, ppt, Slide-15)

opportunity to increase the number of lessons it will perform. This solution would utilize Bear

This Wave consisted of a total of 38 agents and was divided into two classes of 19 students each, one in the morning and one in the afternoon. Both classes followed the same agenda and the Language trainers ensured that all activities held were similar if not the same, this helps reduce knowledge gaps between both classes.

Thus, the cost per trainee is low. However, it has certain limitations also. The learners may be passive. It violates the principle of learning by doing and constitutes one-way communication. However, if learners are permitted to ask questions, it provides feedback to the instructor. Lectures can easily be combined with other techniques. Thus, a teacher may conduct a class by the combined lecture-cum-discussion method. He may lecture only to add new information that the group does not possess. Formal reading assignment may be given, demonstration may be presented and video films may be shown along with the lecture. The lectures are supplemented with discussions, film shows, case studies, role playing, etc. This method is useful when concepts, attitudes, theories and problem solving have to be discussed. These are essential when technical or special information of a complex nature is to be

Inspired by the teaching - learning process in the classroom. It is a population –based evolutionary computer algorithm that modeled on transferring knowledge in the classroom and use student result to proceed on global solution. TLBO does not need any specific parameters; it only requires common controlling parameters like population size and number of generations, so it is called parameter less optimization algorithm. TLBO is divided into two phases: ‘Teacher Phase’ and ‘Learner Phase’. In Teacher phase learners gain knowledge from teacher and then Learner also gain knowledge from their classmates by mutual interaction, group-discussion etc. in ‘Learning

the New England Complex Systems Institute elaborate on. Bar-Yam and her colleagues suggest considering the following: adapting to the different teaching abilities by the characteristics of each individual and integrating the goal of transmitting knowledge to each person and letting them contribute. Not only do they acknowledge the difficulty of the increasing problems but also provide direction.

There are several examples I could provide for each of the three learning theories discussed in the above section. The first example is in support of the behaviorism learning theory. At the beginning of each school year, I establish procedures with all of my classes. This creates an optimal learning environment for all students to allow for the greatest amount of success. Each student is then conditioned to follow each of the established procedures the same way through practicing over and over again with a reward, if students carryout the procedure correctly. If the students do not follow the procedures correctly, there is a

It is not easy to become a successful, professional engineer. A professional engineer requires high responsibility, especially problem-based solving skills to cope with workplace environment and compete with others. Problem-based learning (PBL) is really important to reflect the professional practice of engineers. There have been several definitions of PBL. According to Lizinger, Lattuca, Hadgraft and Newstetter (2011, p.134), PBL is the approach that allows learners to practice finding solutions throughout their academy. Learners in a PBL process have to reflect, identify and learn about the complex problem and the cycle is repeated until learners find a solution that they can do both in writing and presentation. There are some basics of PBL such as decision-making, communication, collaboration, self-regulation, self-motivation and flexibility. These basics are the base for every engineer to become a successful professional practicing engineer.

MFO is a recently proposed promising optimization algorithm that mimics the moving behavior of moths. MFO is exploited

It is a swarm-based intelligence algorithm influenced by the social behavior of animals cherishes a flock of birds finding a food supply or a school of fish protecting themselves from a predator. A particle in PSO is analogous to a bird or fish flying through a search (problem) area. The movement of every particle is coordinated by a rate that has each magnitude and direction. Every particle position at any instance of your time is influenced by its best position and also the position of the most effective particle in an exceedingly drawback area. The performance of a particle is measured by a fitness worth that is drawback specific. The PSO rule is analogous to different biological process algorithms.

This suggests that in order to achieve an effective learning situation between a teacher and a student, the former must discover the reference points from which the learner starts and consequently, maintain a meaningful and effective endeavor to plan his efforts with consideration of some identified points in the students life and milieu. Only then can he/she be sure that the learning experience is pleasant, relevant and realistic to the learner.