This research paper demonstrates untraditional learning and teaching method that is developed from combination of experimentation, usage of computer simulations and problem based learning. Taking all previously mentioned methods together there can be created very successful learning environment which provides students to master electromagnetism more effectively. Research focuses on proper use of data acquisition modules and computer simulations in PBL teaching method. Based on the results of the research experimental PBL in various cases provides better learning outcomes, but there are also a few occasions where the results aren’t so pleasing. Overall PBL provides results that are at least as promising as results of other teaching and learning methods. Therefore this method will be utilized in Liepaja University to teach physics, especially electromagnetism.
INTRODUCTION Virtual laboratories have been used for several decades as a tool to help in the educational process. However, it is not always clear the purpose and correct way to use this tool. Educators need to improve their work so that the teaching process is more efficient. A wide range of virtual resources is available in different languages. Most of the resources are in English and some in Latvian, therefore the use of virtual laboratories is made very simple. Hypothesis: The efficient use of a virtual laboratory depends on multiple circumstances. The concept of the virtual laboratory is understood and used differently by many authors and researchers because that depends on the purpose of use. In the most general terms, a virtual laboratory is a computer-based activity where students interact with an experimental apparatus or other activity via a computer interface. Typical examples is a simulation of an experiment, whereby a student interacts with programmed-in behaviours, and a remote-controlled experiment where a student interacts with real apparatus via a computer link. This kind of process allows students to explore a topic by comparing and contrasting different scenarios, to pause and restart an application for reflection and note taking, to get practical experimentation experience over the Internet. The most recognizable are computer simulations that allow us to examine basic concepts in physics. These simulations are broadly used in the teaching and learning process in different ways. The purpose of this research is to study through virtual laboratories that are broadly used in educational institutions and to examine the usefulness and impact of using such laboratories. MATERIAL AND METHODS To find out the circumstances for the efficient use of a virtual laboratory, research has been made to understand the key factors. A criterion for effectiveness of the virtual laboratory is made depending on other experiences over the past ten years. Mostly through literature studies and depending on experience, all the assumptions are justified. RESULTS What people mean by a virtual laboratory, to understand what value it can bring, and importantly what it cannot and must not do. A virtual laboratory must bring as close a connection to reality as possible, to as many students as possible. The key areas of benefit are accessibility, training and augmentation. Nothing can replace the experience of working hands-on with laboratory equipment, the virtual laboratory should not be used to provide a full experience. In some cases, learning a new environment or software for simulations can be difficult and time consuming. In the context of geographical location or mobility issues, the use of a virtual laboratory may provide a substitution for a real experiment. A substitution is also necessary due to lack of equipment. DISCUSSION In recent years, researchers do not try to prove that virtual experiments are better than experiments in real life because such researches were made and the results in most cases do not prove that virtual experiments bring better results in students’ exams. Different researchers try to prove that using virtual environment in some cases changes the attitude towards physics and science. In future authors will make and use virtual laboratory not only as computer simulations but also as a whole environment for learning and teaching physics and science. CONCLUSION The results are theoretical. However, this research is significant for future work because it helps to prevent failures and focus on things that have not been done before. There exist some limitations due to a lack of students. Therefore, the authors can also focus on different stages of education.
INTRODUCTION Development of mechatronic systems involves finding an optimal balance between the basic mechanical structure, sensor and actuator implementation, automatic information processing and overall control. Mechatronic systems are characterized by a combination of basic mechanical devices with a processing unit monitoring and controlling it via number of actuators and sensors. Therefore sensors are significant in the process of providing usable output to microcontrollers. Wide range of sensors are available for constructing mechatronic systems. Sensors can be divided into two big groups: Active and Passive. Other type of classification is by the means of detection used in the sensor. Some of the means are electric, chemical, radioactive etc. Various types of sensors are classified by their measuring objectives for example light sensors, temperature sensors, flow sensors etc. MATERIALS AND METHODS In the process of constructing a mechatronic system a proper setup and signal processing must be provided. There exist certain problems with several sensors, therefore sometimes additional circuits for signal conditioning are made to linearize the output with hardware, but some researchers and developers try to linearize the signal using software. In modern manufacturing equipment very complex systems of devices and sensors are made therefore, they must function correctly because they are the main control parameters. It is particularly important that such control parameters that bring about a correct actual behavior in relation to the reference behavior of such a system are available as a function of time. This means that the parameters must be such that the actual behavior of the system corresponds as closely as possible to the reference behavior. Some examples of such systems are: Robot arms, which move a tool, such as a laser or burr removing tool, for example, which is to be guided along a particulary contour line of a workpiece. Heating systems which are intended to impart a particulary temperature profile to a workpiece. The input data of sensors is crucial for mechatronic systems. A large part of the system is equipped with sensors that read the most important parameters – location coordinates, altitude, compass readings, distance to the barrier (for robots and unmanned aerial vehicles), temperature (heaters and coolers), lighting, etc. Often, some types of sensors give floating data, processing which, a computer or controller acting under an algorithm develops non-physical, inexecutable commands for the final control elements. This results in an increasing load of engines, heating elements, and other actuators, as well as inappropriately increasing energy consumption. The well-known PID algorithm and numerical approximation with built-in MatLab or MATCAD functions does not provide a solution for autonomous systems with controllers that have limited memory and speed of operation. RESULTS New methods that approximate sensor data and are applicable to both analogue and PWM (Pulse-Width-Modulation)-controlled devices have been developed in the paper. The first proposed – derivative - method relates to the restriction of the function direction coefficient module. The second method – the growth bisection method enables smooth sensor data to be obtained. The derivation method is based on limitation of the maximum function increment to a specified level. The growth bisection (proportional) method is based on comparison of the increment module with the increment in the previous step and its proportional decrease by multiplying by a predefined constant. Both methods take up some lines in the control program code, and most mechatronic equipment is capable of real-time operation. CONCLUSIO Dynamic data background connection allows to obtain a self-learning system adapting to the nature of incoming data – a higher number of data will be used in case of minor changes; in contrast, only the last data saved will be used for a rapid change. A system response delay is negligible.
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