This system decreases the mental effort required for the integration of the hypertext nodes with semantic space. For example, researchers have found that lead-augmented hypertext system improves the learning performance by visualization of interacting information elements as one undivided unit 5. It could also provide implications for the development of effective instructions to improve their learning performance. Studying the mental effort in problem-solving helps to determine the online processes while individuals are working on these problems and provides a deep understanding of their learning outcomes 9, 10. Furthermore, developed expertise resulting from repeated practice lowers the mental effort imposed by a familiar task 7, 8. Thus, deeper processing of presented information requires higher mental effort than a more surface level encoding. Deeper processing implies a greater degree of semantic or cognitive analysis of input for meaning extraction and comprehension with their existing knowledge, while surface level processing is more concerned with the recognition of physical or sensory features of the stimulus 6. Depth of processing refers to the degree to which a person encodes information. Accordingly, the more the number of interacting information a task contains, the higher the cognitive demand and mental effort it elicits 5. On the other hand, highly interacting information consists of elements that heavily interrelate, which must be combined and processed simultaneously. When element interaction is low, task information can be extracted and learned for one single element at a time since individual elements have minimal references to one another. Perceived demand characteristics mainly depend on the inherent complexity of the task content, which is related to the degree of interaction between various information elements 4. It is seen as a combination of the perceived demand characteristics, depth of information processing, and personal expertise 1, 4. Mental effort represents the quantity of cognitive resources involved in performing a task 1, 2, 3. This study suggests the reliability of EEG to evaluate the mental effort in an educational context and provides valuable insights into improving the problem-solving abilities of students in educational practice. According to the functional topography of brain activity in the theta and alpha frequency, our results suggest that the mental effort while solving scientific problems is related to working memory, visuospatial processing, semantic processing and magnitude manipulation. Also, upper alpha desynchronization demonstrates a widespread enhancement across the whole brain. With the increase of mental effort, theta synchronization in the frontal region and lower alpha desynchronization in the parietal and occipital regions significantly increase. By manipulating the complexity of the scientific problem, the level of mental effort also changes. In this study, we investigate the EEG indicators of mental effort while solving scientific problems. The electroencephalogram is a promising physiological approach to assessing the online mental effort.

Understanding the structure of the subject Teacher presents examples Students discover the interrelationships Inductive reasoning Requires intuitive thinking Guided vs.Studying the mental effort in problem-solving is important to the understanding of how the brain allocates cognitive resources to process information. Less obvious examples help prevent undergeneralization Close non-examples help prevent overgeneralization Think back on hypothesis Extending and connecting concepts Abstractions Ways to organize information.ĭefining attributes Prototype Exemplars Concepts and schemasĤ Strategies for Teaching Concepts: ComponentsĮxamples and non-examples Relevant and irrelevant attributes Name of the concept Definition of the concept General category Defining attributes (if appropriate) Use visual aids 2 Concepts Learning Categories of similar ideas, events, objects, people, etc.

Complex cognitive processes