Learning Technology in Scheduling Based on the Mixed Graphs

We propose the adaptive algorithm for solving a set of similar scheduling problems using learning technology. It is devised to combine the merits of an exact algorithm based on the mixed graph model and heuristics oriented on the real-world scheduling problems. The former may ensure high quality of the solution by means of an implicit exhausting enumeration of the feasible schedules. The latter may be developed for certain type of problems using their peculiarities. The main idea of the learning technology is to produce effective (in performance measure) and efficient (in computational time) heuristics by adapting local decisions for the scheduling problems under consideration. Adaptation is realized at the stage of learning while solving a set of sample scheduling problems using a branch-and-bound algorithm and structuring knowledge using pattern recognition apparatus.

Learning Profile Identification Based on the Analysis of the User Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve an efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analyzed in different dimensions allows the system to deal with the different focus of the learning.

Why fear snakes and spiders? Evidence for a learning-based account of fear-relevant stimulus-processing

Fear-relevant stimuli, such as snakes, spiders and heights, preferentially capture attention as compared to nonfear-relevant stimuli. This is said to reflect an encapsulated mechanism whereby attention is captured by the simple perceptual features of stimuli that have evolutionary significance. Research, using pictures of snakes and spiders, has found some support for this account; however, participants may have had prior fear of snakes and spiders that influenced results. The current research compared responses of snake and spider experts who had little fear of snakes and spiders, and control participants across a series of affective priming and visual search tasks. Experts discriminated between dangerous and nondangerous snakes and spiders, and expert responses to pictures of nondangerous snakes and spiders differed from those of control participants. The current results dispute that stimulus fear relevance is based purely on perceptual features, and provides support for the role of learning and experience.