While robots are extensively used in factories, our industry hasn't yet been able to prepare them for working in human environments - for instance in houses or in human-operated factories. The main obstacle to these applications lies in the amplitude of the uncertainty inherent to the environments humans are used to work in, and in the difficulty in programming robots to cope with it. For instance, in robot-oriented environments, robots can expect to find specific tools and objects in specific places. In a human environment, obstacles may force one to find a new way of holding a tool, and new objects appear continuously and need to be dealt with. As it proves difficult to build into robots the knowledge necessary for coping with uncertain environments, the robotics community is turning to the development of agents that acquire this knowledge progressively and that adapt to unexpected events. This thesis studies the problem of vision-based robotic grasping in uncertain environments. We aim to create an autonomous agent that develops grasping skills from experience, by interacting with objects and with other agents. To this end, we present a 3D object model for autonomous, visuomotor interaction. The model represents grasping strategies along with visual features that predict their applicability. It provides a robot with the ability to compute grasp parameters from visual observations. The agent acquires models interactively by manipulating objects, possibly imitating a teacher. With time, it becomes increasingly efficient at inferring grasps from visual evidence. This behavior relies on (1) a grasp model representing relative object-gripper configurations and their feasibility, and (2) a model of visual object structure, which aligns the grasp model to arbitrary object poses (3D positions and orientations). The visual model represents object edges or object faces in 3D by probabilistically encoding the spatial distribution of small segments of object edges or the distribution of small patches of object surface. A model is learned from a few segmented 3D scans or stereo images of an object. Monte Carlo simulation provides robust estimates of the object's 3D position and orientation in cluttered scenes. The grasp model represents the likelihood of success of relative object-gripper configurations. Initial models are acquired from visual cues or by observing a teacher. Models are then refined autonomously by ``playing' with objects and observing the effects of exploratory grasps. After the robot has learned a few object models, learning becomes a combination of cross-object generalization and interactive experience: grasping strategies are generalized across objects that share similar visual substructures; they are then adapted to new objects through autonomous exploration. The applicability of our model is supported by numerous examples of pose estimates in cluttered scenes, and by a robot platform that shows increasing grasping capabilities as it explores its environment.

This study presents three of the many strategies that pupils can use, in order to learn vocabulary. The aim of this essay is to study the combination of three strategies of learning and three methods of teaching, and compare the three different combinations in order to find out which one of them is the most efficient for pupils who learn new vocabulary. The three strategies are visual, auditory and kinesthetic strategies.               This is a pilot study that takes place in a group of students that study Spanish as a foreign language in a Swedish school. Each combination of strategy and method is tested on the group of pupils and thereafter evaluated in a semi-structured, qualitative way that consists of a questionnaire that the students fill in after each combination has been tested.               The results of the study show that, according to the pupils, the visual and the kinesthetic strategies are the most efficient ones for the pupils that participated in the study, whereas the results of the tests that were conducted show that the visual strategy was the most efficient one. The study also concludes that every student needs to find their individual strategy to learn vocabulary in a foreign language.

Research into the usefulness of scientific diagrams as teaching and learning tools has revealed their great effectiveness in reinforcing and replacing text; summarizing, clarifying, grouping and comparing information; illustrating abstract concepts and spatial relations between concepts; and aiding understanding and integration of knowledge. However, these advantages are not always realised as diagram effectiveness depends on the student's cognitive ability, visual literacy and prior knowledge. In biochemistry, flow diagrams are used as tools for the visualisation of biochemical processes, the abstract nature of which presents problems to students, probably because the depicted content is beyond their perceptual experience. In this study, we define visualisation as the entire process from the perception of an external representation (e.g. diagram), its internal processing, and the expression of a mental model of the represented content. Therefore, visualisation incorporates reasoning processes and interactions with a student's conceptual knowledge, in their construction of a mental model. Students' visualisation difficulties, in terms of conceptual and reasoning difficulties, have been well researched in areas such as physics and chemistry, but neglected in biochemistry, especially with respect to the use of diagrams as visualisation tools. Thus the aim of this study was to investigate students' use of diagrams for the visualisation of biochemical processes, and to identify the nature, and potential sources of students' conceptual, reasoning and diagram-related difficulties revealed during the visualisation process. The study groups ranged from 27 to 95 biochemistry students from the University of Natal and 2 to 13 local and international experts. Propositional knowledge was obtained from textbooks and from a questionnaire to experts. Data on student visualisation of biochemical processes was obtained from their responses to written and interview probes as well as student-generated diagrams. All data was subjected to inductive analysis according to McMillan and Schumacher (1993) and any difficulties that emerged were classified at levels 1- 3 on the framework of Grayson et al. (2001). The possible sources of difficulties were considered in terms of a model by Schonborn et al. (2003 & 2002). The results revealed the following major findings. The meaning of linear, cyclic and cascade biochemical processes was partially resolved by means of an extensive list of generic and distinguishing functional features obtained from experts. Attempts to clarify propositional knowledge of the complement system revealed a deficiency in our understanding of the functional relationship between the complement pathways and highlighted the need for further experimental laboratory work. Several students literally interpreted diagrams of the functional characteristics of biochemical processes (e.g. cyclic) as the spatial arrangement of the intermediates within cells (e.g. occur in "circles"), although in some cases, their verbal responses revealed that they did not hold this difficulty suggesting that they might hold more than one internal model of the process. Some students also showed difficulty using textbook diagrams to visualise the chemistry of glycolytic and complement reactions. In this regard, besides students' conceptual knowledge and reasoning ability, a major source of these difficulties included misleading symbolism and visiospatial characteristics in the diagrams, suggesting the need for improvement of diagram design through the use of clearer symbolism, the standardization of conventions, and improvement of visiospatial properties of diagrams. The results constituted further empirical evidence for the model of Schonbom et al. (2003 & 2002) and led to the proposal of a model of visualisation aimed at clarifying the highly complex and cognitive processes involved in individuals' visualisation of biochemical processes in living systems. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2003.

Thesis (Ph. D.)--University of Oregon, 2008. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 147-151). Also available online in ProQuest, free to University of Oregon users.

Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2008. / Committee Chair: Goel, Ashok; Committee Member: Eastman, Charles; Committee Member: Ferguson, Ronald; Committee Member: Glasgow, Janice; Committee Member: Nersessian, Nancy; Committee Member: Ram, Ashwin.

Thesis supervisor: Steven J. Luck. Thesis supervisor: Andrew Hollingworth. Includes bibliographical references (p. 125-128).

Thesis (Ph. D.)--University of California at Berkeley, 1991. / Includes bibliographical references (leaves 236-247).

Thesis (Ph. D.)--University of California at Berkeley, 1991. / Includes bibliographical references (leaves 236-247).

Thesis (M.Ed. )--Kutztown University of Pennsylvania, 2006. / Source: Masters Abstracts International, Volume: 45-06, page: 2704. Typescript. Abstract precedes thesis as 1 leaf (iii). Includes bibliographical references (leaves 42-46).

This paper proposes a holistic approach to designing for the promotion of team and social competences in blended learning courses. Planning and modelling cooperative learning scenarios based on a domain specific modelling notation in the style of UML activity diagrams, and comparing evaluation results with planned outcomes allows for iterative optimization of a course's design. In a case study - a course on project management for computer science students - the instructional design including individual and cooperative learning situations was modelled. Specific emphasis was put on visualising the hypothesised development of team competences in the course design models. These models were subsequently compared to evaluation results obtained during the course. The results show that visual modelling of planned competence promotion enables more focused design, implementation and evaluation of collaborative learning scenarios.