Cooperative learning is a major teaching method which is used by many instructors as their teaching framework. It has been proven better than individual and competitive teaching methods by raising higher group achievements and individual achievements with more diverse reasoning and logic thinking, and more creative ideas. Many researchers make their efforts in promoting the outcomes of cooperative learning from different viewpoints. One of them is Zone of Proximal Development (ZPD), which emphasizes that teachers and learners with higher capability can support other learners to develop their ZPD through proper scaffolding. Prior Knowledge plays an important role in scaffolding. In order to maximize the effect of scaffolding, learners supported by scaffolding must possess enough prior knowledge. At the same time, teachers must consider the whole teaching progress and it is difficult to be aware of every individual learner¡¦s learning progress and offer adaptive assistance to each of them. Thus, we propose two mechanisms: knowledge diagnostic and learning material developed by using IT techniques to promote learners¡¦ prior knowledge in a specific domain. Students were given different mechanisms and divided into twenty groups to resolve their cooperative tasks. Their cooperative learning outcomes were measured by tasks achievement and perceived of cooperation process that is composed of perceived of cooperation extent, perceived of task conflict and perceived of emotional conflict. The result shows that the combination of knowledge diagnostic and learning material can promote students¡¦ prior knowledge in the domain we selected. The result of cooperative learning outcomes shows that there is a positive relationship between prior knowledge and task achievement, and a positive relationship between prior knowledge and perceived of task conflict, but there is no significant relationship between prior knowledge and perceived of cooperation extent and nor between prior knowledge and perceived of emotional conflict.

Opportunity has emerged as a focal point in the field of entrepreneurship and recognizing potential opportunities is the first step of new venture creation. A number of studies acknowledge the contributions of prior knowledge and social networks in explaining opportunity recognition. However, previous research ignored the importance of cognitive process. Thus, the purpose of this study is filling the research gap of the mediation mechanism in this process. Through the entrepreneurial cognitions view lens, this research goes a step further by asking:¡¨how do individuals conclude that an opportunity from their prior knowledge and information?¡¨ Entrepreneurial alertness, first used by Kirzner (1973) has been identified as a major factor in the process of opportunity recognition. We equate this cognitive ability with entrepreneurial alertness, proposing that this bridges the gap between knowledge/information and the innovativeness/number of opportunities. There are 3 studies in this research; first, Study 1 developed a scale of entrepreneurial alertness. Secondly, Study 2 conducted an experimental design research to test the relationships between prior knowledge, entrepreneurial alertness, and the number of opportunities. Thirdly, Study 3 collected large numbers sample to test the full model. Results indicate that prior knowledge and social networks had positive effects on entrepreneurial alertness. Moreover, entrepreneurial alertness was positively associated with the innovativeness and number of opportunities. In the mediating effect, interpretation ability was partially mediated the relationship between prior knowledge of markets, professional networks and opportunity recognition. This study contributed theoretical and practical implications. Future directions for research are described, and its practical implications for entrepreneurship education and entrepreneurs are examined.

Thesis (Ph. D.)--Open University. BLDSC no. DX231196.

Consumer behavior has long suggested the importance of prior knowledge in understanding behavior. In spite of the vast amount of research in this area, there is a vacuum regarding to what extent an individual applies his/her knowledge in decision-making situations (a concept from economic psychology). An individual may have the knowledge but might not use it or apply it when making decisions. This is of great importance, especially within a health context where decisions may result in life or death situations. In addition, operationalizations of dimensions of prior knowledge within the consumer behavior field have been inconsistent. To eliminate these gaps in prior research and extend the consumer behavior literature this dissertation draws upon the consumer behavior and economic-psychology literatures to investigate the impact of six dimensions of prior knowledge on health information-seeking and disease prevention behaviors. The case of HPV is used here to examine the theoretical relationships. This dissertation is also of particular interest to better understanding direct-to-consumer (DTC) advertising. DTC advertising usually provides information to consumers through the listing of sources consumers can go to, other than providing disease prevention information within the message itself. Hence, examining how prior knowledge impacts information-seeking and prevention behaviors can help guide the development of more effective DTC messages. Results show that information-seeking intentions are predicted by how much consumers think they know and how much of their knowledge they apply in decision-making situations. Also, consumers who have high confidence in using their knowledge are more likely to use external (as opposed to internal) sources of information. In addition, prevention behaviors are predicted by how much consumers know about the disease, how much they think they know and their experience with the disease. This investigation helps guide the development of future DTC campaigns, in terms of motivating consumers to seek additional information, and take the recommended preventative actions; based on consumers’ prior knowledge set. In conclusion, this dissertation extends the literature on the role of prior knowledge in consumer decision-making on multiple levels and provides interesting findings for future research. / text

Using cross-correlation analysis, we demonstrate that flux measurements at observation locations during hydraulic tomography (HT) surveys carry non-redundant information about heterogeneity that are complementary to head measurements at the same locations. We then hypothesize that a joint interpretation of head and flux data can enhance the resolution of HT estimates. Subsequently, we use numerical experiments to test this hypothesis and investigate the impact of stationary and non-stationary hydraulic conductivity field, and prior information such as correlation lengths, and initial mean models (uniform or distributed means) on HT estimates. We find that flux and head data from HT have already possessed sufficient heterogeneity characteristics of aquifers. While prior information (as uniform mean or layered means, correlation scales) could be useful, its influence on the estimates is limited as more non-redundant data are used in the HT analysis (see Yeh and Liu [2000]). Lastly, some recommendation for conducting HT surveys and analysis are presented.

Learners are exquisitely attuned to statistical information in their language input. We tested how prior experience impacts such sensitivity, particularly whether prior experience serves as a bootstrap by enabling acquisition of more complex structure. Experiments 1 and 2 tested whether giving adult learners experience with adjacent category-dependencies in an artificial language facilitates subsequent learning of a novel language containing more complex nonadjacent dependencies. Prior experience had a facilitating effect, both when it preceded exposure to the nonadjacent language by just a few minutes (Experiment 1), and also by 24 hours (Experiment 2). Prior experience with the vocabulary and prosodic characteristics of the language did not facilitate more complex learning. Experiments 3 and 4 tested whether infants also benefit from prior experience in learning nonadjacent dependencies between categories. While 12-month-olds learn adjacent dependencies between word categories (Gómez & Lakusta, 2004), they do not track nonadjacent word dependencies until 15 months (Gómez & Maye, 2005). We asked whether experience with adjacent word-category dependencies enables 12-month-olds to generalize these relations to nonadjacent occurrences. Infants were familiarized to an artificial language containing adjacent category dependencies, and were habituated to strings in which those dependencies were nonadjacent. Infants dishabituated to strings containing violations of the nonadjacent dependencies when the dependencies had been adjacent during previous familiarization (Experiment 3), and when they were novel (Experiment 4). Infants familiarized to a language lacking co-occurrence restrictions, but otherwise matched to the experimental language, failed to become sensitive to the nonadjacent category dependencies during habituation. These findings demonstrate that prior experience can bootstrap acquisition of more complex language structure.

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains vi, 65 p. Includes abstract. Includes bibliographical references (p. 62-65).

Latent class analysis explains dependency structures in multivariate categorical data by assuming the presence of latent classes. We investigate the specification of suitable priors for the Bayesian latent class model to determine the number of classes and perform variable selection. Estimation is possible using standard tools implementing general purpose Markov chain Monte Carlo sampling techniques such as the software JAGS. However, class specific inference requires suitable post-processing in order to eliminate label switching. The proposed Bayesian specification and analysis method is applied to the Hungarian heart disease data set to determine the number of classes and identify relevant variables and results are compared to those obtained with the standard prior for the component specific parameters.

Foggy and hazy weather conditions considerably effect visibility distance which impacts speed, flow of traffic, travel time delay and increases the risk accidents. Bad weather condition is considered a cause of road accidents, since it the poor conditions can effect drivers field of vision. In addition, fog, haze and mist can have negative influences on visual applications in the open air since they decrease visibility by lowering the contrast and whitening the visible color palette. The poor visibility in these images leads to some failures in recognition and detection of the outdoor object systems and also in Intelligent Transportation Systems (ITS). In this thesis, we propose an image visibility restoration algorithm under foggy weather in intelligent transportation systems. Various camera based Advanced Driver Assistant Systems (ADAS), which can be improved by applying the visibility restoration algorithm, have been applied in this field of study to enhance vehicle safety by displaying the image from a frontal camera to driver after visibility enhancement. To remove fog, automatic methods have been proposed which are categorized into two approaches based on the number of input images: 1) methods which are using polarizing filters, 2) methods which are using captured images from different fog densities. In both of these approaches multiple images are required which have to be taken from exactly the same point of view. While these applications can generate good results, their requirements make them impractical, particularly in real time applications, such as intelligent transportation systems. Therefore, in this thesis we introduce a high-performance visibility restoration algorithm only using a single foggy image which applies a recursive filtering to preserve the edge of images and videos in real time and also compute depth map of the scene to restore image. The applied edge preserving filtering is based on a domain transform in which 1-Dimensional edge-preserving filtering is performed by preserving the geodesic distance between points on the curves that is adaptable with wrapping the input signal. The proposed algorithm can be applied in intelligent transportation system applications, such as Advanced Driver Assistance Systems (ADAS). The main features of the proposed algorithm are its speed, which plays a main role in real time applications, since 1-Dimensional operations are used in the applied filtering leads to remarkable speedups in comparison with classical median filters and robust bilateral lfilters. Potential of memory saving is considered as another one advantage of the proposed model and also the parameters of applied edge-preserving filtering do not effect on its computational cost. It is the first edge-preserving filter for color images with arbitrary scales in real time. The proposed algorithm is also able to handle both color and gray-level images and achieves the restored image without the presence of artifacts in comparison with other state-of-the-art algorithms.

This study was conducted at the school where I teach which is a semi-rural secondary school (Grade 8-12) situated in Omuthiya Town in Oshikoto region, Namibia. The new curriculum that has been implemented in Namibia, has posed challenges in how best to put the curriculum into practice at this school. It was this challenge that triggered my interest in doing a research study with the aim of improving my practice. Essentially, the study sought to gain insight into whether integrating learners’ prior everyday knowledge and experiences of acids and bases in conjunction with practical activities using easily accessible materials enabled or constrained meaning-making of this topic. This study is located within an interpretive paradigm. Within this paradigm, a qualitative case study approach was adopted with my Grade 9 class. Data were gathered using document analysis, videotaped lessons and observations by a critical friend, stimulated recall discussions while watching the videotaped lessons as well as focus group interviews with the learners. An inductive analysis to discover patterns and themes was applied during the data analysis process. The themes were further turned into analytical statements. Data sets were also analysed in relation to the research questions posed by this study. The validation process was achieved by using a variety of data gathering techniques. I watched the videotaped lessons with a teacher who observed the lessons and transcripts of the interviews and a summary of discussions were given back to the respondents to verify their responses and check for any misinterpretations, a process known as member checking. I also translated what the learners said in Oshiwambo, their home language, into English. The findings from the study revealed that the use of learners’ prior everyday knowledge and experiences of acids and bases facilitated meaningful learning during teaching and learning. Furthermore, linking learning to learners’ everyday experiences enabled them to learn scientific concepts in a relaxed and non-threatening environment. However, linking learners’ prior knowledge and experiences to conventional science (textbook science) proved to be a challenge. My recommendations are that teachers need support in their endeavors to incorporate learners’ real life experiences into their teaching and learning repertoires. Another aspect of my study that deserves further research is the role that language plays in implementing the curriculum.