Pavement performance prediction using adaptive logic networks

Pavement performance is one of the most important components of the pavement management system. Prediction of the future performance of a pavement section is important in programming maintenance and rehabilitation needs. Models for predicting pavement performance have been developed on the basis of traffic and age. The purpose of this research is to extend the use of a relatively new approach to performance prediction in pavement performance modeling using adaptive logic networks (ALN). Adaptive logic networks have recently emerged as an effective alternative to artificial neural networks for machine learning tasks. ^ The ALN predictive methodology is applicable to a wide variety of contexts including prediction of roughness based indices, composite rating indices and/or individual pavement distresses. The ALN program requires key information about a pavement section, including the current distress indexes, pavement age, climate region, traffic and other variables to predict yearly performance values into the future. ^ This research investigates the effect of different learning rates of the ALN in pavement performance modeling. It can be used at both the network and project level for predicting the long term performance of a road network. Results indicate that the ALN approach is well suited for pavement performance prediction modeling and shows a significant improvement over the results obtained from other artificial intelligence approaches. ^

Assessing the construct validity of a measure of learning agility

This study examined the construct validity of the Choices questionnaire that purported to support the theory of Learning Agility. Specifically, Learning Agility attempts to predict an individual's potential performance in new tasks. The construct validity will be measured by examining the convergent/discriminant validity of the Choices Questionnaire against a cognitive ability measure and two personality measures. The Choices Questionnaire did tap a construct that is unique to the cognitive ability and the personality measures, thus suggesting that this measure may have considerable value in personnel selection. This study also examined the relationship of this pew measure to job performance and job promotability. Results of this study found that the Choices Questionnaire predicted job performance and job promotability above and beyond cognitive ability and personality. Data from 107 law enforcement officers, along with two of their co-workers and a supervisor resulted in a correlation of .08 between Learning Agility and cognitive ability. Learning Agility correlated .07 with Learning Goal Orientation and. 17 with Performance Goal Orientation. Correlations with the Big Five Personality factors ranged from −.06 to. 13 with Conscientiousness and Openness to Experience, respectively. Learning Agility correlated .40 with supervisory ratings of job promotability and correlated .3 7 with supervisory ratings of overall job performance. Hierarchical regression analysis found incremental validity for Learning Agility over cognitive ability and the Big Five factors of personality for supervisory ratings of both promotability and overall job performance. A literature review was completed to integrate the Learning Agility construct into a nomological net of personnel selection research. Additionally, practical applications and future research directions are discussed. ^

Group presence in virtual worlds: Supporting collaborative e-learning

Even though e-learning endeavors have significantly proliferated in recent years, current e-learning technologies provide poor support for group-oriented learning. The now popular virtual world's technologies offer a possible solution. Virtual worlds provide the users with a 3D - computer generated shared space in which they can meet and interact through their virtual representations. Virtual worlds are very successful in developing high levels of engagement, presence and group presence in the users. These elements are also desired in educational settings since they are expected to enhance performance. The goal of this research is to test the hypothesis that a virtual world learning environment provides better support for group-oriented collaborative e-learning than other learning environments, because it facilitates the emergence of group presence. To achieve this, a quasi-experimental study was conducted and data was gathered through the use of various survey instruments and a set of collaborative tasks assigned to the participants. Data was gathered on the dependent variables: Engagement, Group Presence, Individual Presence, Perceived Individual Presence, Perceived Group Presence and Performance. The data was analyzed using the statistical procedures of Factor Analysis, Path Analysis, Analysis of Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA). The study provides support for the hypothesis. The results also show that virtual world learning environments are better than other learning environments in supporting the development of all the dependent variables. It also shows that while only Individual Presence has a significant direct effect on Performance; it is highly correlated with both Engagement and Group Presence. This suggests that these are also important in regards to performance. Developers of e-learning endeavors and educators should incorporate virtual world technologies in their efforts in order to take advantage of the benefit they provide for e-learning group collaboration.

An Industry View of Experiential Learning

With the downsizing of hotel companies in the 1980s and 1990s. internships became a way of supplementing staff during peak seasons and auditioning students for full-time positions upon graduation. The authors surveyed corporate directors of human resources on specific activities and tasks associated with experiential learning in the guest services areas of hotels.