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Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill, or knowledge component (KC), at a given time. However, current student models do not tell us exactly at which point a KC is learned. In this paper, we present a machine-learned model that assesses the probability that a student learned a KC at a specific problem step (instead of at the next or previous problem step). We use this model to analyze which KCs are learned gradually, and which are learned in "eureka" moments. We also discuss potential ways that this model could be used to improve the effectiveness of cognitive mastery learning. (Contains 5 figures and 2 tables.)

This paper explores the academic success of NCAA Division I collegiate student athletes that enroll in engineering majors. At the University of South Carolina, which is a member of the NCAA Division I Southeastern Conference, nineteen engineering students were on an active athletic roster during the spring semester of 2005. The mean cumulative grade point average of these engineering student athletes is 3.24 on a 4.00 scale, which is well above the college mean. Twelve of these student athletes responded to an academic survey that was developed to identify what attributes and behaviors contribute to their success in engineering. It was found that time management, organization, and problem solving are skills that engineering student athletes excel at. In addition, these students take from athletics the ability to concentrate and the desire to succeed and apply them to academics. Even though the demands on time are high, engineering student athletes spend out-of-classroom time with their engineering peers, their professors, and their faculty advisor. Time spent with the faculty advisor is perceived as being especially critical, since all twelve students agreed that course planning and management is very important to their academic performance. (Contains 3 tables and 3 figures.)

In the marketing research class taught at University of South Carolina, Upstate School of Business and Economics, pedagogical emphasis is placed on the marketing research process as it relates to the policies and functions of the business enterprise. Historically, the marketing research project has been used in this undergraduate class to convey the nuances of market research to students. Participating students define the problem or opportunity for their client, determine budgets and timetables, select and design the research instrument(s), collect the data, organize and analyze the data, and present the project findings. A recent project, which involved work for the City of Sunshine, identified, measured, and analyzed specific "quality of life" variables and assessed the impact of the city's revitalization efforts on the perceptions of its citizens. The study targeted three population sectors using questionnaires for data collection. The data was consolidated and analyzed by the students using WebSurveyor's online survey tools. Comparative analysis was also performed using secondary data sources such as city tourism and visitor information, as well as sundry information from previous projects and similar city revitalization plans. The results of their analysis and corresponding conclusions are outlined in this paper. (Contains 3 charts.)

Stochastic unit commitment is an efficient method for grid operation in the presence of significant uncertainties. An example is an operation during a predicted hurricane with uncertain line out-ages. However, the solution quality comes at the cost of substantial computational burden, which makes its adoption challenging. This paper evaluates some possible ways that machine learning can be used to reduce this computational burden. First, a set of feasibility studies is conducted. Results suggest that using machine learning as an assistant to the stochastic unit commitment solver is more advantageous than using it as a standalone solver. In particular, the machine learning model is trained to facilitate solving the problem by determining the unnecessary constraints that can be removed from the original problem without affecting the final accuracy. The variables that can be used as input features/predictors or outputs for the machine learning model are determined through feasibility studies. Then, an algorithm to train and utilize a machine learning model is proposed. The method is tested on a 500-bus synthetic South Carolina system. Various test cases show an average reduction in solution time by more than 90% by using the trained machine learning model to assist the stochastic unit commitment solver. [ABSTRACT FROM AUTHOR]

• A novel two-stage formulation for AC optimal transmission switching problems. • A Generalized Benders Decomposition based algorithm to solve the problem. • Be able to handle grid uncertainties in optimal transmission switching problems. • Guaranteed convergence and accuracy for the AC optimal transmission problems. • Numerical results on the IEEE 118-bus system and South Carolina 500-bus system. Optimal transmission switching is proposed in recent years to optimize the power system operational cost in deterministic studies. With the rapid growth of renewable generations, the grid uncertainties have greatly increased, which cannot be ignored in the decision-making of optimal transmission switching problems. This paper proposes a novel two-stage stochastic optimization formulation with a convex relaxation for AC optimal transmission switching problems. A generalized Benders decomposition based algorithm, including an inner loop and an outer loop, is proposed to solve the AC optimal transmission switching problem with grid uncertainties. The optimal switching plan and the expected system cost will be found in the iterative calculation without any sacrifice of accuracy. Numerical studies on the IEEE 118-bus system and the South Carolina 500-bus confirm the effectiveness of the proposed decomposition approach in solving the AC optimal transmission switching problems with grid uncertainties. The scalability analysis shows the proposed approach is efficient in dealing with a large number of scenarios. [ABSTRACT FROM AUTHOR]