Both traditional teaching and online teaching advocate individualized education. One of the difficulties on exploring possible improvements of instructional design is the challenging process of data collection. Existing research mainly focuses on the exam score of students but pays little attention to students' daily practice. As an effective method to handle time-series dataset, the generalized estimating equations (GEE) have not been used in this research field. Considering above issues, we first propose an experimental paradigm of programming performance analysis based on the performance record of students’ daily practice-exam and finish collecting a complete time-series dataset in one semester, including students’ individual attributes, learning behavior, and learning performance. Then, we propose an approach that analyzes practice-exam time-series dataset based on GEE to study the influence of individual attributes and learning behavior on learning performance. It is the first time to apply the GEE method for ordinal multinomial responses in this research field, by which we conclude several results that gender or major does have a certain difference on the programming learning. The longer the answer time and the less the cost time, the better the students' performance. Regardless of gender, students tend to cram for the exam and perform a little worse in the daily exercise. Finally, targeting at two important individual attributes, we give corresponding teaching mode decisions that university should teach students programming by major and teacher should give different teaching methods to students of different genders at different time points.

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This paper is a description of the successful implementation of a model-merge approach for creating and updating the power system model of the new Energy Management System (EMS) for the China Southern Grid Company (CSG). The power-system model of this new EMS is a merged model of the five models in the five EMS's serving the five Province power systems that constitute the China Southern Grid. The merging and the continued updating of the merged model is an automated process. The same process is employed to update the displays in this CSG EMS using updated models from the Province EMS's. All data exchanges are in accordance with CIM, XML, SVG, and TASE2 standards.

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