Today, blended learning is widely carried out in many colleges. Different online learning platforms have accumulated a large number of fine granularity records of students’ learning behavior, which provides us with an excellent opportunity to analyze students’ learning behavior. In this paper, based on the behavior log data in four consecutive years of blended learning in a college’s programming course, we propose a novel multiclassification frame to predict students’ learning outcomes. First, the data obtained from diverse platforms, i.e., MOOC, Cnblogs, Programming Teaching Assistant (PTA) system, and Rain Classroom, are integrated and preprocessed. Second, a novel error-correcting output codes (ECOC) multiclassification framework, based on genetic algorithm (GA) and ternary bitwise calculator, is designed to effectively predict the grade levels of students by optimizing the code-matrix, feature subset, and binary classifiers of ECOC. Experimental results show that the proposed algorithm in this paper significantly outperforms other alternatives in predicting students’ grades. In addition, the performance of the algorithm can be further improved by adding the grades of prerequisite courses.
In recent years, transportation engineering and construction of urban infrastructures have been developing rapidly in China. On the other hand, green construction demands recycling materials from industrial facilities and exploring the potential uses of waste during a second life cycle. Emerging soil stabilized materials, as alternatives of traditional cement materials, greatly improve the recycling of waste materials and the ground performance. Gypsum-slag (GS) curing agent is a new type of water-hardening inorganic gelling material composed of cement, steel slag, mineral slag, desulfurization gypsum, and additive. The unconfined compressive strength of soils stabilized by GS and by cement was investigated in this study. Meanwhile, the microstructure of stabilized soils was detected by means of scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) and X-ray diffraction (XRD) tests. The relationship between apparent porosity and unconfined compressive strength was established by the Python image processing method. The results show that presence of the GS soil hardening agent changes the microstructure and increases the unconfined compressive strength of the original soils. A colloidal substance is generated to envelop the soil particles and fill the pores when the curing agent and soil are fully mixed and reacted. In comparison, the effect of stabilization generated by GS is proven to be greater than that of cement in view of both microstructure and macroscopic strength.
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