Benefits and Problems: Centralized Administration on College English Course in Chinese Higher Education Institutions

It’s arguable that more involvement of government with centralized policies causes less efficiency on education progresses. The article reviewed documents dedicated to or related to College English (CE), which have been released by Ministry of Education of People’s Republic of China (MOE)and other institutions / organizations after 1949. Exploring CE’s goal, assessment and national impacts, it's substantially analyzed the benefits and disadvantages of centralized governance conducted on CE, and proposed an integral strategy potentially developed: conducting centralized administration while highlighting and enhancing diversity and individuality on CE for reaching the goal of CE in China higher education.

Computational Aspects of the Intelligent Tutoring System MetaTutor

We present in this chapter the architecture of the intelligent tutoring system MetaTutor that trains students to use metacognitive strategies while learning about complex science topics. The emphasis of this chapter is on the natural language components. In particular, we present in detail the natural language input assessment component used to detect students’ mental models during prior knowledge activation, a metacognitive strategy, and the micro-dialogue component used during sub-goal generation, another metacognitive strategy in MetaTutor. Sub-goal generation involves sub-goal assessment and feedback provided by the system. For mental model detection from prior knowledge activation paragraphs, we have experimented with three benchmark methods and six machine learning algorithms. Bayes Nets, in combination with a word-weighting method, provided the best accuracy (76.31%) and best human-computer agreement scores (kappa=0.63). For sub-goal assessment and feedback, a taxonomy-driven micro-dialogue mechanism yields very good to excellent human-computer agreement scores for sub-goal assessment (average kappa=0.77).