The use of programming online judges(POJs)has risen dramatically in recent years,owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming.Since POJs have greater numb...The use of programming online judges(POJs)has risen dramatically in recent years,owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming.Since POJs have greater number of pro-gramming problems in their repository,learners experience information overload.Recommender systems are a common solution to information overload.Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’current context,like learning goals and current skill level(topic knowledge and difficulty level).To overcome the issue,we propose a context-aware practice problem recommender system based on learners’skill level navigation patterns.Our system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and tofind learners’learning goals.Collaborativefiltering(CF)and con-tent-basedfiltering approaches are employed to recommend problems in the cur-rent and next skill levels based on frequent skill level navigation patterns.The sequence similarity measure is used tofind the top k neighbors based on the sequence of problems solved by the learners.The experiment results based on the real-world POJ dataset show that our approach considering the learners’cur-rent skill level and learning goals outperforms the other approaches in practice problem recommender systems.展开更多
Pattern design and technology play a very important role in the garment industry. In order to improve the level of pattern making and design of the garment industry, a survey was conducted to investigate the industria...Pattern design and technology play a very important role in the garment industry. In order to improve the level of pattern making and design of the garment industry, a survey was conducted to investigate the industrial needs in pattern design and technology in China's Mainland. The data were collected from the employers and employees from the garment industry and students in the major of fashion and clothing studies. It indicated that there was a gap between the employer and employee, especially the requirements of the industrial needs and the course contents covered by the tertiary schools. The employers expected to recruit more experienced pattern designers, at the same time, they were not reluctant to hire fresh graduates and spent more resources on the training of employees. The students knew little about their employment situation of the garment industry, spent too little time on the course study and learned too little practical skills in pattern design. They could not make use of the knowledge which prevented them from being employed by the garment industry. Efforts should be taken by both the tertiary schools and the garment industry. The students should be aspirated towards the profession of pattern cutters and the syllabuses of pattern making should be more practical and industrial orientated. The solution might benefit the garment industry a lot in a long run.展开更多
文摘The use of programming online judges(POJs)has risen dramatically in recent years,owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming.Since POJs have greater number of pro-gramming problems in their repository,learners experience information overload.Recommender systems are a common solution to information overload.Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’current context,like learning goals and current skill level(topic knowledge and difficulty level).To overcome the issue,we propose a context-aware practice problem recommender system based on learners’skill level navigation patterns.Our system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and tofind learners’learning goals.Collaborativefiltering(CF)and con-tent-basedfiltering approaches are employed to recommend problems in the cur-rent and next skill levels based on frequent skill level navigation patterns.The sequence similarity measure is used tofind the top k neighbors based on the sequence of problems solved by the learners.The experiment results based on the real-world POJ dataset show that our approach considering the learners’cur-rent skill level and learning goals outperforms the other approaches in practice problem recommender systems.
文摘Pattern design and technology play a very important role in the garment industry. In order to improve the level of pattern making and design of the garment industry, a survey was conducted to investigate the industrial needs in pattern design and technology in China's Mainland. The data were collected from the employers and employees from the garment industry and students in the major of fashion and clothing studies. It indicated that there was a gap between the employer and employee, especially the requirements of the industrial needs and the course contents covered by the tertiary schools. The employers expected to recruit more experienced pattern designers, at the same time, they were not reluctant to hire fresh graduates and spent more resources on the training of employees. The students knew little about their employment situation of the garment industry, spent too little time on the course study and learned too little practical skills in pattern design. They could not make use of the knowledge which prevented them from being employed by the garment industry. Efforts should be taken by both the tertiary schools and the garment industry. The students should be aspirated towards the profession of pattern cutters and the syllabuses of pattern making should be more practical and industrial orientated. The solution might benefit the garment industry a lot in a long run.