Case: On July 9, 2003, the people’s procuratorate of Ansai County, Shaanxi Province arrested three judges, Zhang Hongming, Liu Jianzhong and Xue Sheng of the people’s court of Zichang County of the same province on ...Case: On July 9, 2003, the people’s procuratorate of Ansai County, Shaanxi Province arrested three judges, Zhang Hongming, Liu Jianzhong and Xue Sheng of the people’s court of Zichang County of the same province on charges of "unlawful detention." On September 29, 2003, Ansai people’s court declared the three judges innocent and immediately released them. On September 30, 2003, Ansai people’s procuratorate protested the judgment and appealed to the intermediate people’s court of Yan’an city. On December 18, 2003, the Yan’an展开更多
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
文摘Case: On July 9, 2003, the people’s procuratorate of Ansai County, Shaanxi Province arrested three judges, Zhang Hongming, Liu Jianzhong and Xue Sheng of the people’s court of Zichang County of the same province on charges of "unlawful detention." On September 29, 2003, Ansai people’s court declared the three judges innocent and immediately released them. On September 30, 2003, Ansai people’s procuratorate protested the judgment and appealed to the intermediate people’s court of Yan’an city. On December 18, 2003, the Yan’an
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.
