让自适应测验更知人善选——基于推荐系统的选题策略

基于推荐系统中协同过滤推荐的思想, 提出两种可以利用已有答题者数据的CAT选题策略：直接基于答题者推荐(DEBR)和间接基于答题者推荐(IEBR)。通过两个模拟研究, 在不同题库和不同长度的测验中, 比较了两种推荐选题策略与两种传统选题策略(FMI和BAS)在测量精度和对题目曝光率控制上的表现, 以及影响推荐选题策略表现的因素。结果发现：两种推荐选题策略对题目曝光率的控制优于两种传统选题策略, 测量精度不亚于BAS方法, 其中DEBR侧重选题精度, IEBR对题目曝光率控制最好。已有答题者数据的特点和质量是影响推荐选题策略表现的主要因素。

Make adaptive testing know examinees better: The item selection strategies based on recommender systems

Better CAT item selection strategies may be designed by making better use of information from previous examinees’ responses. The past examinees’ data serve as a valuable reference for selecting items more accurately and evenly for new examinees. However, most of the existing strategies proposed under the theoretical framework of IRT only use information from the current examinee and fail to take full advantage of past examinees’ data. A collaborative filtering recommender approach from the recommender system literature is able to find items that best match one’s preference by utilizing information from others, which shares the similar goal as the item selection strategy of CAT. Therefore, the present study adapted the underlying assumptions of collaborative filtering recommender and proposed new item selection strategies which take advantage of past examinees’ data, and then investigated the potential factors that might affect the performance of new strategies.