Optimal Hierarchical Learning Path Design With Reinforcement Learning

E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the learners’ current progresses and skills, learning material contents. In this article, the authors address the problem of finding the optimal learning policy. To this end, a model for learners’ hierarchical skills in the E-learning system is first developed. Based on the hierarchical skill model and the classical cognitive diagnosis model, a framework to model various mastery levels related to hierarchical skills is further developed. The optimal learning path in consideration of the hierarchical structure of skills is found by applying a model-free reinforcement learning method, which does not require any assumption about learners’ learning transition processes. The effectiveness of the proposed framework is demonstrated via simulation studies.     

概率估计的趋势效应及其对决策的影响机制——基于心理动量的视角

概率是反映风险与不确定性的重要指标, 概率估计具有趋势效应, 会对决策产生影响。文章描述了概率估计趋势效应的两种表现形式, 概率估计变化的趋势性(即不同时间点概率估计变化产生的趋势作用)与单边概率估计的趋势性(即高于或低于某个概率区间范畴的上界或下界的估计表述所产生的趋势作用), 揭示了概率估计趋势效应对于个体判断、决策行为和非理性决策偏差的影响; 基于心理动量的理论视角提出了一个整合模型, 阐述了概率估计趋势效应催生心理动量体验继而引发后续决策行为的内在机理。未来的研究可进一步关注：多方信息来源主体下概率估计的趋势效应; 动态趋势效应与静态概率估计的交互作用; 风险沟通中的概率估计变化趋势。     

Sequential Detection of Compromised Items Using Response Times in Computerized Adaptive Testing

Item compromise persists in undermining the integrity of testing, even secure administrations of computerized adaptive testing (CAT) with sophisticated item exposure controls. In ongoing efforts to tackle this perennial security issue in CAT, a couple of recent studies investigated sequential procedures for detecting compromised items, in which a significant increase in the proportion of correct responses for each item in the pool is monitored in real time using moving averages. In addition to actual responses, response times are valuable information with tremendous potential to reveal items that may have been leaked. Specifically, examinees that have preknowledge of an item would likely respond more quickly to it than those who do not. Therefore, the current study proposes several augmented methods for the detection of compromised items, all involving simultaneous monitoring of changes in both the proportion correct and average response time for every item using various moving average strategies. Simulation results with an operational item pool indicate that, compared to the analysis of responses alone, utilizing response times can afford marked improvements in detection power with fewer false positives.     

Conditional Covariance Theory and Detect for Polytomous Items

This paper extends the theory of conditional covariances to polytomous items. It has been proven that under some mild conditions, commonly assumed in the analysis of response data, the conditional covariance of two items, dichotomously or polytomously scored, given an appropriately chosen composite is positive if, and only if, the two items measure similar constructs besides the composite. The theory provides a theoretical foundation for dimensionality assessment procedures based on conditional covariances or correlations, such as DETECT and DIMTEST, so that the performance of these procedures is theoretically justified when applied to response data with polytomous items. Various estimators of conditional covariances are constructed, and special attention is paid to the case of complex sampling data, such as those from the National Assessment of Educational Progress (NAEP). As such, the new version of DETECT can be applied to response data sets not only with polytomous items but also with missing values, either by design or at random. DETECT is then applied to analyze the dimensional structure of the 2002 NAEP reading samples of grades 4 and 8. The DETECT results show that the substantive test structure based on the purposes for reading is consistent with the statistical dimensional structure for either grade. This research was supported by the Educational Testing Service and the National Assessment of Educational Progress (Grant R902F980001), US Department of Education. The opinions expressed herein are solely those of the author and do not necessarily represent those of the Educational Testing Service. The author would like to thank Ting Lu, Paul Holland, Shelby Haberman, and Feng Yu for their comments and suggestions. Requests for reprints should be sent to Jinming Zhang, Educational Testing Service, MS 02-T, Rosedale Road, Princeton, NJ 08541, USA. E-mail: jzhang@ets.org     

Look-ahead content balancing method in variable-length computerized classification testing

Content balancing is one of the most important issues in computerized classification testing. To adapt to variable-length forms, special treatments are needed to successfully control content constraints without knowledge of test length during the test. To this end, we propose the notions of ‘look-ahead’ and ‘step size’ to adaptively control content constraints in each item selection step. The step size gives a prediction of the number of items to be selected at the current stage, that is, how far we will look ahead. Two look-ahead content balancing (LA-CB) methods, one with a constant step size and another with an adaptive step size, are proposed as feasible solutions to balancing content areas in variable-length computerized classification testing. The proposed LA-CB methods are compared with conventional item selection methods in variable-length tests and are examined with different classification methods. Simulation results show that, integrated with heuristic item selection methods, the proposed LA-CB methods result in fewer constraint violations and can maintain higher classification accuracy. In addition, the LA-CB method with an adaptive step size outperforms that with a constant step size in content management. Furthermore, the LA-CB methods generate higher test efficiency while using the sequential probability ratio test classification method.     

气功对老年人性格的影响

用艾森克人格问卷和A型行为类型问卷对122名练气功二年以上60岁以上的老年人及90名60岁以上从未练过气功的正常老人进行了测查。发现气功组神经质得分显著低于对照组。A型行为类型者气功组虽稍低于对照组,但无显著差异。练气功5年以上者A型行为类型得分显著低于练气功2—4年的。上述结果表明气功可能对改善老年人性格中的神经质维度有作用;气功可能会改变A型行为类型,但练功时间至少需5年以上才可能发生改变。