Learning to align organizational design and data

The relationship between organizational learning and organizational design is explored. In particular, we examine the information processing aspects of organizational learning as they apply to a two-valued decision making task and the relation of such aspects to organizational structure. Our primary contribution is to extend Carley's (1992) model of this process. The original model assumes that all data input into the decision making processes are of equal importance or weight in determining the correct overall organizational decision. The extension described here allows for the more natural situation of non-uniform weights of evidence. Further extensions to the model are also discussed. Such organizational learning performance measures provide an interesting framework for analyzing the recent trend towards flatter organizational structures. This research suggests that flatter structures are not always better, but rather that data environment, ultimate performance goals, and relative need for speed in learning can be used to form a contingency model for choosing organizational structure.     

构建网络学习资源提高民族高校有机化学课程教学质量

High-quality online course materials were reconstructed through feedback from undergraduate students and online data analysis regarding constructed learning materials on the Chao Xing Learning Platform. They were used for studying organic chemistry courses online and offline in university for nationalities. This model, based on student-centered teaching, can effectively transform students from passively accepting knowledge to actively learning and internalizing knowledge. This strategy can also enhance the students' learning initiative and the effects of learning, and hopefully be helpful to the universities in multi-ethnic areas in developing fundamental disciplined construction of organic chemistry and other courses.     

以学生为中心的物理化学远程教学初探

During the fight against the new coronavirus epidemic, the "Physical Chemistry" team of Beijing Normal University used various network platforms and tools, such as MOOC of China University, Rain Classroom and WeChat Group in the teaching process. The exploration and practice of online learning of "Physical Chemistry" is carried out based on MOOC teaching, with student learning as the center and teacher guidance as the support. Taking the most difficult chapter of "fundamentals for statistical thermodynamic" as an example, we designed a complex teaching program that includes previews, watching MOOC videos in class, real-time online exercises, teachers' answers, and after-class reflection and questioning. The students were encouraged to learn independently through the reserved information, designed activities and assignments. Practice shows that the online course has cultivated the students' self-management ability, trained their critical thinking skills, and achieved good teaching results.     

Student enrichment in mathematics: a case study with first year university students

ABSTRACT

This paper presents an enrichment case study to showcase a possible avenue for attending to the needs of academically strong mathematics students. We report on a group of university students who were presented with the opportunity of exploring a specific first year mathematics topic deeper, using an inquiry-based learning approach as part of an enrichment programme. Following the intervention, students completed a questionnaire and a few were interviewed to establish their experiences of the enrichment programme. We discuss the successes and pitfalls of the intervention and report on the impact it had on the participants.     

‘New mathematics’ and teaching

Inside the scientific world it is not always understood that the mood of mathematics, which is a product and a part of culture, can change with time. This is partly why many have been surprised by the coming of the so‐called new mathematics.

In the truly creative mathematical mind two opposite tendencies coexist: the logical and the imaginative. Apparently it seems that new mathematics can be reduced to a purely logical machinery. In fact it contains as much imaginative contributions as classical mathematics. But it is difficult to show simultaneously the logical sequence of propositions and the clumsy progression of research itself. Mathematical exposition does not always follow the ‘ most natural slopes’ of the mind. Unfamiliar presentations often give an impression of ‘ abstraction ‘, more familiar ones an impression of concreteness ‘.

So it appears that difficulties with new mathematics are mostly of psychological origin. Misuses of it can easily raise up intolerance reactions and emotional blocks. Perhaps insisting upon the fact that, here as elsewhere, it is important to be able to guess, to realize that intuition and imagination are essential, could help to make new mathematics better understood, more useful and more able to be considered as a unifing element among sciences.     

Chaos in social learning with multiple true states

Most existing social learning models assume that there is only one underlying true state. In this work, we consider a social learning model with multiple true states, in which agents in different groups receive different signal sequences generated by their corresponding underlying true states. Each agent updates his belief by combining his rational self-adjustment based on the external signals he received and the influence of his neighbors according to their communication. We observe chaotic oscillation in the belief evolution, which implies that neither true state could be learnt correctly by calculating the largest Lyapunov exponents and Hurst exponents.     

中学生学习自我效能感、学习策略与学业成就的关系

以学习自我效能感和学习策略量表为工具,通过方差分析、相关分析和路径分析等方法,重点考察了学习自我效能感和学习策略这两个因素对学业成就的影响路径以及相互间的关系.发现中学生的学习自我效能感与学习策略间存在着显著的相关关系;学习自我效能感和学习策略对初中生的学业成就有显著回归效应,而学习策略对高中生的学业成就有显著回归效应;学习自我效能感可通过影响学习策略来间接影响学业成就.     

Quantum chemical electron impact mass spectrum prediction for de novo structure elucidation: Assessment against experimental reference data and comparison to competitive fragmentation modeling

We investigate the success of the quantum chemical electron impact mass spectrum (QCEIMS) method in predicting the electron impact mass spectra of a diverse test set of 61 small molecules selected to be representative of common fragmentations and reactions in electron impact mass spectra. Comparison with experimental spectra is performed using the standard matching algorithms, and the relative ranking position of the actual molecule matching the spectra within the NIST‐11 library is examined. We find that the correct spectrum is ranked in the top two matches from structural isomers in more than 50% of the cases. QCEIMS, thus, reproduces the distribution of peaks sufficiently well to identify the compounds, with the RMSD and mean absolute difference between appropriately normalized predicted and experimental spectra being at most 9% and 3% respectively, even though the most intense peaks are often qualitatively poorly reproduced. We also compare the QCEIMS method to competitive fragmentation modeling for electron ionization, a training‐based mass spectrum prediction method, and remarkably we find the QCEIMS performs equivalently or better. We conclude that QCEIMS will be very useful for those who wish to identify new compounds which are not well represented in the mass spectral databases.