学生成绩视角下“一课多师”教学效果研究——以数据模型与决策课程为例

“一课多师”协同教学模式在教学领域普遍存在,但在实践过程中,尚未形成成熟的模式,特别是对其教学效果的研究和分析也未形成一致的标准,使其在应用和推广上存在着一定的不足。为了解决对“一课多师”教学效果的评价问题,本文选取MBA必修课程数据模型与决策作为研究的案例对象,提出了从学生成绩角度进行评价的思路,采用定量的对比研究方法,给出采取“一课多师”模式下的课程教学效果评价的案例和方法。

MBA《数据模型与决策》课程的教学方法探究

随着我国经济的不断发展,我国的MBA教育也取得了巨大的成绩。论文从《数据模型与决策》这一课程的特点出发,深入分析了教学过程中出现的各种问题以及产生的原因。在此基础上,从改进教学方法入手为提高该课程的教学效果提出了相应的对策建议。

更正

本刊2023年第7期发表的文章《学生成绩视角下“一课多师”教学效果研究——以数据模型与决策课程为例》的第二作者应为李皛。

微塑料污染监测现状与研究进展

微塑料(粒径小于5 mm的塑料颗粒和碎片)普遍存在于水体、陆地表层土壤和空气中,严重影响着生态环境安全及人类健康。微塑料污染监测是环境污染治理与生态保护的重要数据基础。首先,在介绍国内外微塑料监测和研究进展的基础上,分析了微塑料污染的来源及其在陆地和水环境中的赋存状态、迁移转化路径。其次,分析了发达国家和地区在微塑料原位监测网布设、样品采集、分析测试和数据模型等方面的技术特征,对国内微塑料监测技术与方法进行了总结。最后,结合现阶段我国在微塑料监测和污染防治方面的不足,初步提出了针对我国微塑料污染监测技术发展和监测体系建设的对策建议,阐述了数据模型方法在微塑料污染监测中的重要作用。研究成果可为我国“十四五”期间微塑料监测网络和监测体系建设等提供参考和借鉴。

Decision Model of Knowledge Transfer in Big Data Environment

A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.

Organizational Data Management： Proposing a Metadata-Driven Data Warehouse （DW） Model

Discussing the matter of organizational data management implies, almost automatically, the concept of data warehousing as one of the most important parts of decision support system （DSS）, as it supports the integration of information management by aggregating all data formats and provisioning external systems with consistent data content and flows, together with the metadata concept, as one of the easiest ways of integration for software and database systems. Since organizational data management uses the metadata channel for creating a bi-directional flow, when correctly managed, metadata can save both time and resources for organizations. This paperI will focus on providing theoretical aspects of the two concepts, together with a short brief over a proposed model of design for an organizational management tool.

Research on Speech Visualization Technology and Its Application in English Listening Teaching

In order to improve the automatic retrieval ability of English vocabulary, for the distribution of semantic attributes in English vocabulary, an automatic classification method of English vocabulary is proposed based on association rules, English vocabulary data storage model is constructed, a two element linguistic feature function is constructed for describing the directionality of English lexical retrieval scheduling, English vocabulary classification decision making model is constructed based on contextual relations of English vocabulary, the features of the association rules of English vocabulary are extracted, the adaptive learning method is used to realize the automatic classification of English vocabulary. The simulation results show that the method of English vocabulary classification has good performance, the classification error rate is low, the retrieval precision is high, and the computational overhead is small.

A New Approach for Knowledge Discovery in Distributed Databases Using Fragmented Data Storage Model

Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new generations of databases. These models have a deep impact on evolving decision-support systems. But they suffer a variety of practical problems while accessing real-world data sources. Specifically a type of data storage model based on data distribution theory has been increasingly used in recent years by large-scale enterprises, while it is not compatible with existing decision-support models. This data storage model stores the data in different geographical sites where they are more regularly accessed. This leads to considerably less inter-site data transfer that can reduce data security issues in some circumstances and also significantly improve data manipulation transactions speed. The aim of this paper is to propose a new approach for supporting proactive decision-making that utilizes a workable data source management methodology. The new model can effectively organize and use complex data sources, even when they are distributed in different sites in a fragmented form. At the same time, the new model provides a very high level of intellectual management decision-support by intelligent use of the data collections through utilizing new smart methods in synthesizing useful knowledge. The results of an empirical study to evaluate the model are provided.

Restricted variation in data envelopment analysis with undesirable factors in nature

In standard data envelopment analysis （DEA） models, inefficient decision-making units （DMUs） should change their inputs and outputs arbitrarily to meet the efficient frontier. However, in many real applications of DEA, because of some limitations in resources and DMU＇s ability, these variations cannot be made arbitrarily. Moreover, in some situations, undesirable factors with different disposability, strong or weak disposability, are found. In this paper, a DEA-based model is proposed to determine the relative efficiency of DMUs in such a restricted environment and in presence of undesirable factors. Indeed, variation levels of inputs and outputs are pre-defined and are considered to evaluate the performance of DMUs. Numerical examples are utilized to demonstrate the approach.