组织学习的时空理论

从动态和系统的角度,从组织学习的来源出发,首先提出了组织学习的来源存在时间和空间两个维度,然后建立了基于时间和空间维度的组织学习时空理论。从时间维度(过去、现在和未来)和空间维度(内部和外部)构建了组织学习的6种基本模式,通过排列组合建立了64种组织学习方法,并进一步提出组织要根据外部环境的客观要求和内部实现的可行性选择合适的学习方法,以及组织选择的学习方法可能随着发展阶段的推移而变化等观点。     

“教学做”一体化的高职土木工程专业实训室建设研究

本文分析了土木工程实训室建设的现状,探讨了"教学做"一体化实训室建设的措施,并预测了"教学做"一体化实训室的实训效果。     

研学结合教学理念在微观经济学课程中的应用

为了探索适合高职学生学习特点的教学方法，提高教学质量，本文就研学结合教学理念的内涵、实施路径、特征和目的进行了探索和研究，并在微观经济学课程中进行试验。结果显示教学效果良好，教学质量得到提升，学生的学习兴趣得以提高，主动参与的积极性增加，培养了学生分析问题和解决问题的能力，为专业训练和实践打好基础。论文最后就研学结合教学理念在微观经济学课程实施中存在的问题进行了剖析，并提出了进一步探索和努力地方向。     

Thinking critically about critical thinking: Assessing critical thinking of business students using multiple measures

Critical thinking is a skill that potential employers expect all graduates to possess. Hence, most business management programs consider critical thinking as an important student learning goal. Unfortunately, there is ambiguity about how to best assess critical thinking, both as a skill and a learning outcome. The authors empirically demonstrate how they measure the critical thinking ability of their students in different settings, and how their critical thinking ability improves as they progress through the business program.     

How collaborative routines improve dynamic innovation capability and performance in tourism industry? A path-dependent learning model

Current results of direct relationships between collaboration and innovation capability on performance in tourism contexts are inconsistent. This research is to uncover roles of collaborative routines on promoting relationships between internal and service innovation capabilities as two distinct mechanisms, and performance. The study also examines the contingent effect of knowledge search on performance. Survey data from 181 samples from the Thai tourism industry are gathered for analyses. Findings demonstrate that knowledge search and internal innovation capability have a direct impact on performance, whereas the effect of service innovation capability on performance is significant only in the presence of collaborative routines.     

Does online observational learning matter? Empirical evidence from panel data

The information presented on a product sales page plays an important role in consumers' purchase decisions. This study examines the persuasive effect, whether a customer's choice is heavily driven by information inferred from others' behavior, and how these impacts are moderated by market age and product type. Results show that online customer choice was significantly affected by historical cumulative sales and times saved. Positive cumulative sales and shop service quality have a significant positive impact on product sales. The times saved have no direct impact on product sales. For different types of products, the times saved of experiential products has a significant impact on product sales, while the shop service quality information of search products has a greater impact on product sales. The influence of online observation learning on product sales will be significantly moderated by a combination of product type and market age. These findings not only offer important theoretical contributions to e-commerce research but also provide practical implications for online sellers and managers of social commerce platforms.     

基于建构主义理论的外语语音教学策略简

扼要介绍了建构主义学习理论的基本内涵，指出当前外语语音教学存在的主要问题及其根源，并有针对性地提出了基于建构主义学习理论的外语语音教学策略。     

A gradient boosting approach to the Kaggle load forecasting competition

We describe and analyse the approach used by Team TinTin (Souhaib Ben Taieb and Rob J Hyndman) in the Load Forecasting track of the Kaggle Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem for a US utility with 20 geographical zones. The data available consisted of the hourly loads for the 20 zones and hourly temperatures from 11 weather stations, for four and a half years. For each zone, the hourly electricity loads for nine different weeks needed to be predicted without having the locations of either the zones or stations. We used separate models for each hourly period, with component-wise gradient boosting for estimating each model using univariate penalised regression splines as base learners. The models allow for the electricity demand changing with the time-of-year, day-of-week, time-of-day, and on public holidays, with the main predictors being current and past temperatures, and past demand. Team TinTin ranked fifth out of 105 participating teams.     

Escape dynamics: A continuous-time approximation

We extend a continuous-time approximation approach to the analysis of escape dynamics in economic models with constant gain adaptive learning. This approach is based on the application of the results of continuous-time version of large deviations theory to the linear diffusion approximation of the original discrete-time dynamics under learning. We characterize escape dynamics by analytically deriving the most probable escape point and mean escape time. The approximation is tested on the Phelps problem of a government controlling inflation while adaptively learning a misspecified Phillips curve, studied previously by Sargent (1999) and Cho et al. (2002) (henceforth, CWS), among others. We compare our results with simulations extended to very low values of the constant gain and show that, for the lowest gains, our approach approximates simulations relatively well. We express reservations regarding the applicability of any approach based on large deviations theory to characterizing escape dynamics for economically plausible values of constant gain in the model of CWS when escapes are not rare. We show that for these values of the gain it is possible to derive first passage times for learning dynamics reduced to one dimension without resort to large deviations theory. This procedure delivers mean escape time results that fit the simulations closely. We explain inapplicability of large deviations theory by insufficient averaging near the point of self-confirming equilibrium for relatively large gains which makes escapes relatively frequent, suggest the changes which might help approaches based on the theory to work better in this gain interval, and describe a simple heuristic method for determining the range of constant gain values for which large deviations theory could be applicable.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.