Incorporating Information Quality Management into EAI Processes

Nowadays Enterprise Application Integration (EAI) is rather a technical problem than an organizational issue in enterprise systems. Due to the increased complexity of EAI processes, it is difficult to guarantee the information quality (IQ) without scientific process control. Information Quality Management (IQM) highlights the control of the business processes by resolving IQ problems through IQ definition, measurement, analysis and improvement. When considering EAI’s emphasis on the integration of business processes and the information flow within an enterprise, they are potentially mutually complementary for the IQ improvement. However, there are very few studies carefully examining IQ improvement in EAI processes, and complementary nature of IQM and EAI. By analyzing the challenges of EAI to IQ, and theoretical foundations of incorporating IQM into EAI processes, this study proposes a framework to effectively improve the IQ of EAI processes, and suggests six propositions for future research

Bridging Role Of Absorptive Capacity For Knowledge Management Systems Success

This paper aims to gain a better understanding of KMS success. Based on the absorptive capacity theory, we develop a research model to explain how the use of KMS increases organizational performance by increasing the organizational absorptive capacity and the higher order capabilities. The absorptive capacity plays an important role in transforming KMS usage into agility and innovativeness and the sequent organizational performance. The model is empirically tested with a survey. The results support the mediation effect of absorptive capacity on the use of KMS and the two higher order organizational capabilities, the mediation effects of the two superior organizational capabilities on the relationship between KMS usage and organizational performance and the mediation effect on the link between absorptive capacity and performance

The Learning Impacts of a Concept Map based Classroom Response System

Concept map is a powerful tool to achieve meaningful learning. In order to improve the capabilities of traditional classroom response systems to foster students’ higher-order thinking, in this study we propose an innovative Concept Map based Classroom Response System characterized by interactivity, diagnosticity and enjoyment, and empirically evaluate its effectiveness on improving students\u27 cognitive and affective levels in learning. This research entails important pedagogical implications and demonstrates the appropriateness of applying the system into higher education

Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes

In this paper, we move towards combining large parametric models with non-parametric prototypical networks. We propose prototypical fine-tuning, a novel prototypical framework for fine-tuning pretrained language models (LM), which automatically learns a bias to improve predictive performance for varying data sizes, especially low-resource settings. Our prototypical fine-tuning approach can automatically adjust the model capacity according to the number of data points and the model's inherent attributes. Moreover, we propose four principles for effective prototype fine-tuning towards the optimal solution. Experimental results across various datasets show that our work achieves significant performance improvements under various low-resource settings, as well as comparable and usually better performances in high-resource scenarios.Comment: Published as a conference paper at AAAI 202

Automatic Semantic Causal Map Integration

Causal map integration is helpful to broaden group member’s eyesight and sheds insight on the detection of overall group’s cognition tendencies. However the existing causal map integration approaches are either based on human intervention mechanism that is criticized with researcher bias, or based on syntactic mechanism that lacks of semantic. In order to improve the current causal map integration methodology and practice, this study proposes the conceptualization and formalization of an innovative causal map integration approach, automatic semantic causal map integration, grounded on the Sowa’s Conceptual Graph Theory and Kosko’s Fuzzy Knowledge Combination Theory. The system prototype with an example is also illustrated

REVISITING THE EFFECT OF SOCIAL CAPITAL ON KNOWLEDGE SHARING IN WORK TEAMS: A MULTILEVEL APPROACH

Given the nested nature of work teams, this study distinguishes social capital between team-levels and individual-levels to investigate their effects on individual knowledge sharing in work teams. A survey was conducted to test the hypotheses involving 343 participants who were nested in 47 knowledge-intensive teams across 9 Chinese organizations. Our results reveal that social capital at different levels conjointly influences individuals’ sharing of their explicit and tacit knowledge and also plays distinct roles on the individuals’ sharing behavior in work team context. The results also demonstrate that an optimal social network configuration maximizes team members’ knowledge sharing. Our investigation from a multilevel approach articulates how social capital at different levels in conjunction influences individual sharing behavior, contributing to the existing social capital and social network theories as well as the literature of knowledge management

Predicting Problem-Solving Performance Using Concept Map

A growing community of researchers applies the concept map for elicitation and representation individual’s knowledge structure especially within knowledge-intensive processes in organizations. As an extension of prior works on concept map, this study aims to explore a new indicator of structural properties of concept map from an information entropy perspective to predict an individual’s problem-solving performance. From the information processing view of problem-solving, Information Theory provides the framework to formulate a new indicator called EntropyAvg. A controlled experiment was carried out to validate the predictive ability of the new indicator. The results demonstrate that EntropyAvg is able to estimate an individual’s problem-solving performance beyond two other widely adopted indicators, i.e., complexity and integration. The theoretical and practical contributions of this study are also discusse

SkCoder: A Sketch-based Approach for Automatic Code Generation

Recently, deep learning techniques have shown great success in automatic code generation. Inspired by the code reuse, some researchers propose copy-based approaches that can copy the content from similar code snippets to obtain better performance. Practically, human developers recognize the content in the similar code that is relevant to their needs, which can be viewed as a code sketch. The sketch is further edited to the desired code. However, existing copy-based approaches ignore the code sketches and tend to repeat the similar code without necessary modifications, which leads to generating wrong results. In this paper, we propose a sketch-based code generation approach named SkCoder to mimic developers' code reuse behavior. Given a natural language requirement, SkCoder retrieves a similar code snippet, extracts relevant parts as a code sketch, and edits the sketch into the desired code. Our motivations are that the extracted sketch provides a well-formed pattern for telling models "how to write". The post-editing further adds requirement-specific details to the sketch and outputs the complete code. We conduct experiments on two public datasets and a new dataset collected by this work. We compare our approach to 20 baselines using 5 widely used metrics. Experimental results show that (1) SkCoder can generate more correct programs, and outperforms the state-of-the-art - CodeT5-base by 30.30%, 35.39%, and 29.62% on three datasets. (2) Our approach is effective to multiple code generation models and improves them by up to 120.1% in Pass@1. (3) We investigate three plausible code sketches and discuss the importance of sketches. (4) We manually evaluate the generated code and prove the superiority of our SkCoder in three aspects.Comment: Accepted by the 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023

Can Lean Media Enhance Large Group Learning? An Empirical Investigation of Mobile Information and Communication Technology

A mobile information and communication technology, namely the Mobile Interactive Learning System (MILS), was used to enhance large group learning in a university setting. Expectations concerning learning outcomes, based on the social construction perspective of media richness and constructivist pedagogical principles, were supported. Under similar study conditions, learners with the MILS system demonstrated better (perceived) understanding than those without. Furthermore, learning satisfaction among MILS users was significantly higher. The results were drawn from an empirical evaluation of a structural equation model, and from analyses of variance between the two users groups (with versus without MILS). The results support our hypotheses concerning the impact on understanding and satisfaction. They also suggest that mobile technology affects the learning process, leading to more individual practice and peer influenced learning