scholarly journals Evaluation Method of IT English Blended Teaching Quality Based on the Data Mining Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lin Shao
Keyword(s):  
Data Mining ◽  
Evaluation Method ◽  
Reference Method ◽  
Teaching Quality ◽  
Quality Of Education ◽  
Data Mining Algorithm ◽  
Mining Algorithm ◽  
Mixing Method ◽  
Education Model

As the mixed education model gradually becomes widespread in various universities in Japan, the evaluation of the quality of IT English mixed education has become a very important issue, and it is worth considering the corresponding evaluation method. In this paper, we use a data mining algorithm to implement an evaluation method for the interconversion of quantitative data and qualitative concepts and use the IT English mixed teaching model to evaluate and analyze the teaching quality of the course. The evaluation method is feasible and provides a mixing method. Evaluation of the quality of education. Reference method.

Research Challenge of Locally Computed Ubiquitous Data Mining

Data Mining ◽  
2013 ◽  
pp. 1960-1978
Author(s):  
Aysegul Cayci ◽  
João Bártolo Gomes ◽  
Andrea Zanda ◽  
Ernestina Menasalvas ◽  
Santiago Eibe
Keyword(s):  
Data Mining ◽  
Situational Factors ◽  
Wireless Sensor ◽  
Data Mining Algorithm ◽  
Future Directions ◽  
Wearable Technologies ◽  
Mining Algorithm ◽  
New Challenges ◽  
Research Challenge

Advances in wireless, sensor, mobile and wearable technologies present new challenges for data mining research on providing mobile applications with intelligence. Autonomy and adaptability requirements are the two most important challenges for data mining in this new environment. In this chapter, in order to encourage the researchers on this area, we analyzed the challenges of designing ubiquitous data mining services by examining the issues and problems while paying special attention to context and resource awareness. We focused on the autonomous execution of a data mining algorithm and analyzed the situational factors that influence the quality of the result. Already existing solutions in this area and future directions of research are also covered in this chapter.

Research Challenge of Locally Computed Ubiquitous Data Mining

2011 ◽  
pp. 576-594
Author(s):  
Aysegul Cayci ◽  
João Bártolo Gomes ◽  
Andrea Zanda ◽  
Ernestina Menasalvas ◽  
Santiago Eibe
Keyword(s):  
Data Mining ◽  
Situational Factors ◽  
Wireless Sensor ◽  
Data Mining Algorithm ◽  
Future Directions ◽  
Wearable Technologies ◽  
Mining Algorithm ◽  
New Challenges ◽  
Research Challenge

Advances in wireless, sensor, mobile and wearable technologies present new challenges for data mining research on providing mobile applications with intelligence. Autonomy and adaptability requirements are the two most important challenges for data mining in this new environment. In this chapter, in order to encourage the researchers on this area, we analyzed the challenges of designing ubiquitous data mining services by examining the issues and problems while paying special attention to context and resource awareness. We focused on the autonomous execution of a data mining algorithm and analyzed the situational factors that influence the quality of the result. Already existing solutions in this area and future directions of research are also covered in this chapter.

scholarly journals A product-centric data mining algorithm for targeted promotions

2020 ◽  
Vol 54 ◽  
pp. 101940 ◽  
Author(s):  
Raymond Moodley ◽  
Francisco Chiclana ◽  
Fabio Caraffini ◽  
Jenny Carter
Keyword(s):  
Data Mining ◽  
Data Mining Algorithm ◽  
Mining Algorithm

scholarly journals Life-cycle Asset Management in Residential Developments Building on Transport System Critical Attributes via a Data-mining Algorithm

Buildings ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Umair Hasan ◽  
Andrew Whyte ◽  
Hamad Al Jassmi
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Built Environment ◽  
Transport System ◽  
Asset Management ◽  
Public Transport ◽  
Categorical Variables ◽  
Data Mining Algorithm ◽  
Large Set ◽  
Mining Algorithm

Public transport can discourage individual car usage as a life-cycle asset management strategy towards carbon neutrality. An effective public transport system contributes greatly to the wider goal of a sustainable built environment, provided the critical transit system attributes are measured and addressed to (continue to) improve commuter uptake of public systems by residents living and working in local communities. Travel data from intra-city travellers can advise discrete policy recommendations based on a residential area or development’s public transport demand. Commuter segments related to travelling frequency, satisfaction from service level, and its value for money are evaluated to extract econometric models/association rules. A data mining algorithm with minimum confidence, support, interest, syntactic constraints and meaningfulness measure as inputs is designed to exploit a large set of 31 variables collected for 1,520 respondents, generating 72 models. This methodology presents an alternative to multivariate analyses to find correlations in bigger databases of categorical variables. Results here augment literature by highlighting traveller perceptions related to frequency of buses, journey time, and capacity, as a net positive effect of frequent buses operating on rapid transit routes. Policymakers can address public transport uptake through service frequency variation during peak-hours with resultant reduced car dependence apt to reduce induced life-cycle environmental burdens of buildings by altering residents’ mode choices, and a potential design change of buildings towards a public transit-based, compact, and shared space urban built environment.

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