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Electronic learning (e‐learning) has gradually become an important part of university education. There is a trend among universities in Taiwan to offer more and more e‐learning courses. The effectiveness of teaching or learning in an e‐learning system can be quantified by multi‐criteria measures. The purpose of this study was to evaluate the effectiveness of teaching or learning in an e‐learning system measures in linguistic preferences.

A generalized quantitative evaluation model that considers both the interdependence among measures and the fuzziness of subjective perception is currently lacking in the literature. The results indicated that the fuzzy analytical network process is a simple, suitable, and effective method of identifying the primary measures that influence the effectiveness of e‐learning, specifically in the context of interdependent measures and varying linguistic preferences.

The most significant measures of e‐learning effectiveness were the quality of the e‐learning system and learner attractiveness. Enhanced usage of multimedia features can attract learner attention and may eventually increase learner attractiveness. Reducing the waiting time for learning materials to load may improve the quality of the system. Furthermore, the management should actively maintain and improve the responsiveness of instructors to learner inquiries.

The main contributions of this study are twofold. First, the evaluation can be considered as a complex‐dependence, hierarchical decision‐making problem. This study contains a review of the literature and identifies 21 criteria and five aspects to measure e‐learning system effectiveness. Second, this study integrates fuzzy set theory and the ANP to develop an evaluation model that prioritizes the relative weights of the proposed measures. The proposed method can be used to handle dependence within a set of measures and to construct a hierarchical structure.

Driving cycle is an essential requirement to evaluate the exhaust emissions of various types of vehicles on the chassis dynamometer test. This study presents a real world comparison of the driving cycles of Edinburgh motorcycles in two world cities; Edinburgh in Scotland and Delhi in India. The two driving cycles (EMDC & DMDC) driving cycle (EMDC) that were was developed through the analysis of experimental data. This data was collected from trips on a number of routes in each city. In Edinburgh, five different routes between the home addresses in the surrounding areas and place of work at Edinburgh Napier University in Edinburgh were selected. In Delhi data were collected in East Delhi (Geeta Calony) to Central Delhi (Raisena Road). The data collected data was divided into two categories of urban and rural roads in the case of Edinburgh while it was only the urban route in Delhi.. Forty four trips were made on the five designated routes in both urban and rural areas and 12 trips were made in Delhi. The aims of the study were to assess the various parameters (i.e. motorcycle speed, cruise, accelerations and decelerations and percentage time spent in idling) and their statistical validity over total trip lengths for producing a real world EMDC in each of the two cities. The results show that EMDC in Edinburgh, the EMDC has a cycle length of 770 and 656 seconds for urban and rural trips, respectively, which was found more than ECE cycle length. Time spent in acceleration and deceleration modes were found to be significantly higher than any other driving cycle reported to date for motorcycles, reflecting a typical characteristic of the driving cycle in Edinburgh; this was presumably due to diverse driving conditions of motorcycles in the city. In Delhi on the other hand, the DMDC has a cycle length of 847.5 seconds for the urban trips, which higher than that of the EMDC length. The overall percentage time spent in acceleration in Delhi was higher than that of Edinburgh while the time spent in deceleration was lower in Delhi. The overall average speed in the case of Delhi was slightly higher than that of Edinburgh.

The scientific community shares a heritage of knowledge generated by several different fields of research. Identifying how scientific interest evolves is relevant for recording and understanding research trends and society’s demands.

This article presents SciBR-M, a novel method to identify scientific interest evolution from bibliographic material based on Formal Concept Analysis. The SciBR-M aims to describe the thematic evolution surrounding a field of research. The method begins by hierarchically organising sub-domains within the field of study to identify the themes that are more relevant. After this organisation, we apply a temporal analysis that extracts implication rules with minimal premises and a single conclusion, which are helpful to observe the evolution of scientific interest in a specific field of study. To analyse the results, we consider support, confidence, and lift metrics to evaluate the extracted implications.

The authors applied the SciBR-M method for the Educational Data Mining (EDM) field considering 23 years since the first publications. In the digital libraries context, SciBR-M allows the integration of the academy, education, and cultural memory, in relation to a study domain.

Cultural changes lead to the production of new knowledge and to the evolution of scientific interest. This knowledge is part of the scientific heritage of society and should be transmitted in a structured and organised form to future generations of scientists and the general public.

The method, based on Formal Concept Analysis, identifies the evolution of scientific interest to a field of study. SciBR-M hierarchically organises bibliographic material to different time periods and explores this hierarchy from proper implication rules. These rules permit identifying recurring themes, i.e. themes subset that received more attention from the scientific community during a specific period. Analysing these rules, it is possible to identify the temporal evolution of scientific interest in the field of study. This evolution is observed by the emergence, increase or decrease of interest in topics in the domain. The SciBR-M method can be used to register and analyse the scientific, cultural heritage of a field of study. In addition, the authors can use the method to stimulate the process of creating knowledge and innovation and encouraging the emergence of new research.