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Computers and computer‐assisted instruction are being used with increasing frequency in the area of health science student education, yet students’ attitudes towards the use of e‐learning technology and computer‐assisted instruction have received limited attention to date. The purpose of this study was to investigate the significant predictors of health science students’ attitudes towards e‐learning and computer‐assisted instruction. All students enrolled in health science programmes (n=2885) at a large multi‐campus Australian university in 2006‐2007, were asked to complete a questionnaire. This included the Online Learning Environment Survey (OLES), the Computer Attitude Survey (CAS), and the Attitude Toward Computer‐Assisted Instruction Semantic Differential Scale (ATCAISDS). A multiple linear regression analysis was used to determine the significant predictors of health science students’ attitudes to e‐learning. The Attitude Toward Computers in General (CASg) and the Attitude Toward Computers in Education (CASe) subscales from the CAS were the dependent (criterion) variables for the regression analysis. A total of 822 usable questionnaires were returned, accounting for a 29.5 per cent response rate. Three significant predictors of CASg and five significant predictors of CASe were found. Respondents’ age and OLES Equity were found to be predictors on both CAS scales. Health science educators need to take the age of students and the extent to which students perceive that they are treated equally by a teacher/tutor/instructor (equity) into consideration when looking at determinants of students’ attitudes towards e‐learning and technology.

Through an examination of macro-historical studies on the governance of Iran, the purpose of this study is to identify the most significant and important events and trends in the rise and fall of Iranian governments and introduce alternative futures in a range of possible, plausible and preferable forms of future governance. To carry out a foresight study of alternative futures of Iranian governance, the authors used futures studies, based on the detection of the most critical driving forces, which are also the most important uncertainties. Futures studies as an interdisciplinary field of study help to identify the events and trends that affect political change and offer scenarios of four alternative futures for the governance of Iran: Smart and Stable Government, Authoritarian Development-oriented Government, Irrational Government and Irrational Breakable Government. The authors believe that Iran’s endeavors to promote democracy, taking the changing international trends into account, make a more trustworthy future for Iran both possible plausible.

Based on macro-history approach and by using “shared history”, future triangle and then scenario planning, the future of Governance in Iran has been analyzed.

Whenever the government has distanced itself from the public and has neglected the trend of international change, it has been faced with a period of collapse and annihilation. And whenever these two important factors are understood, the result is a trend of development and growth. Therefore, the most favored image of Iran’s future relies on the maintenance and promotion of public participation and on increasing attention to the sustainable realities of international relations.

The complexities of events and trends affecting the rise and fall of previous governments of Iran make it necessary to use an interdisciplinary approach to understand the events that have emerged or are emerging in its governance. In this study, from futures studies point of view, transformation of governance has been studied.

The construction industry is a key driver of economic growth. However, the adverse impacts of construction and demolition waste (CDW) resulted from the active construction projects on the economy, environment, public health and social life necessitates an appropriate control and management of this waste stream. Developing and promoting the construction and demolition waste management (CDWM) hierarchy program at the strategic level is essential.

This study aims to propose a hybrid decision model that hybridizes the Integrated Determination of Objective Criteria Weights (IDOCRIW) and weighted aggregated sum product assessment (WASPAS) under a fuzzy environment.

The proposed method ranks the potential strategic alternatives by the sustainable development criteria to improve the performance of CDWM. As indicated in the results, the fuzzy approach in the decision-making process enables the transformation of linguistic variables into fuzzy numbers that show uncertainty and ambiguity in real-world systems. Moreover, the close correlation between the final ranking of the proposed methodology and the average priority order of the strategic alternatives obtained by five different multi-criteria decision-making (MCDM) methods implies the validity of the model performance.

This proposed model is an appropriate tool to effectively decide on the development of CDWM from a strategic point of view. It aims to establish an MCDM framework for the evaluation of effective strategies for CDWM according to the indices of sustainable development. Implementing proper operational plans and conducting research in CDWM has the highest priority, and enacting new and more stringent laws, rules and regulations against the production of CDW has secondary priority. This study contributes to the field by optimizing the CDWM by applying the top-priority strategies resulted from the proposed fuzzy hybrid MCDM methodology by the decision-makers or policy-makers to reach the best managerial strategic plan.

In the proposed methodology, the IDOCRIW technique is utilized and updated with the triangular fuzzy numbers for the first time in the literature to derive the weights of sustainable development criteria. The fuzzy WASPAS method is utilized for evaluation and providing a final ranking of the strategic alternatives.

Incorporating users’ behavior patterns could help in the ranking process. Different click models (CMs) are introduced to model the sophisticated search-time behavior of users among which commonly used the triple of attractiveness, examination and satisfaction. Inspired by this fact and considering the psychological definitions of these concepts, this paper aims to propose a novel learning to rank by redefining these concepts. The attractiveness and examination factors could be calculated using a limited subset of information retrieval (IR) features by the random forest algorithm, and then they are combined with each other to predicate the satisfaction factor which is considered as the relevance level.

The attractiveness and examination factors of a given document are usually considered as its perceived relevance and the fast scan of its snippet, respectively. Here, attractiveness and examination factors are regarded as the click-count and the investigation rate, respectively. Also, the satisfaction of a document is supposed to be the same as its relevance level for a given query. This idea is supported by the strong correlation between attractiveness-satisfaction and the examination-satisfaction. Applying random forest algorithm, the attractiveness and examination factors are calculated using a very limited set of the primitive features of query-document pairs. Then, by using the ordered weighted averaging operator, these factors are aggregated to estimate the satisfaction.

Experimental results on MSLR-WEB10K and WCL2R data sets show the superiority of this algorithm over the state-of-the-art ranking algorithms in terms of P@n and NDCG criteria. The enhancement is more noticeable in top-ranked items which are reviewed more by the users.

This paper proposes a novel learning to rank based on the redefinition of major building blocks of the CMs which are the attractiveness, examination and satisfactory. It proposes a method to use a very limited number of selected IR features to estimate the attractiveness and examination factors and then combines these factors to predicate the satisfactory which is regarded as the relevance level of a document with respect to a given query.