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This study aims to propose a blackboard approach using multistrategy machine learning student modeling techniques to learn the properties of students' inconsistent behaviors during their learning process.

These multistrategy machine learning student modeling techniques include inductive reasoning (similarity‐based learning), deductive reasoning (explanation‐based learning), and analogical reasoning (case‐based reasoning).

According to the properties of students' inconsistent behaviors, the ITS (intelligent tutoring system) may then adopt appropriate methods, such as intensifying teaching and practicing, to prevent their inconsistent behaviors from reoccurring.

This research sets the learning object on a single student. After the inferences are accumulated from a group of students, what kinds of students tend to have inconsistent behaviors or under what conditions the behaviors happened for most students can be learned.

The purpose of this paper is to provide adaptive access to learning resources in the digital library.

A novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.

This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.

The results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.

The proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.

This paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.

The purpose of this paper is to shed light upon the connections between quality management, employee health and organisational learning in a school setting.

The study is based on a quantitative survey. Items measuring health status and values of quality management were included in a questionnaire addressed to teachers in a random sample of 20 schools. The items were checked for reliability with Cronbach's alpha tests and the correlation was measured with Pearson's correlation test.

The Cronbach's alpha tests showed that the reliability of all the indices measured was sufficient. Moreover, correlations were found between all the indices of quality management values and the health index. This indicates that the health status of the school employees is related to the level of adoption of the quality management values. A framework depicting the findings from an organisational learning perspective is proposed.

The study strengthens the knowledge of the connections between quality management and health. The study was carried out in Sweden and the possibilities for generalising the findings to other countries are not certain. In addition, the relevance of the study for other organisations than schools remains unproven. A discussion regarding the possibilities of generalising the findings is included.

The findings and the proposed framework are helpful for improving school quality and employee health in schools.

Improved school quality is important for society as a whole.

Previous research has indicated links between quality management and health in industrial manufacturing. This is the first study to explore this link in the school sector. Furthermore, the connections to organisational learning represent a novel approach.

Capital project delivery, such as the delivery of transportation networks and industrial facilities, often suffers losses due to overly aggressive planning. Planners often are overly optimistic about the chance of success while underestimating risks. The purpose of this paper is to examine the hypothesis that these biases are from the difficulties most decision makers face when interpreting probabilistic information.

Three behavioral experiments were conducted to test the theoretical fitness of the paradigms proposed by the description–experience gap literature, namely, the sampling errors effect, the recency effect and statistical information format. College students were recruited to participate in a series of estimating tasks. And their estimating results were compared given different levels of information completeness.

It was found that the existing paradigms could predict risk decision making in the risk-averse estimating scenarios where test subjects were required to give a relatively conservative estimate, but they seemed to be less effective in predicting decisions in the risk-seeking estimating scenario, where test subjects were asked to give a relatively aggressive estimate.

Based on these findings, an integrative model is proposed to explain the observations pertaining to aggressive planning in capital projects. Two dimensions are deemed to be relevant: including risk-taking intentions, and an information uncertainty continuum that ranges from an implicit experience-based information representation to an explicit description-based information representation.

Firms often use upward product line extensions to achieve gains in brand evaluations and in overall demand. Despite the prevalence of such extensions, previous research has provided little guidance about how upward line extensions influence overall revenue when they are launched as a core product as opposed to a peripheral product. The purpose of this study is to fill this research gap.

Using data from the quick service restaurant industry, this study looks at the effects of upwardly extended core and peripheral products on product line revenue. The empirical study uses a quasi-experiment to compare customer purchases across the pre- and post-launch of upward line extensions.

The results of this study reveal that launching core and peripheral products as upward line extensions can each increase total product line revenue. In addition, findings illustrate that as compared to a core launch, this total product line revenue increase is substantially higher in the case of a peripheral launch.

First, the estimated model does not include supply availability and competition. Second, the data span only six months and this restriction prohibits us from investigating alternative sources of the causal effect. Third, the empirical setting in this study is limited to financial data in the quick service restaurant industry as a proxy of actual behavior. Finally, given that customers are not randomly assigned to treatment and control groups, the author is unable to definitively rule out the effect of unobservable attributes.

The findings suggest that firms should prioritize peripheral upward line extensions but use both types considering resource constraints (cost and human resources) and strategic importance to the firm.

This study bolsters the extant literature related to upward product line extensions by providing an empirical framework that evaluates the causal effect of upward line extension on total revenue, using field data in a real-life setting (as opposed to survey or lab experiment data) and actual firm revenue (as opposed to a perceptual outcome measure such as behavioral intentions). In addition, findings contribute to the new product development literature.

In a multi-stakeholder recommender system (MSRS), stakeholders are the multiple entities (consumer, producer, system, etc.) benefited by the generated recommendations. Traditionally, the exclusive focus on only a single stakeholders' (for example, only consumer or end-user) preferences obscured the welfare of the others. Two major challenges are encountered while incorporating the multiple stakeholders' perspectives in MSRS: designing a dedicated utility function for each stakeholder and optimizing their utility without hurting others. This paper proposes multiple utility functions for different stakeholders and optimizes these functions for generating balanced, personalized recommendations for each stakeholder.

The proposed methodology considers four valid stakeholders user, producer, cast and recommender system from the multi-stakeholder recommender setting and builds dedicated utility functions. The utility function for users incorporates enhanced side-information-based similarity computation for utility count. Similarly, to improve the utility gain, the authors design new utility functions for producer, star-cast and system to incorporate long-tail and diverse items in the recommendation list. Next, to balance the utility gain and generate the trade-off recommendation solution, the authors perform the evolutionary optimization of the conflicting utility functions using NSGA-II. Experimental evaluation and comparison are conducted over three benchmark data sets.

The authors observed 19.70% of average enhancement in utility gain with improved mean precision, diversity and novelty. Exposure, hit, reach and target reach metrics are substantially improved.

A new approach considers four stakeholders simultaneously with their respective utility functions and establishes the trade-off recommendation solution between conflicting utilities of the stakeholders.

Producers and consumers – who represent opposing sides of the market – have different frames of reference, which may result in differences in classification of the same products. The authors aim to demonstrate that “classification gaps” have a negative effect on the performance of products and that these effects play a role in different stages of consumers’ decision process.

The data collection consisted of three comprehensive parts covering production and consumption in the music festival market in The Netherlands. The first part focused on festival organizers who were asked to classify their own music festival in terms of musical genres. In total, 70 festival organizers agreed to participate. The second part measured the genre classification of 540 consumers. In the third part, the authors interviewed 1,554 potential visitors of music festivals in The Netherlands about their awareness of the festival and if they considered visiting or actually visited the festival.

This paper provides empirical evidence that a classification gap between the production side and the consumption side of the market has negative effects on music festival performance. In addition, the authors found that this is in part because of lower activation of potential consumers in the marketplace.

An important practical implication of this study is that – in general – producers should be aware that classification gaps can occur – even if they are sure about the classification of their products – and that this can have serious consequences. The category membership of products is often seen as a given, whereas it cannot be assumed that the classification perceived by different economic groups is the same – as demonstrated in this paper.

This paper demonstrates that a fundamental – but understudied – disconnect between the two opposing sides of the market (i.e. producers and consumers) regarding the classification of the same products can have negative effects on performance of these products.

Online health monitoring of large complex equipment has become a trend in the field of equipment diagnostics and prognostics due to the rapid development of sensing and computing technologies. The purpose of this paper is to construct a more accurate and stable grey model based on similar information fusion to predict the real-time remaining useful life (RUL) of aircraft engines.

First, a referential database is created by applying multiple linear regressions on historical samples. Then similarity matching is conducted between the monitored engine and historical samples. After that, an information fusion grey model is applied to predict the future degradation trajectory of the monitored engine considering the latest trend of monitored sensory data and long-term trends of several similar referential samples, and the real-time RUL is obtained correspondingly.

The results of comparative analysis reveal that the proposed model, which is called similarity-based information fusion grey model (SIFGM), could provide better RUL prediction from the early degradation stage. Furthermore, SIFGM is still able to predict system failures relatively accurately when only partial information of the referential samples is available, making the method a viable choice when the historical whole life cycle data are scarce.

The prediction of SIFGM method is based on a single monotonically changing health indicator (HI) synthesized from monitoring sensory signals, which is assumed to be highly relevant to the degradation processes of the engine.

The SIFGM can be used to predict the degradation trajectories and RULs of those online condition monitoring systems with similar irreversible degradation behaviors before failure occurs, such as aircraft engines and centrifugal pumps.

This paper introduces the similarity information into traditional GM(1,1) model to make it more suitable for long-term RUL prediction and also provide a solution of similarity-based RUL prediction with limited historical whole life cycle data.