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The ultimate objective of the any e-Learning system is to meet the specific need of the online learners and provide them with various features to have efficacious learning experiences by understanding their complexities. Any e-Learning system could be much more improved by tracking students commitment and disengagement on that course, in turn, would allow system to have personalized involvements at appropriate times in order to re-engage learners. Motivations play a important role to get back the learners on the track could be done by analyzing of several attributes of the log files. This paper aims to analyze the multiple attributes which cause the learners to disengage from an online learning environment.

For this improvisation, Web based learning system is researched using data mining techniques in education. There are various attributes characterized for the disengagement prediction using web log file analysis. Though, there have been several attempts to include motivating characteristics in e-Learning systems are adapted, presently influence on cognition is acknowledged mostly.

Classification is one of the predictive data mining technique which makes prediction about values of data using known results found from different data sets. To find out the optimal solution for identifying disengaged learners in the online learning systems, Naive Bayesian (NB) classifier with Particle Swarm Optimization (PSO) algorithm is used which will classify the data set and then perform the independent analysis.

The experimental results shows that the use of unrelated variables in the class attributes will reduce the accuracy and reliability of a any classification model. However, the hybrid PSO algorithm is clearly more apt to find minor subsets of attributes than the PSO with NB classifier. The NB classifier combined with hybrid PSO feature selection method proves to be the best feature selection capability without degrading the classification accuracy. It is further proved to be an effective method for mining large structural data in much less computation time.

Short-term motivation encompasses specific, challenging and attainable goals that develop in the limited timespan. On the other hand, long-term motivation indicates a sort of continuing commitment that is required to complete assigned task. As short-term motivational problems span for a limited period of time, such as a session, therefore, they need to be addressed in real time to keep the learner engaged in the learning process. Similarly, long-term learners’ motivation plays an equally important role to retain the learner in the long run and minimize the risk of dropout. Therefore, the purpose of this study is to incorporate a comprehensive learner motivation model that is based on short-term and long-term aspects of the learners' motivation. This approach enables Web-based educational systems to identify the real-time motivational state of the learner and provide personalized interventions to keep the learners engaged in learning process.

Recent research regarding personalized Web-based educational systems demonstrates learner’s motivation to be an essential component of the learning model. This is because of the fact that low motivation results in either students’ less engagement or complete drop out from the learning activities. A learner motivation model is considered to be a set of perceptions and beliefs that the system has developed about a learner. This includes both short-term and long-term motivations of leaners.

This study proposed a framework of a domain independent learners’ motivation model based on firm educational theories. The proposed framework consists of two modules. The primary module deals with real-time identification of motivation and logging off activities such as login, forum participation and adherence to assessment deadline. Secondary module maintains the profile of leaners associated with both short-term and long-term motivation. A study was conducted to verify the impact of learners’ motivation model and personalized interventional strategies based on proposed model, using Systematical Information Education Method assessment standards. The results show an increase in motivational index and the characteristics associated with motivation during the conducted study.

Motivational diagnosis is important for both traditional classrooms and Web-based education systems. It is one of the major elements that contribute in the success of the learning process. However, dropout rate among online students is very high, which leads to incorporate motivational elements in more personalized way because motivated students will retain the course until they successfully complete it. Hence, identifying learner’s motivation, updating learners’ motivation model based on this identification and providing personalized interventions are the key for the success of Web-based educational systems.

Purpose — The research was designed to explore people's willingness/ability to understand complex road user charges. However, the results raise issues about respondent engagement and ecological validity and so have important implications for questionnaire practice.

Methodology — Computer-based experiments administered in the United Kingdom and Germany gathered respondents' estimates of road user charges along with their response latencies, personal characteristics, acceptance of road charging, assessments of task complexity and attitudes to analytical tasks.

Findings — The results demonstrate questionnaire learning effects and show the effect of personal characteristics on the accuracy and speed of questionnaire completion. The tendency of males, younger people and students to complete the task more quickly is interesting as is the fact that fewer and smaller errors were made by participants who claimed to gain satisfaction from completing a task which has involved mental effort. Engagement was seen to vary with personal characteristics, attitudes to decision making, task complexity and acceptance of the policy being tested. A key finding is that disengagement was more evident among participants who were broadly supportive of road charging than among those who were not.

Implications — The findings have important implications for the design of data collection exercises and for the interpretation of resulting data. It is concluded that repeated choice experiments are an inappropriate source of data on responses to unfamiliar circumstances. The collection of data on response latencies and the inclusion of questions on respondents' attitudes to task completion is a strongly recommended addition to standard questionnaire practice. The extent to which disengagement in an experimental context is, or is not, indicative of real-world behaviour is an important and urgent subject for further research.

Affective states in learning have gained immense attention in education. The precise affective-states prediction can increase the learning gain by adapting targeted interventions that can adjust the changes in individual affective states of students. Several techniques are devised for predicting the affective states considering audio, video and biosensors. Still, the system that relies on analyzing audio and video cannot certify anonymity and is subjected to privacy problems.

A new strategy, termed rider squirrel search algorithm-based deep long short-term memory (RiderSSA-based deep LSTM) is devised for affective-state prediction. The deep LSTM training is done by the proposed RiderSSA. Here, RiderSSA-based deep LSTM effectively predicts the affective states like confusion, engagement, frustration, anger, happiness, disgust, boredom, surprise and so on. In addition, the learning styles are predicted based on the extracted features using rider neural network (RideNN), for which the Felder–Silverman learning-style model (FSLSM) is considered. Here, the RideNN classifies the learners. Finally, the course ID, student ID, affective state, learning style, exam score and course completion are taken as output data to determine the correlative study.

The proposed RiderSSA-based deep LSTM provided enhanced efficiency with elevated accuracy of 0.962 and the highest correlation of 0.406.

The proposed method based on affective prediction obtained maximal accuracy and the highest correlation. Thus, the method can be applied to the course recommendation system based on affect prediction.

The authors present a quantitative analysis of the effect that organizational change has on work stress, work attitudes and perceptions, and cognitive utilization in a task.

First, the authors study the role organizational change has on work stress, attitudes and perceptions, including the role of attitudes toward change. The authors do so by examining differences across employees who are and are not undergoing change, as well as across two change phases. Second, the authors take advantage of the ongoing organizational change to study how people's anxiety about such change affects their cognitive utilization. They use an innovative approach to measure attention disengagement in a cognitive utilization task – a proxy for task-related performance – through a letter detection exercise. Third, the authors examine the role of work stress and change-related anxiety on attention disengagement among employees undergoing change. For this test, they use two organizational change-related texts to function as an anxiety-inducing and a calming-inducing prime.

Organization change is associated with higher work stress, lower job satisfaction and perceptions of institutional effectiveness and support. Further, organizational change-related anxiety adversely affects cognitive utilization, showing that employees undergoing change have higher attention disengagement relative to those not experiencing change. Among employees undergoing change, those receiving an anxiety-inducing prime show better cognitive utilization (lower attention disengagement) than those receiving the calming-inducing prime.

The rare merger of two public universities provides a natural experiment and a source of exogenous variation to examine the effects of radical organizational change on employees' attitudes, perceptions and task performance.

The purpose of this paper is to report the findings of a study that used measurements of shaft relative rotational position, made using inexpensive Hall Effect sensors and magnets mounted at the ends of a gearbox input and output shafts, to determine gear “transmission variance.” The transmission variance signals, as a function of gear/shaft rotational position, were then used to detect and diagnose gear faults.

Two sets of spur gears (one plastic and one steel) were used to experimentally determine the relative shaft rotational position between the input and the output gearbox shafts. Fault-free and faulted (damaged tooth faces and cracked tooth bases) gears were used to collect representative dynamic signals. Signal processing was used to extract transmission variance values as a function of shaft rotational position and then used to detect and diagnose gear faults.

The results show that variations in the relative rotational position of the output shaft relative to that of the input shaft (the transmission variance) can be used to reveal gear mesh characteristics, including faults, such as cracked or missing gear teeth and flattened gear tooth faces, in both plastic gears and steel gears under appropriate (realistic) loads and speeds.

The operational mode of the non-contact rotational position sensors and the dynamic accuracy limitations are explained along with the basic signal processing required to extract transmission variance values.

The results show that shaft rotational position measurements can be made accurately and precisely using relatively inexpensive sensors and can subsequently reveal gear faults.

The inexpensive and yet trustworthy fault detection methodology developed in this work should help to improve the efficiency of maintenance actions on gearboxes and, therefore, improve the overall industrial efficiency of society.

The method described has distinct advantages over traditional analysis methods based on gearbox vibration and/or oil analysis.

Organizations are keenly interested to find out the causes of work disengagement that are harmful to achieve desired level of performance. Antecedents and levels of work disengagement vary across organizations and sectors due to differences in organizational culture. Therefore, the purpose of this paper is to determine the antecedents of work disengagement in the public sector organizations.

The research data were obtained from 303 employees of the public sector organizations using the self-administered questionnaires and cluster sampling technique. The research model proposed in this study has been examined by using the regression analysis and Hayes’s (2013) guidelines for moderation.

It is found that work disengagement increases because of managers’ personal preferences, unfairness, above the rule practices, negative political influence, work overload, and a lack of accountability in the workplace. The results reveal a positive association among organizational injustice, organizational politics, work overload, and work disengagement. Moreover, it is also found that organizational injustice is a strongest predictor of work disengagement. Bureaucratic culture of the public sector organizations has a strong strengthening effect on above-stated relationships.

The study has identified various practical implications related to top management, employees, union, and researchers. The study provides new avenues for senior managers of the services sector to eradicate the levels of work disengagement by improving fairness and perception of organizational politics in the workplace.

There is rare literature that investigates the link between work disengagement and organizational injustice, organizational politics, and work overload especially in the presence of interactive effects of a bureaucratic culture. Most of the studies on employee disengagement did not use the unbiased and significant sample size so their results cannot be generalized to larger population. Therefore, the current study has aimed to overcome the shortcomings of previous studies and brings a novel conceptual model on work disengagement.

Motivational interviewing (MI) is a collaborative communication style designed to help clients achieve desired behavior changes. MI includes communication methods along with a mindset that avoids attempting to impose behavior change. Relying on the conservation of resources theory, this research report argues that learning MI helps practitioners communicate more effectively and preserve their own psychological health resources.

We tested whether MI training resulted in beneficial effects on practitioners' resilience and burnout (i.e. exhaustion and disengagement), surveying participants (N = 42) from various disciplines who learned MI at a training institute. Subjects received a questionnaire before and one month after MI training. The post-training questionnaire also assessed whether participants applied the training content in practice.

The results revealed that the training reduced participants' disengagement. Practical application was a predictor for this decrease as well as an increase in resilience.

Due to the small sample size and self-reported data, this paper should be considered an experimental study that could inspire future research in this area, using more elaborate research designs.

Learning MI not only helps in facilitating behavior change in clients but also in bolstering practitioners' own resources. MI novices should aim to apply their newly acquired skills.

This study is among the first to explicitly hint at the possibility that learning MI helps practitioners preserve their psychological resources.

This study aims to investigate the effects of both internal and external corporate social irresponsibility (CSI) on organizational workplace deviant behaviours (OWDB) by using social cognitive theory. The study also explores the role of moral disengagement as a mediator in this relationship.

Data was collected from a sample of 321 individuals employed in the textile industry of Pakistan. The study used partial least square-structural equation modelling (PLS-SEM) to estimate the relationships within the model.

The findings indicate that both internal and external CSI have a positive impact on moral disengagement. Secondly, moral disengagement drives OWDB positively. Thirdly, moral disengagement is a significant mediator that mediates between both internal and external CSI and OWDB positively.

This research offers novel perspectives to organizational leaders, highlighting the significance of addressing CSI in conjunction with sustainability endeavours. It is imperative for business managers to prioritize the morality of their employees.

This study’s novelty lies in its confirmation of the mediating role of moral disengagement in the relationship between internal and external CSI and OWDB.