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The purpose of this paper is to improve users’ search results relevancy by manipulating their explicit feedback.

CoRRe – an explicit feedback model integrating three popular feedback, namely, Comment-Rating-Referral is proposed in this study. The model is further enhanced using case-based reasoning in retrieving the top-5 results. A search engine prototype was developed using Text REtrieval Conference as the document collection, and results were evaluated at three levels (i.e. top-5, 10 and 15). A user evaluation involving 28 students was administered, focussing on 20 queries.

Both Mean Average Precision and Normalized Discounted Cumulative Gain results indicate CoRRe to have the highest retrieval precisions at all the three levels compared to the other feedback models. Furthermore, independent t-tests showed the precision differences to be significant. Rating was found to be the most popular technique among the participants, producing the best precision compared to referral and comments.

The findings suggest that search retrieval relevance can be significantly improved when users’ explicit feedback are integrated, therefore web-based systems should find ways to manipulate users’ feedback to provide better recommendations or search results to the users.

The study is novel in the sense that users’ comment, rating and referral were taken into consideration to improve their overall search experience.

In the article ‘Approaches to Social Skills Training’ we outlined three levels at which social skills training can be conducted in terms of learning by Thinking, Doing and Feeling. We pointed out that often trainers concentrate on learning in one or two of these areas rather than mixing the approaches and integrating them in one learning process. We summarised some of the ingredients and consequences of these three approaches and presented a broad spectrum of training styles from which the social skills trainer can select. Finally we pointed out the need to distinguish between training and therapy at one end of the spectrum, and suggested how the trainer could establish a clear boundary. In this article we examine feedback in detail as one aspect of social skills training and we explore the range of choices the trainer has in this area.

Seeking and retrieving information is an essential aspect of knowledge workers' activities during problem‐solving and decision‐making tasks. In recent years, user‐oriented Information Seeking (IS) research methods rooted in the social sciences have been integrated with Information Retrieval (IR) research approaches based on computer science to capitalize on the strengths of each field. Given this background, the objective is to develop a topic‐needs variation determination technique based on the observations of IS&R theories.

In this study, implicit and explicit methods for identifying users' evolving topic‐needs are proposed. Knowledge‐intensive tasks performed by academic researchers are used to evaluate the efficacy of the proposed methods. The paper conducted two sets of experiments to demonstrate and verify the importance of determining changes in topic‐needs during the IS&R process.

The results in terms of precision and discounted cumulated gain (DCG) values show that the proposed Stage‐Topic_W (G,S) and Stage‐Topic‐Interaction methods can retrieve relevant document sets for users engaged in long‐term tasks more efficiently and effectively than traditional methods.

The improved precision of the proposed methods means that they can retrieve more relevant documents for the searcher. Accordingly, the results of this research have implications for enhancing the search function in enterprise content management (ECM) applications to support the execution of projects/tasks by professionals and facilitate effective ECM.

The model observes a user's search behavior pattern to determine the personal factors (e.g. changes in the user's cognitive status), and content factors (e.g. changes in topic‐needs) simultaneously. The objective is to capture changes in the user's information needs precisely so that evolving information needs can be satisfied in a timely manner.

A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems.

The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system.

The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations.

The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.

This study aims to examine the influence of task interdependence on team members’ Moqi in virtual teams in China. The authors also aim to identify virtual collaboration as a mediator and distributive justice climate as a moderator in this relationship.

The data were collected from a sample of 87 virtual teams (including 349 individuals) from various Chinese companies through a three-wave survey. Hierarchical regression analysis, path analysis, bootstrapping method and multiple validity tests were used to examine the research model.

In virtual teams in China, task interdependence has a significantly positive influence on team members’ Moqi; Virtual collaboration mediates the relationship between task interdependence and team members’ Moqi; The distributive justice climate positively moderates the relationship between task interdependence and virtual collaboration, as well as the indirect effect of virtual collaboration on the relationship between task interdependence and team members’ Moqi.

In virtual teams, leaders can facilitate team members’ Moqi by designing highly interdependent tasks, encouraging team members to engage in virtual collaboration and cultivating a climate of high attention distributive justice.

This is one of the first studies to pay to the Moqi among team members rather than supervisor-subordinate relationships and further examine how team members’ Moqi is predicted by task interdependence via the mediation of virtual collaboration with the distributive justice climate playing a moderating role.

This study intends to add to the existing body of literature and provides a strong advocacy for the use of the computer-mediated corrective feedback by university lecturers in Nigeria and elsewhere. The purpose of this paper is to explore the perceptions and experiences of lecturers toward students' research project supervision using the computer-mediated corrective feedback, factors that facilitate its use, the most preferred computer-mediated corrective feedback types and the extent of its usage in project supervision.

This research relies on both the Dialectical Theory and the Unified Theory of Acceptance and Use of Technology. This study adopted the interpretivist philosophical paradigm. The case study approach of the qualitative design was used in this investigation. The research participants were selected using the multi-stage sampling procedure. In all, twenty-four (24) lecturers (four from each university, comprising 16 males and 8 females and their ages ranged from 37 years to 61 years) made up the study sample. In-depth interviews were held with these lecturers. The collected data were transcribed and coded and themes were generated based on the responses of research participants using inductive-thematic analysis (ATLAS.ti version 22).

The results indicated that lecturers' perceptions towards the computer-mediated corrective feedback in students’ research project supervision are positive, as they considered it flexible, speedy and economical. Users' personal and device-related factors affect the deployment of computer-mediated corrective feedback for students' research project supervision. E-mail, WhatsApp and Zoom are the three themes that emerged as computer-mediated corrective feedback types that lecturers adopt while supervising students’ research projects. Therefore, the study recommends that lecturers should take full advantage of computer-mediated corrective feedback in supervising students' research projects in lieu of the Fourth Industrial Revolution. Universities should also provide an enabling environment that facilitates computer-mediated corrective feedback.

Studies (outside Nigeria) have been conducted on CMCF using predominantly the experimental and the quantitative research designs in ascertaining the impact of this mode of feedback on students' writing performances. Other studies examined students' perceptions toward CMCF. However, little or no attention has been given to the use of CMCF in the supervision of students' research project writing, especially in Nigeria. Moreover, calls for more qualitative research into lecturer-student interactions and the assessment of educational issues have emerged in recent times. It is against this backdrop that this study explored university lecturers' perceptions and experiences of CMCF on students' research project supervision in Nigerian universities.

The aim of the study is to explore perceptions of the impact of assessment feedback by international undergraduate nursing students. Research to date indicates that summative assessment feedback may impact significantly on student achievement but if it is undertaken sub optimally or does not provide students with the opportunity to engage with the process and reflexively respond, it can also be exceptionally damaging to the learning experience.

A scoping exercise of overall student feedback experience was initially collated via the adoption of an Interpretive Phenomenological Approach (IPA). Participants were recruited via purposive sampling and the LEGO® Serious Play® method was used to collect data. Analysis with Quirkos software was used to examine the salience as well as commonality of findings as an integral part of a recognised five-step thematic analytical approach.

Feedback was perceived, by students, as significantly impacting factor in relation to their overall progression, attainment and retention rates. Themes generated from the findings evidenced student perceptions that summative feedback is a positive driver and source of motivation for academic success and progression. It was perceived that levels of attainment were related to the clarity, quality and individualised nature of feedback that students received and that this was perceived to be evident in their final grades. These were accompanied by perceptions that feedback clarity also determined the potential of breaking down perceived student barriers to learning, their perceived capacity for effective assignment planning and preparation and the likelihood of them having any positive collective or individual interpersonal relationships with their tutors. Summarised, students perceived that feedback ought to lead to student empowerment in managing their studies and as such it ought to be clear, straightforward and non-ambiguous.

The methodological design of the study means that generalisability from its findings was never intended or possible. However, there may be the potential transferability of findings to similar institutions and contexts of nurse education with students who have similar demographic profiling. The study was also a means of providing an insight into the lived experience of students which could be used in the prospective adaptation of feedback mechanisms for staff at a local level within Higher Education.

The study reveals the perceived impact of gamification as a mechanism of summative assessment as conveyed by a designated group of students. Whilst specific recommendations for change can only be made within the context specificity of the research, there may be aspects of the findings which are potentially transferable to other similar contexts of Higher Education delivery whose pedagogical approaches mirror those in operation at the institution where the research was undertaken. It became apparent that the standardisation of feedback approaches offered many opportunities to improve existing systems. The issue of monitoring workloads is also of significance in terms of the level and degree of summative assessment and feedback that academic staff can undertake.

The study revealed the perceived magnitude of assessment feedback on progression, attainment and retention rates, alongside the perceived need for a universal feedback template and the opportunity to provide audio-video feedback. This study adds to existing knowledge in the field of pedagogic practice about both the execution of LEGO® Serious Play® as a research methodology and why the perceptions of feedback as articulated and illuminated by a group of contemporary nursing students ought to matter in the context of Higher Education.

Innovation models should give insight into the success and failure of generating new business. Considering the high degree of complexity, it is proposed to view such models at different levels of abstraction. It is also proposed to make feedback an essential property of the process model. The result of this line of thinking is an integrated environment for the creation of new business. In this multi-layer environment, innovation is positioned as the interconnecting activity between the development of new technology and business, and the involved process model is represented by a circle of change.

Good abandonment behavior refers to users obtaining direct answers via search engine results pages (SERPs) without clicking any search result, which occurs commonly in mobile search. This study aims to better understand users' good abandonment behavior and perception, and then construct a good abandonment prediction model for mobile search with improved performance.

In this study, an in situ user mobile search experiment (N = 43) and a crowdsourcing survey (N = 1,379) were conducted. Good abandonment behavior was analyzed from a quantitative perspective, exploring users' search behavior characteristics from four aspects: session and query, SERPs, gestures and eye-tracking data.

Users show less engagement with SERPs in good abandonment, spending less time and using fewer gestures, and they pay more visual attention to answer-like results. It was also found that good abandonment behavior is often related to users' perceived difficulty of the searching tasks and trustworthiness in the search engine. A good abandonment prediction model in mobile search was constructed with a high accuracy (97.14%).

This study is the first to explore eye-tracking characteristics of users' good abandonment behavior in mobile search, and to explore users' perception of their good abandonment behavior. Visual attention features are introduced into good abandonment prediction in mobile search for the first time and proved to be important predictors in the proposed model.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.