Search results

In this chapter, the authors studied cognitive biases such as certainty effect, isolating effect, and overconfidence effect in the Turkish version of “Who Wants to be a Millionaire” TV show. The research was carried out by watching the show during different dates between September 2013 and April 2015 and filling in a questionnaire, which consists of 25 questions. A total of 408 contestants were observed and evidence was found for both certainty and isolation effects.

Generative artificial intelligence (GenAI) has progressed in its ability and has seen explosive growth in adoption. However, the consumer’s perspective on its use, particularly in specific scenarios such as financial advice, is unclear. This research develops a model of how to build trust in the advice given by GenAI when answering financial questions.

The model is tested with survey data using structural equation modelling (SEM) and multi-group analysis (MGA). The MGA compares two scenarios, one where the consumer makes a specific question and one where a vague question is made.

This research identifies that building trust for consumers is different when they ask a specific financial question in comparison to a vague one. Humanness has a different effect in the two scenarios. When a financial question is specific, human-like interaction does not strengthen trust, while (1) when a question is vague, humanness builds trust. The four ways to build trust in both scenarios are (2) human oversight and being in the loop, (3) transparency and control, (4) accuracy and usefulness and finally (5) ease of use and support.

This research contributes to a better understanding of the consumer’s perspective when using GenAI for financial questions and highlights the importance of understanding GenAI in specific contexts from specific stakeholders.

This study proposes an integrated Machine Learning and simulated framework for a personalized learning system. This framework aims to improve the integrity of the provided tasks, adapt to each student individually and ultimately enhance students' academic performance.

This methodology comprises two components. (1) A simulation-based system that utilizes reinforcement algorithms to assign additional questions to students who do not reach pass grade thresholds. (2) A Machine Learning system that uses the data from the system to identify the drivers of passing or failing and predict the likelihood of each student passing or failing based on their engagement with the simulated system.

The results of this study offer preliminary evidence of the effectiveness of the proposed simulation system and indicate that such a system has the potential to foster improvements in learning outcomes.

As with all empirical studies, this research has limitations. A simulation study is an abstraction of reality and may not be completely accurate. Student performance in real-world environments may be higher than estimated in this simulation, reducing the required teacher support.

The developed personalized learning (PL) system demonstrates a strong foundation for improving students' performance, particularly within a blended learning context. The findings indicate that simulated performance using the system exhibited improvement when individual students experienced higher learning benefits tailored to their needs.

The research offers evidence of the effectiveness of personalized learning systems and highlights their capacity to drive improvements in education. The proposed system holds the potential to enhance learning outcomes by tailoring tasks to meet the unique needs of each student.

This study contributes to the growing literature on personalized learning, emphasizing the importance of leveraging machine learning in educational technologies to enable precise predictions of student performance.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

The purpose of this paper is to show how an academic library added access points to its digital reference service outside its traditional library web pages (e.g. online catalog, subscription databases). It seeks to determine whether, how, and to what extent these access points were used by patrons.

Almost 1,200 chat, e‐mail, and instant message digital reference transactions were examined. The data collected included: status of user; format by which questions were submitted (chat, e‐mail, IM); subject area of the question; type of question, and the access point from which the patron submitted the question. The data were analyzed using SPSS statistical software.

Patrons used the access points in external resources over 25 per cent of the time. They took advantage of the access points as their research needs arose. An increase in the amount of reference transactions received was observed after the addition of the external access points.

This study may be useful in planning, administering, and staffing digital reference services.

This is currently the only comprehensive study that has examined digital reference transactions in multiple formats, the correlation between access point and information need, and the impact of adding access points to digital reference in external resources.

Recent studies show that many Web users only submit short queries and conduct short search sessions. This paper examines aspects of users’ attempting longer more complex queries. Web search services such as Ask Jeeves – publicly accessible question and answer (Q&A) search engines – encourage queries in question or request format. In light of this trend, this study examines whether general Web queries are shifting towards a more question/request format. Previous studies show that some users were submitting question or request format queries to general non‐Q&A Web search engines. This paper re‐examines this issue by analysing large‐scale Web query data from two different (US and European) Web query data sets, including 1.2 million Excite queries (www.excite.com) and 1.2 million AlltheWeb.com (http://AlltheWeb.com) queries from 2001.

To provide educationalists with an understanding of the key quality issues relating to multiple‐choice tests, and a set of guidelines for the quality assurance of such tests.

The discussion of quality issues is structured to reflect the order in which those issues naturally arise. It covers the design of individual multiple‐choice questions, issues relating to the question bank as a whole, choice of test format, and what can be learned through post‐test analysis. The paper offers practical advice, with an emphasis on maximising test reliability.

It is recognised that considerable expertise and effort is required to undertake a thorough post‐test statistical analysis, but pre‐test quality assurance is relatively straightforward, if labour‐intensive. The question of which is best amongst the various alternative test formats is left open.

The general issue of quality assurance of multiple‐choice tests is surely an important one, yet the author is not aware of any other publication that deals directly with this topic.

Even though young children ask many questions, the request for a question usually receives a story in response. The purpose of this action research was to determine if the implementation of questioning activities throughout the curriculum would make kindergartners aware of questions and enhance the questioning skills of these young learners.

In our constant asking of questions in our organizations we use a combination of open and closed questions. This is generally done in a haphazard manner according to custom or spontaneity. Argues that research evidence suggests there are appropriate contexts and occasions for the use of either type. Sets out the pros and cons in tabular form. Finds it is possible to obtain different responses according to the type of question. Claims that this is an unrecognized finding which has to be worrying to managers and organizations which may be making wrong decisions due to such misplaced questioning. States that a more conscious approach to questioning, with an awareness of what is likely to succeed in obtaining accurate response and what is not, should improve the efficiency and effectiveness of an organization′s workings.

Everyone thinks that they can ask a question that will produce an appropriate range of answers. Research suggests the opposite. Reference is made to Dr Belson's study of 29 questions. These gave rise to a wide variety of unintended meanings and hence responses. Dr Belson's research results are reduced to ten hypotheses, and these are translated into an audit context. Finally, action points are provided so that the auditor can improve on his/her performance.