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An educator’s goal is to create experiences that provide students with opportunities to learn. This goal is the same whether the opportunities are presented face-to-face, fully online or hybrid formats in both formal and informal educational settings. All of the examples presented in this chapter come from experiences in a R1 university, however, the information presented is just as valid in any educational setting. For more than a decade, the authors have used knowledge checking to help students test their understanding of the content whether it is using clickers in large-enrollment lectures or embedding questions directly into the course content online. Knowledge-checks and other types of low-stakes quizzing are examples of a learning strategy called retrieval practice. This chapter briefly reviews the rich history of retrieval practice and what the research says about it, mainly that students benefit when they take time as they learn to practice retrieving stored information (Roediger & Butler, 2011). Examples from current courses as well as a fictional case study illustrate how retrieval practice can be used in online courses to keep students engaged and give them opportunities to practice retrieving information that they have learned.

Good pedagogy is good pedagogy whether being taught in a traditional brick and mortar building, fully online using a course management system or leveraging the best of both formats using a hybrid model. Retrieval practice is an example of this. In this chapter, the authors will provide a brief history and literature review on retrieval practice. Following our literature review, the authors provide examples of how different retrieval practice options have been incorporated into the online courses they design at Penn State, as well as a case example of a course redesigned with retrieval practice in mind.

This article characterises the questioning behaviour in reference interviews preceding delegated online searches of bibliographic databases and relates it to questioning behaviour in other types of interviews/settings. With one exception, the unit of analysis is the question (N=610), not the interview. The author uses A.C. Graesser‘s typology of questions to analyse type of question and M.D. White’s typology of information categories to determine the question‘s content objective; this is the first application of Graesser’s typology to interview questions in any setting. Graesser‘s categories allow for a more subtle understanding of the kind of information need underlying a question. Comparisons are made between questions asked by the information specialist and those asked by the client. Findings show that the information specialist dominates the interview, about half the questions were verification questions and about 22% were judgemental questions or requests; all but four types of questions from Graesser’s categories appeared in the interviews, but no new question types were discovered. Clients often phrase questions as requests. In content, both clients and information specialists focus on the subject and service requested, but the clients ask also about search strategy and output features. Both parties ask predominantly short‐answer questions. Results are related to interface design for retrieval systems.

The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing.

This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics.

The experiment results show that the proposed method outperforms the baseline methods.

This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

In community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites.

In the paper, an algorithm for recommending explanatory Q&A documents is proposed. Q&A documents are modeled with the biterm topic model (BTM) (Yan et al., 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&A documents. To train multiple classifiers, three features are extracted from the Q&A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&A documents are recommended.

The GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm.

The proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated.

A novel explanatory Q&A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&A documents. The method of ranking the explanatory Q&A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&A documents. It assists users in grasping answers easily.

The purpose of this paper is to examine some implications for library and information science (LIS) of the book Geometry of Information Retrieval and the associated issues that arise from it.

The paper presents a conceptual and methodological analysis.

It is found that the epistemology, methodology, and subject scope of LIS are at risk from current developments in information retrieval (IR).

The paper is limited to raising questions but not answering them: the implications are that LIS needs to revisit and enhance its methods.

The paper is wholly original and valuable for initiating discussion.

Librarians and teachers have essentially the same function—providing information. However, they cannot claim with Dr Johnson: ‘Sir, I have found you an argument; but I am not obliged to find you an understanding.’ Both librarian and teacher should present knowledge in such a way that the inquirer is able to comprehend it. One way is to guide the inquirer into working out ideas for himself. Such guidance involves presenting the information in a coherent sequence of easily assimilated steps, after each of which a question is posed to check that that step has been understood. This is what a good tutor does. This, too, is what Programmed Instruction does.

Social question and answer (SQA) site is one of the factors that boosted up and popularized the vision of social web. It enables community members to post highly valued answers to globally asked questions and information seekers to grab intellectual information in a contextual, concise, and meaningful format at the cost of investing a few minutes. The purpose of this paper is to present a common architecture, history, and a comprehensive review of such sites.

A critical and analytical investigation of the state-of-the-art SQA sites and relevant literature has been carried out with the intention to explore the noticeable features of such sites.

By studying relevant literature, and analysing a number of existing systems, a number of research challenges are identified and a generic architecture of SQA sites is contributed.

The review contributes a comprehensive knowledge about SQA systems and aims to be helpful to new researchers who want to get a broad picture of SQA systems on a single platform. The domain is in its infancy and requires tremendous efforts from the research community to explore its salient aspects with respect to the human world.

The study inspects SQA sites on a large scale and makes an original contribution by presenting a comprehensive review, future research challenges, and a generic architecture of SQA sites.

Eyewitness testimony can determine the outcome of criminal investigations. The cognitive interview (CI) has been widely used to collect informative and accurate accounts. However, face-to-face interviews have been restricted during the current pandemic, raising the need for using video-conferencing. The authors tested whether virtual interviews could produce elaborate accounts from eyewitnesses and if the CI superiority effect against a structured interview (SI) could be fully replicated online.

The authors used a 2 × 2 factorial design with interview condition (CI vs SI) and environment (face-to-face vs virtual) manipulated between-subjects. A total of 88 participants were randomly assigned to one of the four conditions. Participants watched a mock robbery and were interviewed 48 h later using either the SI or the CI. Both interviews contained the same structure and interview phases but only the CI included its key cognitive mnemonics/ instructions. Both sessions were either face-to-face or online.

Participants interviewed with the CI recalled more information than participants interviewed with the SI, regardless of the interview environment. Both environments produced a comparable amount of recall. Report accuracy was high for all groups.

This can be crucial to inform police practices and research in this field by suggesting investigative interviews can be conducted virtually in situations such as the current pandemic or when time and resources do not allow for face-to-face interviewing.

To the best of the authors’ knowledge, this is the first study showing that the CI superiority effect can be replicated online and that a fully remote CI can produce elaborate accounts.

With the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG.

The authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user’s question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method.

The designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches.

Although the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user’s information query based on the emerging KG.

Assessment is a driving force in the library and services offered are constantly evaluated for their merit. This study seeks to review student use of an academic library during late‐night hours to determine the effectiveness of the service.

Late‐night library users were surveyed about the frequency and reasons for their late‐night library use. They were given the opportunity to voice their opinions about the value of library services provided from midnight to 2.00 a.m. Face‐to‐face interviews were conducted. Numbers of users were tallied and student behavior was observed and noted. Websites were reviewed to determine open hours at other libraries.

The investigators found that the patrons viewed the library as a quiet, peaceful place conducive to study and placed high value on late‐night access to the library. This was confirmed by the fact that there were requests to further extend the late‐night hours and to offer access to more library space. The research established the library as a haven required by a dedicated group of students who greatly needed a late‐night place to work.

The investigators plan to extend the study of late‐night use to determine where the students go at 11.30 p.m., when all floors except first floor close. In addition, more questions will relate to student classification, area of study, late‐night safety, food, age, gender, mode of transportation, and housing. Participant recruitment will be enhanced to increase participation.

These outcomes could result in changes to the hours and services provided after midnight and have an impact on other libraries following similar late‐night practices.

There is little information in the literature about student behavior during hours after midnight. No reference was made to investigators observing student behavior first‐hand during late‐night hours.