Guest editorial: special issue on ECIR 2021

Information Retrieval Journal -     

Expert group formation using facility location analysis

In this paper, we propose an optimization framework to retrieve an optimal group of experts to perform a multi-aspect task. While a diverse set of skills are needed to perform a multi-aspect task, the group of assigned experts should be able to collectively cover all these required skills. We consider three types of multi-aspect expert group formation problems and propose a unified framework to solve these problems accurately and efficiently. The first problem is concerned with finding the top k experts for a given task, while the required skills of the task are implicitly described. In the second problem, the required skills of the tasks are explicitly described using some keywords but each expert has a limited capacity to perform these tasks and therefore should be assigned to a limited number of them. Finally, the third problem is the combination of the first and the second problems. Our proposed optimization framework is based on the Facility Location Analysis which is a well known branch of the Operation Research. In our experiments, we compare the accuracy and efficiency of the proposed framework with the state-of-the-art approaches for the group formation problems. The experiment results show the effectiveness of our proposed methods in comparison with state-of-the-art approaches.     

Predicting relevance based on assessor disagreement: analysis and practical applications for search evaluation

Evaluation of search engines relies on assessments of search results for selected test queries, from which we would ideally like to draw conclusions in terms of relevance of the results for general (e.g., future, unknown) users. In practice however, most evaluation scenarios only allow us to conclusively determine the relevance towards the particular assessor that provided the judgments. A factor that cannot be ignored when extending conclusions made from assessors towards users, is the possible disagreement on relevance, assuming that a single gold truth label does not exist. This paper presents and analyzes the predicted relevance model (PRM), which allows predicting a particular result’s relevance for a random user, based on an observed assessment and knowledge on the average disagreement between assessors. With the PRM, existing evaluation metrics designed to measure binary assessor relevance, can be transformed into more robust and effectively graded measures that evaluate relevance towards a random user. It also leads to a principled way of quantifying multiple graded or categorical relevance levels for use as gains in established graded relevance measures, such as normalized discounted cumulative gain, which nowadays often use heuristic and data-independent gain values. Given a set of test topics with graded relevance judgments, the PRM allows evaluating systems on different scenarios, such as their capability of retrieving top results, or how well they are able to filter out non-relevant ones. Its use in actual evaluation scenarios is illustrated on several information retrieval test collections.     

TIJAH: Embracing IR Methods in XML Databases

This paper discusses our participation in INEX (the Initiative for the Evaluation of XML Retrieval) using the TIJAH XML-IR system. TIJAHs system design follows a standard layered database architecture, carefully separating the conceptual, logical and physical levels. At the conceptual level, we classify the INEX XPath-based query expressions into three different query patterns. For each pattern, we present its mapping into a query execution strategy. The logical layer exploits score region algebra (SRA) as the basis for query processing. We discuss the region operators used to select and manipulate XML document components. The logical algebra expressions are mapped into efficient relational algebra expressions over a physical representation of the XML document collection using the pre-post numbering scheme. The paper concludes with an analysis of experiments performed with the INEX test collection.     

The older adult as learning participant

The older adult as learner has special needs. Planners of educational programs must take into account such needs. Instrumental (basic, delayed gratification types) and expressive (self-enjoyment, immediate gratification types) needs are two broad categories within which learning activities can be planned. Older adults appear to demonstrate preference for instrumental activities but most current learning opportunities are expressive in nature. In addition, older adults prefer to engage in self-directed planning if given the opportunity. It is suggested that a better scheme for assessing needs and for reaching the self-fulfillment desires of older persons is required of program administrators in institutions of higher education and other agencies.     

ELDERLY INTERESTS IN THE EXPRESSIVE DOMAIN

Several researchers have examined elderly learning preferences and involvement in terms of the instrumental (basic or skill mastery) and expressive (enjoyment or self‐fulfillment) dichotomy. An overall preference for instrumental types of learning usually is found; however, certain types of older individuals will report preferences for expressive types of activities. A factor analysis of data on expressive and instrumental choices resulted in ten factors with more than one item loading at 0.40 or higher. The first factor, Expressive Arts Interest, accounted for 31% of the variance. Five other factors were also expressive in nature. Some implications in terms of assessing needs, planning programs, teaching, and research are presented.     

THE CONTRIBUTIONS OF HOWARD YALE McCLUSKY TO AN EVOLVING DISCIPLINE OF EDUCATIONAL GERONTOLOGY

The development of interest, knowledge, and professional involvement in educational gerontology has been evolutionary. Knowledge from various disciplines has contributed to this development. Howard Mc‐Clusky, professor emeritus of adult education and educational psychology at the University of Michigan, has made many valuable contributions. His advocacy of limitless human potential throughout life has included work toward the development of knowledge about adults as learners and corresponding instructional needs. McClusky's development of a “theory of margin” has facilitated an understanding of the need to balance in the later years those stresses and demands (load) on a person with his or her coping resources (power). He also has provided insights to program developers regarding the importance of time perceptions and differences in how learning needs are categorized. He calls for intergenerational approaches to older adult instruction efforts and provides some optimistic hope for the future of educational gerontology.     

OLDER ADULT LEARNING: INSTRUMENTAL AND EXPRESSIVE CATEGORIES

This article examines older adult preferences for instrumental vs. expressive learning activities. Course title selections and actual learning activity information were analyzed. Interviews with 256 Nebraskans, 55 or older, (average age was 68.11) were obtained. Hypotheses and results were (a) predicted preference for instrumental learning was supported; (b) greater preference for instrumental learning by blue‐collar workers and the less educated received partial support as no differences existed for the occupational category but individuals without college degrees preferred instrumental courses; and (c) predicted differences in learning activity received partial support as younger people, white‐collar workers, college graduates, nonwhites, and married people were more involved with instrumental learning. It was concluded that more instrumental learning opportunities must be made available to older people.