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The purpose of this study is, First, to leverage the limitation of annotated data and to identify the cognitive level of learning objectives efficiently, this study adopts transfer learning by using word2vec and a bidirectional gated recurrent units (GRU) that can fully take into account the context and improves the classification of the model. This study adds a layer based on attention mechanism (AM), which captures the context vector and gives keywords higher weight for text classification. Second, this study explains the authors’ model’s results with local interpretable model-agnostic explanations (LIME).

Bloom's taxonomy levels of cognition are commonly used as a reference standard for identifying e-learning contents. Many action verbs in Bloom's taxonomy, however, overlap at different levels of the hierarchy, causing uncertainty regarding the cognitive level expected. Some studies have looked into the cognitive classification of e-learning content but none has looked into learning objectives. On the other hand, most of these research papers just adopt classical machine learning algorithms. The main constraint of this study is the availability of annotated learning objectives data sets. This study managed to build a data set of 2,400 learning objectives, but this size remains limited.

This study’s experiments show that the proposed model achieves highest scores of accuracy: 90.62%, F1-score and loss. The proposed model succeeds in classifying learning objectives, which contain ambiguous verb from the Bloom’s taxonomy action verbs, while the same model without the attention layer fails. This study’s LIME explainer aids in visualizing the most essential features of the text, which contributes to justifying the final classification.

In this study, the main objective is to propose a model that outperforms the baseline models for learning objectives classification based on the six cognitive levels of Bloom's taxonomy. In this sense, this study builds the bidirectional GRU (BiGRU)-attention model based on the combination of the BiGRU algorithm with the AM. This study feeds the architecture with word2vec embeddings. To prove the effectiveness of the proposed model, this study compares it with four classical machine learning algorithms that are widely used for the cognitive classification of text: Bayes naive, logistic regression, support vector machine and K-nearest neighbors and with GRU. The main constraint related to this study is the absence of annotated data; there is no annotated learning objective data set based on Bloom’s taxonomy's cognitive levels. To overcome this problem, this study seemed to have no choice but to build the data set.

Projects have high stakes in how they are categorized. The final place of a project within a classification scheme depends on the inclusion or exclusion of certain classification criteria. So far, many researchers and organizations have used a variety classification criteria to construct different project classification schemes. However, most of these classification criteria have been taken for granted and the process of selecting them to categorize projects still remains a black box. The purpose of this paper is to open the black box of classification process and explain how it is reflected in picking the classification criteria.

Drawing on insights from cognitive psychology’s literature, the authors examine the main views of classification process to provide insight into the unknown or implicit reasons that one might have to pick particular attributes as project classification criteria.

The authors argue that classification occurs in the eye of the beholder; it is not only the project’s features per se but also the classifier’s “goals, ideal and preference” or “knowledge of causal relations” that are reflected in the classification criteria.

By elaborating the classification process, the authors brought the project context into the big picture of classification and provide a more rational, and coherent picture of how project classification works. This contributes to a theoretical blind spot, raised by prior researchers, related to the selection of project classification criteria.

Understanding classification processes will reduce the ambiguities, inconsistencies and multiple interpretations of project categories and help practitioners increase their projects’ visibility and legitimacy within an already established classification scheme. These implications help organizations in addressing some of the main obstacles to using categorization in project management practice.

The review of prior work in the category research literature and the insights from this paper will provide project management scholars with a useful toolbox for future research on project classification, which has long been understudied.

The purpose of this paper is to establish pluralism as the basis for bibliographic classification theory and practice and examine the possibility of establishing trustworthy classifications.

The paper examines several key notions in classification and extends previous frameworks by combining an explanation‐based approach to classification with the concepts of cognitive authority and trust.

The paper presents an understanding of classification that allows designers and editors to establish trust through the principle of transparency. It demonstrates that modern classification theory and practice are tied to users' activities and domains of knowledge and that trustworthy classification systems are in close dialogue with users to handle bias responsible and establish trust.

The paper establishes a foundation for exploring trust and authority for classification systems.

The purpose of this paper is twofold. First, the authors provide a survey of operations management (OM) research applications of traditional hierarchical and nonhierarchical clustering methods with respect to key decisions that are central to a valid analysis. Second, the authors offer recommendations for practice with respect to these decisions.

A coding study was conducted for 97 cluster analyses reported in six OM journals during the period spanning 1994-2015. Data were collected with respect to: variable selection, variable standardization, method, selection of the number of clusters, consistency/stability of the clustering solution, and profiling of the clusters based on exogenous variables. Recommended practices for validation of clustering solutions are provided within the context of this framework.

There is considerable variability across clustering applications with respect to the components of validation, as well as a mix of productive and undesirable practices. This justifies the importance of the authors’ provision of a schema for conducting a cluster analysis.

Certain aspects of the coding study required some degree of subjectivity with respect to interpretation or classification. However, in light of the sheer magnitude of the coding study (97 articles), the authors are confident that an accurate picture of empirical OM clustering applications has been presented.

The paper provides a critique and synthesis of the practice of cluster analysis in OM research. The coding study provides a thorough foundation for how the key decisions of a cluster analysis have been previously handled in the literature. Both researchers and practitioners are provided with guidelines for performing a valid cluster analysis.

To the best of the authors’ knowledge, no study of this type has been reported in the OM literature. The authors’ recommendations for cluster validation draw from recent studies in other disciplines that are apt to be unfamiliar to many OM researchers.

This article explores how the commentary of intermediaries – third-party entities that do not have direct economic stakes in the sales of goods – can contribute to the creation of new market categories comprising preexisting but neglected and undervalued goods. Specifically, I study how the Sundance Institute facilitated the creation of a market for independent cinema in the United States, suggesting that intermediaries create market categories by defining boundaries, generating criteria of evaluation, and setting standards for measuring and establishing hierarchies of quality, which help audiences understand and value the category. The study, thus, adds nuance to our understanding of markets and categories.

One major aspect of T.D. Wilson’s research has been his insistence on situating the investigation of information behaviour within the context of its occurrence Ö within the everyday world of work. The significance of this approach is reviewed in light of the notion of embodied cognition that characterises the evolving theoretical episteme in cognitive science research. Embodied cognition employs complex external props such as stigmergic structures and cognitive scaffoldings to reduce the cognitive burden on the individual and to augment human problem‐solving activities. The cognitive function of the classification scheme is described as exemplifying both stigmergic structures and cognitive scaffoldings. Two different but complementary approaches to the investigation of situated cognition are presented: cognition‐as‐scaffolding and cognition‐as‐infrastructure. Classification‐as‐scaffolding views the classification scheme as a knowledge storage device supporting and promoting cognitive economy. Classification‐as‐infrastructure views the classification system as a social convention that, when integrated with technological structures and organisational practices, supports knowledge management work. Both approaches are shown to build upon and extend Wilson’s contention that research is most productive when it attends to the social and organisational contexts of cognitive activity by focusing on the everyday world of work.

Organizational scholars increasingly appreciate the role of categories as bases of order or “cognitive infrastructures” in markets. Yet they construe categories as disciplinary devices. They understand category formation, implementation, and revision as the purview of professionals. And they tie those processes to notions of institutional development that sharply distinguish settled from unsettled or disordered eras. We challenge these conceptions through a historical study of how manufacturers, associations, and cost accountants broke from the disciplinary functions of accounting categories underlying mass production to create new categorical schemes devoted to learning, innovation, and improvement. Reformers reconfigured the uses of categories in markets, mobilizing classifications to spark reflection, experimentation, and improvement among firms by perturbing taken-for-granted assumptions. They also redesigned the practices of producing, implementing, and revising categories. Manufacturers themselves produced and routinely revised classifications through collective deliberation, while accountants served as their consultants, rather than autonomous authorities who monopolized category formation and implementation. In so doing, reformers forged foundations for upgrading markets and fostering competition based on innovation and improvement in a variety of industries. Based on these findings, we extend existing research beyond categorical imperatives to highlight how categories also serve as cognitive infrastructures for learning, discovery, and innovation in markets.

When decision makers encounter new assurance services that can be customized for individual clients, they must include them in their pre-existing categorization of assurance, a cognitive task known as postclassification. This paper draws upon three literatures (classification research in accounting, theory of assurance, and cognitive psychology) in order to suggest how this task might be modeled and studied empirically, using the example of SysTrust™. The role of a necessary condition for successful postclassification called the category use effect (Ross, 2000), in which decision makers are reminded of pre-existing categories when they learn to use new categories, is explained.

Cognitive style has been identified to be significantly influential in deciding users' preferences of search engines. In particular, Witkin's field dependence/independence has been widely studied in the area of web searching. It has been suggested that this cognitive style has conceptual links with the holism/serialism. This study aims to investigate the differences between the field dependence/independence and holism/serialism.

An empirical study was conducted with 120 students from a UK university. Riding's cognitive style analysis (CSA) and Ford's study preference questionnaire (SPQ) were used to identify the students' cognitive styles. A questionnaire was designed to identify users' preferences for the design of search engines. Data mining techniques were applied to analyse the data obtained from the empirical study.

The results highlight three findings. First, a fundamental link is confirmed between the two cognitive styles. Second, the relationship between field dependent users and holists is suggested to be more prominent than that of field independent users and serialists. Third, the interface design preferences of field dependent and field independent users can be split more clearly than those of holists and serialists.

The contributions of this study include a deeper understanding of the similarities and differences between field dependence/independence and holists/serialists as well as proposing a novel methodology for data analyses.

People in all cultures of the world classify other people most readily in easily identifiable categories. Examples of such categories are race, gender, economic, and physical difference. These categories make the world intelligible because they assign roles and functions attached to the individuals that fill the category. Racial, gender, and other categories that reflect difference may change over time as to the meaning and assigned roles and functions, but the very fact of them being a criterion for classification remains rather unchallenged. Yet the very fact of classification may question whether individuals with disabilities belong to the most essential of all categories, the human category. With classification, a statement of exclusion or inclusion in the human category is imminent.