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This study developed new measures of the routine and non-routine task contents of managerial, professional, technical, and clerical occupations from a workload perspective. Here, we present a comparative analysis of the workload structures of state and industrial sector employees.

Our method involves detailed descriptions of work processes and an element-wise time study. We collected and analysed data to obtain a workload structure that falls within three conceptual task categories: (i) non-routine analytic tasks, (ii) non-routine interactive tasks and (iii) routine cognitive tasks. A total of 2,312 state and industrial sector employees in Kazakhstan participated in the study. The data were collected using a proprietary web application that resembles a timesheet.

The study results are consistent with the general trend reported by previous studies: the higher the job level, the lower the occupation’s routine task content. In addition, the routine cognitive task contents of managerial, professional, technical, and clerical occupations in the industrial sector are higher than those in local governments. The work of women is also more routinary than that of men. Finally, vthe routine cognitive task contents of occupations in administrative units are higher than those of occupations in substantive units.

Our study sought to address the challenges of using the task-based approach associated with measuring tasks by introducing a new measurement framework. The main advantage of our task measures is a direct approach to assessing workloads consisting of routine tasks, which allows for an accurate estimation of potential staff reductions due to the automation of work processes.

In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task.

To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning.

Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets.

This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.

Preservice teachers (PSTs) typically have few opportunities to observe social studies instruction in PSTs' elementary field placements. To practice effective integration as a pathway to include more social studies instruction, PSTs developed a unit plan based on inquiry during an undergraduate methods course. The purpose of this study was to explore what scaffolds were effective in PSTs' development of social studies inquiry projects.

The author used a multiple case study approach, examining initial submissions, feedback and resubmissions as PSTs developed PSTs' inquiry projects. The data were analyzed with an eye on PSTs' ability to plan a unit according to the four dimensions of the C3 Framework's inquiry arc (National Council for the Social Studies [NCSS], 2013).

The author analyzed data related to PSTs’ area of highest need from PSTs' initial submissions: staging the task, formative tasks and resources. PSTs were able to develop inquiry projects after being supported in their (1) organization, (2) clarity, (3) alignment and (4) developmental appropriateness.

This study shows that novice teachers can create high-quality social studies learning experiences in elementary school when provided appropriate supports (in this case, feedback about organization, clarity, alignment and developmental appropriateness). With the tools to develop such projects, new teachers may be able to increase the amount of social studies taught in elementary classrooms.

As an emerging tool for data discovery, data retrieval systems fail to effectively support users' cognitive processes during data search and access. To uncover the relationship between data search and access and the cognitive mechanisms underlying this relationship, this paper examines the associations between affective memories, perceived value, search effort and the intention to access data during users' interactions with data retrieval systems.

This study conducted a user experiment for which 48 doctoral students from different disciplines were recruited. The authors collected search logs, screen recordings, questionnaires and eye movement data during the interactive data search. Multiple linear regression was used to test the hypotheses.

The results indicate that positive affective memories positively affect perceived value, while the effects of negative affective memories on perceived value are nonsignificant. Utility value positively affects search effort, while attainment value negatively affects search effort. Moreover, search effort partially positively affects the intention to access data, and it serves a full mediating role in the effects of utility value and attainment value on the intention to access data.

Through the comparison between the findings of this study and relevant findings in information search studies, this paper reveals the specificity of behaviour and cognitive processes during data search and access and the special characteristics of data discovery tasks. It sheds light on the inhibiting effect of attainment value and the motivating effect of utility value on data search and the intention to access data. Moreover, this paper provides new insights into the role of memory bias in the relationships between affective memories and data searchers' perceived value.

This research explores the appropriateness of using a digital learning system based on a multiplayer online animated game in firefighting education and training. Use of the game improved learning was effective. It can increase learners' understanding of the taught content and their willingness to learn.

The research applies digital learning based on a multiplayer online animated game coupled with 3D and virtual reality (VR) technology. To verify the effectiveness of this approach, participants were divided into two groups, an experimental group (using the game) and a control group (not using the game). T-tests were used to compare the learning of the two groups.

Firefighting knowledge was significantly better among the experimental group than the control group. Learning was explored in three different domains: basic firefighting knowledge, fire extinguisher operation and fire source identification; the latter two showed statistically significant differences. After learners acquired knowledge from multimedia, the addition of a VR exercise effectively helped them to remember what they learned and increased their understanding of the taught content.

The approach proposed in this study applies digital learning via a multiplayer online animated game to explore the appropriateness of such games in firefighting education and training. Use of the game improved learning and was effective. It can not only increase learners' understanding of the taught content, but also their willingness to learn.

Despite the technological paradigm shift presented to the architecture, engineering, construction and operations sector (AECO), the full-fledged acceptance of the building information modelling (BIM) methodology has been slower than initially anticipated. Indeed, this study aims to acknowledge the need for increasing supportive technologies enabling the use of BIM, attending to available human resources, their requirements and their tasks.

A complete case study is described, including the development process centred on design science research methodology followed by the usability assessment procedure validated by construction projects facility management operational staff.

Results show that participants could interact with BIM using openBIM processes and file formats naturally, as most participants reached an efficiency level close to that expected for users already familiar with the interface (i.e. high-efficiency values). These results are consistent with the reported perceived satisfaction and analysis of participants’ discourses through 62 semi-structured interviews.

The contributions of the present study are twofold: a proposal for a virtual reality openBIM framework is presented, particularly for the semantic enrichment of BIM models, and a methodology for evaluating the usability of this type of system in the AECO sector.

This article aims to address the confusion related to the meanings of interorganisational cooperation, control, coordination and collaboration in collaborative projects by developing a conceptual framework. From this, the authors aim to describe the links among these concepts in terms of development levels of stakeholder relationships. In addition, the authors aim to identify challenges and preconditions in relation to developing relationships at different levels.

The authors have adopted the directed approach of qualitative content analysis method to validate and extend the conceptual framework of this study. The context of this study is a large hospital construction project located in northern Finland.

The findings of this study suggest that collaboration is a multilevel process of active engagement of multiple stakeholders. These stakeholders must have a high degree of shared understanding in terms of cooperation, control and coordination to achieve the mutually desired outcomes. This study also identifies the challenges that project stakeholders could face in developing collaborative relationships and propose preconditions for the same.

This study provides a better understanding for project managers to manage interorganisational collaborative construction projects successfully. The outcome of this research would be beneficial to project management team to deliver dispute-free construction projects.

Existing practical research on the development of relationships at different levels in collaborative construction projects is limited. This study offers a framework for the same which is validated in a real-life project.

This study aims to investigate the relationship between information and communication technologies (ICTs) use and work performance during the coronavirus disease 2019 (COVID-19) pandemic. Specifically, it aims to understand what the role of task–technology fit is, and what effect this has on feelings of loneliness of individuals and their subsequent work performance. As a large proportion of workers are required to work from home during the ongoing COVID-19 pandemic, understanding what aspects contribute to higher performance and reduced negative psychological outcomes is of increased practical and research interest.

To explore these questions, a quantitative approach that employed a sample population of 357 individuals who worked from home during the COVID-19 pandemic was used. Using a convenience, purposive and snowball sampling approach the authors collected data through a custom-built online questionnaire, and analyzed the data using structural equation modeling (SEM).

The results highlight the effect that high task–technology fit has in both directly and indirectly influencing work performance of individuals. The authors find that by designing ICTs based on task–technology fit principles, individuals are less likely to experience feelings of loneliness while working from home and are more prone to perform better in their work-related tasks.

This study offers a new perspective on the role fit of tasks with technology have on influencing psychological states, and indirectly influencing work-related outcomes. The authors, therefore, expand the understanding about why task–technology fit is sought after by explaining part of the psychological mechanisms through which it has an effect on work performance.

In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.

The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.

The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.

The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.

Robots face fundamental challenges in achieving reliable and stable operations for complex home service scenarios. This is one of the crucial topics of robotics methods to imitate human beings’ advanced cognitive characteristics and apply them to solve complex tasks. The purpose of this study is to enable robots to have the ability to understand the scene and task process in complex scenes and to provide a reference method for robot task programming in complex scenes.

This paper constructs a task modeling method for robots in complex environments based on the characteristics of the perception-motor memory model of human cognition. In the aspect of episodic memory construction, the task execution process is included in the category of qualitative spatio-temporal calculus. The topology interaction of objects in a task scenario is used to define scene attributes. The task process can be regarded as changing scene attributes on a time scale. The qualitative spatio-temporal activity graphs are used to analyze the change process of the object state with time during the robot task execution. The tasks are divided according to the different values of scene attributes at different times during task execution. Based on this, in procedural memory, an object-centered motion model is developed by analyzing the changes in the relationship between objects in the scene episode by analyzing the scene changes before and after the robot performs the actions. Finally, the task execution process of the robot is constructed by alternately reconstructing episodic memory and procedural memory.

To verify the applicability of the proposed model, a scenario where the robot combines the object (one of the most common tasks in-home service) is set up. The proposed method can obtain the landscape of robot tasks in a complex environment.

The robot can achieve high-level task programming through the alternating interpretation of scenarios and actions. The proposed model differs from traditional methods based on geometric or physical feature information. However, it focuses on the spatial relationship of objects, which is more similar to the cognitive mechanism of human understanding of the environment.