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Effective project learning can prevent projects from repeating the same mistakes; however, knowledge codification is required for project-to-project learning to be up-scaled across the temporal, geographical and organisational barriers that constrain personalised learning. This paper explores the state of practice for the structuring of codified project learnings as concrete boundary objects with the capacity to enable externalised project-to-project learning across complex boundaries. Cross-domain reconceptualisation is proposed to enable further research and support the future development of standardised recommendations for boundary objects that can enable project-to-project learning at scale.

An integrative literature review method has been applied, considering knowledge, project learning and boundary object scholarship as state-of-practice sources.

It is found that the extensive body of boundary object literature developed over the last three decades has not yet examined the internal structural characteristics of concrete boundary objects for project-to-project learning and boundary-spanning capacity. Through a synthesis of the dispersed structural characteristic recommendations that have been made across examined domains, a reconceptualised schema of 30 discrete characteristics associated with boundary-spanning capacity for project-to-project learning is proposed to support further investigation.

This review makes a novel contribution as a first cross-domain examination of the internal structural characteristics of concrete boundary objects for project-to-project learning. The authors provide directions for future research through the reconceptualisation of a novel schema and the identification of important and previously unidentified research gaps.

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

The aim of this study is to illuminate the perceptions of the essential factors for sustaining Learning Study of the curriculum leaders who have led Learning Study in schools over a sustained period.

This study adopted a case study research approach to explore the perspectives of the curriculum leaders regarding the sustainability of Learning Study. Data were collected through interviews, observations and document analysis. To analyse the data, thematic analysis was conducted to identify themes related to the research aim.

Four themes were deemed crucial by the curriculum leaders for sustaining Learning Study: (1) integrating Learning Study into the overall development plan of the school, with milestones recognisable by all stakeholders; (2) developing a shared understanding of and patience towards the different developmental needs of stakeholders; (3) developing a sustained programme of professional development for teachers regarding variation theory of learning, which underpins Learning Study; and (4) creating an improvement culture and a safe environment for sustaining professional development. We argue that Learning Study models should be flexible that they fit various school contexts while retaining the aim of enabling learning.

Sustaining Learning Study in schools becomes a challenge if support in the form of government funding and research involvement from tertiary institutions is withdrawn. This study is the first to voice the opinions of school curriculum leaders regarding this complex issue, who play a key role in initiating, implementing and sustaining Learning Study.

The purpose is to design a system in which drones can control traffic most effectively using a deep learning algorithm.

Drones have now started entry into each facet of life. The entry of drones has made them a subject of great relevance in the present technological era. The span of drones is, however, very broad due to various kinds of usages leading to different types of drones. Out of the many usages, one usage which is presently being widely researched is traffic monitoring as traffic monitoring can hover over a particular area. This paper specifically brings out the basic algorithm You Look Only Once (YOLO) which may be used for identifying the vehicles. Consequently, using deep learning YOLO algorithm, identification of vehicles will, therefore, help in easy regulation of traffic in streetlights, avoiding accidents, finding out the culprit drivers due to which traffic jam would have taken place and recognition of a pattern of traffic at various timings of the day, thereby announcing the same through radio (namely, Frequency Modulation (FM)) channels, so that people can take the route which is the least jammed.

The study found that the object(s) detected by the deep learning algorithm is almost the same as if seen from a naked eye from the top view. This led to the conclusion that the drones may be used for traffic monitoring, in the days to come, which was not the case earlier.

The main research content and key algorithm have been introduced. The research is original. None of the parts of this research paper has been published anywhere.

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.

The Open University (OU) in the UK has been providing distance learning since 1969. The purpose of this case study is to outline the impact that The OU Library in the UK has had on student learning outcomes by embedding academic literature and digital and information literacy (DIL) skills materials in the curriculum.

The case study presents an overview of the university context, including how the curriculum is developed. It discusses the role of the library in this process, outlining how librarians work with academic staff to embed skills and literature in the curriculum. Unique in-house technical solutions are presented to aid future approaches to providing distance library services.

The impact of the library on university education is discussed. Findings from qualitative research are presented, outlining the value the university places on the role of the library as an educational partner. Quantitative research studies are also presented, outlining the positive relationships between library content access and training attendance with student success.

As universities are considering their distance-learning offerings post-COVID-19, it is hoped that this case study will help both library and university administrators examine the role of their libraries in this strategy.

A case study on the approach The OU Library takes to support education in its broadest sense has not been published before.

The purpose of the paper is to propose a semantic model for describing open source software (OSS) in a machine–human understandable format. The model is extracted to support source code reusing and revising as the two primary targets of OSS through a systematic review of related documents.

Conducting a systematic review, all the software reusing criteria are identified and introduced to the web of data by an ontology for OSS (O4OSS). The software semantic model introduced in this paper explores OSS through triple expressions in which the O4OSS properties are predicates.

This model improves the quality of web data by describing software in a structured machine–human readable profile, which is linked to the related data that was previously published on the web. Evaluating the OSS semantic model is accomplished through comparing it with previous approaches, comparing the software structured metadata with profile index of software in some well-known repositories, calculating the software retrieval rank and surveying domain experts.

Considering context-specific information and authority levels, the proposed software model would be applicable to any open and close software. Using this model to publish software provides an infrastructure of connected meaningful data and helps developers overcome some specific challenges. By navigating software data, many questions which can be answered only through reading multiple documents can be automatically responded on the web of data.

The purpose of this study is to theorize that computer-assisted language learning (CALL) can be integrated in English language learning with a focus on cultural learning of both home and target language.

The present study used a systematic methodology to conceive the language and home-culture integrated online learning (LHIOL) curriculum design based on relevant conceptual frameworks and gather qualitative data from focused group interviews of 30 teachers and 3,000 students’ open-ended questionnaires, along with learning artifacts to identify major themes.

CALL, used as cultural and linguistic material, helps students embrace their cultural identities, especially ethnic minorities, capitalize on their distinctive values, and appreciate and empathize with other languages and cultures. The instructors advocate for localizing intercultural communicative competence (ICC) educational content into Vietnamese culture, using real multimedia resources. However, the LHIOL curriculum faced systemic constraints regarding competitions between linguistic and cultural instruction, teachers’ refusal to recognize ICC’s importance and recognition of an explicit link between virtual cultural learning and their lives.

LHIOL is a preliminary practical effort to suggest how a cultural education from one’s native tongue can be integrated into a culture-focused English/Western language environment. By incorporating fundamental concepts that underpin the integration of language and culture as well as CALL, improving ICC offers a framework that can be applied to elucidate cultural learning.

This study aims to investigate the literature related to the use of digital technologies for promoting circular economy (CE) in the construction industry.

A comprehensive approach was adopted, involving bibliometric analysis, text-mining analysis and content analysis to meet three objectives (1) to unveil the evolutionary progress of the field, (2) to identify the key research themes in the field and (3) to identify challenges hindering the implementation of digital technologies for CE.

A total of 365 publications was analysed. The results revealed eight key digital technologies categorised into two main clusters including “digitalisation and advanced technologies” and “sustainable construction technologies”. The former involved technologies, namely machine learning, artificial intelligence, deep learning, big data analytics and object detection and computer vision that were used for (1) forecasting construction and demolition (C&D) waste generation, (2) waste identification and classification and (3) computer vision for waste management. The latter included technologies such as Internet of Things (IoT), blockchain and building information modelling (BIM) that help optimise resource use, enhance transparency and sustainability practices in the industry. Overall, these technologies show great potential for improving waste management and enabling CE in construction.

This research employs a holistic approach to provide a status-quo understanding of the digital technologies that can be utilised to support the implementation of CE in construction. Further, this study underlines the key challenges associated with adopting digital technologies, whilst also offering opportunities for future improvement of the field.