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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.

Together, learning objects (LOs) and e-pedagogical practices have the potential to improve the performance of e-learning systems in several ways. They can make e-learning more personalised and adaptable, providing students with a more engaging and effective learning experience.

The development of LOs and e-pedagogical practices have significantly influenced and changed the performance of e-learning systems. LOs are self-contained, reusable units of instructional content that create instructional materials, such as online courses, tutorials and assessments. They provide a flexible and modular approach to designing and delivering e-learning content, allowing educators to easily customise and adapt their materials to the needs of their students. e-pedagogical practices refer to the use of technology to enhance and support the teaching and learning process. They include strategies such as online collaboration, gamification and adaptive learning to improve student engagement, motivation and achievement.

To achieve this objective, this study consists of two main phases. First, the authors extract metadata from LOs using latent semantic analysis algorithms, which are considered a strong tool in web-mining exploration techniques. Second, they identify LOs according to a particular form of similarity using fuzzy c-means (FCM) algorithms. To improve classification accuracy, the FCM is used as a clustering algorithm.

Finally, in order to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset are conducted. The results of this study indicate that the proposed approach exceeds the traditional approach and produces good results.

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.

Ancient Chinese architecture is famous for its wooden frame structure and unique position in world architecture history. As numerous types of components and complex combinations exist, the overall structural system and how they are interlocked have always been crucial but challenging parts of the study. Students find it hard to understand and remember the concepts using traditional 2D paper media, making such knowledge unattractive to pass on to the new generation. To overcome the challenges, this research aims to examine the effect of combining Virtual Reality (VR) with digital interaction games in learning structural characteristics (dougong) of ancient Chinese architecture.

This research develops an interactive cognitive system in the VR environment based on constructivist theory to improve the learning approach of ancient Chinese architecture. Applying an experimental procedure, the feedback of an experimental group using the VR cognition system and a control group using traditional learning 2D media are collected to examine the differences in learning effectiveness and user experience.

This study develops an interactive cognitive system to aid in learning the structural system of ancient Chinese architecture. The results indicate that integrating VR and interactive learning games can increase students' positive attitudes and learning effectiveness towards ancient Chinese architecture.

This study integrates VR technology and interactive games to improve the learning approach. It examines the effect of applying the concept of human–computer interaction in learning ancient Chinese buildings. The concept of designing the interactive cognitive system is expected to guide students gradually to be the main body of learning and stimulate their learning enthusiasm and motivation.

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.