The impact of learning styles on student grouping for collaborative learning: a case study

Learning style models constitute a valuable tool for improving individual learning by the use of adaptation techniques based on them. In this paper, we present how the benefit of considering learning styles with adaptation purposes, as part of the user model, can be extended to the context of collaborative learning as a key feature for group formation. We explore the effects that the combination of students with different learning styles in specific groups may have in the final results of the tasks accomplished by them collaboratively. With this aim, a case study with 166 students of computer science has been carried out, from which conclusions are drawn. We also describe how an existing web-based system can take advantage of learning style information in order to form more productive groups. Our ongoing work concerning the automatic extraction of grouping rules starting from data about previous interactions within the system is also outlined. Finally, we present our challenges, related to the continuous improvement of collaboration by the use and dynamic modification of automatic grouping rules.     

Next Generation University Library Information Systems Based on Cooperative Learning

An integrated library information system is a resource planning system for a library, used to track resources owned, bills paid, orders made, and patrons who have borrowed. In our research, we focused on university library information systems (ULISs). We identified an important research question regarding their main limitation in offering intelligent help to the students in their documentation/learning. We identified the importance of the endowment of ULISs with artificial intelligence. In this article, we analyzed different aspects related to the presence of computational intelligence in ULISs and intelligence of ULISs. Finally, we proposed a complex next generation ULIS based on a hybrid cooperative learning, being able to offer an intelligent help for personalized learning of students. We defined some novel paradigms in the context of a novel kind of cooperative hybrid personalized learning, such as learning role and sub-role; and learning intelligence level.     

Integrating affective learning into intelligent tutoring systems

Affectivity has influence in learning face-to-face environments and improves some aspects in students, such as motivation. For that reason, it is important to integrate affectivity elements into virtual environments. We propose a conceptual model that suggests which elements of tutor, student and dialogue should be integrated and implemented into learning systems. We design an ontology guided by methontology, and apply a mathematical evaluation (OntoQA) to determine the richness of the proposed model. The mathematical evaluation states that the proposed model has relationship richness and horizontal nature. We developed a software application implementing the conceptual model in order to prove its effectivity to generate students’ motivation. The findings suggest that the implemented affective learning ontology impacts positively the motivation in students with low academic performance, in female students and in engineering students.     

Ontology-based healthcare context information model to implement ubiquitous environment

To establish real u-healthcare environments, it is necessary to receive the context information obtained from various platforms at the proper time in portable devices which operate using both wired and wireless communication. Moreover, a knowledge model is required that reflects the information and characteristics needed for such services while remaining appropriate for medical reference. This paper develops an ontology-based healthcare context information model to implement a ubiquitous environment. Contextual information will be extracted and classified to implement the healthcare services using the context information model. The healthcare context information model can be defined using the ontology, and a common healthcare model will be developed by considering medical references and service environments. Application and healthcare service developers can use the sensed information in various environments by authoring device- and space-specific ontologies based on this common ontology. In addition, this paper designs a personalized u-healthcare service system. The validity of the model used in this study is evaluated for the food and exercise recommendation in u-healthcare services.     

A comprehensive approach to model and use context for adapting applications in pervasive environments

With an increasing diversity of pervasive computing devices integrated in our surroundings and an increasing mobility of users, it will be important for computer systems and applications to be context-aware. Lots of works have already been done in this direction on how to capture context data and how to carry it to the application. Among the remaining challenges are to create the intelligence to analyze the context information and deduce the meaning out of it, and to integrate it into adaptable applications. Our work focuses on these challenges by defining generic context storage and processing model and by studying its impact on the application core. We propose a reusable context ontology model that is based on two levels: a generic level and a domain specific level. We propose a generic adaptation framework to guarantee adaptation of applications to the context in a pervasive computing environment. We also introduce a comprehensive adaptation approach that involves content adaptation and presentation adaptation inline with the adaptation of the core services of applications. Our case study shows that the context model and the application adaptation strategies provide promising service architecture.     

一个情境相关的双层室内空间数据模型

在室内空间的移动对象管理中,如何建立数据模型是要解决的首要问题。随着情境感知信息系统的发展,情境的概念得到越来越多的关注,如何在室内空间数据管理中融入情境信息已经成为人们关注的焦点问题。针对这一问题,综合考虑几何、拓扑、情境3类信息,建立了一个情境相关的双层室内空间数据模型。在分析了经典的空间划分方法后,提出了室内空间细粒度划分方法,并给出其形式化定义;引入层次互补的思想来组织室内空间,采用本体的方法将情境信息加入到模型中,使该模型对室内空间的表达更加灵活;最后,通过实例分析和列举模型的优势说明了建模方法的可行性和有效性。     

Ontology learning and its application to automated terminology translation

Our OntoLearn system is an infrastructure for automated ontology learning from domain text. It is the only system, as far as we know, that uses natural language processing and machine learning techniques, and is part of a more general ontology engineering architecture. We describe the system and an experiment in which we used a machine-learned tourism ontology to automatically translate multiword terms from English to Italian. The method can apply to other domains without manual adaptation.     

A hierarchical ontology context model for work-based learning

Context modelling involves a) characterizing a situation with related information, and b) dealing and storing the information in a computer-understandable form. It is the keystone to enable a system to possess the perception capacity and adapt its functionality properly for different situations. However, a context model focusing on the characteristics of work-based learning is not well studied by pioneering researchers. For addressing this issue, in this work we firstly analyze several existing context models to identify the essentials of context modelling, whereby a hierarchical ontology context model is proposed to characterize work-based learning. Subsequently, we present the application of the proposed model in work-based learning scenario to provide adapted learning supports to professionals. Hence, this work has significance in both theory and practice.     

Context based object categorization: A critical survey

The goal of object categorization is to locate and identify instances of an object category within an image. Recognizing an object in an image is difficult when images include occlusion, poor quality, noise or background clutter, and this task becomes even more challenging when many objects are present in the same scene. Several models for object categorization use appearance and context information from objects to improve recognition accuracy. Appearance information, based on visual cues, can successfully identify object classes up to a certain extent. Context information, based on the interaction among objects in the scene or global scene statistics, can help successfully disambiguate appearance inputs in recognition tasks. In this work we address the problem of incorporating different types of contextual information for robust object categorization in computer vision. We review different ways of using contextual information in the field of object categorization, considering the most common levels of extraction of context and the different levels of contextual interactions. We also examine common machine learning models that integrate context information into object recognition frameworks and discuss scalability, optimizations and possible future approaches.     

内容适配中基于本体的传输上下文模型*

在内容适配的研究中,传输上下文的描述语言得到了广泛的关注,然而现有的描述语言在语义表达能力和用户偏好描述方面存在不足。为此提出了一种基于本体的传输上下文模型,通过将本体引入建模过程,提高了模型的语义表达能力和用户偏好的描述能力,较好地满足了e learning背景下的内容适配的需求。     

本体在E learning系统中的应用研究*

探讨了Elearning及本体,提出了基于本体的Elearning系统层次结构模型,并重点研究了本体在其中的应用：用于描述学习材料语义的内容本体,用于定义学习材料上下文的上下文本体以及用于在学习课程中组织学习材料的结构本体。     

一种基于情景感知的学习服务发现算法

化学计算模型是基于化学反应和计算之间比喻的并行计算模型,其内在的并行性及不确定性可以有效地消除与计算逻辑本身无关的人为顺序性,从而能够以更为直观和抽象的方式来描述并行计算。但也正是由于其内在的并行性和高层抽象性,使得特定的控制机制难以描述。高阶化学编程语言是对传统化学计算模型的扩展和泛化,在保留传统化学计算特征的同时,不仅可以有效地描述传统的控制机制,也可以方便地定义新的控制机制。通过从简单面向对象语言到高阶化学语言的转换,给出了面向对象语言的一种化学语义描述,提供了一种描述面向对象系统的新视角,也为结合面向对象编程和化学编程提供了一种可能。     

本体在E-learning系统中的应用研究

探讨了E-learning及本体,提出了基于本体的E-learning系统层次结构模型,并重点研究了本体在其中的应用：用于描述学习材料语义的内容本体,用于定义学习材料上下文的上下文本体以及用于在学习课程中组织学习材料的结构本体。     

AiCEF: an AI-assisted cyber exercise content generation framework using named entity recognition

Content generation that is both relevant and up to date with the current threats of the target audience is a critical element in the success of any cyber security exercise (CSE). Through this work, we explore the results of applying machine learning techniques to unstructured information sources to generate structured CSE content. The corpus of our work is a large dataset of publicly available cyber security articles that have been used to predict future threats and to form the skeleton for new exercise scenarios. Machine learning techniques, like named entity recognition and topic extraction, have been utilised to structure the information based on a novel ontology we developed, named Cyber Exercise Scenario Ontology (CESO). Moreover, we used clustering with outliers to classify the generated extracted data into objects of our ontology. Graph comparison methodologies were used to match generated scenario fragments to known threat actors’ tactics and help enrich the proposed scenario accordingly with the help of synthetic text generators. CESO has also been chosen as the prominent way to express both fragments and the final proposed scenario content by our AI-assisted Cyber Exercise Framework. Our methodology was assessed by providing a set of generated scenarios for evaluation to a group of experts to be used as part of a real-world awareness tabletop exercise.

     

基于FCA的对象与属性交叉渐进的情感本体学习

提出了一种基于形式概念分析的渐进式情感本体学习的算法。该算法通过动态交叉添加对象和属性来生成概念格,从而从概念格当中抽取情感本体,最终实现情感本体的学习过程。给出了算法的描述,并结合实例说明了本体学习过程。     

The design and implementation of a competency-based intelligent mobile learning system

Mobile learning provides a ubiquitous learning context for the learners to select appropriate learning paths and learning objects. Adaptive learning methods and correct learning path planning can help to achieve the goal of learning anytime and anywhere. Moreover, the display ability of mobile learning devices has become a key factor affecting the interest and acquisition time of learners. Achieving the desired functionality is currently an important topic in the field of mobile learning. This paper uses competency-based learning as the basis to evaluate the knowledge deficiency that the learner must overcome. We then use carrier selection, fuzzy interpolation computation, and ant-genetic algorithm techniques to select the appropriate learning paths and objects. Finally, we use NFC’s point-to-point technology to transfer the learning content in the learning device to a larger screen with NFC capability in the user’s environment to display the same content, thus providing a complete learning system.     

Web 3.0 – Based personalisation of learning objects in virtual learning environments

The paper aims at research on Web 3.0 – based personalisation of learning objects (LOs) while learning in virtual learning environments. Learning personalisation is analysed in terms of suitability of LOs and VLEs to particular learning styles. The novel sets portrait analysing interconnections between students’ learning styles, their preferred learning activities, relevant teaching/learning methods, and LOs types is presented in more detail. The well-known standardised vocabularies of teaching/learning methods and LOs types were used to establish these interconnections. The sets portrait of these interconnections is followed by the appropriate ontology. The ontology is considered as an appropriate tool to create learners’ personalised learning environments consisting of LOs, suitable teaching/learning methods and activities according to their preferred learning styles. The ontology should help the learner to find suitable LOs according to preferred learning methods/activities, and vice versa, and thus to personalise learning. The presented Web 3.0 – based approaches are the typical cases of information and communication technologies (ICT’s) application for development of human capital.     

A cloud-based learning environment for developing student reflection abilities

Students learn new knowledge effectively through relevant reflection. Reflection affects how students interact with learning materials. Studies have found that good reflection abilities allow students to attain better learning motivation, comprehension, and performance. Thus, it is important to help students develop and strengthen their reflection abilities as this can enable them to engage learning materials in a meaningful manner. Face-to-face dialectical conversations are often used by instructors to facilitate student reflection. However, such conventional reflection methods are usually only usable in classroom environments, and could not be adopted for distance learning or after class. Cloud computing could be used to solve this issue. Instructor guidance and prompting for initiating reflection could be seamlessly delivered to the students’ digital devices via cloud services. Thus, instructors would be able to facilitate student reflective activities even when outside the classroom. To achieve this objective, this study proposed a cloud-based reflective learning environment to assist instructors and students in developing and strengthening reflection ability during and after actual class sessions. An additional experiment was conducted to evaluate the effectiveness of the proposed approach in an industrial course. Results show that the learning environment developed by this study is able to effectively facilitate student reflection abilities and enhance their learning motivation.