Five facets of social presence in online distance education

Social presence in online learning environments refers to the degree to which a learner feels personally connected with other students and the instructor in an online learning community. Based on a 19 item Online Social Presence Questionnaire (OSPQ) given to college students in two different online learning courses, a series of exploratory and confirmatory factor analyses consistently revealed five factors representing facets of social presence in online learning environments: social respect (e.g. receiving timely responses), social sharing (e.g., sharing information or expressing beliefs), open mind (e.g., expressing agreement or receiving positive feedback), social identity (e.g., being called by name), and intimacy (e.g., sharing personal experiences). Together, the five factors accounted for 58% of the variance and were based on 19 items. Although much previous research focuses on cognitive aspects of learning in online environments, understanding the role of the learner’s sense of presence may be particularly important in distance learning situations in which students and the instructor are physically separated.     

Presence: a unique characteristic in educational virtual environments

This article investigates the effect of presence on learning outcomes in educational virtual environments (EVEs) in a sample of 60 pupils aged between 11 and 13 years. We study the effect of personal presence, social presence and participant’s involvement on certain learning outcomes. We also investigate if the combination of the participant’s representation model in the virtual environment (VE) with the way it is presented gives a higher sense of presence that contributes to learning outcomes. Our results show that the existence of an avatar as the pupils’ representation enhanced presence and helped them to successfully perform their learning tasks. The pupils had a high sense of presence for both cases of the EVE presentation, projection on a wall and through a head mounted display (HMD). Our socialized virtual environment seems to play an important role in learning outcomes. The pupils had a higher sense of presence and completed their learning tasks more easily and successfully in the case of their egocentric representation model using the HMD.     

电子词典与词汇知识表达

词汇知识的表达与取得是自然语言处理极须克服的问题,本论文提出一个初步的架构与常识的抽取机制。语言处理系统是以词为讯息处理单元,登录在词项下的讯息可以包括统计、语法、语义、常识等。语言分析系统利用〈词〉为引得取得输入语句中相关词汇的语法、语义、常识等信息,让语言处理系统有更好的聚焦能力,可以藉以解决分词歧义、结构的歧义。对于不易以人工整理取得的常识,本论文也提出计算机自动学习的策略,以渐进式的方式累积概念与概念之间的语义关系,来增进语言系统的分析能力。这个策略可行的几个关键技术,包括(1)未登录词判别及语法语义自动分类, (2)词义分析, (3)应用语法语义及常识的剖析系统。     

Guest Editors' Introduction: Emerging Internet Technologies for E-Learning

As a flourishing field, e-learning at large (including distance learning, Web-based learning, and digital game-based learning) has attracted increasing attention from both industry and academic sectors. To facilitate the development of effective e-learning systems, scalable technologies that support an arbitrary number of users while providing them with a good learning environment are needed. In this introduction, we provide an overview of e-learning system development with respect to a layered reference architecture, including the Internet infrastructure layer, the conceptual/modeling layer, and the application layer. In addition, the three articles included in this special issue cover the issues of managing the learning objects in an open and scalable architecture, incorporation of learners' pedagogical features in Web-based learning environments, and support of digital game-based learning. All these issues, though not exhaustive, are important to ensure successful development of e-learning systems on an Internet platform.     

Level of intrinsic motivation of distance education students in e‐learning environments

According to researches, motivation that initiates and sustains behaviour is one of the most significant components of learning in any environment. Accordingly, level of intrinsic motivation triggers and sustains the interest of the open and distance education students when it comes to learning on their own in e‐learning environments. Despite a comprehensive literature regarding the motivation of those learning in traditional learning environments, the number of studies addressing the motivation of open and distance education students in e‐learning environments is not sufficient. In this context, this study aims at determining the level of intrinsic motivation of open and distance education students. Thus, data were collected from 1,639 distance education students in 22 programmes, through Intrinsic Motivation in e‐Learning Questionnaire developed and validated to that end. Analyses carried out indicate that the level of intrinsic motivation of open and distance education students is high in e‐learning environments, but there is not a statistically significant difference by gender, programme structure (graduate/undergraduate), instruction type (distance–blended), and academic disciplines.     

The impact of transactional distance dialogic interactions on student learning outcomes in online and blended environments

This study measured how student interactions (as captured by Transactional Distance dialogue (Moore, 1993)) in online and blended learning environments impacted student learning outcomes, as measured by student satisfaction and student grades. Dialogue was measured as student interactions with other students (student–student interaction), the technologies used (student–technology interaction), the instructors (student–teacher interaction), and the course contents (student–content interaction). In addition, moderating effects of media and modality of interactions and individual differences on student learning outcomes were also measured. Data was obtained from 342 online and blended students between 2010 and 2013. Findings indicate that student–content interaction had a larger effect on student learning outcomes than other forms of dialogue. Implications for educational policies that require teacher-presence (student–teacher) and student–student interactions in distance learning environments are also discussed.     

Design requirements, student perception indicators and validation metrics for intelligent exploratory learning environments

The new forms of interaction afforded by innovative technology and open-ended environments provide promising opportunities for exploratory learning. Exploratory environments, however, require appropriate support to lead to meaningful learning outcomes. This paper focuses on the design and validation of intelligent exploratory environments. The goal is twofold: requirements that guide the operationalisation of pedagogical strategies to computer-based support and methodology for the validation of the system. As designers, we need to understand what kind of interaction is conducive to learning and aligned with the theoretical principles behind exploratory learning. We summarise this in the form of three requirements—rare interruption of interaction, co-location of feedback and support towards specific goals. Additionally, developing intelligent systems requires many resources and a long time to build. To facilitate their evaluation, we define three indicators— helpfulness, repetitiveness and comprehension—of students’ perception of the intelligent system and three metrics—relevance, coverage, and scope—which allow the identification of design or implementation problems at various phases of the development. The paper provides a case study with a mathematical microworld that demonstrates how the three requirements are taken into account in the design of the user-facing components of the system and outline the methodology for formative validation of the intelligent support.     

A research initiative on the construction of innovative environments for teaching and learning. Montessori and Munari based psycho-pedagogical insights in computers and human behavior for the “new school”

Italians children-students live a strong technological gap among different education instances: on the one hand, they are attending schools technologically still to the '80 years, on the other hand, they can rely on hyper-technological domestic-family environments where videogames, smartphones, internet are always available. In the school, all learnings take place under the supervision of the teacher that stimulates, directs and corrects these important steps in the basic training. On the contrary, in the domestic environment the presence of technology is increasingly pervasive. These new technologies cognitively stimulate the children, but they entertain the little ones often alone and without the participation and supervision of an adult audience.Thus, the technology gap results in a pedagogical clash among different educational instances and this is the “space” addressed by our research initiative whose objective is the construction of innovative teaching and learning environments for children between 3 and 6 years of age. The specific quantitative outcomes can be defined with respect to three main families of indicators: measures to detect the use of learning environments; indicators of the level of satisfaction and involvement of the various involved actors; real impact on the socio-cognitive development of children produced by the introduction of methodologies and technologies.     

Norwegian students using digital artifacts in project-based learning

Abstract Discussion about information and communication technologies in education are often conducted at a general level. To increase understanding about new technologies and learning it is necessary to be more specific about what kinds of tools are being used and how they are related to knowledge construction by students within specific subject domains. Three Norwegian cases studies are reported so that different school cultures on how the learning environments are implemented are exposed. One important conclusion from the cross‐case analysis is the diversity among cases. What is the added value or changes that these technologies represent? Within a Norwegian context the paper shows how technology contributes to the design of new learning environments and how it might stimulate knowledge construction among students.     

Students' perceptions of instructors' roles in blended and online learning environments: A comparative study

This study develops an instrument—the Online Instructor Role and Behavior Scale (OIRBS)—and uses it to examine students' perceptions of instructors' roles in blended and online learning environments. A total sample of 750 university students participated in this study. Through a confirmatory factor analysis, the OIRBS was validated in five constructs: course designer and organizer (CDO), discussion facilitator (DF), social supporter (SS), technology facilitator (TF), and assessment designer (AD). The results show that the five factor structures remained invariant across the blended learning and online learning. Both students in blended learning environments and students in online learning environments exhibited the greatest weight in the CDO dimension, followed by the TF and DF dimensions. In addition, students in the online learning environments scored higher in the DF dimension than did those in the blended learning environments.     

Learning support environments: Rationale and evaluation

In this paper we argue that existing understanding of human cognition has much to offer the design of instructional systems and materials, and that new technologies, such as hypertext, in harness with traditional techniques, provide opportunities for extending the mapping of cognitive principles to instructional design. Following a review of types of knowledge, cognitive styles and strategies within a CAL framework, the advantages of learning support environments over other CAL approaches are discussed. Such environments are presented as extensions to hypertext which incorporate various generic features for learning applications. The need for evaluation of learning which is dynamic and internal to the learning tasks is stressed.     

Research and development of web-based virtual online classroom

To build a web-based virtual learning environment depends on information technologies, concerns technology supporting learning methods and theories. A web-based virtual online classroom is designed and developed based on learning theories and streaming media technologies. And it is composed of two parts: instructional communicating environment (ICE) and collaborative learning environment (CLE). ICE provides learners with learning materials, lecture videos, and interactive environment etc. CLE supports active learning by providing the environment with learning tools, learning materials and contextual discussion for learners. The environments are designed with event-based synchronous strategies and e-learning technologies standards.     

个性化远程教学系统的模型研究

个性化远程教学系统是基于网络暨Internet技术的远程教学系统的最新发展。文章根据学习对象元和国际IEEEP1484LOM标准以及其他相关技术,提出了个性化远程教学系统的概念以及特征,并且依据NCEC项目实践,对于系统的层次模型、功能模型和数据模型进行了深刻的阐述与剖析。     

Virtual classroom extension for effective distance education.

Key requirements of effective distance learning are interactivity among participants and the student's sense of presence in the classroom. This system meets those requirements, letting the instructor perceive remote students' body language and facial expressions as they listen and speak, and letting remote students participate in the on-campus classroom.     

CBDL: Context‐based distance learning for categorical attributes

Distance learning is an important notion and has played a critical role in success of various machine learning algorithms. Any learning algorithm that requires dissimilarity/similarity measures has to assume some forms of distance functions, either explicitly or implicitly. Hence, in recent years a considerable amount of research has been devoted to distance learning. Despite great achievements in this field, a number of important issues need to be further explored for real world datasets mainly containing categorical attributes. Based on these considerations, the current research presents a Context‐Based Distance Learning approach (CBDL) to advance the state of the art existing researches on distance metric learning for categorical datasets. CBDL is designed and developed based on the idea that distance between two values of a given categorical attribute can be estimated by using information inherently exists within subset of attributes called context. CBDL composes of two main components: context extraction component and distance learning component. Context extraction component is responsible for extracting the relevant subset of feature set for a given attribute, while distance learning component tries to learn distance between each pair of values based on the extracted context. To have a comprehensive analysis, we conduct wide range of experiments in both supervised and unsupervised environments in the presence of noise. Our experimental results reveal that CBDL is the method of choice distance learning approach by offering a comparable or better performance compared to the state of the art existing distance learning schemes according to studied evaluation measures. © 2011 Wiley Periodicals, Inc.     

Guest Editors' Introduction: Intelligent Educational Systems of the Present and Future

Education in the 21st century faces an increasing number of challenges that require intelligent systems. The learning environments of today and tomorrow must handle distributed and dynamically changing content, a geographical dispersion of students and teachers, and generations of learners who spend hours a day interacting with multimillion-dollar multimedia environments. The new learning environments are moving beyond kindergarten through college classrooms to distance learning, lifelong education, and on-the-job training; they're entering the multifaceted universe of virtual worlds and ambient spaces. The articles in this special issue report novel, cutting-edge, intelligent learning environments. The systems not only have intelligent computational architectures but also are empirically tested on humans. Empirical research is of course essential to test system fidelity in delivering the intended pedagogy and to assess learning gains, usability, and learner satisfaction. This article is part of a special issue on intelligent educational systems.     

A NEW E‐LEARNING ACHIEVEMENT EVALUATION MODEL BASED ON ROUGH SET AND SIMILARITY FILTER

The trend of utilizing information and Internet technologies as teaching and learning tools is rapidly expanding into education. E‐learning is one of the most popular learning environments in the information era. The Internet enables students to learn without limitations of space and time. Furthermore, the learners can repeatedly review the context of a course without the barrier of distance. Recently, student‐centered instruction has become the primary trend in education, and the e‐learning system, which is considered with regard to of personalization and adaptability, is more and more popular. By means of e‐learning systems, teachers can adjust the learning schedule instantly for each learner according to a student's achievements and build more adaptive learning environments. Sometimes, teachers give biased assessments of students’ achievements under uncontrollable conditions (i.e., tiredness, preference) and are in dire need of overcoming this predicament. To solve the drawback mentioned, a new model to evaluate learning achievements based on rough set and similarity filter is proposed. The proposed model includes four facets: (1) select important features (attributes) to enhance classification performance by feature selection methods; (2) utilize minimal entropy principle approach (MEPA) to fuzzify the quantitative data; (3) select linguistic values for each feature and delete inconsistent data using the similarity threshold (similarity filter); and (4) generate rules based on rough set theory (RST). The practical e‐learning achievement data sets are collected by an e‐learning online examination system from a university in Taiwan. To verify our model, the performances of the proposed model are compared with the listing models. Results of this study demonstrate that the proposed model outperforms the listing models.     

A generalized learning algorithm for an automaton operating in amultiteacher environment

Learning algorithms for an automaton operating in a multiteacher environment are considered. These algorithms are classified based on the number of actions given as inputs to the environments and the number of responses (outputs) obtained from the environments. In this paper, we present a general class of learning algorithm for multi-input multi-output (MIMO) models. We show that the proposed learning algorithm is absolutely expedient and epsilon-optimal in the sense of average penalty. The proposed learning algorithm is a generalization of Baba's GAE algorithm and has applications in solving, in a parallel manner, multi-objective optimization problems in which each objective function is disturbed by noise.     

An online collaborative document creation exercise in an ODL research project module

Online technologies to support collaborative learning provide lecturers in distance higher education with ever more opportunities to extend their teaching in beneficial ways. Students are also increasingly familiar with the online environment via social networking sites. How well this familiarity with online environments translates into the online collaborative learning space in an open distance learning context was examined. We report here on one cycle of an action research project involving 11 fourth-year computing students producing shared survey questionnaires and interview questions collaboratively online. Feedback on the collaboration exercise was obtained by means of a questionnaire which was analysed using grounded theory techniques. The expectation was that collaborative text creation software would be used but somewhat surprisingly, given their academic background, the students used email as the tool of choice. The students' reflections on the collaborative process showed that they noted and appreciated the benefits of collaborative work but also confirmed some of the previously reported frustrations of online collaborative work – in particular differences in levels of commitment and effort, and the free rider effect. Overall, the results pointed to the need for lecturers to be involved in a collaborative process to critically question and guide choices being made.