Does instructor's image size in video lectures affect learning outcomes?

One of the most commonly used forms of video lectures is a combination of an instructor's image and accompanying lecture slides as a picture‐in‐picture. As the image size of the instructor varies significantly across video lectures, and so do the learning outcomes associated with this technology, the influence of the instructor's image size should be evaluated. This study tested the effect of image size of the instructor on learners' experience of social presence, cognitive load, learning performance and satisfaction. In the study, 87 Chinese undergraduates were randomly assigned to view a video lecture with a small image of the instructor, with a medium image size of the instructor or with a large image of the instructor. The results of analyses of variances and analysis of covariance suggested that learners perceived the same level of social presence and cognitive load across video lectures with different image sizes of the instructor; learners who watched the video lecture with the small image of the instructor learned better than those who watched the video lectures with the medium and the large images of the instructors. Furthermore, learners who watched the video lecture with the small image and medium image of the instructor experienced more learning satisfaction. The findings highlight the importance of image size of the instructor in video lecture learning.     

Context‐aware language‐learning application in the green technology building: Which group can benefit the most?

To assess the effects and learner perceptions of context‐aware ubiquitous language learning (CAULL), a green‐building English‐learning application (GBELA) employing sensing technology was created to develop participants' English listening and reading skills using smartphones and QR codes. This study investigated the effects of 40 participants' perceived ease of use, usability, usefulness, learner attitude, satisfaction with the use of GBELA, and self‐efficacy in smartphone and GBELA usage. Quantitative and qualitative data through pretest/post‐test, questionnaires, and semistructured interviews were collected with a focus on green building–based English (GBbE) reading and listening skills. Results proved the effectiveness of the GBELA for both high‐achievement (HA) and low‐achievement (LA) groups. Furthermore, correlations were found between the HA group and ease of use of the GBELA. The correlations among learner perceptions and self‐efficacy showed that a well‐designed context‐aware learning system can help learners enhance self‐efficacy in CAULL mode. Implications for the design of effective context‐ and knowledge‐specific ubiquitous learning systems are provided in the study.     

Student interactions in online discussion forums: their perception on learning with business simulation games

Digital technology offers new teaching methods with controversial results over learning. They allow students to develop a more active participation in their learning process although it does not always drive to unequivocal better learning outcomes. This study aims to offer additional evidence on the contribution of business simulation games to students’ learning outcomes, considering student interactions in online discussion forums. We conducted a qualitative research with the online discussion forums of 5 different courses at bachelor and master levels, which involves 41 students’ teams. The final sample was composed of 3681 messages posted by the students. The results reveal that some generic and specific managerial skills exert a positive influence on learning outcomes. Students mostly highlighted teamwork, decision-making, information processing, reaching agreements, and dealing with uncertainty as the most relevant contributions of the game towards their learning. These results have instructional and pedagogical implications for determining the best way to enhance students’ motivation and learning outcomes when using digital technology methods, which involves recommendations that affect their design and monitoring.     

Impact of intangibility on perceived risk associated with online games

Online computer gaming is growing at a rapid pace. However, this phenomenon is stigmatised by many negative connotations. This study investigated the influence of intangibility on perceived risks (social, time, financial, physical, performance and psychology) among online gamers. The self-completed market survey questionnaire employs the Intangibility and Perceived Risk scales. Data were gathered in Taipei city, the biggest metropolitan areain Taiwan. Both public (1018) and online (400) voluntary interviews were conducted. The collected data were analysed with a structural equation model. There is a significant positive relationship between intangibility and alldimensions of perceived risk. The findings have managerial implications and future research is suggested.     

Peer interaction: the experience of distance students at university level

This study investigates how university students on distance learning courses experience interaction with their peers. Students on two distance learning courses at The Open University (UK) were interviewed, to investigate learners' experience of interaction on these courses. The analysis, using a grounded‐theory approach, reveals disparities between course designers' and students' reasons for integrating computer‐mediated interaction within the courses. It draws attention to the significant affective challenges that online students face when working together, and demonstrates a need to pay attention to their problems with self‐presentation. The study also reinforces the socio‐cultural view that it is important to consider the various contexts in which online learning takes place. Distance students have opportunities to structure their own learning contexts, and this allows them to source face‐to‐face learning interaction and other forms of support as they engage with individuals in their everyday environment.     

Effects of social-interactive engagement on the dropout ratio in online learning: insights from MOOC

In online learning, the high dropout ratio is a serious problem and reflects a poor level of motivation in e-learning programmes. Social-interactive engagement may greatly affect users’ attitudes and choices in many fields; among these, online learning is inevitably impacted by factors such as social connections. To study the impact of social-interactive engagement on the dropout ratio and learning progress, iMOOC was employed as the study object using data from 619 courses and 2,071,147 learners, as well as 19,451,428 learning records. As engagement is a process of collecting experiences, the learner’s experience plays a significant role in reducing the dropout ratio. Social-interactive engagement helps to reduce the dropout ratio; thus, learners should be encouraged to engage in online activities, such as discussions, note sharing, commenting and Q&A, to alleviate the feelings of being disconnected and isolated. Through an empirical study, we also find that the longer a learner's registered time is, the lower the dropout ratio. From the perspective of the courses themselves, the dropout ratios of shorter or more difficult courses are lower than those of longer or less-difficult courses. This paper provides theoretical and practical recommendations for reducing the dropout ratio in online learning and improving learning efficiency.     

A Safe-Control Paradigm for Human–Robot Interaction

This paper introduces a new approach to control a robot manipulator in a way that is safe for humans in the robot's workspace. Conceptually the robot is viewed as a tool with limited autonomy. The limited perception capabilities of automatic systems prohibits the construction of failsafe robots of the capability of people Instead, the goal of our control paradigm is to make the interaction with a robot manipulator safe by making the robot's actions predictable and understandable to the human operator. At the same time the forces the robot applies with any part of its body to its environment have to be controllable and limited. Experimental results are presented of a human-friendly robot controller that is under development for a Barrett Whole Arm Manipulator robot.     

Recognizing body poses using multilinear analysis and semi-supervised learning

This paper presents an approach to full-body human pose recognition using features extracted from stereo silhouettes via multilinear analysis in a semi-supervised learning framework. Inputs to the proposed approach are pairs of silhouette images obtained from wide baseline binocular cameras. Through multilinear analysis, low dimensional view-invariant pose coefficient vectors can be extracted from these stereo silhouette pairs. Taking these pose coefficient vectors as features, a recently proposed state-of-the-art semi-supervised learning method, Universum, is adopted for pose recognition. Experiment results obtained using real image data showed the efficacy of the proposed approach.     

Motivating Human–Agent Interaction: Transferring Insights from Behavioral Marketing to Interface Design

The understanding of consumer interaction with online EC Websites is one of the big current challenges for online marketers. The present paper investigates what drives and impedes Net customers to interact with a marketer's websites and, more specifically, with decision support interface systems (DSISs) employed in these sites to assist users when shopping or searching for high-involvement consumer goods. DSIS are considered as a predecessor or partially already integrator of more elaborated agent systems that may be used in future EC environments. It is shown that the design of today's DSISs is sub-optimal for both marketers and consumers, because it fails to motivate user interaction. One reason for this might be that little effort has been made to transfer the insights from consumer behavior to the design of EC user interfaces. The current paper aims to address this gap by proposing a number of new DSIS design principles, all of which are based on insights from traditional marketing search- and perceived risk theory. The main contribution of this paper is that it proposes a design approach for EC Websites that intuitively makes the user model available to the user [Shneiderman and Maes, 36].     

How to Better Use Expert Advice

This work is concerned with online learning from expert advice. Extensive work on this problem generated numerous expert advice algorithms whose total loss is provably bounded above in terms of the loss incurred by the best expert in hindsight. Such algorithms were devised for various problem variants corresponding to various loss functions. For some loss functions, such as the square, Hellinger and entropy losses, optimal algorithms are known. However, for two of the most widely used loss functions, namely the 0/1 and absolute loss, there are still gaps between the known lower and upper bounds.In this paper we present two new expert advice algorithms and prove for them the best known 0/1 and absolute loss bounds. Given an expert advice algorithm ALG, the goal is to form an upper bound on the regret L _ALG – L* of ALG, where L _ALG is the loss of ALG and L* is the loss of the best expert in hindsight. Typically, regret bounds of a canonical form C · are sought where N is the number of experts and C is a constant. So far, the best known constant for the absolute loss function is C = 2.83, which is achieved by the recent IAWM algorithm of Auer et al. (2002). For the 0/1 loss function no bounds of this canonical form are known and the best known regret bound is , where C ₁ = e – 2 and C ₂ = 2 . This bound is achieved by a P-norm algorithm of Gentile and Littlestone (1999). Our first algorithm is a randomized extension of the guess and double algorithm of Cesa-Bianchi et al. (1997). While the guess and double algorithm achieves a canonical regret bound with C = 3.32, the expected regret of our randomized algorithm is canonically bounded with C = 2.49 for the absolute loss function. The algorithm utilizes one random choice at the start of the game. Like the deterministic guess and double algorithm, a deficiency of our algorithm is that it occasionally restarts itself and therefore forgets what it learned. Our second algorithm does not forget and enjoys the best known asymptotic performance guarantees for both the absolute and 0/1 loss functions. Specifically, in the case of the absolute loss, our algorithm is canonically bounded with C approaching and in the case of the 0/1 loss, with C approaching 3/ . In the 0/1 loss case the algorithm is randomized and the bound is on the expected regret.