Fostering self‐regulated learning in a blended environment using group awareness and peer assistance as external scaffolds

Most systems for training self‐regulated learning (SRL) behaviour focus on the provision of a learner‐centred environment. Such systems repeat the training process and place learners alone to experience that process iteratively. According to the relevant literature, external scaffolds are more promising for effective SRL training. In this work, group awareness and peer assistance are used as external scaffolds in the process of training SRL behaviour, enhancing opportunities for self‐reflection and stimulating and encouraging learners. This work further develops a system, called self‐regulated learning with group awareness and peer assistance (SRL‐GAPA). Experimental results reveal that SRL‐GAPA provides significantly more participation in online training tasks and learning activities, better SRL behaviour and better learning achievement than the traditional SRL system (i.e,, a learner‐centred environment). The SRL‐GAPA benefited poorly self‐regulated learners more than highly self‐regulated students. Some implications of this finding are discussed.     

The effects of directive self‐explanation prompts to support active processing of multiple representations in a simulation‐based learning environment

Processing of multiple representations in multimedia learning environments is considered to help learners obtain a more complete overview of the domain and gain deeper knowledge. This is based on the idea that relating and translating different representations leads to reflection beyond the boundaries and details of the separate representations. To achieve this, the design of a learning environment should support learners in adequately processing multiple representations. In this study, we compared a scientific inquiry learning environment providing instructional support with directive self‐explanation prompts to relate and translate between representations with a scientific inquiry learning environment providing instructional support with general self‐explanation prompts. Learners who received the directive prompts outperformed the learners who received general prompts on test items assessing domain knowledge. These positive results did not stretch to transfer items and items measuring learners' capabilities to relate and translate representations in general. The results suggest that learner support should promote the active relation of representations and translation between them to foster domain knowledge, and that other forms of support (e.g. extended training) might be necessary to make learners more expert processors of multiple representations.     

Learning with an active e‐course in the Knowledge Grid environment

An active e‐course is an open, self‐representable and self‐organizable media mechanism. Its kernel idea is to organize learning materials in a concept space rather than in a page space. The tailored content and flexible structure of the e‐courses can be dynamically formed to cater for different learners with different backgrounds, capabilities and expectations, at different times and venues. The active e‐course can also assess learners' learning performances and give appropriate suggestions to guide them in further learning. An authoring tool for constructing course ontology and a system prototype have been developed to support an active e‐course, enabling a learner‐centred, highly interactive and adaptive learning approach. The results of an empirical study show that the system can help enhance the effectiveness and efficiency of learning. Copyright © 2005 John Wiley & Sons, Ltd.     

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.     

Tracking,recognition, and distance detection of hand gestures for a 3‐D interactive display

Abstract— This study proposes an interactive system for displays, the technologies of which consists of three main parts: hand‐gesture tracking, recognition, and depth measurement. The proposed interactive system can be applied to a general 3‐D display. In this interactive system, for hand‐gesture tracking, Haar‐like features are employed to detect a specific hand gesture to start tracking, while the mean‐shift algorithm and Kalman filter are adopted for fast tracking. First, for recognizing hand gestures, a principal component analysis (PCA) algorithm is used to localize colored areas of skin, and then hand gestures are identified by comparison with a prepared database. Second, a simple optical system is set up with an infrared laser source and a grid mask in order to project a proposed horizontal stripe pattern. Third, the projected patterns are deciphered to extract the depth information using the Hough‐transform algorithm. The system containing hand‐gesture localization, recognition, and associated depth detection (the distance between the display and the hand), was included in a prototype of an interactive display. Demonstration of rotation recognition of a finger‐pointing hand gesture was successful by using the algorithm of radar‐like scanning.     

Dance performance evaluation using hidden Markov models

We present in this paper a hidden Markov model‐based system for real‐time gesture recognition and performance evaluation. The system decodes performed gestures and outputs at the end of a recognized gesture, a likelihood value that is transformed into a score. This score is used to evaluate a performance comparing to a reference one. For the learning procedure, a set of relational features has been extracted from high‐precision motion capture system and used to train hidden Markov models. At runtime, a low‐cost sensor (Microsoft Kinect) is used to capture a learner's movements. An intermediate step of model adaptation was hence requested to allow recognizing gestures captured by this low‐cost sensor. We present one application of this gesture evaluation system in the context of traditional dance basics learning. The estimation of the log‐likelihood allows giving a feedback to the learner as a score related to his performance. Copyright © 2016 John Wiley & Sons, Ltd.     

Self‐efficacy,learning method appropriation and software skills acquisition in learner‐controlled CSSTS environments

A computer‐simulated software training system (CSSTS) delivers a specific form of computer‐based training in which participants are allowed to select various training features within a simulated software environment. Given the growing use of these systems as end‐user training (EUT) aids, there is a need for greater understanding of how participants use these systems, as well as whether participant‐controlled learning environments are truly effective. The present research examines how a particular learner characteristic, software self‐efficacy, drives appropriation in a high learner control, CSSTS environment. Contrary to notions in the literature, results from spreadsheet and database software training courses reveal that pre‐training specific software self‐efficacy constitutes a significant, negative predictor of faithful appropriations of the CSSTS. This research also establishes a positive relationship between faithful appropriation and increases in software self‐efficacy (SSE). In essence, faithful appropriations lead to greater increases in SSE, which influences software skills performance. In addition, the research validates prior EUT research by extending prior findings to a database training environment. A psychometrically sound measure is put forth to capture database self‐efficacy.     

Data‐Driven Crowd Motion Control With Multi‐Touch Gestures

Controlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run‐time gesture, our system extracts the K nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run‐time control. Our system is accurate and efficient, making it suitable for real‐time applications such as real‐time strategy games and interactive animation controls.     

Designing on‐demand education for simultaneous development of domain‐specific and self‐directed learning skills

On‐demand education enables individual learners to choose their learning pathways according to their own learning needs. They must use self‐directed learning (SDL) skills involving self‐assessment and task selection to determine appropriate pathways for learning. Learners who lack these skills must develop them because SDL skills are prerequisite to developing domain‐specific skills. This article describes the design of an on‐demand learning environment developed to enable novices to simultaneously develop their SDL and domain‐specific skills. Learners received advice on their self‐assessments and their selections of subsequent learning tasks. In the domain of system dynamics – a way to model a dynamic system and draw graphs depicting the system's behaviour over time – advice on self‐assessment is provided in a scoring rubric containing relevant performance standards. Advice on task selection indicates all relevant task aspects to be taken into account, including recommendations for suitable learning tasks which meet the individual learner's needs. This article discusses the design of the environment and the learners' perceptions of its usefulness. Most of the times, the learners found the advice appropriate and they followed it in 78% of their task selections.     

Effectiveness of feedback for enhancing English pronunciation in an ASR‐based CALL system

This paper presents a study on implementing the ASR‐based CALL (computer‐assisted language learning based upon automatic speech recognition) system embedded with both formative and summative feedback approaches and using implicit and explicit strategies to enhance adult and young learners' English pronunciation. Two groups of learners including 18 adults and 16 seventh graders participated in the study. The results indicate that the formative feedback had a positive impact on improving the learners' speaking articulation, and the summative feedback aided the learners' self‐reflection and helped them to track their speaking progress. Furthermore, the implicit information such as model pronunciation with full sentences and audio recast benefitted the adult learners, whereas the young learners preferred the explicit learning information such as textual information of individual words for self‐correction. In addition, the results of this study also confirm that learners have different perceptions of the media modalities designed with implicit and explicit strategies in the feedback. Feedback with audio modality is more suitable for adults, whereas juxtaposed textual and audio modalities are better for young learners.     

Gestural cue analysis in automated semantic miscommunication annotation

The automated annotation of conversational video by semantic miscommunication labels is a challenging topic. Although miscommunications are often obvious to the speakers as well as the observers, it is difficult for machines to detect them from the low-level features. We investigate the utility of gestural cues in this paper among various non-verbal features. Compared with gesture recognition tasks in human-computer interaction, this process is difficult due to the lack of understanding on which cues contribute to miscommunications and the implicitness of gestures. Nine simple gestural features are taken from gesture data, and both simple and complex classifiers are constructed using machine learning. The experimental results suggest that there is no single gestural feature that can predict or explain the occurrence of semantic miscommunication in our setting.     

Three‐dimensional interaction and autostereoscopic display system using gesture recognition

We propose a 3D interaction and autostereoscopic display system that use gesture recognition, which can manipulate virtual objects in the scene directly by hand gestures and can display objects in 3D stereoscopy. The system consists of a gesture recognition and manipulation part as well as an autostereoscopic display as an interactive display part. To manipulate the 3D virtual scene, a gesture recognition algorithm is proposed, which use spatial‐temporal sequences of feature vectors to match predefined gestures. To get smooth 3D visualization, we utilize the programmable graphics pipeline in graphic processing unit to accelerate data processing. We develop a prototype system for 3D virtual exhibition. The prototype system reaches frame rates of 60 fps and operates efficiently with a mean recognition accuracy of 90%.     

Understanding mobile learning from the perspective of self‐regulated learning

Cognizant of the research gap in the theorization of mobile learning, this paper conceptually explores how the theories and methodology of self‐regulated learning (SRL), an active area in contemporary educational psychology, are inherently suited to address the issues originating from the defining characteristics of mobile learning: enabling student‐centred, personal, and ubiquitous learning. These characteristics provide some of the conditions for learners to learn anywhere and anytime, and thus, entail learners to be motivated and to be able to self‐regulate their own learning. We propose an analytic SRL model of mobile learning as a conceptual framework for understanding mobile learning, in which the notion of self‐regulation as agency is at the core. The rationale behind this model is built on our recognition of the challenges in the current conceptualization of the mechanisms and processes of mobile learning, and the inherent relationship between mobile learning and SRL. We draw on work in a 3‐year research project in developing and implementing a mobile learning environment in elementary science classes in Singapore to illustrate the application of SRL theories and methodology to understand and analyse mobile learning.     

An activity‐based analysis of hands‐on practice methods

Abstract The success of exploration‐based training is likely to be strongly influenced by what activities the learner undertakes during training. This paper presents a study of the activities undertaken during training by 51 experienced computer users learning to use an application package through exercises, exploration or a combined approach to training. Results suggest that exploration learners practice procedures selectively, fail to consolidate skills through repetition, and do not devise activities which extend their knowledge beyond the scope of the training materials. It is argued that these characteristics may lead to subsequent difficulties in performance.     

Design and development of an innovative individualized adaptive and intelligent e-learning system for teaching–learning of probability unit: Details of UZWEBMAT

In this study, an innovative adaptive and intelligent web based e-learning system, UZWEBMAT (Turkish abbreviation of Adaptive and INtelligent WEB based MAThematics teaching–learning system) was designed, developed and implemented. This e-learning system was intended for learning and teaching secondary school level permutation-combination-binomial expansion and probability subjects. Content which was prepared according to Turkish curriculum for secondary school mathematics course was transformed into learning objects in three different ways in accordance with VAK (Visual–Auditory–Kinesthetic) learning styles. Primary/secondary/tertiary learning styles of learners registering the system are determined and each learner receives the content appropriate for his/her dominant learning style. Also, they can be directed to contents of other styles according to their performances thanks to an expert system. Learning objects constituting the content were prepared according to constructivist approach. An active role for the learner was the purpose. Tips and intelligent solution supports within the learning objects were presented with expert system support to the learners. With this structure, UZWEBMAT bears the characteristics of intelligent tutoring system as well as an adaptive e-learning environment. All the movements of learners studying with UZWEBMAT are recorded and the necessary information is reported to both learners and teachers in a visualized way.     

Easy gesture recognition for Kinect

Recent progress in entertainment and gaming systems has brought more natural and intuitive human–computer interfaces to our lives. Innovative technologies, such as Xbox Kinect, enable the recognition of body gestures, which are a direct and expressive way of human communication. Although current development toolkits provide support to identify the position of several joints of the human body and to process the movements of the body parts, they actually lack a flexible and robust mechanism to perform high-level gesture recognition. In consequence, developers are still left with the time-consuming and tedious task of recognizing gestures by explicitly defining a set of conditions on the joint positions and movements of the body parts. This paper presents EasyGR (Easy Gesture Recognition), a tool based on machine learning algorithms that help to reduce the effort involved in gesture recognition. We evaluated EasyGR in the development of 7 gestures, involving 10 developers. We compared time consumed, code size, and the achieved quality of the developed gesture recognizers, with and without the support of EasyGR. The results have shown that our approach is practical and reduces the effort involved in implementing gesture recognizers with Kinect.     

基于视觉的动态手势识别研究综述

手势自古以来在人类交流方面扮演着非常重要的角色,而基于视觉的动态手势识别技术是利用计算机视觉、物联网感知等新兴技术和3D视觉传感器等新型设备让机器能够理解人类的手势,从而让人类能和机器更好地交流,因此对于人机交互等领域的研究很有意义。介绍了动态手势识别中所用到的传感器技术,并比较了相关传感器的技术参数。通过追踪近年来国内外关于视觉的动态手势识别技术,陈述了动态手势识别的处理流程：手势检测与分割、手势追踪、手势分类。通过对比各流程所涉及的方法,可以发现深度学习具有较强的容错性、高度并行性、抗干扰性等一系列优点,在手势识别领域取得了远高于传统学习算法的成就。最后分析了动态手势识别目前遇到的挑战和未来可能的发展方向。     

e‐Learning recommender system for a group of learners based on the unified learner profile approach

In the age of information explosion, e‐learning recommender systems (eL_RSs) have emerged as effective information filtering techniques that attempt to provide the most appropriate learning resources for learners while using e‐learning systems. These learners are differentiated on the basis of their learning styles, goals, knowledge levels and others. Several attempts have been made in the past to design eL_RSs to recommend resources to individuals; however, an investigation of recommendations to a group of learners in e‐learning is still in its infancy. In this paper, we focus on the problem of recommending resources to a group of learners rather than to an individual. The major challenge in group recommendation is how to merge the individual preferences of different learners that form a group and extract a pseudo unified learner profile (ULP) that closely reflects the preferences of all learners. Firstly, we propose a profile merging scheme for the ULP by utilizing learning styles, knowledge levels and ratings of learners in a group. Thereafter, a collaborative approach is proposed based on the ULP for effective group recommendations. Experimental results are presented to demonstrate the effectiveness of the proposed group recommendation strategy for e‐learning.     

Visual learning by imitation with motor representations.

We propose a general architecture for action (mimicking) and program (gesture) level visual imitation. Action-level imitation involves two modules. The viewpoint Transformation (VPT) performs a "rotation" to align the demonstrator's body to that of the learner. The Visuo-Motor Map (VMM) maps this visual information to motor data. For program-level (gesture) imitation, there is an additional module that allows the system to recognize and generate its own interpretation of observed gestures to produce similar gestures/goals at a later stage. Besides the holistic approach to the problem, our approach differs from traditional work in i) the use of motor information for gesture recognition; ii) usage of context (e.g., object affordances) to focus the attention of the recognition system and reduce ambiguities, and iii) use iconic image representations for the hand, as opposed to fitting kinematic models to the video sequence. This approach is motivated by the finding of visuomotor neurons in the F5 area of the macaque brain that suggest that gesture recognition/imitation is performed in motor terms (mirror) and rely on the use of object affordances (canonical) to handle ambiguous actions. Our results show that this approach can outperform more conventional (e.g., pure visual) methods.     

基于Leap Motion的动态手势识别方法

伴随虚拟现实（Virtual Reality,VR）技术的发展,以及人们对人机交互性能和体验感的要求提高,手势识别作为影响虚拟现实中交互操作的重要技术之一,其精确度急需提升^[1].针对当前手势识别方法在一些动作类似的手势识别中表现欠佳的问题,提出了一种多特征动态手势识别方法.该方法首先使用体感控制器Leap Motion追踪动态手势获取数据,然后在特征提取过程中增加对位移向量角度和拐点判定计数的提取,接着进行动态手势隐马尔科夫模型（Hidden Markov Model,HMM）的训练,最后根据待测手势与模型的匹配率进行识别.从实验结果中得出,该多特征识别方法能够提升相似手势的识别率.