Simulation-based learning: The learning–forgetting–relearning process and impact of learning history

The results of empirical experiments evaluating the effectiveness and efficiency of the learning–forgetting–relearning process in a dynamic project management simulation environment are reported. Sixty-six graduate engineering students performed repetitive simulation-runs with a break period of several weeks between the runs. The students used a teaching tool called the project management trainer (PMT) that simulates a generic dynamic, stochastic project management environment. In this research, we focused on the effect of history recording mechanism on the learning forgetting process. Manual or automatic history recording mechanisms were used by the experimental group, while the control group did not use any history recording mechanism. The findings indicate that for the initial learning phase, the manual mechanism is better than the automatic mechanism. However, for the relearning phase, the break period length influenced the performance after the break. When the break period is short, the manual history keeping mechanism is better, but for a long period break, there is no significant difference. A comparison between the experimental group and the control group revealed that using any history recording mechanism reduced forgetting. Based on the findings, some practical implications of using simulators to improve the learning–forgetting process are discussed.     

Heterogeneousmulti-player imitation learning

This paper studies imitation learning in nonlinear multi-player game systems with heterogeneous control input dynamics. We propose a model-free data-driven inverse reinforcement learning (RL) algorithm for a leaner to find the cost functions of a N-player Nash expert system given the expert’s states and control inputs. This allows us to address the imitation learning problem without prior knowledge of the expert’s system dynamics. To achieve this, we provide a basic model-based algorithm that is built upon RL and inverse optimal control. This serves as the foundation for our final model-free inverse RL algorithm which is implemented via neural network-based value function approximators. Theoretical analysis and simulation examples verify the methods.     

Top-down learning strategies: can they facilitate stylus keyboard learning?

Learning a new stylus keyboard layout is time-consuming yet potentially rewarding, as optimized virtual keyboards can substantially increase performance for expert users. This paper explores whether the learning curve can be accelerated using top-down learning strategies. In an experiment, one group of participants learned a stylus keyboard layout with top-down methods, such as visuo-spatial grouping of letters and mnemonic techniques, to build familiarity with a stylus keyboard. The other (control) group learned the keyboard by typing sentences. The top-down learning group liked the stylus keyboard better and perceived it to be more effective than the control group. They also had better memory recall performance. Typing performance after the top-down learning process was faster than the initial performance of the control group, but not different from the performance of the control group after they had spent an equivalent amount of time typing. Therefore, top-down learning strategies improved the explicit recall as expected, but the improved memory of the keyboard did not result in quicker typing speeds. These results suggest that quicker acquisition of declarative knowledge does not improve the acquisition speed of procedural knowledge, even during the initial cognitive stage of the virtual keyboard learning. They also suggest that top-down learning strategies can motivate users to learn a new keyboard more than repetitive rehearsal, without any loss in typing performance.     

Student teachers’ satisfaction for blended learning via Edmodo learning management system

In a digitally driven world, behaviours of future teachers for blended learning (both face-to-face and on-line classes) need to be examined. This study serves three purposes. The first is to examine student teachers’ preferences for Community-of-Inquiry model-driven blended learning via Edmodo. Second, predicting student satisfaction on b-learning from a combination of four variables (gender, having internet access, using the internet for information access, and previous experience in on-line learning) was questioned. And third, b-learning orientations of participants were investigated. One of the mixed methods, the concurrent triangulation design was employed in which both qualitative and quantitative methods were applied. The study group included 135 freshmen and junior students (29 males and 106 females) from a western Turkish educational faculty. The findings for the first question indicated that 70.4% of student teachers prefer b-learning. For the second, 15% of the variance in satisfaction on b-learning was explained by the proposed model with a medium effect. And for the third, the qualitative findings were discussed under Perceived Usefulness (PU) and Perceived Uselessness and Unease-of-Use (PU-UU) themes. Although less than a quarter of participants found b-learning useless, most held positive notions for b-learning practices via Edmodo.     

Applying online externally-facilitated regulated learning and computational thinking to improve students’ learning

This study aims to explore, via quasi-experiments, the effects of online externally-facilitated regulated learning (ERL) and computational thinking (CT) on improving students’ computing skills in a blended learning environment. Four classes in a one-semester course entitled ‘Applied Information Technology: Data Processing’ were the samples for this research. The first class (C1, ERL&CT group) simultaneously received the interventions regarding online ERL and CT, the second class (C2, CT group) received the intervention regarding online CT, and the third class (C3, ERL group) received the intervention regarding online CT, while the last group (C4, control group) received a traditional teaching method, although teaching was also conducted in a blended computing class. Students in ERL&CT group and CT group came from the Department of Finance, while the ERL group and control group came from the Department of Law at a comprehensive university. According to the posttest analysis, the results indicate that students who received the intervention of online ERL had statistically better development of computing skills for using Excel by semester-end than those without. In addition, this study also reveals that the application of online CT alone could be helpful in students’ development of computing skills. Furthermore, the results indicate that students’ computing skills could be improved under the condition of simultaneously applying ERL and CT. Based on the findings of this study, the authors present implications for online teachers and educators, particularly for those teaching computing courses.     

Re-transfer learning and multi-modal learning assisted early diagnosis of Alzheimer’s disease

Nowadays more and more elderly people are suffering from Alzheimer’s disease (AD). Finely recognizing mild cognitive impairment (MCI) in early stage of the symptom is vital for AD therapy. However, brain image samples are relatively scarce, meanwhile have multiple modalities, which makes finely classifying brain images by computers extremely difficult. This paper proposes a fine-grained brain image classification approach for diagnosing Alzheimer’s disease, with re-transfer learning and multi-modal learning. First of all, an end-to-end deep neural network classifier CNN4AD is designed to finely classify diffusion tensor image (DTI) into four categories. And according to the characteristics of multi-modal brain image dataset, the re-transfer learning method is proposed based on transfer learning and multi-modal learning theories. Experimental results show that the proposed approach obtain higher accuracy with less labeled training samples. This could help doctors diagnose Alzheimer’s disease more timely and accurately.

     

A survey on ensemble learning

Despite significant successes achieved in knowledge discovery,traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data,such as imbalanced,high-dimensional,noisy data,etc.The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data.In this context,it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining model.Ensemble learning,as one research hot spot,aims to integrate data fusion,data modeling,and data mining into a unified framework.Specifically,ensemble learning firstly extracts a set of features with a variety of transformations.Based on these learned features,multiple learning algorithms are utilized to produce weak predictive results.Finally,ensemble learning fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive way.In this paper,we review the research progress of the mainstream approaches of ensemble learning and classify them based on different characteristics.In addition,we present challenges and possible research directions for each mainstream approach of ensemble learning,and we also give an extra introduction for the combination of ensemble learning with other machine learning hot spots such as deep learning,reinforcement learning,etc.     

Has Web 2.0 revitalized informal learning? The relationship between Web 2.0 and informal learning

Learning is becoming increasingly self‐directed and often occurs away from schools and other formal educational settings. The development of a myriad of new technologies for learning has enabled people to learn anywhere and anytime. Web 2.0 technology allows researchers to shed a new light on the importance and prevalence of informal learning. However, there are few empirical studies that support the claim that this technology facilitates informal learning. The present study investigates the relationship between Web 2.0 levels and the evaluation of informal learning websites. For this purpose, 287 informal learning websites were selected and their Web 2.0 levels were rated based upon eight criteria proposed in the Web 2.0 exploratory literature. In addition, previously examined informal learning evaluation results were employed. The results showed that current informal learning websites have moderately adopted the most heavily promoted features of Web 2.0. Correlation analyses showed a positive relationship between Web 2.0 features and informal learning website ratings. The implications for the relationship and internal correlations of variables were summarized and discussed.     

Implementing digital game-based learning in schools: augmented learning environment of ‘Europe 2045’

It is widely agreed that the traditional process of schooling can benefit from the usage of computers as supportive tools. Of various approaches using computers in education over the last decade, e-learning and edutainment have become the most prominent. Recently, a number of authors have criticised these approaches arguing that they conserve traditional ‘drill and practice’ behaviouristic methods of teaching instead of enhancing and augmenting them. It has been proposed that a ‘paradigm shift’ is needed and that this shift may come through utilizing all the advantages of full-fledged video games, so-called digital game-based learning (DGBL). However, several case studies reported serious problems with the DGBL. Among the most notable issues are the lack of acceptance of games as an educational tool, problems with integration of games into formal schooling environments, and the so-called transfer problem, which is the problem of the inherent tension between game play and learning objectives, the tension that mitigates the ability of students to transfer knowledge gained in the video game to the real-world context. Here, we present a framework for an augmented learning environment (ALE), which verbalises one way of how these problems can be challenged. The ALE framework has been constructed based on our experience with the educational game, Europe 2045, which we developed and which has been implemented in a number of secondary schools in the Czech Republic during 2008. The key feature of this game is that it combines principles of on-line multi-player computer games with social, role-playing games. The evaluation which we present in this paper indicates the successful integration of the game and its acceptance by teachers and students. The ALE framework isolates key principles of the game contributing to this success, abstracts them into theoretical entities we call action-based spaces and causal and grounding links, and condenses them in a coherent methodological structure, which paves the way for further exploitation of the DGBL by educational game researchers and designers.     

Pointwise manifold regularization for semi-supervised learning

Manifold regularization(MR)provides a powerful framework for semi-supervised classification using both the labeled and unlabeled data.It constrains that similar instances over the manifold graph should share similar classification out-puts according to the manifold assumption.It is easily noted that MR is built on the pairwise smoothness over the manifold graph,i.e.,the smoothness constraint is implemented over all instance pairs and actually considers each instance pair as a single operand.However,the smoothness can be pointwise in nature,that is,the smoothness shall inherently occur“everywhere"to relate the behavior of each point or instance to that of its close neighbors.Thus in this paper,we attempt to de-velop a pointwise MR(PW_MR for short)for semi-supervised learning through constraining on individual local instances.In this way,the pointwise nature of smoothness is preserved,and moreover,by considering individual instances rather than instance pairs,the importance or contribution of individual instances can be introduced.Such importance can be described by the confidence for correct prediction,or the local density,for example.PW.MR provides a different way for implementing manifold smoothness Finally,empirical results show the competitiveness of PW_MR compared to pairwise MR.     

走近神州数码e—learning系统

影响企业发展的因素有很多,包括外部的市场环境和内部的管理体制、企业文化、领导能力等等,但最根本的还是企业员工的素质。企业之间的竞争,最终还是要落实到企业人员之间的竞争上。要想在当今迅速变化的技术环境中取得成功和保持竞争力,员工不断扩展其知识和专业技能至关重要,     

Object–object interaction affordance learning

This paper presents a novel object–object affordance learning approach that enables intelligent robots to learn the interactive functionalities of objects from human demonstrations in everyday environments. Instead of considering a single object, we model the interactive motions between paired objects in a human–object–object way. The innate interaction-affordance knowledge of the paired objects are learned from a labeled training dataset that contains a set of relative motions of the paired objects, human actions, and object labels. The learned knowledge is represented with a Bayesian Network, and the network can be used to improve the recognition reliability of both objects and human actions and to generate proper manipulation motion for a robot if a pair of objects is recognized. This paper also presents an image-based visual servoing approach that uses the learned motion features of the affordance in interaction as the control goals to control a robot to perform manipulation tasks.     

Label distribution for multimodal machine learning

Multimodal machine learning(MML)aims to understand the world from multiple related modalities.It has attracted much attention as multimodal data has become increasingly available in real-world application.It is shown that MML can perform better than single-modal machine learning,since multi-modalities containing more information which could complement each other.However,it is a key challenge to fuse the multi-modalities in MML.Different from previous work,we further consider the side-information,which reflects the situation and influences the fusion of multi-modalities.We recover multimodal label distribution(MLD)by leveraging the side-information,representing the degree to which each modality contributes to describing the instance.Accordingly,a novel framework named multimodal label distribution learning(MLDL)is proposed to recover the MLD,and fuse the multimodalities with its guidance to learn an in-depth understanding of the jointly feature representation.Moreover,two versions of MLDL are proposed to deal with the sequential data.Experiments on multimodal sentiment analysis and disease prediction show that the proposed approaches perform favorably against state-of-the-art methods.     

Could a mobile‐assisted learning system support flipped classrooms for classical Chinese learning?

In this study, the researcher aimed to develop a mobile‐assisted learning system and to investigate whether it could promote teenage learners' classical Chinese learning through the flipped classroom approach. The researcher first proposed the structure of the Cross‐device Mobile‐Assisted Classical Chinese (CMACC) system according to the pilot survey and reviewed literature, and then adopted a quasi‐experimental design to understand whether the developed system could promote and support flipped classroom learning for classical Chinese. A total of 56 eleventh graders from two classes participated in the experiment. The learners in the experimental group learned classical Chinese with the flipped classroom learning strategy with the assistance of the CMACC system, while the control group adopted the flipped classroom learning strategy without using the CMACC system. The results reveal that all of the students improved their Chinese performance, but it was noticeable that the learners who used the CMACC system showed better motivation in terms of self‐directed preview learning, while those who only learned with the traditional textbooks tended to be more passive. In sum, the mobile‐assisted learning system added value in providing learners with opportunities to achieve anytime and anywhere flipped classroom learning. The integration of ubiquitous mobile learning technology and the flipped classroom strategy can be viewed as a critical factor leading to students achieving self‐regulated learning. It is also suggested that instructors should carefully take the targeted learners' cultural background and the availability of supporting learning devices into consideration so as to prevent the flipped classroom from exacerbating the digital divide. Other suggestions for educators and instructional designers are also proposed.     

Iterative learning control approach for ramp metering

An iterative learning control scheme is developed to the traffic density control in a macroscopic level freeway environment. With rigorous analysis, the proposed intelligent control scheme guarantees the asymptotic convergence of the traffic density to the desired one. The control scheme is applied to a freeway model, and simulation results confirm the efficacy of the proposed approach.     

A new learning algorithm with reference-following variables

A learning algorithm is presented for the learning of neural networks, in which the learning trajectory is convergence without any over-learning by changing of topological construction of the algorithm near any local minimum points of learning error. Became the topological construction is not convergent for some functions by usual BP method near some local minimum points, there is an over-learning phenomenon. To avoid the over-learning phenomenon, reference-foUowing variables are used to change the topological construction of this algorithm. The theoretical analysis and the simulation results indicate that the proposed method is simple and useful.     

Predicting students’ learning style using learning analytics: a case study of business management students from India

ABSTRACT

Business Management Education in India has shown an upward growth trend in the last couple of decades. Due to the diverse nature of the course, students from diverse academic backgrounds are being admitted to the course. Therefore, differences in students’ abilities and their learning styles have a significant effect on their learning outcomes. Meanwhile, with the development of learning technologies, learners can be provided a more effective learning environment to optimise their learning. The purpose of this study was to develop a model to automatically detect the students’ learning styles from their personal, academic and social media data and make recommendations for students, teachers, educators and administrators for overall improvement of learning outcomes. Data analysis in this research was represented using data collected from post-graduate business management students in India. A 10-fold cross-validation was used to create and test the models. The data were analysed by R and R-Studio. Classification accuracy, Precision, Recall, Kappa, ROC curve and F measure were observed. The results showed that the accuracy of classification by the C4.5 technique had the highest value at 95.7%, and it could be applied to develop Felder–Silverman’s learning style while taking into consideration students’ academic, personal information and social media preferences.     

The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review

Functional Magnetic Resonance Imaging (fMRI) is presently one of the most popular techniques for analysing the dynamic states in brain images using various kinds of algorithms. From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature. It takes much time for each researcher to know about the various approaches and algorithms which are in use for fMRI data. This paper provides a review in a systematic manner for the present literature of fMRI data that makes use of the machine and deep learning algorithms. The major goals of this review paper are to (a) identify machine learning and deep learning research trends for the implementation of fMRI; (b) identify usage of Machine Learning Algorithms and deep learning in fMRI, and (c) help new researchers based on fMRI to put their new findings appropriately in existing domain of fMRI research. The results of this systematic review identified various fMRI studies and classified them based on fMRI types, mental diseases, use of machine learning and deep learning algorithms. The authors have provided the studies with the best performance of machine learning and deep learning algorithms used in fMRI. The authors believe that this systematic review will help incoming researchers on fMRI in their future works.     

Pre-service teachers’ skills and perceptions about the use of virtual learning environments to improve teaching and learning

This paper explores data retrieved from Educational Immersive Virtual Worlds to describe pre-service teachers’ skills and perceptions about the simulation tasks. This project had 10 participants who were immersed for 3 years in the Technology and Pedagogical Models in Immersive Worlds island, a multi-user virtual environment in Second Life and Open Simulator. In this project, we evaluated how three-dimensional virtual environments can facilitate the achievement of teaching and learning processes. Based on quantitative and qualitative methodologies, two data collection instruments were applied. Through observation grids and personal log books, professional performance of the 18 pedagogical challenges implemented was collected. The statistical analysis shows that the students improved their technology skills and educational aspects about good practices in classes, regardless of the type of platform used. The analysis through Constant Comparing Method reported a positive assessment of the use of virtual environments, especially about the use of teaching strategies. Main conclusions regarding the pedagogical context reflect the importance of peer assessment on teaching performance, as well as the complexity of role-plays as intellectual challenges to enhance pre-service teachers’ skills. The main difficulties identified during the development of the activities were technical in nature, reporting hardware and connectivity issues.     

Playing ‘catch up’ with blended learning: performance impacts of augmenting classroom instruction with online learning

This study examines the efficacy of blended learning – an approach that combines in-class and online methods in a way that leverages the strengths of both – using a field experiment spanning 16 weeks. An information-processing model of learning suggests that learners will weigh the cost of information accessibility against its value in ways that will impact their interactions with the available information sources, which will consequently affect learning outcomes. Results of our study suggest that such an assessment did indeed occur and that it impacted learning performance. Specifically, our results support the idea that providing high-value content in both settings – the classroom (rich, yet high cost) and online (efficient, yet low cost), enhances performance. The largest gains in performance were seen by those who used the blended learning system the most, with the lowest gains by those who did not use the system at all (i.e. the control group).