Evaluating students’ learning achievement based on the eigenvector method

In this paper, we present a new method for students’ learning achievement evaluation based on the eigenvector method. The proposed method considers the “accuracy rate”, the “time rate”, the “importance” and the “complexity” for evaluating students’ learning achievement. First, the proposed method transforms the attributes “accuracy rate” and “time rate” into the “effect of accuracy rate” and the “effect of time rate”, respectively. Then, it generates the relative importance degrees of the attributes “effect of accuracy rate”, “effect of time rate”, “importance” and “complexity” based on the eigenvector method. Then, it uses the correlation coefficients between the attribute vectors and the standard deviations of the elements in the attribute vectors to calculate the fitness degrees of the attributes, where the attribute vectors represent the relationships between the attributes and the questions. Then, it generates the weights of the attributes based on the relative importance degrees of the attributes and the fitness degrees of the attributes. Then, it generates the importance degrees of the questions according to the weights of the attributes and the relation matrix representing the relationships between the questions and the attributes. Finally, based on the importance degrees vector of the questions, the grade matrix, the accuracy rate matrix, it calculates the learning achievement index of each student having the same original total score for students’ learning achievement evaluation. The proposed method provides us with a useful way for students’ learning achievement evaluation based on the eigenvector method.     

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.     

Patterns of students' collaborations by variations in their learning orientations in blended course designs: How is it associated with academic achievement?

Background

While a number of learner factors have been identified to impact students' collaborative learning, there has been little systematic research into how patterns of students' collaborative learning may differ by their learning orientations.

Objectives

This study aimed to investigate: (1) variations in students' learning orientations by their conceptions, approaches, and perceptions; (2) the patterns of students' collaborations by variations in their learning orientations and (3) the contribution of patterns of collaborations to academic achievement.

Methods

A cohort of 174 Chinese undergraduates in a blended engineering course were surveyed for their conceptions of learning, approaches to learning and to using online learning technologies, and perceptions of e-learning, to identify variations in their learning orientations. Students' collaborations and mode of collaborations were collected through an open-ended social network analysis (SNA) questionnaire.

Results and Conclusions

A hierarchical cluster analysis identified an ‘understanding’ and ‘reproducing’ learning orientations. Based on students' learning orientations and their choices to collaborate, students were categorized into three mutually exclusive collaborative group, namely Understanding Collaborative group, Reproducing Collaborative group and Mixed Collaborative group. SNA centrality measures demonstrated that students in the Understanding Collaborative group had more collaborations and stayed in a better position in terms of capacity to gather information. Both students' approaches to learning and students' average collaborations significantly contributed to their academic achievement, explaining 3% and 4% of variance in their academic achievement respectively. The results suggest that fostering a desirable learning orientation may help improve students' collaborative learning.     

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.

     

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.     

A quantitative multimodal discourse analysis of teaching and learning in a web-conferencing environment – The efficacy of student-centred learning designs

This paper presents a quantitative approach to multimodal discourse analysis for analyzing online collaborative learning. The coding framework draws together the fields of systemic functional linguistics and Activity Theory to analyze interactions between collaborative-, content- and technology-related discourse. The approach is used to examine how the task subject matter, the activity design, and the choice of interface affected interaction and collaboration for a computing course conducted in a web-conferencing environment. The analysis revealed the critical impact of activity design on the amount and type of discourse that transpired. Student-centred designs resulted in over six times more student discourse as compared to teacher-centred designs and created a learning environment where students took greater ownership over the tasks and contributed more to the content-based discussion. The paper also incorporates a rationale for the approach to coding and a reflection on its efficacy for discourse analysis in technology-based learning environments.     

Algorithms of Dynkin diagrams in Lie group machine learning

This paper uses the geometric method to describe Lie group Machine Learning （LML） based on the theoretical framework of LML, which gives the geometric algorithms of Dynkin diagrams in LML. It includes the basic conceptions of Dynkin diagrams in LML, the classification theorems of Dynkin diagrams in LML, the classification algorithm of Dynkin diagrams in LML and the verification of the classification algorithm with experimental results.     

Sequencing of multi‑robot behaviors using reinforcement learning

Given a collection of parameterized multi-robot controllers associated with individual behaviors designed for particular tasks, this paper considers the problem of how to sequence and instantiate the behaviors for the purpose of completing a more complex, overarching mission. In addition, uncertainties about the environment or even the mission specifications may require the robots to learn, in a cooperative manner, how best to sequence the behaviors. In this paper, we approach this problem by using reinforcement learning to approximate the solution to the computationally intractable sequencing problem, combined with an online gradient descent approach to selecting the individual behavior parameters, while the transitions among behaviors are triggered automatically when the behaviors have reached a desired performance level relative to a task performance cost. To illustrate the effectiveness of the proposed method, it is implemented on a team of differential-drive robots for solving two different missions, namely, convoy protection and object manipulation.     

Incremental learning of gestures for human–robot interaction

For a robot to cohabit with people, it should be able to learn people’s nonverbal social behavior from experience. In this paper, we propose a novel machine learning method for recognizing gestures used in interaction and communication. Our method enables robots to learn gestures incrementally during human–robot interaction in an unsupervised manner. It allows the user to leave the number and types of gestures undefined prior to the learning. The proposed method (HB-SOINN) is based on a self-organizing incremental neural network and the hidden Markov model. We have added an interactive learning mechanism to HB-SOINN to prevent a single cluster from running into a failure as a result of polysemy of being assigned more than one meaning. For example, a sentence: “Keep on going left slowly” has three meanings such as, “Keep on (1)”, “going left (2)”, “slowly (3)”. We experimentally tested the clustering performance of the proposed method against data obtained from measuring gestures using a motion capture device. The results show that the classification performance of HB-SOINN exceeds that of conventional clustering approaches. In addition, we have found that the interactive learning function improves the learning performance of HB-SOINN.     

Improving accuracy of automatic optical inspection with machine learning

Electronic devices require the printed circuit board(PCB)to support the whole structure,but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices(SMDs)resistors.The automated optical inspection(AOI)machine,widely used in industrial production,can take the image of PCBs and examine the welding issue.However,the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs.This paper proposes a machine learning based method to improve the accuracy of AOI.In particular,we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image.We present a practical scheme including two machine learning procedures to mitigate AOI errors.We conduct experiments with the real dataset from a production line for three months,the experimental results show that our method can reduce the rate of misjudgment from 0.3%–0.5%to 0.02%–0.03%,which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.     

Mine,ours, and yours: Whose engagement and prior knowledge affects individual achievement from online collaborative learning?

In computer-supported collaborative learning research, studies examining the combined effects of individual level, group level and within-group differences level measures on individual achievement are scarce. The current study addressed this by examining whether individual, group and within-group differences regarding engagement and prior knowledge predict individual achievement. Engagement was operationalised as group members' exhibited activities in the task space (i.e., discussing domain-content) and social space (i.e., regulating ideas, actions and socioemotional processes). Prior knowledge and achievement were operationalised as group members' performance on a domain-related pre-test and post-test, respectively. Data was collected for 95 triads of secondary education students collaborating on a complex business-economics problem. Subsequently, three different multilevel models were tested to examine the combined effect. First a model with the individual level measures (model 1) was tested and in subsequent models the group level measures (model 2) and within-group levels measures (model 3) were added. Findings indicate model 2 showed the best fit; group members' individual engagement in the social space activities as well as the groups' average prior knowledge positively predicts individual achievement. No effects were found for either group members' or groups' engagement in the task space and for the within-group differences.     

Can differences in learning strategies explain the benefits of learning from static and dynamic visualizations?

The effects of dynamic and static visualizations in understanding physical principles of fish locomotion were investigated. Seventy-five students were assigned to one of three conditions: a text-only, a text with dynamic visualizations, or a text with static visualizations condition. During learning, subjects were asked to think aloud. Learning outcomes were measured by tests assessing verbal factual knowledge, pictorial recall as well as transfer. Learners in the two visualization conditions outperformed those in the text-only condition for transfer and pictorial recall tasks, but not for verbal factual knowledge tasks. Analyses of the think-aloud protocols revealed that learners had generated more inferences in the visualization conditions as opposed to the text-only condition. These results were mirrored by students’ self-reported processing demands. No differences were observable between the dynamic and the static condition concerning any of the learning outcome measures. However, think-aloud protocols revealed an illusion of understanding when learning with dynamic as opposed to static visualizations. Furthermore, learners with static visualizations tended to play the visualizations more often. The results stress the importance of not only using outcome-oriented, but also process-oriented approaches to gain deeper insight into learning strategies when dealing with various instructional materials.     

Effects of network communications on a class of learning controlled non-linear systems†

In this article, an iterative learning control approach is proposed for a class of sampled-data non-linear systems over network communication channels. The effects of constant time delays and stochastic packet loss are discussed and demonstrated by simulation results. The focus of this article is to study the remote control problems when the environment is periodic or repeatable over iterations in a fixed finite interval. Because of the existence of time delays and packet loss in input and output signal transmissions, it is not trivial to accomplish the remote stabilisation task of any system. Moreover, to track a desired trajectory through a remote controller is even more difficult. Previous cycle-based learning method is incorporated into the network-based control for a class of non-linear systems which satisfies a global Lipschitz condition. The convergence property of this approach is proven. Furthermore, the convergence in the iteration domain is also discussed when there exists packet loss in both transmission channels of the system. Finally, one single-link rigid robot is given as an example to show the effectiveness of the proposed approach.     

Almost sure convergence of iterative learning control for stochastic systems

This paper proposes an iterative learning control (ILC) algorithm with the purpose of controling the output of a linear stochastic system presented in state space form to track a desired realizable trajectory. It is proved that the algorithm converges to the optimal one a.s. under the condition that the product input-output coupling matrices are full-column rank in addition to some assumptions on noises. No other knowledge about system matrices and covariance matrices is required.     

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.     

SO（3） classifier of Lie group machine learning

This paper gives the method to design classifiers of Lie group Machine Learning （LML） based on the basic conceptions and the theory framework of it, which contains the following steps： （1） Map the observed data set in the learning system to the nonempty set G. （2） Construct the corresponding Lie group structure according to G. （3） Apply the obtained Lie group to build the LML model. （4） Form the corresponding classifier. （5） Test classification abilities and so on. At last, the design of SO（3） classifier and an example are given to test its classification ability.     

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).     

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.