MOOC study group: Facilitation strategies,influential factors,and student perceived gains

“Join a Meetup Group” (face-to-face study group) has been propagated by Coursera to build rapport and provide mutual support among MOOC learners; however, studies remain scant regarding its effectiveness and sustainability. This interpretive case study documents our facilitation process, key influential factors, as well as student perceived gains in a six-week MOOC study group. Data sources include discussion recordings, end-of-course interviews, goal setting sheets, weekly reflection journals, and researchers' observation notes. Results showed that, cognitively, participants broadened their perspective of thinking, raised cultural awareness, and shared many learning strategies. Affectively, they established a strong sense of community and gained motivation for learning. Participants also increased action tendencies toward trying out Coursera functions, new courses, and learning strategies, and they became more cognizant of the benefits and procedures of the MOOC study group. Our findings suggest that, with proper design and facilitation, face-to-face study group would be a practicable and effective approach to leverage MOOC students' motivation, engagement, and deeper learning. Implications are discussed in terms of potential gains, challenges, key influential factors, as well as future design and implementation of MOOC study groups.     

Lecture capture with real‐time rearrangement of visual elements: impact on student performance

The primary goal of this study is to create and test a lecture‐capture system that can rearrange visual elements while recording is still taking place, in such a way that student performance can be positively influenced. The system we have devised is capable of integrating and rearranging multimedia sources, including learning content, the instructor and students' images, into lecture videos that are embedded in a website for students to review after school. The present study employed a two‐group experimental design, with 153 participants (145 females and 8 males) making up an experimental group in which lecture courses were recorded using the new lecture‐capture system, and 149 participants (140 females and 9 males) forming a control group whose lectures were recorded by traditional means. All participants were in the freshman college and studying Introduction to Computer and Information Science in one of six classes, and were randomly assigned to one of the two groups. The participants' midterm examination and final examination scores were collected as indicators of their academic performance, with their mathematics entrance scores used as a pre‐test. The findings obtained from analysis of covariance (ANCOVA) suggest that appropriate rearrangement of visual elements in lecture videos can significantly impact students' learning performance.     

Programming assistance tools to support the learning of IT programming in South African secondary schools

Novice programmers struggle to understand introductory programming concepts and this difficulty, associated with learning to program, contributes mainly to the lack of interest in the field of Computer Science at tertiary level. Programming assistance tools have been used to assist novice programmers extensively at education institutions. A programming assistance tool (PAT) is a software program that can be used by novice programmers to learn how to program and/or improve their understanding of programming concepts.This research proposes that novice programmers, specifically Information Technology (IT) scholars in South African secondary schools, could be supported by PATs. The main objective of this research was to determine whether the use of a PAT impacted IT scholars' understanding of programming concepts and their motivation towards programming in general. Criteria for the selection of PATs were derived from the programming difficulties identified in literature and from surveys to IT teachers and scholars. The selection criteria were grouped into programming knowledge and programming skills categories. Existing PATs were evaluated using the selection criteria and three PATs, namely, RoboMind, Scratch and B#, were selected for evaluation in this research study. A convenience sample of schools participated in the study. The three PATs provided different approaches while being able to support the Delphi programming language used in schools that participated in the study.The findings of this research indicated that, although scholars perceived the PATs to be useful in the explanation of certain of the programming concepts, there was no conclusive evidence that IT scholars who used a PAT had a significantly better understanding of programming concepts and motivation towards programming than scholars who did not use a PAT. Participant feedback was used to identify the strengths and shortcomings of the three PATs and to provide recommendations for the development of future PATs specifically designed to support IT scholars.     

Automatic analysis and identification of verbal aggression and abusive behaviors for online social games

Online multiplayer games create new social platforms, with their own etiquette, social rules of conduct and ways of expression. What counts as aggressive and abusing behavior may change depending on the platform, but most online gaming companies need to deal with aggressive and abusive players explicitly. This usually is tied to a reporting mechanism where the offended player reports an offense. In this paper, we develop tools for validating whether a verbal aggression offense report refers to a real offense or not, in the context of a very popular online social game, called Okey. Our approach relies on the analysis of player behavior and characteristics of offending players. In the proposed system, chat records and other social activities in the game are taken into account, as well as player history. This methodology is sufficiently generic, and it can be applied to similar gaming platforms, thus describing a useful tool for game companies. We report our results on data collected over a six months period, involving 100,000 users and 800,000 game records, and illustrate the viability of such analysis, while providing insights on the factors associated with verbal aggression and abusive behavior for social games.     

Subsampled terahertz data reconstruction based on spatio-temporal dictionary learning

In this paper, the problem of terahertz pulsed imaging and reconstruction is addressed. It is assumed that an incomplete (subsampled) three dimensional THz data set has been acquired and the aim is to recover all missing samples. A sparsity-inducing approach is proposed for this purpose. First, a simple interpolation is applied to incomplete noisy data. Then, we propose a spatio-temporal dictionary learning method to obtain an appropriate sparse representation of data based on a joint sparse recovery algorithm. Then, using the sparse coefficients and the learned dictionary, the 3D data is effectively denoised by minimizing a simple cost function. We consider two types of terahertz data to evaluate the performance of the proposed approach: THz data acquired for a model sample with clear layered structures (e.g., a T-shape plastic sheet buried in a polythene pellet), and pharmaceutical tablet data (with low spatial resolution). The achieved signal-to-noise-ratio for reconstruction of T-shape data, from only 5% observation was 19 dB. Moreover, the accuracies of obtained thickness and depth measurements for pharmaceutical tablet data after reconstruction from 10% observation were 98.8%, and 99.9%, respectively. These results, along with chemical mapping analysis, presented at the end of this paper, confirm the accuracy of the proposed method.     

Engineering accidents in society: A comparison of Chinese and American railway accident investigation

Based on the investigation reports for two major railway accidents in China and the USA, as well as the analysis of related systems, this paper compares the differences between Chinese and American railway accident investigation modes along different dimensions. The four aspects attended to are the investigation purpose, institutional basis, scope and process, and type of conclusion. The results reveal the role of social factors in shaping the investigation pattern of engineering accidents, and show engineering accidents can serve as a window to understanding a society and the ways that learning from engineering accidents is a socially institutionalized process. A final discussion considers possible improvements that might be introduced into the Chinese mode of engineering accident investigation.     

Hierarchical power management of a system with autonomously power-managed components using reinforcement learning

This paper presents a hierarchical dynamic power management (DPM) framework based on reinforcement learning (RL) technique, which aims at power savings in a computer system with multiple I/O devices running a number of heterogeneous applications. The proposed framework interacts with the CPU scheduler to perform effective application-level scheduling, thereby enabling further power savings. Moreover, it considers non-stationary workloads and differentiates between the service request generation rates of various software application. The online adaptive DPM technique consists of two layers: component-level local power manager and system-level global power manager. The component-level PM policy is pre-specified and fixed whereas the system-level PM employs temporal difference learning on semi-Markov decision process as the model-free RL technique, and it is specifically optimized for a heterogeneous application pool. Experiments show that the proposed approach considerably enhances power savings while maintaining good performance levels. In comparison with other reference systems, the proposed RL-based DPM approach, further enhances power savings, performs well under various workloads, can simultaneously consider power and performance, and achieves wide and deep power-performance tradeoff curves. Experiments conducted with multiple service providers confirm that up to 63% maximum energy saving per service provider can be achieved.     

Nonlinear robust dual-loop control for electro-hydraulic load simulator

This paper investigates on the high performance torque control of electro-hydraulic load simulator (EHLS). In order to suppress actuator׳s motion disturbance, a nonlinear robust dual-loop control scheme is developed, which consists of a open-loop nonlinear velocity feed-forward compensator and a closed-loop nonlinear deterministic robust torque controller. The main function of the open-loop compensator is to decouple actuator׳s active motion disturbance, whereas the torque loop controller aims at guaranteeing the dynamics performance of tracking torque reference. Besides actuator׳s motion disturbance, both the nonlinearity characteristics and friction problem of the EHLS system are taken into consideration in this paper. The effectiveness of the developed method are verified through comparative co-simulations and experiments.     

Nonlinear adaptive filtering using kernel‐based algorithms with dictionary adaptation

Nonlinear adaptive filtering has been extensively studied in the literature, using, for example, Volterra filters or neural networks. Recently, kernel methods have been offering an interesting alternative because they provide a simple extension of linear algorithms to the nonlinear case. The main drawback of online system identification with kernel methods is that the filter complexity increases with time, a limitation resulting from the representer theorem, which states that all past input vectors are required. To overcome this drawback, a particular subset of these input vectors (called dictionary) must be selected to ensure complexity control and good performance. Up to now, all authors considered that, after being introduced into the dictionary, elements stay unchanged even if, because of nonstationarity, they become useless to predict the system output. The objective of this paper is to present an adaptation scheme of dictionary elements, which are considered here as adjustable model parameters, by deriving a gradient‐based method under collinearity constraints. The main interest is to ensure a better tracking performance. To evaluate our approach, dictionary adaptation is introduced into three well‐known kernel‐based adaptive algorithms: kernel recursive least squares, kernel normalized least mean squares, and kernel affine projection. The performance is evaluated on nonlinear adaptive filtering of simulated and real data sets. As confirmed by experiments, our dictionary adaptation scheme allows either complexity reduction or a decrease of the instantaneous quadratic error, or both simultaneously. Copyright © 2015 John Wiley & Sons, Ltd.     

Creating formative feedback spaces in large lectures

Large lectures are the predominant way of teaching first-year students at universities in Norway. However, this forum for education is seldom discussed as a context for a formative feedback practice. The purpose of this sequential mixed methods study was to address whether and how a student-response system can open for a formative feedback practice in lectures and thereby support students' ability to monitor their own learning, as well as supply insight into how students engage with the feedback in their course work. The context for the study was large lectures (150–200 students) in a qualitative method course for first-year psychology students. Findings from the survey (n = 149) showed a positive correlation between the extent to which students report that they use clickers to monitor their own learning, and the extent to which they report that they used the feedback in their own course work. However, findings indicate that students valued the process of monitoring their own learning during the lectures to a greater extent than they actually used the feedback in their course work. Findings from interviews (n = 6) illustrated various ways students applied feedback in their course work.