Does the emotional design of scaffolds enhance learning and motivational outcomes in game-based learning?

Background

In recent years, the importance of emotions in learning has been increasingly recognized. Applying emotional design to induce positive emotions has been considered a means to enhance the instructional effectiveness of digital learning environments. However, only a few studies have examined the specific effects of emotional design in game-based learning.

Objectives

This quasi-experimental study utilized a value-added research approach to investigate whether emotional design applied to scaffolding in a game-based learning environment improves learning and motivational outcomes more than emotionally neutral scaffolding.

Methods

A total of 138 participants, mean age of 11.5 (SD = 0.73) participated in the study. A total of 68 participants played the base version of a fraction learning game (Number Trace), where scaffolding was provided with emotionally neutral mathematical notations, and 70 participants played the value-added version of the game using emotionally designed animated scaffolding agents. Pre-and post-tests were used to measure conceptual fraction knowledge and self-reported measures of situational interest and situational self-efficacy to evaluate motivational outcomes.

Results and Conclusions

Our results indicate that the emotional design applied to scaffolds can improve the educational value of a game-based learning environment by enhancing players' situational interest and situational self-efficacy. However, although the intervention improved the participants' conceptual fraction knowledge, there was no significant difference between the scaffolding conditions in participants' learning outcomes.

Takeaways

The results suggest that emotional design can increase the educational impact of game-based learning by promoting the development of interest, as well as improving self-efficacy.     

Can we get from liking to buying? Behavioral differences in hedonic and utilitarian Facebook usage

Companies’ Facebook pages have emerged as a commonly used marketing channel and their importance as a sales channel is likely to increase. Details about consumers’ underlying motivations to use these pages need to be linked to their effect on the host company’s business. This study distinguishes between consumers’ hedonic and utilitarian motivations for using company-hosted Facebook pages and relates them to two types of community usage behavior: browsing and participation. The effects on variables closely linked to business performance are examined. Analysis of data collected from 1162 members of a travel agency’s Facebook page reveals that hedonic motivations indicate a higher propensity to participate in the community whereas utilitarian motivations relate more strongly to merely browsing the community page. The participating members, however, do not show intentions to buy from the host company or refer it to others, while the browsers do. For practitioners, the finding that hedonic community participants are needed to maintain the community, but that they are unlikely to profit the company, is crucial.     

Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning

Learning analytics is the analysis of electronic learning data which allows teachers, course designers and administrators of virtual learning environments to search for unobserved patterns and underlying information in learning processes. The main aim of learning analytics is to improve learning outcomes and the overall learning process in electronic learning virtual classrooms and computer-supported education. The most basic unit of learning data in virtual learning environments for learning analytics is the interaction, but there is no consensus yet on which interactions are relevant for effective learning. Drawing upon extant literature, this research defines three system-independent classifications of interactions and evaluates the relation of their components with academic performance across two different learning modalities: virtual learning environment (VLE) supported face-to-face (F2F) and online learning. In order to do so, we performed an empirical study with data from six online and two VLE-supported F2F courses. Data extraction and analysis required the development of an ad hoc tool based on the proposed interaction classification. The main finding from this research is that, for each classification, there is a relation between some type of interactions and academic performance in online courses, whereas this relation is non-significant in the case of VLE-supported F2F courses. Implications for theory and practice are discussed next.     

Hybrid static–dynamic selection of implementation alternatives in heterogeneous environments

The Journal of Supercomputing - With the emergence of heterogeneous architectures, developing parallel software has become an increasingly complex task. The ability of using multiple devices in a...     

An optimization methodology for machine learning strategies and?regression problems in ballistic impact scenarios

In domains with limited data, such as ballistic impact, prior researches have proven that the optimization of artificial neural models is an efficient tool for improving the performance of a classifier based on MultiLayer Perceptron. In addition, this research aims to explore, in the ballistic domain, the optimization of other machine learning strategies and their application in regression problems. Therefore, this paper presents an optimization methodology to use with several approaches of machine learning in regression problems, maximizing the limited dataset and locating the best network topology and input vector of each network model. This methodology is tested in real regression scenarios of ballistic impact with different artificial neural models, obtaining substantial improvement in all the experiments. Furthermore, the quality stage, based on criteria of information theory, enables the determination of when the complexity of the network design does not penalize the fit over the data and thereby the selection of the best neural network model from a series of candidates. Finally, the results obtained show the relevance of this methodology and its application improves the performance and efficiency of multiple machine learning strategies in regression scenarios.     

Fuzzy recurrence plot-based analysis of dynamic and static spiral tests of Parkinson’s disease patients

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Can bigravity gravitational baryogenesis explain the CMB 143 GHz excess line?

It has been recently claimed [arXiv: 1510.00126], that the 143 GHz excess line in the Cosmic MicrowaveBackground (CMB) spectrumcould be explained by a collision of our Universe with an alternate Universe in which the baryon to entropy ratio is 65 times larger than the corresponding value measured for ourUniverse. This 143 GHz excess line is due to baryons, as was claimed, since the excess line corresponds to the recombination epoch, while the rest of the CMB signal is free of such excess lines. Thus the excess line is ascribed to an effect of collision of our Universe with a parallel universe. In this paper, we propose an alternative mechanism to explain the 143 GHz excess CMB line by using a simple bimetric gravity model which makes use of two metrics, the foreground metric g _μν and the background metric f _μν. The foreground Universe describes our Universe, and the background Universe is assumed to be underlying. The metrics are chosen to satisfy \({f_{\mu \nu }} = D_{{g_{\mu \nu }}}^2\), and the bimetric gravity model is constrained in such a way that the resulting Einstein equations for the background and foreground Universe are identical. In effect, the foreground and background Universe are indistinguishable at the cosmological solutions level. However, for the choices of the metrics we made, the scalar curvatures of the foreground and background Universes, namely R _g and R _f, are related by R _f = 1/D ², which can effectively result in different baryon to entropy ratios for the two Universes, via the gravitational baryogenesis mechanism. According to the gravitational baryogenesis mechanism, the baryon to entropy ratio is \(\eta B = \dot R/M_*^2T\), which means that the baryon to entropy ratio for the foreground and the background Universes we chose, namely ηB(g) and ηB(f), satisfy the relation \(\eta B\left( f \right) = \frac{1}{{{D^2}}}\eta B\left( g \right)\), and if D is chosen as \(D = 1/\sqrt {65} \), this could explain the 143 GHz excess line. The resulting phenomenological picture is quite appealing, since in the context of our bimetric gravity model, the foreground and background Universes coexist and are indistinguishable at the cosmological solutions level, but they are distinguishable only via the gravitational baryogenesis mechanism, which results in a baryon to entropy ratio for the background Universe, which is 65 time larger from the one corresponding to our Universe.     

Leading in Difficult Times: What Can We Learn from the Literature?

This article reviews the recent management literature and LIS literature looking for practical help for academic librarians seeking to lead effectively in the current economic recession. A number of pointers are found under the broad headings of embracing change, focusing on people and focusing on value. There is a lot of overlap and agreement between the two literatures on what is needed, and although both call for a different style of leadership for the future, the difference appears to be one of emphasis rather than a fundamental change.     

Improving static resolution of dynamic class loading in Java using dynamically gathered environment information

In Java software, one important flexibility mechanism is dynamic class loading. Unfortunately, the vast majority of static analyses for Java treat dynamic class loading either unsoundly or too conservatively. We present a novel semi-static approach for resolving dynamic class loading by combining static string analysis with dynamically gathered information about the execution environment. The insight behind the approach is that dynamic class loading often depends on characteristics of the environment that are encoded in various environment variables. Such variables are not static elements; however, their run-time values typically remain the same across multiple executions of the application. Thus, the string values reported by our technique are tailored to the current installation of the system under analysis. Additionally, we propose extensions of string analysis to increase the number of sites that can be resolved purely statically, and to track the names of environment variables. An experimental evaluation on the Java 1.4 standard libraries shows that a state-of-the-art purely static approach resolves only 28% of non-trivial sites, while our approach resolves 74% of such sites. We also demonstrate how the information gained from resolved dynamic class loading can be used to determine the classes that can potentially be instantiated through the use of reflection. Our extensions of string analysis greatly increase the number of resolvable reflective instantiation sites. This work is a step towards making static analysis tools better equipped to handle the dynamic features of Java. This material is based upon work supported by the National Science Foundation under CAREER grant CCF-0546040.     

Can more interactivity improve learning achievement in an online course? Effects of college students' perception and actual use of a course-management system on their learning achievement

This study aims to investigate how interactivity influence learners' use of interactive functions in the course-management system (CMS) and their online learning performance. A two-tier mediation framework is proposed to examine the mediating effects of different actual-use records concerning the CMS's interactive functions. Data are collected from 381 undergraduate students who enrolled in a general-education asynchronous online course from three universities in Taiwan. The results indicate that the relationships among students' self-reported use of interactive functions, students' perceptions of the usefulness of interactive functions, and students' actual-use logs have some direct influences on students' online learning performance (online-discussion scores, exam scores, and group-project scores). In addition, students' actual-use logs (the number of times of log-ins to the online course, the number of times students read learning materials, and the number of postings on the discussion board) have a mediated effect on students' self-reported frequency of logging into the CMS, students' self-reported frequency of using the learner–instructor/learner–learner interactive functions, and online learning performance. The findings and implications could constitute a useful guide for educational practitioners and designers concerned with the effective integration of interactivity into future online courses.     

Reconfiguring and ramping-up ventilator production in the face of COVID-19: Can robots help?

As the COVID-19 pandemic expands, the shortening of medical equipment is swelling. A key piece of equipment getting far-out attention has been ventilators. The difference between supply and demand is substantial to be handled with normal production techniques, especially under social distancing measures in place. The study explores the rationale of human-robot teams to ramp up production using advantages of both the ease of integration and maintaining social distancing. The paper presents a model for faster integration of collaborative robots and design guidelines for workstations. The scenario is evaluated for an open source ventilator through continuous human-robot simulation and amplification of results in a discrete event simulation.     

Should we collude? Analyzing the benefits of bidder cooperation in online group-buying auctions

Cooperation among bidders in traditional auctions is typically forbidden. This is because it is viewed as being harmful to the interests of sellers, who hope to obtain fair prices for their sale items. It also may be harmful to other bidders who are not able to take advantage of any cooperation that is occurring. In online group-buying auctions, in contrast to traditional auctions, cooperation results in higher welfare, leading to market expansion that benefits buyers and sellers, as well as the auction intermediary. This has not been well understood in prior research, however. In this article, we show how the online group-buying auction mechanism on the Internet can be effectively enhanced to produce higher welfare for the participants. The key to achieving this, we find, is for the auction intermediary to provide a means for bidders to cooperate, so as to collectively express greater demand. Such cooperation, it turns out, permits the group-buying auction mechanism to dominate the fixed-price mechanism from the seller’s point of view under some circumstances. Through an analytical modeling analysis, we offer insights into how sellers can set their group-buying auction price curves more effectively, so as to take advantage of bidder cooperation to improve auction performance. We further argue that the goal of the auction intermediary should be to offer an information sharing mechanism to facilitate bidding ring formation, as a means to maximize the value of this market mechanism.     

Coupled particle swarm optimization method with genetic algorithm for the static–dynamic performance of the magneto-electro-elastic nanosystem

As a first attempt, Fourier series expansion (FSE), particle swarm optimization (PSO), and genetic algorithm (GA) methods are coupled for analysis of the static–dynamic performance and propagated waves in the magneto-electro-elastic (MEE) nanoplate. The FSE method is presented for solving the motion equations of the MEE nanoplate. For increasing the performance of genetic algorithms for solving the problem, the particle swarm optimization technique is added as an operator of the GA. Accuracy, convergence, and applicability of the proposed mixed approach are shown in the results section. Also, we prove that for obtaining the convergence results of the PSO and GA, we should consider more than 16 iterations. Finally, it is shown that if designers consider the presented algorithm in their model, the results of phase velocity of the nanosystem will be increased by 27%. A useful suggestion is that there is a region the same as a trapezium in which there are no effects from magnetic and electric potential of the MEE face sheet on the phase velocity of the smart nanoplate, and the region will be bigger by increasing the wavenumber.

     

Learning the structure of dynamic Bayesian networks from time series and steady state measurements

Dynamic Bayesian networks (DBN) are a class of graphical models that has become a standard tool for modeling various stochastic time-varying phenomena. In many applications, the primary goal is to infer the network structure from measurement data. Several efficient learning methods have been introduced for the inference of DBNs from time series measurements. Sometimes, however, it is either impossible or impractical to collect time series data, in which case, a common practice is to model the non-time series observations using static Bayesian networks (BN). Such an approach is obviously sub-optimal if the goal is to gain insight into the underlying dynamical model. Here, we introduce Bayesian methods for the inference of DBNs from steady state measurements. We also consider learning the structure of DBNs from a combination of time series and steady state measurements. We introduce two different methods: one that is based on an approximation and another one that provides exact computation. Simulation results demonstrate that dynamic network structures can be learned to an extent from steady state measurements alone and that inference from a combination of steady state and time series data has the potential to improve learning performance relative to the inference from time series data alone.     

Characterization of static bifurcations for n-dimensional flows in the frequency domain

In this paper n-dimensional flows （described by continuous-time system） with static bifurcations are considered with the aim of classification of different elementary bifurcations using the frequency domain formalism. Based on frequency domain approach, we prove some criterions for the saddle-node bifurcation, transcritical bifurcation and pitchfork bifurcation, and give an example to illustrate the efficiency of the result obtained.     

Can instruction as such affect learning? The case of learner control

This study examined the role of learners’ perceptions in a learner-controlled computer-based learning environment. Computer-based learning environments that offer learner control (LC) to the learners are assumed to enhance motivation and learning outcomes. Recently, the focus of LC research has shifted from measuring the direct effect of LC on learning towards focusing on the underlying mechanisms of effective LC and determining under which conditions LC is most effective (Corbalan, Kester, & van Merriënboer, 2009). There is considerable agreement that learners’ skills, perceptions and the experienced cognitive load interactively affect LC as instructional strategy. For this study, 165 first-year university students participated in an on-line English learning course on verb conjugation. We investigated the effect of learners’ perceptions of LC by comparing the learning outcomes and motivation of learners that received additional instructions on learner control with a group of learners that did not receive additional instruction. Learner characteristics, such as prior knowledge, working memory capacity, self-efficacy and cognitive load, were taken into account. The results indicate that it is not instruction as such, but rather satisfaction with the degree of control that affects learning outcomes and motivation. We suggest that instruction as such does not suffice to enhance perception of control, and that learners’ perceptions play a mere role in the effectiveness of instructional strategies such as learner control.