Why does signaling enhance multimedia learning? Evidence from eye movements

Previous studies have suggested that signaling enhances multimedia learning. However, there is not enough evidence showing why signaling leads to better performance. The goal of this study was to examine the effects of signaling on learning outcomes and to reveal the underlying reasons for this effect by using eye movement measures. The participants were 40 undergraduate students who were presented with either signaled or nonsignaled multimedia materials. Labels in the illustration were signaled by temporarily changing the color of the items. The results suggest that the signaled group outperformed the nonsignaled group on transfer and matching tests. Eye movement data shows that signaling guided attention to relevant information and improved the efficiency and effectiveness of finding necessary information.     

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.     

Is contextual animation needed in multimedia learning games for children? An eye tracker study

The present study investigates affective-motivational, attention, and learning effects of unexplored emotional design manipulation: Contextual animation (animation of contextual elements) in multimedia learning game (MLGs) for children. Participants (N = 134; M_age = 9.25; Grades 3 and 4) learned either from an experimental version of the MLG with a high amount of contextual animation or from an identical MLG with no contextual animation (control). Children strongly preferred (χ² = 87.04, p < .001) and found the experimental version more attractive (p < .001, d = −1.11). No significant differences in overall enjoyment and learning outcomes were found. Attention differences, measured by dwell times and fixation durations, were small and reached only borderline significance (p = .035; d = −0.39). The implication is that contextual animation in MLG for children increases such instructional materials' attractiveness without compromising cognitive processes needed for learning; however, it does not lead to their higher instructional efficiency.     

Does individual or collaborative self-debriefing better enhance learning from games?

The primary aim of this study is to find out whether use of different self-debriefing modes affects learning from a game. In self-debriefing participants are led to reflect upon their game experiences by a set of debriefing questions. Two conditions were compared: Individual and Collaborative self-debriefing. The 45 participants first played the game of Lemonade Tycoon Deluxe, were tested for knowledge and self-debriefed in pairs or alone. Then they played the game once more and were tested again. Game scores increased significantly from the first to the second round of gameplay to an equal degree in both conditions. Knowledge scores of participants in individual self-debriefing increased significantly more than those of participants in the Collaborative condition. The study shows that game-based learning can be effectively scaffolded with self-debriefing. Future studies might investigate whether the type of self-debriefing differentially affects game motivation. In addition, attention to the role of feedback is called for.     

Where do machine learning and human-computer interaction meet?

Implementation of machine learning (ML) in human-computer interaction (HCI) work is not trivial. This article reports on a survey of 112 professionals and academicians specializing in HCI, who were asked to state level of ML use in HCI work. Responses were captured via a structured questionnaire. Analysis showed that about one-third of those who participated in the survey had used ML in conjunction with a variety of different HCI tasks. However, statistically significant differences could not be identified between those who have and those who have not used ML. Using statistics, contingency analysis, and clustering, we modeled interaction between representative HCI tasks and ML paradigms. We discovered that neural networks, rule induction, and statistical learning emerged as the most popular ML paradigms across HCI workers, although intensive learning, such as inductive logic programming, are gaining popularity among application developers. We also discovered that the leading causes for declining use of ML in HCI work are (1) misperceptions about ML, (2) lack of awareness of ML's potential, and (3) scarcity of concrete case studies demonstrating the application of ML in HCI.     

Does modality play a role? Visual‐verbal cognitive style and multimedia learning

The study presented in this paper aimed to examine the effect of visual and verbal cognitive style on learning from different types of visualization and modalities of explanatory text. Learning materials in the form of either computer‐based animation or a series of static pictures with written or spoken explanations were presented to 197 students. We found that a more developed visual cognitive style was related to a better learning outcome, when learning from a combination of static pictures and written text. Higher developed visualizers achieved poorer learning outcomes when learning with an animation and written text. The results are partially in line with an ability‐as‐compensator effect and the expertise reversal effect. Additionally, we found a modality effect as the versions with spoken text provided better results on learning outcome than the versions with written text regardless of the prominence of visual cognitive style. No significant interaction effects were found regarding verbal cognitive style.     

What do students do on-line? Modeling students' interactions to improve their learning experience

In this work, we present an approach to model and analyze students interactions, within a gamified on-line learning environment, in order to assist teachers and tutors (education professionals) decision-making, regarding their students learning experience. We noticed that asking students this information might not bring precise and dynamic results for all students. This way, we characterize the educational resources available in the studied environment, and collected data from students interactions with these resources. Our objective was to generate the students’ interactional profile (a model of their interactions). The information, then, is presented to teachers and tutors, who should use it to guide their pedagogical decision-making process. In this study, the types of interactions were used to personalize gamification elements named missions. We experimented the approach with two groups of users from the studied environment (MeuTutor). The data analysis showed differences in the way these groups were performing, where group B was considerably above group A. We sent the personalized missions (following our approach) to every student from group A, and waited some time for them to interact with it. In the end we checked the effect of this treatment, which, according to the results, promoted relevant improvement in group A interactions.     

Transfer of learning in virtual environments: a new challenge?

The aim of all education is to apply what we learn in different contexts and to recognise and extend this learning to new situations. Virtual learning environments can be used to build skills. Recent research in cognitive psychology and education has shown that acquisitions are linked to the initial context. This provides a challenge for virtual reality in education or training. A brief overview of transfer issues highlights five main ideas: (1) the type of transfer enables the virtual environment (VE) to be classified according to what is learned; (2) the transfer process can create conditions within the VE to facilitate transfer of learning; (3) specific features of VR must match and comply with transfer of learning; (4) transfer can be used to assess a VE’s effectiveness; and (5) future research on transfer of learning must examine the singular context of learning. This paper discusses how new perspectives in cognitive psychology influence and promote transfer of learning through the use of VEs.     

Implementation issues in optimization algorithms: do they matter?

Two factors that have a major impact on the performance of an optimization method are (1) formal algorithm specifications and (2) practical implementations. The impact of the latter is typically ignored, although it defines the results measured in experiments. We present an in-depth study of algorithm implementation issues and ask questions such as Does optimizing the implementation of an optimization algorithm pay off? Do bugs matter? and Is using more complicated but also more efficient data structures worth the effort? The intuitive answer to all of these questions is yes, but there is little published evidence. To bridge this gap, we use one of the most studied combinatorial optimization problems – the Traveling Salesman Problem – as a test bed and implement two state-of-the-art approaches for solving it – the Lin-Kernighan Heuristic and an Ejection Chain Method. We investigate implementation effort and performance gain, in order to provide further insights to the above questions.     

What seams do we remove in mobile-assisted seamless learning? A critical review of the literature

Seamless learning refers to the seamless integration of the learning experiences across various dimensions including formal and informal learning contexts, individual and social learning, and physical world and cyberspace. Inspired by the exposition by Chan et al. (2006) on the seamless learning model supported by the setting of one or more mobile device per learner, this paper aims to further investigate the meaning of seamless learning and the potential ways to put it in practice. Through a thorough review of recent academic papers on mobile-assisted seamless learning (MSL), we identify ten dimensions that characterize MSL. We believe that such a framework allows us to identify research gaps in the stated area. A practitioner interested in adopting an MSL design or doing a new design can use our analysis to situate the dimensional space where the constraints or parameters of his or her design problem lie, and look at relevant design and research-based evidence of other related MSL systems to refine her own design.     

How does desktop virtual reality enhance learning outcomes? A structural equation modeling approach

This study examined how desktop virtual reality (VR) enhances learning and not merely does desktop VR influence learning. Various relevant constructs and their measurement factors were identified to examine how desktop VR enhances learning and the fit of the hypothesized model was analyzed using structural equation modeling. The results supported the indirect effect of VR features to the learning outcomes, which was mediated by the interaction experience and the learning experience. Learning experience which was individually measured by the psychological factors, that is, presence, motivation, cognitive benefits, control and active learning, and reflective thinking took central stage in affecting the learning outcomes in the desktop VR-based learning environment. The moderating effect of student characteristics such as spatial ability and learning style was also examined. The results show instructional designers and VR software developers how to improve the learning effectiveness and further strengthen their desktop VR-based learning implementation. Through this research, an initial theoretical model of the determinants of learning effectiveness in a desktop VR-based learning environment is contributed.     

Environmental support hypothesis in designing multimedia training for older adults: Is less always more?

This study explores younger and older adults learning of MS Publisher functionalities from a multimedia tutorial. Twenty younger and twenty three older adults assigned to a redundant (experimental) or non-redundant (control) condition were taught how to create a greeting card, while the results of their learning were assessed with immediate and delayed performance measures. While younger learners benefited from a non-redundant condition, older learners exhibited an opposite trend. Redundant condition was beneficial for their performance efficiency on a set of a transfer tasks, and their troubleshooting performance during the delayed session. From a cognitive load perspective, using a redundant text along with an audio narration overloads learner’s working memory by placing more strain on the visual pathway and not providing any additional cognitive advantage. From an environmental support standpoint and decreased processing resources view of cognitive aging, providing additional representational support helps reducing the task demands for older adults and results in better learning. The results have cognitive and practical implications for the design of multimedia learning environments for older adults.     

Polite web-based intelligent tutors: Can they improve learning in classrooms?

Should an intelligent software tutor be polite, in an effort to motivate and cajole students to learn, or should it use more direct language? If it should be polite, under what conditions? In a series of studies in different contexts (e.g., lab versus classroom) with a variety of students (e.g., low prior knowledge versus high prior knowledge), the politeness effect was investigated in the context of web-based intelligent tutoring systems, software that runs on the Internet and employs artificial intelligence and learning science techniques to help students learn. The goal was to pinpoint the appropriate conditions for having the web-based tutors provide polite feedback and hints (e.g., “Let’s convert the units of the first item”) versus direct feedback and hints (e.g., “Convert the units of the first item now”). In the study presented in this paper, 132 high school students in a classroom setting, grouped as low and high prior knowledge learners according to a pre-intervention knowledge questionnaire, did not benefit more from polite feedback and hints than direct feedback and hints on either an immediate or delayed posttest, both of which contained near transfer and conceptual test items. Of particular interest and contrary to an earlier lab study, low prior knowledge students did not benefit more from using the polite version of a tutor. On the other hand, a politeness effect was observed for the students who made the most errors during the intervention, a different proxy for low prior knowledge, hinting that even in a classroom setting, politeness may be beneficial for more needy students. This article presents and discusses these results, as well as discussing the politeness effect more generally, its theoretical underpinnings, and future directions.     

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.     

Browser-based applications: popular but flawed?

Browser-based applications (BBAs), applications built on top of web browsers, dominate the world of Internet Applications today but are fundamentally flawed because the web browser is a weak platform for applications. Three characteristics of the browser—page orientation, statelessness, and limited computation—combine to produce a set of practical problems for BBA users. These problems include delays and discontinuities, confusion and errors, clumsy interfacing and limited functionality, printing problems, and filing difficulties. The paper analyzes these usability problems, providing numerous examples and tracing them back to underlying browser characteristics. The paper also examines the factors that make BBAs so popular despite their flaws. The paper concludes by considering directions for understanding the phenomenon better and for improving the current state of Internet Applications.

Can feature information interaction help for information fusion in multimedia problems?

This article presents the information-theoretic based feature information interaction, a measure that can describe complex feature dependencies in multivariate settings. According to the theoretical development, feature interactions are more accurate than current, bivariate dependence measures due to their stable and unambiguous definition. In experiments with artificial and real data we compare first the empirical dependency estimates of correlation, mutual information and 3-way feature interaction. Then, we present feature selection and classification experiments that show superior performance of interactions over bivariate dependence measures for the artificial data, for real world data this goal is not achieved yet.

Collaboration in Multi-Robot Exploration: To Meet or not to Meet?

Work on coordinated multi-robot exploration often assumes that all areas to be explored are freely accessible. This common assumption does not always hold, especially not in search and rescue missions after a disaster. Doors may be closed or paths blocked detaining robots from continuing their exploration beyond these points and possibly requiring multiple robots to clear them. This paper addresses the issue how to coordinate a multi-robot system to clear blocked paths. We define local collaborations that require robots to collaboratively perform a physical action at a common position. A collaborating robot needs to interrupt its current exploration and move to a different location to collaboratively clear a blocked path. We raise the question when to collaborate and whom to collaborate with. We propose four strategies as to when to collaborate. Two obvious strategies are to collaborate immediately or to postpone any collaborations until only blocked paths are left. The other two strategies make use of heuristics based on building patterns. While no single strategy behaves optimal in all scenarios, we show that the heuristics decrease the time required to explore unknown environments considering blocked paths.