Deep Information Processing Research Articles

Learning With Board Games: Interplay of Context and Students’ Prior Knowledge

Board games can support engagement, deep information processing and learning outcomes for university students. The instructional context, notably the way an activity is introduced to students, can influence students’ behavior and learning outcomes. Yet, despite its potential important influence on in-game behavior and learning, the impact of the instructional context used to introduce a game-based learning activity has not received much attention. In two studies, we examined how framing a game-based learning activity with a serious or a non-serious context can impact university students’ behavior and learning outcomes. In the first study, 44 graduate students played the same word association game that was either introduced as a game or as a study activity. Results showed that the serious context increased recall as compared to the gaming context. In the second study, involving 87 participants playing the same game, results showed that the gaming context and prior gaming experience also reduced students’ recall. Overall, grouped analysis showed that the gaming context led to poorer learning than the serious context for recognition and recall measures, even when considering prior knowledge, in-game behavior, and prior gaming experience. Theoretical and practical implications for educators are discussed.

Graphic association learning: Multimodal feature extraction and fusion of image and text using artificial intelligence techniques

With the advancement of technology in recent years, the application of artificial intelligence in real life has become more extensive. Graphic recognition is a hot spot in the current research of related technologies. It involves machines extracting key information from pictures and combining it with natural language processing for in-depth understanding. Existing methods still have obvious deficiencies in fine-grained recognition and deep understanding of contextual context. Addressing these issues to achieve high-quality image-text recognition is crucial for various application scenarios, such as accessibility technologies, content creation, and virtual assistants. To tackle this challenge, a novel approach is proposed that combines the Mask R-CNN, DCGAN, and ALBERT models. Specifically, the Mask R-CNN specializes in high-precision image recognition and segmentation, the DCGAN captures and generates nuanced features from images, and the ALBERT model is responsible for deep natural language processing and semantic understanding of this visual information. Experimental results clearly validate the superiority of this method. Compared to traditional image-text recognition techniques, the recognition accuracy is improved from 85.3% to 92.5%, and performance in contextual and situational understanding is enhanced. The advancement of this technology has far-reaching implications for research in machine vision and natural language processing and open new possibilities for practical applications.

Learning and propagation: Evolutionary dynamics in spatial public goods games through combined Q-learning and Fermi rule

Propagation is crucial for acquiring information, and learning involves deep information processing. Imitation dynamics, commonly used in spatial public goods games, represents strategy propagation in society, while learning dynamics enables agents to self-learn through environmental interactions. In this paper, Q-learning and the Fermi update rule are used to compare differences between learning dynamics and imitation dynamics in simulation experiments. Our study finds that Q-learning is continuously updating the Q-table during its evolution, forming a heterogeneous gain matrix. However, Q-learning’s interaction with the environment is indirect, with almost no network reciprocity, and cannot form clusters in contrast to the Fermi update rule. The Fermi update rule focus only on immediate payoffs. Furthermore, we combine imitation dynamics and learning dynamics, integrating the advantages of both. Defectors and cooperators form a special semi-stable checkerboard-like state. The update rules, the number of states, and the configuration of conversion actions as strategies have led to this special structure. Moreover, we found that parameter choices in the new evolutionary dynamics affect the percentage of cooperation and evolutionary convergence. Overall, this paper offers new insights into learning dynamics and imitation dynamics and provides a foundation for integrating complex mechanisms of evolution.

Beyond beauty: Does visual art facilitate social cognitive skills?

Engaging with art can move individuals through a myriad of emotions, provoke reflective thoughts, and lead to new ideas. Could art also influence interpersonal outcomes pertaining to the ways we interact with others and navigate the social world, that is, our suite of social cognitive skills? Here, we focus on visual art to explore the effect of art engagement on personal aesthetic experience and social cognitive skills. Across two studies, using veridical paintings and matched non-art photos, we examined the effect of art engagement on emotional (e.g., awe, being moved) and eudaimonic experiences (e.g., reflective thoughts), as well as social cognitive skills pertaining to Theory of Mind (ToM) and recognition of other's emotions. Further, we varied the depth with which participants engaged with the experiences of the characters in the artworks, to assess whether deep social information processing could boost the effect of art engagement on social cognitive skills. Our findings showed that art engagement altered personal aesthetic experience through changes in emotional and eudaimonic outcomes. However, we did not find any support for the effect of art engagement on social cognitive skills: Neither engaging with art, nor art in combination with deep social information processing, influenced performance on social cognitive skills of ToM and emotion recognition. The effect of art engagement on personal aesthetic experience and the absence of effect on social cognitive skills highlight the nuanced nature of individuals' interactions with art. We discuss these results considering the varied ways of engagement with different artforms and in relation to different operationalizations of social cognitive skills.

Perception of university students on the role of educational digital stimuli in achieving academic passion in vocational education

Introduction. Digital stimuli are one of the strategies prepared by higher education institutions in university teaching in the field of vocational education, but they do not attach importance to them from other strategies that are used. The investigation of the role of educational digital stimuli in achieving the academic passion of vocational education students in Jordanian universities in light of COVID-19 is important to try to know the effect of digital stimuli on the academic passion of vocational education students. Methodology and research methods. The descriptive survey method was used to analyse the data. To achieve the objectives of the study, a questionnaire consisting of (29) items was designed, divided into three areas: dynamics (11 items), mechanics (7 items), and components (11 items) to collect data. The study sample consisted of 260 male and female students in Jordanian universities. Results and scientific novelty. The results showed that the role of educational digital stimuli in achieving academic passion was ‘high’ with no statistically significant differences due to the gender variable, with statistically significant differences due to the variables of the academic year and university type. Practical significance. The study recommends designing educational environments that are attractive, interactive, and related to the students’ reality, using strategies for deep information processing, problem-solving method, creativity and critical thinking in order to achieve students’ involvement in learning.

Emotional Biases and Equity Investment Decision of Individual Investors

Emotional bias is the presentation of felling of the people about acting something. It affects the human behavior which ultimately influence the decision-making behavior of the individual investors. This study focuses on examining the influence of emotional biases on equity investment decision of individual investors. This study was based on quantitative approach of research with the sample size of 385 individual investors. However, the response rate of the study was 79% while the usable response was only 68.31% of the sample. Emotional biases are measured by loss aversion bias, overconfidence bias, endowment bias, self-control bias, regret aversion bias and status quo bias. Evidence indicates that loss aversion bias, overconfidence bias, self-control bias and regret aversion bias had significant positive influence on equity investment decision. However, endowment bias and status quo bias had no significant contribution to the equity investment decision of the individual investors in Chitwan. Hence, the equity investment decision of investors in Chitwan is broadly depend upon easily and readily available information and strongly based on their own beliefs and emotions without resorting to deep research, investigation, information processing and analysis of the securities and the market with fundamental and technical analysis that leads to overtrading and overreaction to the market, holding on too losing stock enormously, holdings on to enormous risky portfolios, selling off gaining portfolio to early and finally, making investment decisions without proper analysis of the information, decisions are based on their own moods, beliefs, emotions, past experience and stock of knowledge.

Analysis of the Deep Learning Strategies Questionnaire with Ecuadorian students

Measurement of self-regulated learning through self-report questionnaires can help improve educational efforts. The Deep Learning Strategies Questionnaire has been recently validated, which consists of 30 items and four scales: basic self-regulated learning strategies, visual elaboration and summary strategies, deep information processing strategies, and self-regulated social learning strategies. We examined the characteristics of the questionnaire with 694 Ecuadorian students. The exploratory factor analysis resulted in four factors, like the original model. However, the factors identified as basic and social learning strategies included items of visual elaboration and summary and deep processing strategies. Further group comparisons showed that participants with high school finished used fewer visual and verbal elaboration strategies than those with higher education levels and that males use more deep information processing strategies than females. We discuss the difficulty of separating self-regulated learning strategies and conclude with suggestions for future research and recommendations for educational practice.

Students' Entire Deep Learning Personality Model and Perceived Teachers' Emotional Support.

In recent years, deep learning as the requirement of higher education for students has attracted the attention of many scholars, and previous studies focused on defining deep learning as the deep processing of knowledge of the brain, however, in the process of knowledge processing, the brain not only involves the deep processing of information but also participates in learning consciously and emotionally. Therefore, this research proposed a four-factor model hypothesis for deep learning that includes deep learning investment, deep cognitive-emotional experience, deep information processing, and deep learning meta-cognitive. In addition, the research proposed teachers’ emotional support perceived by students has an effect on the four factors of deep learning. Through SPSS 26 and AMOS 24, this research has verified the four-factor model of deep learning applying exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) and verified that the perceived teacher emotional support has an impact on the four factors of students’ deep learning using the SEM.

THE IMPORTANCE OF RELATEDNESS SUPPORT IN EDUCATION

Self Determination Theory, developed by Edward L. Deci and Richard Ryan, is a motivation theory suggesting that if the innate psychological needs of people are met, their motivation will be achieved without external influence and intervention. According to this theory, autonomy (the ability to make decisions about one's own life on its own), competence (having control over outcomes and believing that one has the ability and ability to do any job) and relatedness support (to interact, connect, and value others) are psychological needs and are the optimal conditions for intrinsic motivation. In this study, the relationship between relatedness support provided by teachers in their instructional behavior and its relationship to student engagement was investigated. Students' engagement in the lesson is considered as part of their learning motivation. Designed as a cross-sectional observation study, 191 students from private and public high schools in Ankara were observed at three different times in the same class by two independent observers who received training in Self Determination Theory from the Department of Psychology. With the questions directed to the students, a positive correlation was found between students’ behavioral engagement (raising finger, asking questions, obeying the class rules, completing the given tasks), emotional engagement (enthusiasm, curiosity, interest, optimism), cognitive engagement (advanced deep information processing level and self-regulation skills) and agentic engagement (students' constructive contribution to their own learning, initiating new tasks) and the relatedness support provided by their teachers. When teachers spend time with their students, when they are physically nearby them, when they answer students’ questions, when they are clear and organized in the instructions given about the lesson, students' participation in the lesson has increased. The study also showed that teachers 'awareness of their own instructional behavior was positively correlated with students' intrinsic motivation. Feeling related to the classroom environment, the lesson, peers and the teacher enhances classroom motivation.

Ten-Port Unitary Optical Processor on a Silicon Photonic Chip

Unitary optical processors (UOPs) are task-specific computing units that can ultimately enable ultrafast and energy-efficient unitary matrix-vector multiplications based on the interference between coherent optical beams. The UOP using multiport directional couplers (MDCs) holds great promise in large-scale integration because of its unique robustness against fabrication errors. Here, we demonstrate a 10-port robust UOP on a silicon photonic chip, which is so far the largest-scale UOP using multiport directional couplers. We further show the unique flexibility of this architecture by demonstrating operations for both the transverse electric (TE) and the transverse magnetic (TM) polarizations, which has never been realized in previous UOPs. This flexible and robust architecture is suitable for various scenarios in deep learning, optical communication, and quantum information processing.

Moderators of the Liking Bias in Judgments of Moral Character

Previous research found evidence for a liking bias in moral character judgments because judgments of liked people are higher than those of disliked or neutral ones. This article sought conditions moderating this effect. In Study 1 (N = 792), the impact of the liking bias on moral character judgments was strongly attenuated when participants were educated that attitudes bias moral judgments. In Study 2 (N = 376), the influence of liking on moral character attributions was eliminated when participants were accountable for the justification of their moral judgments. Overall, these results suggest that although liking biases moral character attributions, this bias might be reduced or eliminated when deeper information processing is required to generate judgments of others’ moral character.

Vectorizing posit operations on RISC-V for faster deep neural networks: experiments and comparison with ARM SVE

With the arrival of the open-source RISC-V processor architecture, there is the chance to rethink Deep Neural Networks (DNNs) and information representation and processing. In this work, we will exploit the following ideas: i) reduce the number of bits needed to represent the weights of the DNNs using our recent findings and implementation of the posit number system, ii) exploit RISC-V vectorization as much as possible to speed up the format encoding/decoding, the evaluation of activations functions (using only arithmetic and logic operations, exploiting approximated formulas) and the computation of core DNNs matrix-vector operations. The comparison with the well-established architecture ARM Scalable Vector Extension is natural and challenging due to its closedness and mature nature. The results show how it is possible to vectorize posit operations on RISC-V, gaining a substantial speed-up on all the operations involved. Furthermore, the experimental outcomes highlight how the new architecture can catch up, in terms of performance, with the more mature ARM architecture. Towards this end, the present study is important because it anticipates the results that we expect to achieve when we will have an open RISC-V hardware co-processor capable to operate natively with posits.

The value-reflexive attitude of Russians to the Internet (the case of Moscow and the Moscow Region)

Information and communication technologies, the Internet in particular, have a significant impact on all the aspects of social life. Therefore, it is necessary to study the problems, consequences, and scale of such influence in the sphere of different scientific disciplines (sociology, psychology, and cultural studies) as well as in the sphere of interdisciplinary discourse. There is a need for research adequate to the current situation of transition to a new technological mode, a digital society, which actualizes the demand for the formation of a new type of personality, a new elite. The new elite will be the bearers of the socio-cultural code of deep and systemic information processing, the owners of electronic culture on the basis of critical digital literacy and reflexivity. At the same time, specific empirical studies of recent years abroad and in Russia demonstrate the predominance of competences of information and communication technologies user, which is absolutely insufficient in the digital society. This research aims to analyze the value-reflexive attitude of Russians to the Internet based on the results of a specific sociological survey among the residents of Moscow and the Moscow Region, implemented under the guidance and direct participation of the authors of this article during the period of social restrictions imposed by state authorities in connection with the new coronavirus infection from April to November 2020 by online questionnaire. The research tools included a block of questions on the digitalization of the region, including a number of statements showing the value-reflexive attitude of the respondents to the Internet. The results show that the number of respondents, who regard the Internet as one of the modern civilizational values, exceeds the share of respondents who recognize the dangers of Internet addiction. The authors have revealed that an even smaller percentage of those aware of the relationship of Internet addiction and anonymity on the Internet with the destruction of moral and moral systems, the erasure of the boundaries of personal and public space, psychological problems. A significant proportion of those who exhibit dangerous symptoms of alienation from the real, physical and social world was recorded. It was determined that for the majority of respondents, the Internet space has not become a platform of freedom, creative fulfillment, a friendly environment. The article assumes that economic conditions could become the foundation for bringing people together in the Internet space. Intergenerational differences in the value-reflexive attitude to the Internet space are revealed. The authors draw attention to the fact that the elderly, as the bearers of the deep meanings of traditional cultural forms, reflect the risks and dangers of the Internet space to a greater extent than the young people and adults. The authors link the prospects for further research with the sociological monitoring, which will allow studying the dynamics of digitalization processes in the region, the impact of age characteristics of identity on the attitude to the Internet, and the formation of electronic culture.

The value-reflexive attitude of Russians to the Internet (the case of Moscow and the Moscow Region)

Information and communication technologies, the Internet in particular, have a significant impact on all the aspects of social life. Therefore, it is necessary to study the problems, consequences, and scale of such influence in the sphere of different scientific disciplines (sociology, psychology, and cultural studies) as well as in the sphere of interdisciplinary discourse. There is a need for research adequate to the current situation of transition to a new technological mode, a digital society, which actualizes the demand for the formation of a new type of personality, a new elite. The new elite will be the bearers of the socio-cultural code of deep and systemic information processing, the owners of electronic culture on the basis of critical digital literacy and reflexivity. At the same time, specific empirical studies of recent years abroad and in Russia demonstrate the predominance of competences of information and communication technologies user, which is absolutely insufficient in the digital society. This research aims to analyze the value-reflexive attitude of Russians to the Internet based on the results of a specific sociological survey among the residents of Moscow and the Moscow Region, implemented under the guidance and direct participation of the authors of this article during the period of social restrictions imposed by state authorities in connection with the new coronavirus infection from April to November 2020 by online questionnaire. The research tools included a block of questions on the digitalization of the region, including a number of statements showing the value-reflexive attitude of the respondents to the Internet. The results show that the number of respondents, who regard the Internet as one of the modern civilizational values, exceeds the share of respondents who recognize the dangers of Internet addiction. The authors have revealed that an even smaller percentage of those aware of the relationship of Internet addiction and anonymity on the Internet with the destruction of moral and moral systems, the erasure of the boundaries of personal and public space, psychological problems. A significant proportion of those who exhibit dangerous symptoms of alienation from the real, physical and social world was recorded. It was determined that for the majority of respondents, the Internet space has not become a platform of freedom, creative fulfillment, a friendly environment. The article assumes that economic conditions could become the foundation for bringing people together in the Internet space. Intergenerational differences in the value-reflexive attitude to the Internet space are revealed. The authors draw attention to the fact that the elderly, as the bearers of the deep meanings of traditional cultural forms, reflect the risks and dangers of the Internet space to a greater extent than the young people and adults. The authors link the prospects for further research with the sociological monitoring, which will allow studying the dynamics of digitalization processes in the region, the impact of age characteristics of identity on the attitude to the Internet, and the formation of electronic culture.

Estrategias de aprendizaje profundas: Validación de un modelo situacional y su cuestionario

Measuring self-regulated learning is crucial to improve our educational interventions. Self-report has been the major data collection method and a number of questionnaires exist. Importantly, the vast majority of the questionnaires are constructed from general theoretical models. Our aim was to develop a model and its questionnaire –i.e. Deep Learning Strategies questionnaire- to investigate how students regulate their learning strategies in more realistic learning situations. Four scales were created: (1) Basic learning self-regulation strategies; (2) Visual elaboration and summarizing strategies; (3) Deep information processing strategies; and (4) Social learning self-regulation strategies. A total of 601 higher education students formed the sample. We analyzed, first, the internal validity of the questionnaire. Three structural models were tested: (M1) mono-factor; (M2) scales correlate among them freely, and (M3) the scales are indicators of a general construct. The latter model showed a slight better fit. Additionally, a path analysis was carried out to study the degree in which the use of the Deep learning strategies depends on personal factors and is associated to performance. It was found that the use depends directly and positively on Learning goal orientation, on the self-messages defining the Self-regulation style of emotion and motivation focused on learning, and on Effort. Besides, these two last variables convey the effect of Self-efficacy that, at the same time, affects Effort. Academic performance depends positively on Effort but negatively to the use of Deep learning strategies. It is hypothesized this negative relationship is due to the method of measurement of academic performance.

Deep learning self-regulation strategies: Validation of a situational model and its questionnaire

Measuring self-regulated learning is crucial to improve our educational interventions. Self-report has been the major data collection method and a number of questionnaires exist. Importantly, the vast majority of the questionnaires are constructed from general theoretical models. Our aim was to develop a model and its questionnaire –i.e. Deep Learning Strategies questionnaire- to investigate how students regulate their learning strategies in more realistic learning situations. Four scales were created: (1) Basic learning self-regulation strategies; (2) Visual elaboration and summarizing strategies; (3) Deep information processing strategies; and (4) Social learning self-regulation strategies. A total of 601 higher education students formed the sample. We analyzed, first, the internal validity of the questionnaire. Three structural models were tested: (M1) mono-factor; (M2) scales correlate among them freely, and (M3) the scales are indicators of a general construct. The latter model showed a slight better fit. Additionally, a path analysis was carried out to study the degree in which the use of the Deep learning strategies depends on personal factors and is associated to performance. It was found that the use depends directly and positively on learning goal orientation, on the self-messages defining the self-regulation style of emotion and motivation focused on learning, and on effort. Besides, these two last variables convey the effect of self-efficacy that, at the same time, affects effort. Academic performance, depends positively on effort but negatively to the use of deep learning strategies. It is hypothesized this negative relationship is due to the method of measurement of academic performance.

INFORMATION OVERLOAD: THE PSYCHOLOGICAL AREA

Among the variety of synonymously similar scientific concepts used in psychological research (cognitive overload, sensory overload, knowledge overload, information fatigue syndrome, cognitive information load, information overload, cognitive load), we chose the concept of “information overload” for categorical analysis, because it defines the most holistically and complete the corresponding psychological phenomenon, meaningfully reflecting and naming significant realities. The essential signs of this phenomenon are: the external one – “what caused the state of tension” (informational message) – and the internal one - “what mental state appears as a result” (overload). Information overload is defined as a state of an individual’s mental stress caused by received excessive information messages; this excessiveness is experienced subjectively as a stressful situation associated with worsened efficiency of the individual’s cognitive mental processes and causes a mental protective blocking of high-quality deep intellectual processing of incoming information and its transformation into full-fledged personal knowledge. In the context of multi-sensory perception by a media consumer of modern informational sources, two processes are important: coding by an addressee and decoding by an addressee of an information message; accordingly, in the process of media-perceptual communication, information overload becomes a barrier to knowledge formation by an individual perceiving certain information flows. The content and nature of information overload as a psychological phenomenon is revealed in the measurement of the four aspects we have identified: cognitive, behavioral, emotional-motivational and competence. At the cognitive aspect, information overload is considered in the context of an individual's cognitive activities, affected by the functional limitations of cognitive processes (perception, attention, memory, thinking), and is determined through an individual's cognitive style. At the psychological dimension of the behavioral aspect, information overload is interpreted as a way for mass consciousness manipulating with the political or consumer-related context for individuals’ behavior modeling, in particular, during decision making. At the emotional-motivational aspect, information overload is defined as the emotional state of mental stress associated with unexpected and unfavorable informational influences, accompanied by a deterioration of an individual’s professional efficiency. At the psychological dimension of the competence-based aspect, information overload is interpreted as a psychological threat to an individual’s mental health and is defined as underdeveloped media literacy, as a result of used ineffective strategies by a media consumer in modern mediacommunication. As for counteraction, it is necessary to form media literacy of information consumers, increase their personal self-efficacy.

Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning.

How the brain representation of conceptual knowledge varies as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. In the present functional magnetic resonance imaging study, participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models—associated with the image referents of the words—and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control.

Interaction Analysis of FAIMER Mentor-Learner Web Online Collaborative Learning Session

This report presents the compilation of interaction analysis of a month long online discussion that supplemented regular FAIMER fellowship programme for health professional teachers. Online discussion forums are considered as an effective means and if conducted using appropriate protocols and activities will help learners to share and gain knowledge from each other. Research on interaction analysis stresses the need for proper protocols to evaluate online discussion for meaningful learning. For preparing this report Henri’s (1992) model for interaction analysis of computer-mediated communication was used for the qualitative analysis of electronic discourse that held as part of FAIMER programme among the 2015 and 2016 FAIMER fellows. The key domains examined for reporting are electronic interaction, social cues, cognitive skills and depth of information processing and metacognitive skills and knowledge. Transcript of interaction analysis of 454 mails posted for the entire month revealed most of the interactions were linear and some are of branching type. The social cues used during this computer mediated communication gave recognition to the moderators. Around 60% of participants’ information were at the surface level, 25% were at an in-depth level of information processing, and nearly 15% were of both surface and deep information processing. The metacognitive activities identified by Henri, such as planning, self-awareness, and evaluation were also witnessed in this online discussion making the discussion forum learner-centered. The outcomes of this maiden attempt on the interaction analysis supports the assumption of online discussion forums as an important component of online courses, and the need for usable protocols for analyzing the effectiveness of online discussions as a form of computer-mediated communication. Thus, the FAIMER online discussion forum that was created for collaborative learning on the topic “Giving Feedback” fulfils the criteria proposed in Henri’s protocol for evaluating meaningful collaborative learning through online discussions. Online discussions if conducted following appropriate protocols will have greatest potential on the impact of learning and can serve as easy and effective means of capacity building of health professional educators.

Should learners use their hands for learning? Results from an eye‐tracking study

AbstractGiven the widespread use of touch screen devices, the effect of the users' fingers on information processing and learning is of growing interest. The present study drew on cognitive load theory and embodied cognition perspectives to investigate the effects of pointing and tracing gestures on the surface of a multimedia learning instruction. Learning performance, cognitive load and visual attention were examined in a one‐factorial experimental design with the between‐subject factor pointing and tracing gestures. The pointing and tracing group were instructed to use their fingers during the learning phase to make connections between corresponding text and picture information, whereas the control group was instructed not to use their hands for learning. The results showed a beneficial effect of pointing and tracing gestures on learning performance, a significant shift in visual attention and deeper processing of information by the pointing and tracing group, but no effect on subjective ratings of cognitive load. Implications for future research and practice are discussed.