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Background: Previous studies have shown that dynamic illustrations, as compared to their static counterparts, lead to higher achievement levels, especially for hand‐based procedures. Other researchers have investigated how the presence of seductive details (i.e., appealing but irrelevant adjunct displays) influences students' interest positively but their learning negatively. Objectives: The purpose of the two present studies was to investigate the effects of animated versus static presentations, combined with the presence of seductive details, on student performance on a paper‐folding task (i.e., origami) in a naturalistic school environment. Methods: Fifty‐five children (5–6 years old) participated in the first study and were randomly assigned to one of four groups. The second study was conducted with older children (72 s or third graders) and a more complex origami task. Results and Conclusions: In the first study, results demonstrated negative effects of seductive details on children's performance and time on task, but no effects of presentation format. In the second study, no negative effects of seductive details on student achievement were found, but animated illustrations significantly improved children's performance and reduced time on task. However, seductive details tended to impair learning more greatly given the presence of static, as compared to animated, presentations. Task difficulties and pupils' ability to inhibit irrelevant information may explain these results. Lay Description: What is already known about this topic: Seductive details negatively influence young children's performance.Animated illustrations have been shown to improve students' learning for dynamic procedure. What this paper adds: This is the first study investigating the influence of animated vs. static illustrations combined with seductive details on children's learning performance.Animated illustrations improve children's performance for a challenging dynamic procedure.Seductive details tended to impair more learning from static than from animated presentations.Results are discussed in relation to the task's difficulty as well as students' ability to inhibit irrelevant information. Implications for practitioners: Animated illustrations should be used when presenting dynamic procedures.Instructors should use seductive details cautiously in pedagogical documents particularly when the task is difficult. [ABSTRACT FROM AUTHOR]

Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy. Objectives: This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums. Methods: Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions. Results and Conclusions: Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi‐directional long short‐term memory model for emotion detection yielded the highest predictive performance, with an impressive F‐measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low‐level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups and the utilization of their MOOC log data offer educators the capability to provide adaptive emotional feedback, customize assessments and offer more personalized attention as needed. Lay Description: What is currently known about the subject matter: Emotions are dynamic over time when learners experience cognitive disequilibrium/equilibrium.Online interactions are critical components, which influence learners' emotional state, cognitive processes and learning achievement.It is not clear what are differences in emotion sequence patterns across the groups with different interaction types. What the paper adds: An agglomerative hierarchical algorithm was implemented to cluster learners into three groups by analysing behavioural data.We explore possibilities for automated classification of emotions using deep learning approaches.Learners in the low‐level interaction group experienced more emotion transition from boredom to frustration. Implications for practitioners: A considerable amount of effort should be expended to identify and respond to learners who experience boredom and frustration emotions.Designing interventions or scaffolding to facilitate learners' interaction and promote favourable emotions.Educators could provide more personalized support based on learners' online interaction cluster. [ABSTRACT FROM AUTHOR]

Background: Deep discussions play an important role in students' online learning. However, researchers have largely focused on engaging students in deep discussions in online asynchronous forums. Few studies have investigated how to promote deep discussion via mobile instant messaging (MIM). Objectives: In this study, we applied learning analytical tools (i.e., KBdeX and word clouds) to enhance students' shared epistemic agency and thereby support their deep discussions in MIM. Methods: Forty Chinese college students participated in this study and reflected on their MIM engagement by participating in the learning analytics (LA)‐augmented meta‐discourse sessions. The study used multiple data analysis methods, including content analysis, statistical analysis, epistemic network analysis and lag sequential analysis. Results: We found that LA engaged students in deep discussions and shared epistemic agency‐related discourse, such as creating shared understanding, creating knowledge objects, and projective and regulative processes. In particular, word clouds engaged students in more complete shared epistemic agency discourse trajectory which started from creating awareness of unknowns, then progressed to setting projective plans and sharing information, and ultimately, creating shared understanding. Moreover, our analysis indicated that epistemic agency discourse moves of creating shared understanding led students to a high level of deep discussion. Implications: This study contributes to research by extending the 'comparison paradigm', which focuses on comparing (a)synchronous forums with MIM, to a 'design paradigm', which mobilises design features from (a)synchronous forums to MIM and using learning analytical tools to engage students in deep online discussions by promoting their epistemic agency. Lay Description: What is known in this subject matter: Supporting students' online deep discussions is important.Prior studies have shown multiple technological and pedagogical strategies to support deep discussions in (a)synchronous discussion forums, but few studies in mobile instant messaging (MIM).MIM is a quasi‐synchronous tool. It is difficult to transform the traditional external scaffolds (i.e., scripts or teachers' facilitation) which promote deep discussions from (a)synchronous forums to MIM. What this paper adds to this: We constructed a design framework which used learning analytical tools‐augmented meta‐discourse to engage students in shared epistemic agency discourse moves to promote their deep discussion in MIM.We conducted multiple types of analyses (content analysis, statistical analysis, epistemic network analysis and lag sequential analysis) on students' WeChat message (a major MIM in China).Results indicated both learning analytical tools helped engage students in shared epistemic agency discourse moves, such as creating shared understanding, creating knowledge objects, projective and regulative, thus, to promote students' deep discussions. Word cloud especially engaged students in more complete shared epistemic agency discourse trajectory. Moreover, analysis results indicated that shared epistemic agency discourse moves of creating shared understanding would lead students to a high level of deep discussions. Implications of study findings for practitioners: Our study provides a feasible way to educational practitioners who want to promote students' deep discussions in their online learning course (both fully online and flipped classroom mode).Knowledge Building Discourse Explorer (KBDeX presents the original analysis results of students' written messages in discussion forums as socio‐cognitive networks.) could be implemented in the classroom. The objective is to encourage students to understand the importance of connecting with peers' ideas in a one‐session implementation period.If time allows, teachers could use word cloud and meta‐discourse to engage students in socio‐metacognitive conversations in class to promote their shared epistemic agency discourse in online discussions. [ABSTRACT FROM AUTHOR]

Background: Learning from video lectures with peers, that is, co‐viewing video lectures, is a common mode of learning across a wide range of ages and topics in the information age. Objectives: The present study tested the effects of learners' motivation on co‐viewing video lectures in terms of learning performance, mental effort, and interpersonal brain synchronization (IBS). Methods: The current study was quantitative experimental. In total, 142 participants were recruited and divided into 71 dyads to manipulate task motivation from three aspects: meaning, interest, and reward. Then, they were divided into three experimental conditions according to the level of task motivation: two low task motivation learners (LL condition), a combination of one low and the other high (HL condition), and two high task motivation learners (HH condition). Two participants in a dyad were asked to view a video lecture on a shared screen and not talk until the end of the video lecture, with both learners in full view. Simultaneously, we recorded the cortical hemodynamic activity of two participants in each dyad using functional near‐infrared spectroscopy (fNIRS). Results and Conclusions: The group‐level analysis showed that two high‐task motivation learners had better learning performance, greater mental effort, and stronger IBS than a combination of one low and one high‐task motivation and two low‐task motivation. The results from the individual assessment indicated that pairing a person with low task motivation with another with high task motivation resulted in benefits for the low‐task motivation learner in terms of their self‐perceived learning. However, it negatively affected the performance of the high task motivation learner. Implications: First, high‐task motivation learners were encouraged to co‐view video lectures. Second, instructors were encouraged to intervene between their interactions by providing timely feedback to avoid losses for high‐task motivation learners, as this composition benefits low‐task motivation learners. Lay Description: What is already known about this topic: Most studies consider viewing video lectures as an individual process and test the role of learners' motivation at the individual level.Co‐viewing video lectures is a common mode of learning across a wide range of ages and topics in the information age.Learners' task motivation is essential for optimal learning in various settings. What this paper adds: Having two high task motivation co‐viewers enhanced their learning, encompassing retention, transfer, and confidence in performing well.Having two high task motivation co‐viewers enhanced IBS in the rTPJ‐rTPJ and IPL‐SMA pairings.Pairing a low task motivation learner with a high task motivation learner could be beneficial for the low task motivation learner, but it may result in losses for the high task motivation learner. Implications for practice and/or policy: Future work should verify the conclusions of the current study in natural settings, allowing verbal interaction versus settings permitting only nonverbal interaction and avoiding experimenter expectations.Future research should continue to test the long‐term effects of pairing a low task motivation learner with a high task motivation learner.We encourage high task motivation learners to co‐view video lectures.Instructors are advised to intervene in the interaction of pairs of a low task motivation learner with a high task motivation learner, for instance, by providing timely feedback. [ABSTRACT FROM AUTHOR]

Background: Though pedagogical artificial agents are expected to play a crucial role in the years to come, earlier studies provide inconsistent results regarding their effect on learning. This might be because their potential for exhibiting subtle nonverbal behaviours we know from human teachers has been untapped. What is more, there is little evidence of the processes underlying the effect of nonverbal behaviours of teachers (either human or artificial) on learning, so as to better guide their practical application. Objectives: The aims of the current research were threefold: firstly, to examine the effect of an artificial agent's vocal expressiveness on non‐verbal immediacy (teachers' ability to increase psychological closeness through nonverbal communication). Secondly, to test whether an artificial agent showing strong vocal expressiveness will enhance affective and cognitive learning (perceived and actual), as compared to an artificial agent that shows weak vocal expressiveness. Thirdly, to examine whether the underlying mechanisms of motivation and attention explain the effect of immediacy (and thereby also of vocal expressiveness) on the two learning outcomes. Methods: The study used a between‐participants design, with the participants being randomly assigned to one of the two experimental conditions: artificial modelling with strong expressiveness and artificial modelling with weak vocal expressiveness. Results and conclusions: Results showed that an artificial agent with strong vocal expressiveness increased affective and perceived cognitive learning. Partial support was found for actual cognitive learning. What is more, our findings revealed that vocal expressiveness is related to affective and perceived cognitive learning because it promotes nonverbal immediacy. Finally, results provided evidence of motivation as a mediator of the path from immediacy to affective learning. Major takeaways: The current findings verify the important role of nonverbal immediacy found in traditional educational settings. However, showing that these results also apply to artificial teachers is essential, given that the educational landscape is changing and reshaping by artificial intelligence. Thus, taking into consideration the role of vocal expressiveness in the development of artificial teachers or voice assistants like Alexa, Siri, and Google assistant, as a way to enhance immediacy and affective experience of learners is of imperative value, since they will be found more and more in our societies. Lay Description: What is currently known about the subject matter: There is a positive effect of nonverbal cues of human teachers on students' learningThe artificial agent's voice is important for learning. What their paper adds to this: Provides strong evidence of the positive effect of vocal expressiveness of artificial agents on learning outcomes.Reveals that vocal expressiveness is related to learning outcomes because it enhances immediacy.Examines the proposed mechanisms of motivation and attention as mediators of the path from immediacy to affective and cognitive learning. Implications of study findings for practitioners: Taking into consideration vocal expressiveness of artificial agents during the design process is essential for increased learning.The combination of the vocal parameters of pitch and speech rate is effective for constructing vocal expressiveness.By showing the underlying mechanisms of artificial agents' nonverbal cues (i.e., vocal cues) on learning, it helps designers to make a better decisions during construction.The study's findings are also pertinent for human teachers in improving students' learning in traditional educational settings. [ABSTRACT FROM AUTHOR]

In the increasing pervasiveness of today's digital society, mobile devices are changing the face of education. Students can interact with mobile devices in context‐aware environment. This paper presents a mobile application based on expert system (Plant‐E) for students to acquire knowledge about plant classification by answering decision‐making questions. In order to study effectiveness of Plant‐E and cognitive load of students who experience different kinds of interaction in learning process, another mobile application (Plant‐G) only providing information pages of plants was developed. A quasi‐experiment was conducted with three classes of 137 seventh graders. The three classes, Class A with 46 students using Plant‐E in campus, Class B with 44 students using Plant‐G in campus, and Class C with 47 students using Plant‐G in a traditional classroom, constitute three groups with different interaction complexity. The research conducted pretest, posttest, and delayed test to evaluate learning performance of students in three classes and used a questionnaire to investigate their perceptions and attitudes towards proposed system. Results show that interaction complexity has an impact on students' learning performance and mental effort in mobile learning; the higher the interaction complexity is, the higher mental effort and the better learning performance in mobile learning will be. Lay Description: What is already known about this topic: Mobile devices can be used to meet the urgency of learning need and can provide guidance and clues for learners in context‐aware environment.During mobile learning in context‐aware environment, students need to interact with mobile devices and environment.Mental effort reflects cognitive load related to interaction complexity, and it can affect learning performance.Self‐efficacy affects the degree of mental effort by affecting the firmness of performance goal.Cognitive load and learning performance are the main concerns that need to be considered when evaluating the effectiveness of mobile learning. What this paper adds: Interaction complexity has an impact on students' learning performance and mental effort in mobile learning.In terms of the three levels of interaction complexity in this study, the higher the interaction complexity is, the better the results that students will get in mobile learning.The higher the interaction complexity is, the higher the mental effort that students invested into in mobile learning would be.The medium self‐efficacy group invested the most mental effort and got the best grades, rather than the high or low group.Rule‐based mobile learning expert system is feasible and effective for students to learn by interacting with learning objects in context‐aware environment. Implications for practice and/or policy: Efforts should be made to make mobile learning systems more interactive; for example, learners can learn more effectively by interacting with mobile system, which can improve the frequency of observing learning objects.Rule‐based mobile learning expert system can give learners more opportunities to know how experts do when solving problems and can provide a potential method for students to learn in context‐aware environment. [ABSTRACT FROM AUTHOR]

The instructional effects of customization features in child learning games have rarely been examined. This value‐added study addresses the existing gap with regards to user‐initiated cosmetic customization of environment elements (i.e., non‐avatar customization). Participants (N = 143; Mage = 9.41) studied a biological topic for about 20 min: either using the experimental version of a learning game with customization features, or from a control version without them. Null results were found as concerns between‐group differences: both for motivation‐related variables and learning outcome measures. These findings indicate that user‐initiated cosmetic customization features can be omitted by game designers, especially in settings where children are assigned specific instructional materials from which to study. Lay Description: What is already known: User‐initiated cosmetic customization (the term user‐initiated is afterwards left out of the text for brevity) refers to making choices about a game's visual or audio attributes without directly affecting the content of gameplay itself.Studies with non‐child learners showed that a sub‐type of cosmetic customization referred to as avatar customization (i.e., customization of learners' game characters) can be potentially beneficial for learning.The instructional efficiency of other sub‐types of cosmetic customization (i.e., customization of other elements, such as environmental ones) in game‐based learning for children is less clear. What this paper adds: Children (Grades 3–4) prefer a game with a cosmetically customizable environment over its non‐customizable counterpart: but only when they can contrast these two versions (not when they cannot contrast them).Children enjoy learning and learn equally from customizable and non‐customizable games. Implications for practice: Game‐based learning with cosmetic customization features of game's environment can be used for learning without risk of harming learning outcomes.However, when children's autonomy in choosing instructional materials is restricted, there is little need to invest in the cosmetic customization of a game's environment elements.Cosmetic customization of a game's environment elements may play a more notable role when children are able to choose learning materials. [ABSTRACT FROM AUTHOR]

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; Mage = 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. Lay Description: What is already known about this topic: Contextual animation is frequently used in multimedia learning games (MLGs) for children.Contextual animation is understood to be non‐expository picture motion, which can be added to already existing representational pictures that provide context for target instruction.It is not known whether or not contextual animation, which embellishes already existing representational contextual pictures, harms learning for children in the case of MLGs. What this paper adds: Children (Grades 3 and 4) strongly prefer the game with added contextual animation compared to the game without it.No between‐group differences in overall learning enjoyment and learning outcomes were found.Small attention‐allocation effects of borderline significance were detected. Implications for practice Adding contextual animation (i.e., added non‐expository picture motion) to multimedia learning games for children will increase the attractiveness of the materials and will not hamper learning. Investment into the production of costly contextual animation may be worthwhile in the case of instructional games intended for uncontrolled settings (e.g., at home or during leisure time). In formal contexts (e.g., school), it is less clear whether investment into contextual animation is needed, since children are extraneously motivated to learn and animation does not appear to enhance learning outcomes. [ABSTRACT FROM AUTHOR]

Increasingly, student work is being conducted on computers and online, producing vast amounts of learning‐related data. The educational analytics fields have produced many insights about learning based solely on tutoring systems' automatically logged data, or "log data." But log data leave out important contextual information about the learning experience. For example, a student working at a computer might be working independently with few outside influences. Alternatively, he or she might be in a lively classroom, with other students around, talking and offering suggestions. Tools that capture these other experiences have potential to augment and complement log data. However, the collection of rich, multimodal data streams and the increased complexity and heterogeneity in the resulting data pose many challenges to researchers. Here, we present two empirical studies that take advantage of multimodal data sources to enrich our understanding of student learning. We leverage and extend quantitative models of student learning to incorporate insights derived jointly from data collected in multiple modalities (log data, video, and high‐fidelity audio) and contexts (individual vs. collaborative classroom learning). We discuss the unique benefits of multimodal data and present methods that take advantage of such benefits while easing the burden on researchers' time and effort. Lay Description: What is already known about this topic: When students use education software in the classroom, the software automatically logs their entries, clicks, and other submitted activity.Researchers can use this detailed software‐logged data to infer information about students' learning processes.However, software cannot log some important events that happen during students' learning experience.One example is students' interactions with others while they use the software.What this paper adds: We collected video and audio while students used educational software in the classroom, either individually or working with a partner.The audio and video provided information on students' learning processes and contexts that the software‐logged data could not provide.We were able to gain an improved understanding of students' learning processes through the audio and video data.Implications for practice and/or policy: Students who engage in productive struggle on difficult problems rather than taking an easier or more direct path to the answer show improved learning trajectories following those problems.Features of the language students use with each other when collaborating on math learning predict their math performance. [ABSTRACT FROM AUTHOR]

Many researchers argue that students must be meaningfully engaged in the learning resources for effective learning to occur. However, current online learners still report a problematic lack of attractive and challenging learning resources that engage them in the learning process. This endemic problem is even more evident in online collaborative learning approaches whose resources lack of authentic interactivity, user empowerment, social identity and challenge, thus having a negative effect on learners' self-motivation and engagement. To overcome these and other limitations and deficiencies, in this paper, a new type of learning resource named Collaborative Complex Learning Resources ( CC-LR) is presented based on the virtualization of collaborative learning with the aim of leveraging knowledge elicited during live sessions. During the CC-LR execution, the collaborative sessions are animated so learners can observe how avatars discuss and collaborate, how discussion threads grow and how knowledge is been constructed, refined and consolidated. In addition, complex aspects of the learning process can be incorporated in the CC-LRs during their creation, such as cognitive assessment and emotional awareness. The system produced from this research is tested to evaluate the CC-LR enriched with complex information and analyze its effects in the discussion process. The research reported in this paper was undertaken within the Seventh Framework Programme ( FP7) European project called ' Adaptive Learning via Intuitive/ Interactive, Collaborative and Emotional systems'. [ABSTRACT FROM AUTHOR]