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Background: Learner profiles detected from digital trace data are typically triangulated with survey data to explain those profiles based on learners' internal conditions (e.g., motivation). However, survey data are often analysed with limited consideration of the interconnected nature of learners' internal conditions. Objectives: Aiming to enable a thorough understanding of trace‐based learner profiles, this paper presents and evaluates a comprehensive approach to analysis of learners' self‐reports, which extends conventional statistical methods with psychological networks analysis. Methods: The study context is a massive open online course (MOOC) aimed at promoting physical activity (PA) for health. Learners' (N = 497) perceptions related to PA, as well as their self‐efficacy and intentions to increase the level of PA were collected before and after the MOOC, while their interactions with the course were logged as digital traces. Learner profiles derived from trace data were further examined and interpreted through a combined use of conventional statistical methods and psychological networks analysis. Results and Conclusions: The inclusion of psychological networks in the analysis of learners' self‐reports collected before the start of the MOOC offers better understanding of trace‐based learner profiles, compared to the conventional statistical analysis only. Likewise, the combined use of conventional statistical methods and psychological networks in the analysis of learners' self‐reports before and after the MOOC provided more comprehensive insights about changes in the constructs measured in each learner profile. Major Takeaways: The combined use of conventional statistical methods and psychological networks presented in this paper sets a path for a comprehensive analysis of survey data. The insights it offers complement the information about learner profiles derived from trace data, thus allowing for a more thorough understanding of learners' course engagement than any individual method or data source would allow. Lay Description: What is already known about this topic: Researchers have made extensive use of data from online learning platforms—often referred to as learning trace data—to understand how learners engage in online learning activities.Surveys are often used to gather information about learners' motivation, perceptions, and other internal factors, to complement the insight gleaned from learning traces and thus create a more complete picture of the learning behaviour.When analysing survey data, the focus is usually on individual factors without considering how different factors are connected and affect one another.Psychological networks analysis is a novel analytic approach to studying complex phenomena both in psychology and education. What this paper adds: The paper uses psychological networks as a method for examining mutual relations among learners' internal factors (e.g., motivation, perceptions) measured through self‐reports before and after a course.By combining conventional statistical analysis of self‐reports with psychological networks analysis, the paper develops a comprehensive picture of learners' internal factors measured through self‐reports, one that accounts for both individual and interconnected nature of those factors.This comprehensive approach to information extraction from surveys allows for better understanding of trace‐based learner profiles, that is, profiles mined from data about learners' interaction with online learning activities. Implications for practice and/or policy: The presented method paves a way for a comprehensive analysis of the data collected from learners through surveys.It helps us gain a better understanding of learners' engagement with online learning activities.It can advance the evaluation of digital educational programs that aim to encourage changes in health‐related behaviour. [ABSTRACT FROM AUTHOR]

Background: Despite the significant emphasis on self‐determination in special education and the crucial role of mobile learning, there is a notable absence of path modelling studies that explore the effect of self‐determination on the usage of mobile learning by students with special needs. Objectives: This study sought to investigate the intentions of mobile learning usage among students with special needs by proposing a model rooted in the technology acceptance model, complemented by extraneous constructs from self‐determination theory (competence, autonomy and relatedness). Methods: The data of the study was obtained online from 1298 special needs university students with eight different types of disabilities. Data were analysed with descriptive statistics, confirmatory factor analysis, structural equation modelling and bootstrapping. Results and Conclusions: The proposed model explained 78.4% of ease of use, 85.2% of usefulness, and 76.5% of intention. 11 of the 12 hypotheses tested within the scope of the model were supported. All hypotheses examining the impacts of self‐determination theory constructs on ease of use, usefulness, and intention were validated (8 hypotheses), with the exception of the autonomy → intention relationship. Substantial empirical evidence has been acquired to support the role of the self‐determination theory in exploring the intention toward mobile learning among university students with special needs. Concurrently, a robust theoretical framework has been introduced to the field of special education to elucidate the acceptance and utilization of technology by university students with special needs. Lay Description: What is already known about this topic: Numerous studies have demonstrated the significance of mobile tools and mobile learning in the realm of special education.Self‐determination holds immense importance in fostering independence and social engagement by empowering individuals with special needs to gain confidence, assume control and accountability for their own choices and actions.Self‐determination theory underscores the importance of providing individuals with support for three fundamental psychological needs—autonomy, competence, and relatedness—to foster healthy development. What this paper adds: To the best of the authors' knowledge, this study stands as a unique contribution within its field due to its distinctive characteristics. The model, supported by exceptionally successful outcomes and its ability to elucidate technology acceptance constructs at remarkably high levels, offers a robust theoretical framework for enabling students with special needs to engage effectively with mobile learning in both special and inclusive educational settings.Investigating how self‐determination theory enhances the explanatory power of the technology acceptance model in special education.Illuminating the significance of basic psychological needs in the adoption of mobile learning, employing advanced statistical methods with a substantial participant base, and delivering valuable insights to advance the utilization of mobile learning in special education. Implications for practice and/or policy: Special education settings should encourage students to take initiative, make choices, and exercise autonomy, and curriculum design should include opportunities for students to make choices.Making students part of the design processes and emphasizing usability through the prioritization of customizable, user‐friendly designs should be an inherent aspect of educational technology.Educators and support staff must have training and professional development opportunities to effectively use mobile learning tools and create inclusive learning environments. [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: Japanese EFL learners often fail to identify and categorise the different degrees of certainty associated with CERTAIN, PROBABLE, and POSSIBLE items. This computer‐mediated study evaluated the relative effects of animated versus static illustrations on stimulating the spatial concept of distance for the acquisition of this knowledge. Objectives: This study aimed to: (1) discover whether the spatial concept of distance captured by three‐dimensional (3D) images in animated or static illustrations help learners understand spatial relationships more clearly, and (2) whether a static illustration is sufficient for learning the different degrees of certainty. Method: Animated and static visualisation groups used 3D images of animated versus static illustrations that capture the spatial concept of distance, and a contrasting group did not use illustrations. The former applied the spatial concept of distance to learn the degrees of certainty, using animated or static illustrations. A spatial concept‐oriented approach that visualises concepts of distance enabled participants to understand the degrees of certainty. The contrasting approach involved rote learning of a list of target expressions for the different degrees of certainty in English. Results and Conclusions: Both the animated and static versions of the spatial concept‐oriented approaches were equally effective. The difference between the two versions did not significantly impact the overall performance of the groups. Takeaways: Regardless of the type of effect, the spatial concept of distance captured in words and illustrations helped the participants (1) process the different degrees of certainty cognitively in terms of distances and (2) retain them in long‐term memory. Lay Description: What is already known about this topic: Understanding our experiences in terms of metaphors based on spatially visualised concepts of the physical world leads to mapping the embodied concepts onto non‐embodied concepts and retaining these in long‐lasting memory.3D descriptions direct people's attention to the spatially visualised concepts, which influences people to internalise them with ease.The lack of consensus (in previous studies) on the effect of animated versus static versions of the metaphor awareness‐raising approach with either 2D or 3D images has some relevance to the cognitive theory of multimedia learning. What this paper adds: Computer‐generated 3D images were used to help participants in the metaphor‐based groups understand spatial relationships related to the degrees of certainty among certain, probable, and possible items.This study evaluated the relative effects of animated versus static illustrations on stimulating the spatial concept of distance for the acquisition of this knowledge.The animated and static version of the metaphor‐based approach groups outperformed those in the non‐metaphor‐based and control groups. Implications for practice and/or policy: It is necessary to consider all possible confounding variables and control for their effects when designing future animation versus static studies.Further analysis of the spatial concept‐oriented metaphor‐based approach from the perspectives of the cognitive theory of multimedia learning combined with cognitive linguistic theory would be beneficial for fellow researchers and teachers to gain insight into the nuances of the approach and expand its application in the EFL context. [ABSTRACT FROM AUTHOR]

Background: Mobile‐assisted language learning (MALL) has long been advocated to promote students' language study outcomes. However, little is known about how using instant messaging applications (an example of MALL) affect student motivation, language anxiety, and actual achievement. Objectives: To help close this gap, we have conducted a quasi‐experimental study, in which we have used self‐determination theory to test a mediational process model, in an effort to understand the psychological mechanisms that underlie the effect of instant messaging applications on student outcomes. Methods: Two groups of Saudi undergraduate students (N = 447) were recruited to participate in the study. Participants under the experimental condition were invited to engage in language‐related tasks with their teacher outside of class through the WhatsApp application, whereas participants under the control condition received standard teaching. We applied partial least square structural equation modelling (PLS‐SEM) to test our model. Results and Conclusions: The results of the PLS‐SEM showed that the total effects of using instant messaging applications on autonomous motivation and L2 achievement were significant but that language anxiety was influenced only indirectly and negatively through language achievement. Also, the PLS‐SEM showed that our results have strong predcitive power indicating support for external validity. Implications: Our results show that instant messaging applications can be used to facilitate interest and achievement and to decrease anxiety. Furthermore, we are contributing to L2 methodological literature by using applications of PLS‐SEM in experimental designs that can help researchers and practitioners of motivational processes. Lay Description: What is already known about this topic: Using WhatsApp and more broadly instant messaging applications are conceived to be motivating in language teaching contextStudies indicate students are inclined toward using these applications in language learningResearch shows that using WhatsApp is easy to apply for language teaching purposes What this paper adds: The study examined the effect of using WhatsApp on language learners' motivation, achievement, and language anxietyA pre‐ and post‐test approach with experimental and control condition has been used to evaluate the effect more accuratelyThe study employs Self‐determination Theory as a theoretical framework for testing a motivational process modelResults show that using WhatsApp predicted the outcomes directly and indirectly Implications for practice and/or policy: WhatsApp can significantly encourage university students to better learning of the language skills especially in times where face‐to‐face communication is limitedWhatsApp is conditioned by the guidance of instructors, their ongoing formative and summative assessmentWhatsApp appears to allow students enjoy their learning process due to the opportunities for collaborative work and constant exchange of content [ABSTRACT FROM AUTHOR]

Industrial systems can be complex and not intuitive to perceive. Therefore, students in technology and engineering programs can benefit from developing mental models of industrial systems during their journey in college. However, more often than not, these students do not have access to industrial facilities; thus, developing mental models for systems is a challenge. This paper examines the merit of an Immersive Virtual Reality (IVR) framework application in creating proper mental models for industrial systems in technology and engineering students. Two IVR applications were developed. One IVR application afforded interaction with components of a prefabricated industrial cooling water system (CWVR). In the other application, students designed and built industrial systems with IVR (system designer VR [SDVR]). SDVR facilitated constructive‐generative engagement. A group of 33 students was divided into two; one group (the Design, experimental group) was tasked with building a system with SDVR and interacting with the cooling water system in CWVR. The other group was tasked with directly interacting with the CWVR without building a system with SDVR (the Interaction, comparison group). Students' mental models of the cooling water system in CWVR were evaluated following completing the interaction experience with CWVR. The results demonstrate that the causal model notion of the mental model of the cooling water system was significantly higher in the Design, experimental group. The results suggest that designing a rich IVR application that facilitates constructive‐generative engagements may carry merit in informing student mental models of complex technical concepts. Lay Description: Industrial systems can be complex and not intuitive to perceive.Students in technology‐related programs may struggle to develop mental models for systems.Immersive virtual reality applications for industrial systems were developed and tested.Results indicate that virtual reality might inform mental models of systems in students. [ABSTRACT FROM AUTHOR]