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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: As a result of the COVID‐19 pandemic, many teachers found themselves making a rapid and often challenging shift from in‐person classroom teaching to teaching in an online environment. As teachers continue to learn about working in this new environment, research in cognitive and learning sciences, specifically findings from cognitive load theory and related areas, can provide meaningful strategies for teaching in this 'new normal'. Objectives: This paper describes 12 tips derived from contemporary research in educational psychology, focusing particularly on empirically supported strategies that teachers may apply in their online classroom to ensure that learning is optimized. Implications for Practice: These strategies are generalizable across age groups and learning areas, and are categorized into one of two themes: approaches to optimize the design of online learning materials, and instructional strategies to support student learning. A discussion follows, outlining how teachers may apply these strategies in different contexts, with a brief overview of emerging efforts that aim to bridge cognitive load theory and self‐regulated learning research. Lay Description: What is already known about this topic?: Online teaching and learning have become more prevalent due to the COVID‐19 pandemicScientific research provides guidelines for how to optimize (online) learningA key leading theory that can inform teachers is cognitive load theory (CLT)CLT‐inspired research presents guidelines for improving students' learning What this paper adds?: 12 empirically supported tips are presented to inform online teaching and learning practiceAll 12 tips are inspired by and can be explained by CLTEight tips focus on optimizing the design of online learning materialsFour tips cover instructional strategies that can support students' learning Implications for practitioners: Teachers can use these tips when they design their online learning experiencesAppropriate use of these tips should support the learning processUsing these tips should enhance students' retention and understanding of the materialTherefore, these tips should help students perform better on tests [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]

Summary writing is a useful instructional tool for learning. However, summary writing is a challenge to many students. This mixed‐method study examined the potential of the Student Mental Model Analyzer for Research and Teaching (SMART) system to help students produce summaries that reflect key concepts and relations in a text. SMART uses the students' summary to generate a multi‐dimensional 3S (surface, structure, semantic) evaluation of the students' mental model. This model is then used to drive feedback to help students revise their summary. The current study is an initial investigation examining whether writing and revising in SMART improves students' summary quality. Students (n = 38) in a graduate‐level online course used SMART for seven reading assignments. The 38 students submitted a total of 357 summaries in response to the seven readings. In 47 cases, students produced both an initial draft and a modified revision. These 47 cases were selected for analysis. In the quantitative phase, MANOVA results indicated that students' summaries improved along the 3S dimensions from initial draft to revision. In the qualitative phase, inspection of exemplar cases revealed how students' mental models changed towards more robust and cohesive knowledge structure for texts. Lay Description: Lay DescriptionWhat is already known about this topicTexts are the main means of acquiring new information.Writing a summary is frequently used as a tool for learning from text.Even proficient readers often struggle to learn from text due to insufficient support.Automated summary evaluation (ASE) can help students revise their summaries.What this paper addsWe introduce the Student Mental Model Analyzer for Research and Teaching (SMART) system.SMART uses a written summary to evaluate the quality of the learner's mental models.SMART analyses a student's mental model in surface, structural and semantic dimensions.SMART helps students improve content‐coverage and overall quality of their summaries.Implications for practiceSMART can be a supplement in classrooms to help students learn from complex texts.Teachers can use SMART to provide real‐time, personalized, formative feedback at a scale.Students can use SMART to comprehend the text to prepare for meaningful class discussion.Scholars can use SMART to investigate expertise development in summary via instruction. [ABSTRACT FROM AUTHOR]

This study examines the influence of perceived social presence on online graduate students' value and expectancy beliefs. Forty‐nine participants enrolled in an online teacher leadership graduate program completed questionnaires that measured perceived social presence and value and expectancy beliefs. A series of simple linear regression analyses revealed that social presence positively predicted students' value for online learning. Social presence did not predict participants' expectations of future success in teacher leadership. Results partially supported the assumption of the expectancy‐value model that motivational beliefs are influenced by the information students receive from social partners. Moreover, the study tested and confirmed the application of the model in online settings and for students pursuing graduate education. Lay Description: What is already known about this topic: Value and expectancy motivational beliefs are influenced by perceptions of the classroom social climate.In online learning environments, social presence characterizes the classroom social climate.Existing studies mostly examine the social determinants of value and expectancy beliefs of children in K‐12 face‐to‐face educational settings.The role of social presence in shaping value and expectancy beliefs in fully online graduate programs needs more empirical attention. What this paper adds: A study that examined the impact of social presence on value and expectancy beliefs of online students pursuing graduate education.Social presence predicts value beliefs in online graduate students.Social presence is a social determinant of motivational beliefs. Implications for practice and/or policy: Promotes more careful attention to social presence and motivation in online graduate programs.Identifies social presence as a legitimate social precursor to value and expectancy beliefs.Broadens the scope of research on social determinants of motivational beliefs. [ABSTRACT FROM AUTHOR]

Peer Instruction is an active learning method widely used in higher education, whereby students answer a series of questions twice, once before and once after peer discussion. There is an ongoing debate as to whether a collective feedback should be given after the students' initial answer, and if so, how the frequently observed group conformity can be avoided. This study examined whether guiding on the use of this feedback can reduce group conformity and improve learning using an interactive learning environment to administer a new type of quiz using graphics, and delivering collective feedback to the whole class in a novel heatmap format. In the experimental group, the teacher told the students that the answer indicated by the heatmap was not necessarily the correct one; this information was not given to students in the control group. Results revealed that guided students were less likely to adopt the (incorrect) majority answer than the non‐guided students, and consequently, they were more likely to improve their learning by reaching an agreement about the correct answer through discussion with their peers. These findings suggest that guiding students in their use of collective feedback may have a crucial role in Peer Instruction. [ABSTRACT FROM AUTHOR]

We investigated the effects of readymade versus self‐generated graphic organizers (GOs) on learning, comparing the performances of undergraduates (N = 81) tasked with learning a multimedia document. This document was either presented on its own (control group), with a readymade GO, or with a blank GO that students had to fill in either before or during the learning of the document. In line with previous research, adding a readymade GO increased students' memorization and transfer scores, compared with controls. By displaying the main ideas in the text and their hierarchical relations, GOs act as visual aids to learning. Results showed that self‐generating a GO was no more beneficial than viewing a readymade GO when students were placed in a dual‐task situation (generation + learning). However, when the students' information processing was guided by sequencing these tasks (generation then learning), they outperformed the control and readymade groups on memorization and comprehension. Lay Description: What is already known about this topic: Graphic organizers (GOs) consist in visual displays showing the main ideas of a text and their interrelations.Adding GOs to pedagogical document have been shown to improve students' learning.However, self‐generating GOs has been proven to be detrimental for learning in several studies. What this paper adds: In previous studies, self‐generation of graphic organizers (GOs) have often been carried out while learning, however this task should be separated from the learning task.In the present study, students' learning performances increased when the self‐generated task was separated from the learning task compared to a condition with a readymade GO.These benefits on learning were not observed when the self‐generated activity was not guided. Implications for practitioners: Graphic organizers gathering all the key elements of a multimedia document and highlighting the structure of this document should be used in order to increase students' learning.Students should not be placed in double‐task situation when learning from a pedagogical document.In‐task guidance should be provided when self‐generative tasks are performed. [ABSTRACT FROM AUTHOR]

The study aims to identify the effect of a flipped classroom approach designed according to the cognitive theory of multimedia learning on the academic achievements of eighth‐grade students (aged 14 years) in Saudi Arabia in computer science. To this end, a quasi‐experimental design was used, with a sample of 67 students; 33 students were assigned to the experimental group, whereas 34 comprised the control group. The experimental group was subjected to the flipped classroom approach, whereas the control group was given direct instruction. To measure student achievements, an instrument that measures cognitive skills based on Revised Bloom's taxonomy levels was designed. Findings revealed a positive effect on the experimental group's achievement levels with respect to Bloom's higher order thinking skills, that is, applying, analysing, and evaluating. No difference was found between the two groups in terms of academic achievements at the remembering and understanding levels of Bloom's taxonomy. Moreover, learners with low prior knowledge showed a higher improvement in academic achievements compared with those with high prior knowledge. This corresponds to the assumption of the cognitive theory of multimedia learning that learners with low prior knowledge would benefit from its principles more than learners with high prior knowledge. Lay Description: What is already known about this topic: Increasingly, K‐12 teachers around the world are using the flipped classroom approach (FCA) with their students.Previous studies have shown mixed results on the effects of flipped learning on academic achievements.Scholarly research is limited regarding the use of cognitive theory of multimedia learning (CTML) as a theoretical framework for designing instructional videos in flipped learning.Several flipped learning studies have not provided data of their outcomes or assessment as per level of cognition. What this paper adds: This study presents an improved approach for flipped learning by integrating CTML with flipped learning.A comparison was conducted for the effects of FCA designed according to CTML on the academic achievements of students with high and low prior knowledge.A proposed model has been introduced for the application of flipped learning in K‐12 education. Implications for practice and/or policy: Designing instructional videos in flipped learning should be based on a theoretical framework.Flipped learning research needs to employ Bloom's taxonomy to understand the outcomes or assessments according to cognition levels.Adopting FCA designed according to CTML may be more important for teachers whose students clearly suffer from poor academic achievements. [ABSTRACT FROM AUTHOR]

Humans segment naturalistic actions into meaningful units. These are hierarchically organized: multiple fine events are part of a coarse event. We investigated how repeated practice of a virtual sequential assembly task influences learning of coarse and fine assembly steps. In Experiment 1, participants (N = 10) parsed the task into coarse and fine events. This determined the task's hierarchical structure. In Experiment 2, domain experts (N = 18) and novices (N = 19) practiced the task in a virtual environment three times with intermittent memory testing. We found a hierarchical level effect: memory improved with repeated virtual training for coarse but not for fine steps. Further, experts' memory for fine assembly steps was higher compared with novices' memory. The hierarchical level effect could be explained by higher saliency (and lower similarity, respectively) of the coarse compared with the fine steps. We discuss implications for virtual training, which compose of hierarchical structure into task presentation. Lay Description: What is already known about this topic?: Virtual trainings offer a safe environment for repeated practice in which learners can handle challenging activities.Changes in the working life (e.g., "industry 4.0") require workers to learn new dynamic tasks quickly.Humans perceive dynamic tasks in terms of coarse and fine events, these are hierarchically structured. What this paper adds?: Repeated virtual training improves memory for coarse but not fine events.Domain experts recognize fine events more successfully than novices. Implications for practice and/or policy?: Virtual trainings considering the underlying event structure are useful for training dynamic assembly tasks.Knowledge tests of virtual trainings should be based on the event structure. [ABSTRACT FROM AUTHOR]

The research results we present in this paper reveal that properly calibrated e-learning tools have potential to effectively promote the algorithmic thinking of both science-oriented and humanities-oriented students. After students had watched an illustration (by a folk dance choreography) and an animation of the studied sorting algorithm (bubble sort), they were invited to predict and perform [(1) to reconstruct on the same input; (2) to orchestrate on a random sequence stored in a white array; (3) to orchestrate on a black-box sequence] the entire step sequence of the algorithm (using the interactive visual learning environment we developed). The results of the experiment show that while science-oriented students' performance proved superior to those of their humanities-oriented colleagues, the differences were observed to diminish as both groups advanced with their e-learning tasks. Although drawing general conclusions would be premature, we can conclude that there are no unbridgeable differences in the way these two groups relate to e-learning processes that aim to promote algorithmic thinking. Our findings also emphasize the key importance of some motivational principles in facilitating algorithmic thinking: the principle of moderate and progressive challenge, the principle of gradual shift from concrete to abstract and the principle of genuine active involvement. [ABSTRACT FROM AUTHOR]