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This study investigates the relationship between students' self-efficacy beliefs, goal-setting, and learning tactics in an online business course. Using sequence analysis and process mining techniques, we analyzed log data from 209 students to identify distinct learning tactics and their association with self-efficacy beliefs, inferred from initial goal-setting and final grades. Four learning tactics were identified, with significant differences observed between students with high and low self-efficacy beliefs. High self-efficacy students demonstrated more varied and adaptive tactics, including greater use of quizzes and project-focused activities. In contrast, low self-efficacy students and those who didn't set goals showed less engagement and fewer monitoring activities. The project-focused tactic had the strongest correlation with final grades and goal achievement. Goal-setting at the course's start was linked to more effective learning behaviors and better outcomes. The study reveals how students' online learning behavior changes based on initial goal-setting and subsequent reflection. These findings contribute to research on self-regulated learning in online environments and offer practical implications for designing online courses and learning analytics interventions. Understanding these differences enables the development of targeted interventions to improve learning tactics and self-efficacy beliefs among students, ultimately enhancing their ability to achieve learning goals in online educational settings. [ABSTRACT FROM AUTHOR]

Previous research on player profiles or types is based on players' in-game behaviors and their motivations to play games. However, there are many other activities related to digital games beyond playing the games properly. Using learning analytics methods, the study investigates the prevalence and interconnections between these different metagame activities and classifies gamers based on their use thereof into distinct profiles. The results show that digital game–related information-seeking activities are key metagame activities with connections to other metagame activities. Three distinct profiles of players were identified based on their metagame activities: versatile metagamers, strategizers, and casual metagamers. The results contribute to the existing literature on metagaming and provide insights for game studies, game design and marketing, and digital games and learning. [ABSTRACT FROM AUTHOR]

This study offers a comprehensive analysis of COVID-19 research in education. A multi-methods approach was used to capture the full breadth of educational research. As such, a bibliometric analysis, structural topic modeling, and qualitative synthesis of top papers were combined. A total of 4,201 articles were retrieved from Scopus, mostly published from 2019 to 2021. In this work special attention is paid to analyzing and synthesizing findings about: (i) status of research about COVID-19 regarding frequencies, venues, publishing countries, (ii) identification of main topics in the COVID-19 research, and (iii) identification of the major themes in most cited articles and their impact on the educational community. Structural topic modeling identified three main groups of topics that related to education in general, moving to online education, or diverse topics (e.g., perceptions, inclusion, medical education, engagement and motivation, well-being, and equality). A deeper analysis of the papers that received most attention revealed that problem understanding was the dominating theme of papers, followed by challenges, impact, guidance, online migration, and tools and resources. A vast number of papers were produced. However, thoughtful, well-planned, and meaningful research was hard to conceptualize or implement, and a sense of urgency led to a deluge of research with thin contributions in a time of dire need to genuine insights. [ABSTRACT FROM AUTHOR]

In an era of rapid evolution in educational technologies, Virtual Labs (V-Labs) have emerged as a promising solution, fundamentally altering how learners engage with scientific concepts and experiments. Despite their potential, ensuring their effectiveness and inclusivity in terms of accessibility to diverse students remains a challenging task. Currently, there is limited insight into the accessibility of V-Labs, a gap that our study aims to address. This study seeks to ascertain the effectiveness of V-Labs in terms of accessibility and inclusivity. We synthesized empirical studies, reviewing 36 articles published between 2000 and 2023. Of these articles, 69% of the studies were conducted in higher education and covered a wide range of learning environments. Our study revealed that 47.3% of the studies focused on various engineering subjects. Our findings provide insight into V-Labs' accessibility from four key perspectives: (a) students with limited abilities, (b) diverse cultural and linguistic backgrounds, (c) instructional design features and content availability, and (d) interaction supporting features. We also identified existing gaps in the accessibility of the V-Labs from the four perspectives. Furthermore, we examined the assessment methods of V-Labs, shed light on the aspects that are evaluated, and underscored the need for future work on assessment strategies. [ABSTRACT FROM AUTHOR]

Complex dynamic systems offer a rich platform for understanding the individual or the person‐specific mechanisms. Yet, in learning analytics research and education at large, a complex dynamic system has rarely been framed, developed, or used to understand the individual student where the learning process takes place. Individual (or person‐specific) methods can accurately and precisely model the individual person, create person‐specific models, and devise unique parameters for each individual. Our study used the latest advances in complex systems dynamics to study the differences between group‐based and individual self‐regulated learning (SRL) dynamics. The findings show that SRL is a complex, dynamic system where different sub‐processes influence each other resulting in the emergence of non‐trivial patterns that vary across individuals and time scales, and as such far from the uniform picture commonly theorized. We found that the average SRL process does not reflect the individual SRL processes of different people. Therefore, interventions derived from the group‐based SRL insights are unlikely to be effective in personalization. We posit that, if personalized interventions are needed, modelling the person with person‐specific methods should be the guiding principle. Our study offered a reliable solution to model the person‐specific self‐regulation processes which can serve as a ground for understanding and improving individual learning and open the door for precision education. Practitioner notesWhat is already known about this topic Self‐regulation is a catalyst for effective learning and achievement.Our understanding of SRL personalization comes from insights based on aggregate group‐based data.What this paper adds Every student has their own unique SRL process that varies from the average in non‐trivial ways.We offer a credible method for mapping the individualized SRL process.SRL dynamics vary across time scales. That is, the trait dynamics are different from the state dynamics, and they should be supported differently.Implications for practice and/or policy Personalization can best be achieved if based on unique person‐specific idiographic methods.Supporting learning and SRL in particular can be more efficient when we understand the differences across time scales and persons and apply insights accordingly.The general SRL average should not be expected to work for everyone. [ABSTRACT FROM AUTHOR]

Over the last four decades, studies have investigated the incorporation of Artificial Intelligence (AI) into education. A recent prominent AI-powered technology that has impacted the education sector is ChatGPT. This article provides a systematic review of 14 empirical studies incorporating ChatGPT into various educational settings, published in 2022 and before the 10th of April 2023—the date of conducting the search process. It carefully followed the essential steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, as well as Okoli's (Okoli in Commun Assoc Inf Syst, 2015) steps for conducting a rigorous and transparent systematic review. In this review, we aimed to explore how students and teachers have utilized ChatGPT in various educational settings, as well as the primary findings of those studies. By employing Creswell's (Creswell in Educational research: planning, conducting, and evaluating quantitative and qualitative research [Ebook], Pearson Education, London, 2015) coding techniques for data extraction and interpretation, we sought to gain insight into their initial attempts at ChatGPT incorporation into education. This approach also enabled us to extract insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of this review show that learners have utilized ChatGPT as a virtual intelligent assistant, where it offered instant feedback, on-demand answers, and explanations of complex topics. Additionally, learners have used it to enhance their writing and language skills by generating ideas, composing essays, summarizing, translating, paraphrasing texts, or checking grammar. Moreover, learners turned to it as an aiding tool to facilitate their directed and personalized learning by assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. However, the results of specific studies (n = 3, 21.4%) show that overuse of ChatGPT may negatively impact innovative capacities and collaborative learning competencies among learners. Educators, on the other hand, have utilized ChatGPT to create lesson plans, generate quizzes, and provide additional resources, which helped them enhance their productivity and efficiency and promote different teaching methodologies. Despite these benefits, the majority of the reviewed studies recommend the importance of conducting structured training, support, and clear guidelines for both learners and educators to mitigate the drawbacks. This includes developing critical evaluation skills to assess the accuracy and relevance of information provided by ChatGPT, as well as strategies for integrating human interaction and collaboration into learning activities that involve AI tools. Furthermore, they also recommend ongoing research and proactive dialogue with policymakers, stakeholders, and educational practitioners to refine and enhance the use of AI in learning environments. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field. [ABSTRACT FROM AUTHOR]

Conversational agents (CAs) have come a long way from their first appearance in the 1960s to today's generative models. Continuous technological advancements such as statistical computing and large language models allow for an increasingly natural and effortless interaction, as well as domain-agnostic deployment opportunities. Ultimately, this evolution begs multiple questions: How have technical capabilities developed? How is the nature of work changed through humans' interaction with conversational agents? How has research framed dominant perceptions and depictions of such agents? And what is the path forward? To address these questions, we conducted a bibliometric study including over 5000 research articles on CAs. Based on a systematic analysis of keywords, topics, and author networks, we derive "five waves of CA research" that describe the past, present, and potential future of research on CAs. Our results highlight fundamental technical evolutions and theoretical paradigms in CA research. Therefore, we discuss the moderating role of big technologies, and novel technological advancements like OpenAI GPT or BLOOM NLU that mark the next frontier of CA research. We contribute to theory by laying out central research streams in CA research, and offer practical implications by highlighting the design and deployment opportunities of CAs. [ABSTRACT FROM AUTHOR]

Learning analytics methods have proven useful in providing insights from the increasingly available digital data about students in a variety of learning environments, including serious games. However, such methods have not been applied to the specific context of educational escape rooms and therefore little is known about students' behavior while playing. The present work aims to fill the gap in the existing literature by showcasing the power of learning analytics methods to reveal and represent students' behavior when participating in a computer-supported educational escape room. Specifically, we make use of sequence mining methods to analyze the temporal and sequential aspects of the activities carried out by students during these novel educational games. We further use clustering to identify different player profiles according to the sequential unfolding of students' actions and analyze how these profiles relate to knowledge acquisition. Our results show that students' behavior differed significantly in their use of hints in the escape room and resulted in differences in their knowledge acquisition levels. [ABSTRACT FROM AUTHOR]

Learning analytics provides a novel means to examine various aspects of students' learning and to support them in their individual endeavors. The purpose of this study was to explore the potential of learning analytics to provide insights into non-traditional, vocational practical nurse students' (N = 132) motivational profiles for choosing their studies, using a mixed-methods approach. Non-traditional students were somewhat older learners than those following a more straightforward educational pathway and had diverse educational or professional backgrounds. Institutional admission data and analytics were used to identify their specific study motives and distinct motivational profiles, and to illustrate the connections between the motives emerging in the motivational profiles. Furthermore, the association between the motivational profiles and study performance was examined. The results of qualitative content analysis indicated that non-traditional practical nurse students pursued such specialized training for various reasons, and that pragmatic, professional rationales were emphasized over prosocial, altruistic factors. Through the adoption of person-centered latent class analysis, three motivational profiles were identified: self-aware goal-achievers, qualification attainers, and widely oriented humanitarians. Additionally, the analyses of epistemic networks for the profiles showed the complex interplay between the motives, confirming that some motive connections appear to be more prominent than others. Moreover, the findings indicated that study motives reported at admission did not seem to dictate students' later study performance, as no statistically significant associations were found between the motivational profile and the students' final grade point average or study dropout. This investigation paves the way for more-targeted motivational support and the use of learning analytics in the context of vocational education and training. [ABSTRACT FROM AUTHOR]

Learning analytics is a fast‐growing discipline. Institutions and countries alike are racing to harness the power of using data to support students, teachers and stakeholders. Research in the field has proven that predicting and supporting underachieving students is worthwhile. Nonetheless, challenges remain unresolved, for example, lack of generalizability, portability and failure to advance our understanding of students' behaviour. Recently, interest has grown in modelling individual or within‐person behaviour, that is, understanding the person‐specific changes. This study applies a novel method that combines within‐person with between‐person variance to better understand how changes unfolding at the individual level can explain students' final grades. By modelling the within‐person variance, we directly model where the process takes place, that is the student. Our study finds that combining within‐ and between‐person variance offers a better explanatory power and a better guidance of the variables that could be targeted for intervention at the personal and group levels. Furthermore, using within‐person variance opens the door for person‐specific idiographic models that work on individual student data and offer students support based on their own insights. Practitioner notesWhat is already known about this topicPredicting students' performance has commonly been implemented using cross‐sectional data at the group level.Predictive models help predict and explain student performance in individual courses but are hard to generalize.Heterogeneity has been a major factor in hindering cross‐course or context generalization.What this paper addsIntra‐individual (within‐person) variations can be modelled using repeated measures data.Hybrid between–within‐person models offer more explanatory and predictive power of students' performance.Intra‐individual variations do not mirror interindividual variations, and thus, generalization is not warranted.Regularity is a robust predictor of student performance at both the individual and the group levels.Implications for practiceThe study offers a method for teachers to better understand and predict students' performance.The study offers a method of identifying what works on a group or personal level.Intervention at the personal level can be more effective when using within‐person predictors and at the group level when using between‐person predictors. [ABSTRACT FROM AUTHOR]

Background: Group affective states for learning are constantly formed through socio‐emotional interactions. However, it remains unclear how the affective states vary during collaboration and how they occur with regulation of learning. Appropriate methods are needed to track both group affective states and these interaction processes. Aims: The present study identifies different socio‐emotional interaction episodes during groups' collaborative learning and examines how group affective states fluctuate with regulation of learning during these episodes. Sample: The participants were 54 secondary school students working in groups across four science learning sessions. Methods: Multichannel process data (video, electrodermal activity [EDA]) were collected in an authentic classroom. Groups' affective states were measured with emotional valence captured from video data, and activation captured as sympathetic arousal from EDA data. Regulation of learning was observed from the videotaped interactions. Results: The study disclosed four clusters of socio‐emotional interaction episodes (positive, negative, occasional regulation, frequent regulation), which differed in terms of fluctuation of affective states and activated regulation of learning. These clustered episodes confirm how affective states are constantly reset by socio‐emotional interactions and regulation of learning. The results also show that states requiring regulation do not automatically lead to its activation. Conclusions: By advancing existing understanding of how group level socio‐emotional processes contribute to regulation of learning, the study has implications for educational design and psychological practice. Methodologically, it contributes to collaborative learning research by employing multiple data channels (including biophysiological measures) to explore the various dimensions of socio‐emotional processes in groups. [ABSTRACT FROM AUTHOR]

Remote learning has advanced from the theoretical to the practical sciences with the advent of virtual labs. Although virtual labs allow students to conduct their experiments remotely, it is a challenge to evaluate student progress and collaboration using learning analytics. So far, a study that systematically synthesizes the status of research on virtual laboratories and learning analytics does not exist, which is a gap our study aimed to fill. This study aimed to synthesize the empirical research on learning analytics in virtual labs by conducting a systematic review. We reviewed 21 articles that were published between 2015 and 2021. The results of the study showed that 48% of studies were conducted in higher education, with the main focus on the medical field. There is a wide range of virtual lab platforms, and most of the learning analytics used in the reviewed articles were derived from student log files for students' actions. Learning analytics was utilized to measure the performance, activities, perception, and behavior of students in virtual labs. The studies cover a wide variety of research domains, platforms, and analytical approaches. Therefore, the landscape of platforms and applications is fragmented, small-scale, and exploratory, and has thus far not tapped into the potential of learning analytics to support learning and teaching. Therefore, educators may need to find common standards, protocols, or platforms to build on each others' findings and advance our knowledge. [ABSTRACT FROM AUTHOR]

Remote learning has advanced from the theoretical to the practical sciences with the advent of virtual labs. Although virtual labs allow students to conduct their experiments remotely, it is a challenge to evaluate student progress and collaboration using learning analytics. So far, a study that systematically synthesizes the status of research on virtual laboratories and learning analytics does not exist, which is a gap our study aimed to fill. This study aimed to synthesize the empirical research on learning analytics in virtual labs by conducting a systematic review. We reviewed 21 articles that were published between 2015 and 2021. The results of the study showed that 48% of studies were conducted in higher education, with the main focus on the medical field. There is a wide range of virtual lab platforms, and most of the learning analytics used in the reviewed articles were derived from student log files for students' actions. Learning analytics was utilized to measure the performance, activities, perception, and behavior of students in virtual labs. The studies cover a wide variety of research domains, platforms, and analytical approaches. Therefore, the landscape of platforms and applications is fragmented, small-scale, and exploratory, and has thus far not tapped into the potential of learning analytics to support learning and teaching. Therefore, educators may need to find common standards, protocols, or platforms to build on each others' findings and advance our knowledge. [ABSTRACT FROM AUTHOR]

During the past years scholars have shown an increasing interest in supporting students' self-regulated learning (SRL). Learning analytics (LA) can be applied in various ways to identify a learner's current state of self-regulation and support SRL processes. It is important to examine how LA has been used to identify the need for support in different phases of SRL cycle, which channels are used to mediate the intervention and how efficient and impactful the intervention is. This will help the learners to achieve the anticipated learning outcomes. The systematic literature review followed PRISMA 2020 statement to examine studies that applied LA interventions to enhance SRL. The search terms used for this research identified 753 papers in May 2021. Of these, 56 studies included the elements of LA, SRL, and intervention. The reviewed studies contained various LA interventions aimed at supporting SRL, but only 46% of them revealed a positive impact of an intervention on learning. Furthermore, only four studies reported positive effects for SRL and covered all three SRL phases (planning, performance, and reflection). Based on the findings of this literature review, the key recommendation is for all phases of SRL to be considered when planning interventions to support learning. In addition, more comparative research on this topic is needed to identify the most effective interventions and to provide further evidence on the effectiveness of interventions supporting SRL. [ABSTRACT FROM AUTHOR]

Early research on online PBL explored student satisfaction, effectiveness, and design. The temporal aspect of online PBL has rarely been addressed. Thus, a gap exists in our knowledge regarding how online PBL unfolds: when and for how long a group engages in collaborative discussions. Similarly, little is known about whether and what sequence of interactions could predict higher achievement. This study aims to bridge such a gap by implementing the latest advances in temporal learning analytics to analyze the sequential and temporal aspects of online PBL across a large sample (n = 204 students) of qualitatively coded interactions (8,009 interactions). We analyzed interactions at the group level to understand the group dynamics across whole problem discussions, and at the student level to understand the students' contribution dynamics across different episodes. We followed such analyses by examining the association of interaction types and the sequences thereof with students' performance using multilevel linear regression models. The analysis of the interactions reflected that the scripted PBL process is followed a logical sequence, yet often lacked enough depth. When cognitive interactions (e.g., arguments, questions, and evaluations) occurred, they kindled high cognitive interactions, when low cognitive and social interactions dominated, they kindled low cognitive interactions. The order and sequence of interactions were more predictive of performance, and with a higher explanatory power as compared to frequencies. Starting or initiating interactions (even with low cognitive content) showed the highest association with performance, pointing to the importance of initiative and sequencing. [ABSTRACT FROM AUTHOR]

Predictors of student academic success do not always replicate well across different learning designs, subject areas, or educational institutions. This suggests that characteristics of a particular discipline and learning design have to be carefully considered when creating predictive models in order to scale up learning analytics. This study aimed to examine if and to what extent frequently used predictors of study success are portable across a homogenous set of courses. The research was conducted in an integrated blended problem-based curriculum with trace data (n = 2,385 students) from 50 different course offerings across four academic years. We applied the statistical method of single paper meta-analysis to combine correlations of several indicators with students' success. Total activity and the forum indicators exhibited the highest prediction intervals, where the former represented proxies of the overall engagement with online tasks, and the latter with online collaborative learning activities. Indicators of lecture reading (frequency of lecture view) showed statistically insignificant prediction intervals and, therefore, are less likely to be portable across course offerings. The findings show moderate amounts of variability both within iterations of the same course and across courses. The results suggest that the use of the meta-analytic statistical method for the examination of study success indicators across courses with similar learning design and subject area can offer valuable quantitative means for the identification of predictors that reasonably well replicate and consequently can be reliably portable in the future. [ABSTRACT FROM AUTHOR]

Social Network Analysis (SNA) has enabled researchers to understand and optimize the key dimensions of collaborative learning. A majority of SNA research has so far used static networks, ie, aggregated networks that compile interactions without considering when certain activities or relationships occurred. Compressing a temporal process by discarding time, however, may result in reductionist oversimplifications. In this study, we demonstrate the potentials of temporal networks in the analysis of online peer collaboration. In particular, we study: (1) social interactions by analysing learners' collaborative behaviour, part of a case study in which they worked on academic writing tasks, and (2) cognitive interactions through the analysis of students' self‐regulated learning tactics. The study included 123 students and 2550 interactions. By using temporal networks, we show how to analyse the longitudinal evolution of a collaborative network visually and quantitatively. Correlation coefficients with grades, when calculated with time‐respecting temporal measures of centrality, were more correlated with learning outcomes than traditional centrality measures. Using temporal networks to analyse the co‐temporal and longitudinal development, reach, and diffusion patterns of students' learning tactics has provided novel insights into the complex dynamics of learning, not commonly offered through static networks. [ABSTRACT FROM AUTHOR]

This document is an editorial titled "Diversity in the social sciences: researching digital education in and for the global south." It emphasizes the need for increased diversity in research and representation of humans, particularly in the field of digital education. The editorial argues that digital education is crucial for democratization, especially during times of global change and pandemics. It also highlights the importance of incorporating research from the Global South and alleviating stereotypes and biases. The document provides a brief description of several articles included in the volume, which cover topics such as journalism in Colombia, project-based learning in math education, online education in Panama, social inequalities in Philippine universities, and the adoption of virtual tools by university students in an emerging economy. [Extracted from the article]

In order to successfully implement learning analytics (LA), we need a better understanding of student expectations of such services. Yet, there is still a limited body of research about students' expectations across countries. Student expectations of LA have been predominantly examined from a view that perceives students as a group of individuals representing homogenous views. This study examines students' ideal (i.e., representing their wanted outcomes) and predicted expectations (i.e., unveiling what they realistically expect the LA service is most likely to be) of LA by employing a person-centered approach that allows exploring the heterogeneity that may be found in student expectations. We collected data from 132 students in the setting of Swedish higher education by means of an online survey. Descriptive statistics and Latent Class Analysis (LCA) were used for the analysis. Our findings show that students' ideal expectations of LA were considerably higher compared to their predicted expectations. The results of the LCA exhibit that the Swedish students' expectations of LA were heterogeneous, both regarding their privacy concerns and their expectations of LA services. The findings of this study can be seen as a baseline of students' expectations or a cross-sectional average, and be used to inform student-centered implementation of LA in higher education. [ABSTRACT FROM AUTHOR]

Bibliometric studies on the field of multiple sclerosis (MS) research are scarce. The aim of this study is to offer an overarching view of the body of knowledge about MS research over eight decades–from 1945 to 2021–by means of a bibliometric analysis. We performed a quantitative analysis of a massive dataset based on Web of Science. The analysis included frequencies, temporal trends, collaboration networks, clusters of research themes, and an in-depth qualitative analysis. A total of 48,356 articles, with 1,766,086 citations were retrieved. Global MS research showed a steady increase with an annual growth rate of 6.4%, with more than half of the scientific production published in the last decade. Published articles came from 98 different countries by 123,569 authors in 3,267 journals, with the United States ranking first in a number of publications (12,770) and citations (610,334). A co-occurrence network analysis formed four main themes of research, covering the pathophysiological mechanisms, neuropsychological symptoms, diagnostic modalities, and treatment of MS. A noticeable increase in research on cognition, depression, and fatigue was observed, highlighting the increased attention to the quality of life of patients with MS. This bibliometric analysis provided a comprehensive overview of the status of global MS research over the past eight decades. These results could provide a better understanding of this field and help identify new directions for future research. [ABSTRACT FROM AUTHOR]

This study empirically investigates diffusion-based centralities as depictions of student role-based behavior in information exchange, uptake and argumentation, and as consistent indicators of student success in computer-supported collaborative learning. The analysis is based on a large dataset of 69 courses (n = 3,277 students) with 97,173 total interactions (of which 8,818 were manually coded). We examined the relationship between students' diffusion-based centralities and a coded representation of their interactions in order to investigate the extent to which diffusion-based centralities are able to adequately capture information exchange and uptake processes. We performed a meta-analysis to pool the correlation coefficients between centralities and measures of academic achievement across all courses while considering the sample size of each course. Lastly, from a cluster analysis using students' diffusion-based centralities aimed at discovering student role-taking within interactions, we investigated the validity of the discovered roles using the coded data. There was a statistically significant positive correlation that ranged from moderate to strong between diffusion-based centralities and the frequency of information sharing and argumentation utterances, confirming that diffusion-based centralities capture important aspects of information exchange and uptake. The results of the meta-analysis showed that diffusion-based centralities had the highest and most consistent combined correlation coefficients with academic achievement as well as the highest predictive intervals, thus demonstrating their advantage over traditional centrality measures. Characterizations of student roles based on diffusion centralities were validated using qualitative methods and were found to meaningfully relate to academic performance. Diffusion-based centralities are feasible to calculate, implement and interpret, while offering a viable solution that can be deployed at any scale to monitor students' productive discussions and academic success. [ABSTRACT FROM AUTHOR]

Writing in an academic context often requires students in higher education to acquire a new set of skills while familiarising themselves with the goals, objectives and requirements of the new learning environment. Students' ability to continuously self-regulate their writing process, therefore, is seen as a determining factor in their learning success. In order to study students' self-regulated learning (SRL) behaviour, research has increasingly been tapping into learning analytics (LA) methods in recent years, making use of multimodal trace data that can be obtained from students writing and working online. Nevertheless, little is still known about the ways students apply and govern SRL processes for academic writing online, and about how their SRL behaviour might change over time. To provide new perspectives on the use of LA approaches to examine SRL, this study applied a range of methods to investigate what they could tell us about the evolution of SRL tactics and strategies on a relational, co-temporal, contemporaneous and longitudinal level. The data originates from a case study in which a private Facebook group served as an online collaboration space in a first-year academic writing course for foreign language majors of English. The findings show that learners use a range of SRL tactics to manage their writing tasks and that different tactic can take up key positions in this process over time. Several shifts could be observed in students' behaviour, from mainly addressing content-specific topics to more form-specific and social ones. Our results have also demonstrated that different methods can be used to study the relational, co-temporal, contemporaneous, and longitudinal dynamics of self-regulation in this regard, demonstrating the wealth of insights LA methods can bring to the table. [ABSTRACT FROM AUTHOR]

Functional neuroimaging modalities vary in spatial and temporal resolution. One major limitation of most functional neuroimaging modalities is that only neural activation taking place inside the scanner can be imaged. This limitation makes functional neuroimaging in many clinical scenarios extremely difficult or impossible. The most commonly used radiopharmaceutical in Single Photon Emission Tomography (SPECT) functional brain imaging is Technetium 99 m-labeled Ethyl Cysteinate Dimer (ECD). ECD is a lipophilic compound with unique pharmacodynamics. It crosses the blood brain barrier and has high first pass extraction by the neurons proportional to regional brain perfusion at the time of injection. It reaches peak activity in the brain 1 min after injection and is then slowly cleared from the brain following a biexponential mode. This allows for a practical imaging window of 1 or 2 h after injection. In other words, it freezes a snapshot of brain perfusion at the time of injection that is kept and can be imaged later. This unique feature allows for designing functional brain imaging studies that do not require the patient to be inside the scanner at the time of brain activation. Functional brain imaging during severe burn wound care is an example that has been extensively studied using this technique. Not only does SPECT allow for imaging of brain activity under extreme pain conditions in clinical settings, but it also allows for imaging of brain activity modulation in response to analgesic maneuvers whether pharmacologic or non-traditional such as using virtual reality analgesia. Together with its utility in extreme situations, SPECTS is also helpful in investigating brain activation under typical pain conditions such as experimental controlled pain and chronic pain syndromes. [ABSTRACT FROM AUTHOR]

Objective: Publications are the cornerstone of the dissemination of scientific innovation and scholarly work, but published works are mostly behind paywalls. Therefore, many researchers and institutions are searching for alternative models for disseminating scholarly work that bypasses the current structure of paywalls. This study aimed to determine whether a self-published open access (OA) journal, the International Journal of Health Sciences (IJHS), has been able to reach a global audience in terms of authorship, readership, and impact using the OA model. Methods: All IJHS articles were retrieved and analyzed using scientometric methods. Using the keywords from abstracts and titles, unsupervised clustering was performed to map research trends. Network analysis was used to chart the network of collaboration. The analysis of articles' metadata and the visualizations was performed using R programming language. Results: Using Google Scholar as a source, the general statistics of IJHS from inception to 2019 showed that the average citation per article was 11.29, and the impact factor of the journal was 2.28. The results demonstrate the obvious local and global impact of a locally published journal that allows unrestricted OA and uses an open source publishing platform. The journal's success at attracting diverse topics, authors, and readers is a testament to the power of the OA model. Conclusions: Open source is feasible and rewarding and enables a global reach for research from under-represented regions. Local journals can help the Global South disseminate their scholarly work, which is frequently ignored by commercial and established publications. [ABSTRACT FROM AUTHOR]

The increasing use of digital learning tools and platforms in formal and informal learning settings has provided broad access to large amounts of learner data, the analysis of which has been aimed at understanding students' learning processes, improving learning outcomes, providing learner support as well as teaching. Presently, such data has been largely accessed from discussion forums in online learning management systems and has been further analyzed through the application of social network analysis (SNA). Nevertheless, the results of these analyses have not always been reproducible. Since such learning analytics (LA) methods rely on measurement as a first step of the process, the robustness of selected techniques for measuring collaborative learning activities is critical for the transparency, reproducibility and generalizability of the results. This paper presents findings from a study focusing on the validation of critical centrality measures frequently used in the fields of LA and SNA research. We examined how different network configurations (i.e., multigraph, weighted, and simplified) influence the reproducibility and robustness of centrality measures as indicators of student learning in CSCL settings. In particular, this research aims to contribute to the provision of robust and valid methods for measuring and better understanding of the participation and social dimensions of collaborative learning. The study was conducted based on a dataset of 12 university courses. The results show that multigraph configuration produces the most consistent and robust centrality measures. The findings also show that degree centralities calculated with the multigraph methods are reliable indicators for students' participatory efforts as well as a consistent predictor of their performance. Similarly, Eigenvector centrality was the most consistent centrality that reliably represented social dimension, regardless of the network configuration. This study offers guidance on the appropriate network representation as well as sound recommendations about how to reliably select the appropriate metrics for each dimension. [ABSTRACT FROM AUTHOR]

Background: Although there is a wealth of research focusing on PBL, most studies employ self-reports, surveys, and interviews as data collection methods and have an exclusive focus on students. There is little research that has studied interactivity in online PBL settings through the lens of Social Network Analysis (SNA) to explore both student and teacher factors that could help monitor and possibly proactively support PBL groups. This study adopts SNA to investigate how groups, tutors and individual student’s interactivity variables correlate with group performance and whether the interactivity variables could be used to predict group performance. Methods: We do so by analyzing 60 groups’ work in 12 courses in dental education (598 students). The interaction data were extracted from a Moodle-based online learning platform to construct the aggregate networks of each group. SNA variables were calculated at the group level, students’ level and tutor’s level. We then performed correlation tests and multiple regression analysis using SNA measures and performance data. Results: The findings demonstrate that certain interaction variables are indicative of a well-performing group; particularly the quantity of interactions, active and reciprocal interactions among students, and group cohesion measures (transitivity and reciprocity). A more dominating role for teachers may be a negative sign of group performance. Finally, a stepwise multiple regression test demonstrated that SNA centrality measures could be used to predict group performance. A significant equation was found, F (4, 55) = 49.1, p < 0.01, with an R2 of 0.76. Tutor Eigen centrality, user count, and centralization outdegree were all statistically significant and negative. However, reciprocity in the group was a positive predictor of group improvement. Conclusions: The findings of this study emphasized the importance of interactions, equal participation and inclusion of all group members, and reciprocity and group cohesion as predictors of a functioning group. Furthermore, SNA could be used to monitor online PBL groups, identify important quantitative data that helps predict and potentially support groups to function and co-regulate, which would improve the outcome of interacting groups in PBL. The information offered by SNA requires relatively little effort to analyze and could help educators get valuable insights about their groups and individual collaborators. [ABSTRACT FROM AUTHOR]

Background: Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in online (Problem Based Learning) PBL in particular. Using SNA to study students' positions in information exchange networks, communicational activities, and interactions, we can broaden our understanding of the process of PBL, evaluate the significance of each participant role and learn how interactions can affect academic performance. The aim of this study was to study how SNA visual and mathematical analysis can be sued to investigate online PBL, furthermore, to see if students' position and interaction parameters are associated with better performance. Methods: This study involved 135 students and 15 teachers in 15 PBL groups in the course of "growth and development" at Qassim University. The course uses blended PBL as the teaching method. All interaction data were extracted from the learning management system, analyzed with SNA visual and mathematical techniques on the individual student and group level, centrality measures were calculated, and participants' roles were mapped. Correlation among variables was performed using the non-parametric Spearman rank correlation test. Results: The course had 2620 online interactions, mostly from students to students (89%), students to teacher interactions were 4.9%, and teacher to student interactions were 6.15%. Results have shown that SNA visual analysis can precisely map each PBL group and the level of activity within the group as well as outline the interactions among group participants, identify the isolated and the active students (leaders and facilitators) and evaluate the role of the tutor. Statistical analysis has shown that students' level of activity (outdegree rs(133) = 0.27, p = 0.01), interaction with tutors (rs (133) = 0.22, p = 0.02) are positively correlated with academic performance. Conclusions: Social network analysis is a practical method that can reliably monitor the interactions in an online PBL environment. Using SNA could reveal important information about the course, the group, and individual students. The insights generated by SNA may be useful in the context of learning analytics to help monitor students' activity. [ABSTRACT FROM AUTHOR]

Objective: This study aims to investigate the attitude and practice toward undergraduate research studies among medical students at Qassim University in Buraydah, Saudi Arabia. Methods: An online cross-sectional survey developed based on previous studies. It was announced to all registered medical students who have active college's email (n = 448) at Qassim University in Buraydah, Saudi Arabia during the academic year of 2016. Results: The response rate was 56.6% (n = 252). Less than half of the students have started their research projects (41.6%). Students complained about the lack of free time and the unavailability of a university hospital: 92.4% and 97.1%, respectively. One-third of students participated in extra-curriculum research, and female students were more involved. Only 15.2% have published their research and 26.7% have presented it in conferences. Male students have more journal publication in compared to their female collages while the females have presented their projects more often in conferences. To improve their curriculum vitae, 95.2% stated they are going to participate in extra-curriculum research in the future. Conclusions: Students believe in the importance of research for improving their future work life. The main reason for not participating in research, beyond deficiency of research activities, is lack of free time. Students are unsatisfied with research skills gained through academic life, although their interest toward research increases and they plan on participating in future research. [ABSTRACT FROM AUTHOR]

Social network analysis (SNA) may be of significant value in studying online collaborative learning. SNA can enhance our understanding of the collaborative process, predict the under-achievers by means of learning analytics, and uncover the role dynamics of learners and teachers alike. As such, it constitutes an obvious opportunity to improve learning, inform teachers and stakeholders. Besides, it can facilitate data-driven support services for students. This study included four courses at Qassim University. Online interaction data were collected and processed following a standard data mining technique. The SNA parameters relevant to knowledge sharing and construction were calculated on the individual and the group level. The analysis included quantitative network analysis and visualization, correlation tests as well as predictive and explanatory regression models. Our results showed a consistent moderate to strong positive correlation between performance, interaction parameters and students’ centrality measures across all the studied courses, regardless of the subject matter. In each of the studied courses, students with stronger ties to prominent peers (better social capital) in small interactive and cohesive groups tended to do better. The results of correlation tests were confirmed using regression tests, which were validated using a next year dataset. Using SNA indicators, we were able to classify students according to achievement with high accuracy (93.3%). This demonstrates the possibility of using interaction data to predict underachievers with reasonable reliability, which is an obvious opportunity for intervention and support. [ABSTRACT FROM AUTHOR]

To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students’ interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non-collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content discussion and for realizing the goals of collaboration, we suggest that our SNA-based approach will positively affect teaching and learning in many educational domains. Our study offers a proof-of-concept of what SNA can add to the current tools for monitoring and supporting teaching and learning in higher education. [ABSTRACT FROM AUTHOR]

The number of publications in the field of medical education is still markedly low, despite recognition of the value of the discipline in the medical education literature, and exponential growth of publications in other fields. This necessitates raising awareness of the research methods and potential benefits of learning analytics (LA). The aim of this paper was to offer a methodological systemic review of empirical LA research in the field of medical education and a general overview of the common methods used in the field in general. Search was done in Medline database using the term "LA." Inclusion criteria included empirical original research articles investigating LA using qualitative, quantitative, or mixed methodologies. Articles were also required to be written in English, published in a scholarly peer-reviewed journal and have a dedicated section for methods and results. A Medline search resulted in only six articles fulfilling the inclusion criteria for this review. Most of the studies collected data about learners from learning management systems or online learning resources. Analysis used mostly quantitative methods including descriptive statistics, correlation tests, and regression models in two studies. Patterns of online behavior and usage of the digital resources as well as predicting achievement was the outcome most studies investigated. Research about LA in the field of medical education is still in infancy, with more questions than answers. The early studies are encouraging and showed that patterns of online learning can be easily revealed as well as predicting students' performance. [ABSTRACT FROM AUTHOR]

Aim:Learning analytics (LA) is an emerging discipline that aims at analyzing students’ online data in order to improve the learning process and optimize learning environments. It has yet un-explored potential in the field of medical education, which can be particularly helpful in the early prediction and identification of under-achieving students. The aim of this study was to identify quantitative markers collected from students’ online activities that may correlate with students’ final performance and to investigate the possibility of predicting the potential risk of a student failing or dropping out of a course. Methods:This study included 133 students enrolled in a blended medical course where they were free to use the learning management system at their will. We extracted their online activity data using database queries and Moodle plugins. Data included logins, views, forums, time, formative assessment, and communications at different points of time. Five engagement indicators were also calculated which would reflect self-regulation and engagement. Students who scored below 5% over the passing mark were considered to be potentially at risk of under-achieving. Results:At the end of the course, we were able to predict the final grade with 63.5% accuracy, and identify 53.9% of at-risk students. Using a binary logistic model improved prediction to 80.8%. Using data recorded until the mid-course, prediction accuracy was 42.3%. The most important predictors were factors reflecting engagement of the students and the consistency of using the online resources. Conclusions:The analysis of students’ online activities in a blended medical education course by means of LA techniques can help early predict underachieving students, and can be used as an early warning sign for timely intervention. [ABSTRACT FROM PUBLISHER]

Learning programming is a complex and challenging task for many students. It involves both understanding theoretical concepts and acquiring practical skills. Hence, analyzing learners' data from online learning environments alone fails to capture the full breadth of students' actions if part of their learning process takes place elsewhere. Moreover, existing studies on learning analytics applied to programming education have mainly relied on frequency analysis to classify students according to their approach to programming or to predict academic achievement. However, frequency analysis provides limited insights into the individual time-related characteristics of the learning process. The current study examines students' strategies when learning programming, combining data from the learning management system and from an automated assessment tool used to support students while solving the programming assignments. The study included the data of 292 engineering students (228 men and 64 women, aged 20–26) from the two aforementioned sources. To gain an in-depth understanding of students' learning process as well as of the types of learners, we used learning analytics methods that account for the temporal order of learning actions. Our results show that students have special preferences for specific learning resources when learning programming, namely, slides that support search, and copy and paste. We also found that videos are relatively less consumed by students, especially while working on programming assignments. Lastly, students resort to course forums to seek help only when they struggle. [ABSTRACT FROM AUTHOR]

Productive and effective collaborative learning is rarely a spontaneous phenomenon but rather the result of meeting a set of conditions, orchestrating and scaffolding productive interactions. Several studies have demonstrated that conflicts can have detrimental effects on student collaboration. Through the application of network science, and social network analysis in particular, this learning analytics study investigates the concept of group robustness; that is, the capacity of collaborative groups to remain functional despite the withdrawal or absence of group members, and its relation to group performance in the frame of collaborative learning. Data on all student and teacher interactions were collected from two phases of a course in medical education that employed an online learning environment. Visual and mathematical analysis were conducted, simulating the removal of actors and its effect on the group's robustness and network structure. In addition, the extracted network parameters were used as features in machine learning algorithms to predict student performance. The study contributes findings that demonstrate the use of network science to shed light on essential elements of collaborative learning and demonstrates how the concept and measures of group robustness can increase understanding of the conditions of productive collaborative learning. It also contributes to understanding how certain interaction patterns can help to promote the sustainability or robustness of groups, while other interaction patterns can make the group more vulnerable to withdrawal and dysfunction. The finding also indicate that teachers can be a driving factor behind the formation of rich clubs of well-connected few and less connected many in some cases and can contribute to a more collaborative and sustainable process where every student is included. [ABSTRACT FROM AUTHOR]

The article reports that assessment defines what students consider important and measures their personal achievements across all phases of professional development. It gives institutions a feedback about the success of realizing program goals and institution objectives. Assessment analytics benefits would extend to all stakeholders involved in the educational process.

Background: Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students’ performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance. Methods: Interaction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis. The analysis included visual as well as a statistical analysis. Correlation with students’ performance was calculated, and automatic linear regression was used to predict students’ performance. Results: By using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators. When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user’s position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student’s position and role in information relay in online case discussions, combined with the strength of that student’s network (social capital), can be used as predictors of performance in relevant settings. Conclusion: By using social network analysis, researchers can analyze the social structure of an online course and reveal important information about students’ and teachers’ interactions that can be valuable in guiding teachers, improve students’ engagement, and contribute to learning analytics insights. [ABSTRACT FROM AUTHOR]