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Effective collaboration and teamwork skills are critical in high‐risk sectors, as deficiencies in these areas can result in injuries and risk of death. To foster the growth of these vital skills, immersive learning spaces have been created to simulate real‐world scenarios, enabling students to safely improve their teamwork abilities. In such learning environments, multiple dialogue segments can occur concurrently as students independently organise themselves to tackle tasks in parallel across diverse spatial locations. This complex situation creates challenges for educators in assessing teamwork and for students in reflecting on their performance, especially considering the importance of effective communication in embodied teamwork. To address this, we propose an automated approach for generating teamwork analytics based on spatial and speech data. We illustrate this approach within a dynamic, immersive healthcare learning environment centred on embodied teamwork. Moreover, we evaluated whether the automated approach can produce transcriptions and epistemic networks of spatially distributed dialogue segments with a quality comparable to those generated manually for research objectives. This paper makes two key contributions: (1) it proposes an approach that integrates automated speech recognition and natural language processing techniques to automate the transcription and coding of team communication and generate analytics; and (2) it provides analyses of the errors in outputs generated by those techniques, offering insights for researchers and practitioners involved in the design of similar systems.Practitioner notesWhat is currently known about this topic Immersive learning environments simulate real‐world situations, helping students improve their teamwork skills.In these settings, students can have multiple simultaneous conversations while working together on tasks at different physical locations.The dynamic nature of these interactions makes it hard for teachers to assess teamwork and communication and for students to reflect on their performance.What this paper adds We propose a method that employs multimodal learning analytics for automatically generating teamwork‐related insights into the content of student conversations.This data processing method allows for automatically transcribing and coding spatially distributed dialogue segments generated from students working in teams in an immersive learning environment and enables downstream analysis.This approach uses spatial analytics, natural language processing and automated speech recognition techniques.Implications for practitioners Automated coding of dialogue segments among team members can help create analytical tools to assist in evaluating and reflecting on teamwork.By analysing spatial and speech data, it is possible to apply learning analytics advancements to support teaching and learning in fast‐paced physical learning spaces where students can freely engage with one another. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results and Conclusions Major Takeaways 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.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.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.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.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. [ABSTRACT FROM AUTHOR]

An introduction is presented wherein the editor discusses various reports within the issue on topics, including the role of learning analytics in advancing formative and summative assessment and the use of trace-based learning analytics methods to assess learning processes.

AbstractFeedback has long been considered a significant lever to enhance learning experience and success in higher education. However, students have shown much discontent with the current feedback practice. Feedback experienced as a relational process in which students feel recognized and valued is perceived as paramount for helping support the uptake of feedback and promote positive learning dispositions. However, little attention has been paid to suggesting how instructors in higher educational institutions can facilitate relational feedback. In response, we conducted a rapid literature review with a particular focus on the creation of feedback content. In total, 17 peer-reviewed publications on relational feedback are included. Based on a qualitative analysis of these papers, we developed a framework of 12 characteristics for offering relational feedback. These 12 characteristics were further categorised into four types of feedback conductive to a relational process, including (i) clarifying performance, (ii) suggesting improvement, (iii) inviting further communication, and (iv) evoking positive emotions. We examined how each of these characteristics supports the relational aspects of feedback. Based on the qualitative analysis results, we offered practical recommendations for instructors to apply relational feedback in their pedagogical practices. [ABSTRACT FROM AUTHOR]

The past few years have witnessed an explosion of attention given to the bias displayed by Machine Learning (ML) techniques towards different groups of people (e.g., female vs. male). Although ML techniques have been widely adopted in education, it remains largely unexplored that to what extent such ML bias manifests itself in this specific setting and how it can be reduced and eliminated. Given the increasing importance of ML techniques in empowering educators to teach effectively, this study aimed to quantify the characteristics of the original datasets that might be correlated with the subsequent predictive unfairness displayed by ML models. To this end, we empirically investigated two types of data biases (i.e., distribution bias and hardness bias) towards students of different sexes and first-language backgrounds across a total of five frequently-performed predictive tasks in education. Then, to improve ML fairness, we drew inspiration from the well-established research in Class Balancing Techniques (CBTs), where samples are generated/removed to alleviate the predictive disparity between different prediction classes. We proposed two simple but effective strategies to empower class balancing techniques for alleviating data biases and improving prediction fairness. Through extensive analyses and evaluations, we demonstrated that ML models may greatly improve prediction fairness (improvement up to 66%) with only a small sacrifice (less than 1%) in prediction accuracy by balancing the training data with the use of students' demographic information and the overall hardness bias measure. All data and code used in this study are publicly accessible via https://github.com/lsha49/FairEdu. [ABSTRACT FROM AUTHOR]

Collaborative learning (CL) can be a powerful method for sharing understanding between learners. To this end, strategic regulation of processes, such as cognition and affect (including metacognition, emotion and motivation) is key. Decades of research on self‐regulated learning has advanced our understanding about the need for and complexity of those mediating processes in learning. Recent research has shown that it is not only the individual's but also the group's shared processes that matter and, thus, that regulation at the group level is critical for learning success. A problem here is that the "shared" processes in CL are invisible, which makes it almost impossible for researchers to study and understand them, for learners to recognize them and for teachers to support them. Traditionally, research has not been able to make these processes visible nor has it been able to collect data about them. With the aid of advanced technologies, signal processing and machine learning, we are on the verge of "seeing" these complex phenomena and understanding how they interact. We posit that technological solutions and digital tools available today and in the future will help advance the theory underlying the cognitive, metacognitive, emotional and social components of individual, peer and group learning when seen through a multidisciplinary lens. The aim of this paper is to discuss and demonstrate how multidisciplinary collaboration among the learning sciences, affective computing and machine learning is applied for understanding and facilitating CL. [ABSTRACT FROM AUTHOR]

A common recommendation in critiques of datafication in education is for greater conversation between the two sides of the (critical) divide – what might be characterised as sceptical social scientists and (supposedly) more technically-minded and enthusiastic data scientists. This article takes the form of a dialogue between two academics representing these different perspectives – Dragan Gasevic (a data scientist who is one of the founders of the 'learning analytics' field) and Neil Selwyn (a social scientist and long-time critic of educational technology). Over the course of a six-month email exchange, we explore various arguments for and against the datafication of higher education. Thus, we are able to add a computational dimension to well-worn social criticisms of data representativeness, reductionism and injustice, as well as exploring social tensions inherent in technical claims to data-based precision, clarity and predictability. Crucially, these exchanges allow us to explore ways in which our two viewpoints converge, and therefore highlight opportunities for productive inter-disciplinary exchange and collaboration. [ABSTRACT FROM AUTHOR]

To assist higher education institutions in meeting the challenge of limited student engagement in the implementation of Learning Analytics services, the Questionnaire for Student Expectations of Learning Analytics (SELAQ) was developed. This instrument contains 12 items, which are explained by a purported two‐factor structure of "Ethical and Privacy Expectations" and "Service Feature Expectations." As it stands, however, the SELAQ has only been validated with students from UK university, which is problematic on account of the interest in Learning Analytics extending beyond this context. Thus, the aim of the current work was to assess whether the translated SELAQ can be validated in three contexts (an Estonian, a Spanish, and a Dutch University). The findings show that the model provided acceptable fits in both the Spanish and Dutch samples, but was not supported in the Estonian student sample. In addition, an assessment of local fit is undertaken for each sample, which provides important points that need to be considered in future work. Finally, a general comparison of expectations across contexts is undertaken, which are discussed in relation to the General Data Protection Regulation (2018). Lay Description: What is currently known about the subject matter: Accounting for student expectations allows for a service that is meaningful.SELAQ measures student expectations towards Learning Analytics services.The 12 items of the SELAQ can be explained by a two‐factor structure (Ethical and Privacy Expectations and Service Feature Expectations). What their paper adds to this: Validation of the SELAQ in three contextsA discussion of localized sources of strainAn assessment of measurement quality in each contextA general comparison of student expectations across contexts The implications of study findings for practitioners: SELAQ can be used with Spanish and Dutch students.SELAQ responses provide a much needed input from end‐users in Learning Analytics implementations. [ABSTRACT FROM AUTHOR]

This paper aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. Specifically, an exploratory study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N = 1,134). Trace data about activities were initially coded for the timeliness of activity completion. Such data were then analysed using agglomerative hierarchical clustering based on Ward's algorithm, first order Markov chains, and inferential statistics to (a) detect time management tactics and strategies from students' learning activities and (b) analyse the effects of personalized analytics‐based feedback on time management. The results indicate that meaningful and theoretically relevant time management patterns can be detected from trace data as manifestations of students' tactics and strategies. The study also showed that time management tactics had significant associations with academic performance and were associated with different interventions in personalized analytics‐based feedback. Lay Description: What is currently known about the subject matter: Effective time management behaviour is a vital element for self‐regulation as well as a strong predictor of academic success.The state of the art in time management research is mainly based on self‐reports.External feedback in educational research is one of the most powerful intervention strategies to optimize student learning progress and performance.The advantage of analytics approaches is in their reliance on trace data that can be captured without significant interference with the actual learning process and with low risk of bias that traditional data collection methods are often susceptible to. What our paper adds to this: We detected time management tactics and strategies adopted by students in online component of a flipped classroom through a combined use of trace data and analytics techniques.We identified the association between time management strategy groups (made up from a set of enacted tactics) identified in an online component of a flipped classroom course and their course performance.We analysed the effects of personalized analytics‐based feedback on time management. The implications of study findings for practitioners: From a research perspective, this study proposed methodology that could help both researchers and practitioners improve the interpretation of their results related to time management behaviour.From an instructor perspective, this study makes a step forward to translate time management into actionable feedback to promote better learning engagement.The study also suggests that for instructors to promote effective time management strategies, they can provide personalized analytics‐based feedback to the students in the first half of a semester on a weekly basis rather than providing the feedback throughout the entire duration of a course.From a learner perspective, this study could offer practical guidelines for making necessary adaption and adjustment of their timing of engagement based on a different set of time management tactics, to effectively regulate their learning time, especially during the preparation for face‐to‐face sessions. [ABSTRACT FROM AUTHOR]

Student engagement within the development of learning analytics services in Higher Education is an important challenge to address. Despite calls for greater inclusion of stakeholders, there still remains only a small number of investigations into students' beliefs and expectations towards learning analytics services. Therefore, this paper presents a descriptive instrument to measure student expectations (ideal and predicted) of learning analytics services. The scales used in the instrument are grounded in a theoretical framework of expectations, specifically ideal and predicted expectations. Items were then generated on the basis of four identified themes (Ethical and Privacy Expectations, Agency Expectations, Intervention Expectations, and Meaningfulness Expectations), which emerged after a review of the learning analytics literature. The results of an exploratory factor analysis and the results from both an exploratory structural equation model and confirmatory factor analysis supported a two‐factor structure best accounted for the data pertaining to ideal and predicted expectations. Factor one refers to Ethical and Privacy Expectations, whilst factor two covers Service Feature Expectations. The 12‐item Student Expectations of Learning Analytics Questionnaire (SELAQ) provides researchers and practitioners with a means of measuring of students' expectations of learning analytics services. Lay Description: What is already known about this topic: Understanding student expectations of learning analytics is an important challenge for higher education institutions to address.Research has measured student beliefs regarding the features and ethical procedures of a learning analytics service. What this paper adds: This study builds on prior work by developing an instrument to measure student expectations of learning analytics services.This study proposes that student expectations of learning analytics can be measured using two subscales: (a) ethical and privacy expectations and (b) service expectations. Implications for practice and/or policy: Higher education institutions should understand what students expect from learning analytics services before any implementations are actioned.Higher education institutions have a validated instrument to gauge and understand student expectations of learning analytics services.Results obtained from the instrument can be used to inform the development of specific learning analytics policies for each higher education institution. [ABSTRACT FROM AUTHOR]

Analyzing multimodal multichannel data about self-regulated learning (SRL) obtained during the use of advanced learning technologies such as intelligent tutoring systems, serious games, hypermedia, and immersive virtual learning environments is key to understanding the interplay among cognitive, affective, metacognitive, and social processes and their impact on learning, problem solving, reasoning, and conceptual understanding in learners of all ages and contexts. In this special issue of Computers in Human Behavior , we report six studies conducted by interdisciplinary teams' use of various trace methodologies such as eye tracking, log-files, physiological data, facial expressions of emotions, screen recordings, concurrent think-alouds, and linguistic analyses of discourse. The research studies focus on how these data were analyzed using a combination of traditional statistical techniques as well as educational data-mining procedures to detect, measure, and infer cognitive, metacognitive, and social processes related to regulating the self and others across several tasks, domains, ages, and contexts. The results of these studies point to future work necessitating interdisciplinary researchers' collaboration to use theoretically based and empirically derived approaches to collecting, measuring, and modeling multimodal multichannel SRL data to extend our current models, frameworks, and theories by making them more predictive by elucidating the nature, complexity, and temporality of underlying processes. Lastly, analyses of multimodal multichannel SRL process data can significantly augment advanced learning technologies by providing real-time, intelligent, adaptive, individualized scaffolding and feedback to address learners' self-regulatory needs. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose a novel approach to the analysis of collaborative learning. The approach posits that different dimensions of collaborative learning emerging from social ties and content analysis of discourse can be modeled as networks. As such, the combination of social network analysis (SNA) and epistemic network analysis (ENA) analysis can detect information about a learner's enactment of what the literature on collaborative learning has described as a role: an ensemble of cognitive and social dimensions that is marked by interacting with the appropriate people about appropriate content. The proposed approach is named social epistemic network signature (SENS) and is defined as a combination of these two complementary network analytic techniques. The proposed SENS approach is examined on data produced in collaborative activities performed in a massive open online course (MOOC) delivered via a major MOOC platform. The results of a study conducted on a data set collected in a MOOC suggest SNA and ENA produce complementary results which can i) explain collaboration processes that shaped the creation of social ties and that were associated with different network roles; ii) describe differences between low and high performing groups of learners; and iii) show how combined properties derived from SNA and ENA predict academic performance. Highlights • A network analytics approach for collaborative learning is proposed. • The approach combines social network analysis and epistemic network analysis. • The approach is validated with a dataset from a massive open online course. • Prediction of the structure of social ties and network roles with discourse is shown. • Prediction of performance (groups) with discourse is demonstrated. [ABSTRACT FROM AUTHOR]

This paper presents the results of a natural experiment investigating the effects of instructional conditions and experience on the adoption and sustained use of a learning tool. The experiment was conducted with undergraduate students, enrolled into four performing art courses (N = 77) at a research intensive university in Canada. The students used the video annotation software CLAS for course-based self-assessment on their performances. Although existing research offers insights into the factors predicting students' intentions of accepting a learning tool, much less is known about factors that affect actual adoption and sustained tool use. The study explored the use of CLAS amongst undergraduate students in four courses across two consecutive semesters. Trace data of students' tool use, graph-based measures of metacognitive monitoring, and text cohesion of video annotations were used to estimate the volume of tool use and the quality of the learning strategy and learning products created. The results confirmed that scaffolding (e.g., graded activity with instructional feedback) is required to guide students' initial tool use, although scaffolding did not have an independent significant effect on the quantity of tool use. The findings demonstrated that the use of the tool is strongly influenced by the experience an individual student gains from scaffolded conditions. That is, the students sustained their use of the learning tool in future courses even when the tool use was not graded nor was instructional feedback provided. An important implication is that students’ tool use is not solely driven by motivation – rather, it is shaped by instructional conditions and experience with the tool use. [ABSTRACT FROM AUTHOR]

Learning analytics uses large amounts of data about learner interactions in digital learning environments to understand and enhance learning. Although measurement is a central dimension of learning analytics, there has thus far been little research that examines links between learning analytics and assessment. This special issue of Computers in Human Behavior highlights 11 studies that explore how links between learning analytics and assessment can be strengthened. The contributions of these studies can be broadly grouped into three categories: analytics for assessment (learning analytic approaches as forms of assessment); analytics of assessment (applications of learning analytics to answer questions about assessment practices); and validity of measurement (conceptualization of and practical approaches to assuring validity in measurement in learning analytics). The findings of these studies highlight pressing scientific and practical challenges and opportunities in the connections between learning analytics and assessment that will require interdisciplinary teams to address: task design, analysis of learning progressions, trustworthiness, and fairness – to unlock the full potential of the links between learning analytics and assessment. [ABSTRACT FROM AUTHOR]

This paper reports on the findings of an exploratory study in which the effects of technological scaffolding interventions on micro-level processes of self-regulated learning in the workplace were investigated. Empirical research in the workplace has been much less represented than in formal education. Even less research is available that aimed to identify which technological scaffolding interventions, out of those available in a learning environment, had the highest influence on specific micro-level process of self-regulated learning. This paper reports on the findings of a case study conducted in the naturalistic settings of two organizations in Europe (N = 53) for the period of two months. Trace data about the events of engagement with the technological scaffolding interventions and micro-level processes of self-regulated learning were collected. Both a transition graph based analysis of the temporal dependencies of the collected events and multiple linear regression analyses showed that an intervention that promoted social awareness had consistently the highest effect on all the micro-level processes used in the study. This intervention was followed by the intervention that offered system-generated recommendations about learning paths, learning activities and knowledge assets to stimulate engagement into the micro-level processes within the forethought or preparatory phase of self-regulated learning. These findings suggest that both the social and organizational contexts should be taken into account when developing interventions aimed at supporting the forethought and engagement phases. Further discussion about research, methodological, and learning technology design implications is provided. [ABSTRACT FROM AUTHOR]

This paper reports the results of a study of the association of a set of technological scaffolding interventions – implemented in a learning software called Learn-B – with micro-level processes of self-regulated learning in the workplace. The study was conducted in the context of two European organizations, in which the study participants (N = 53) used the learning software Learn-B for two months in their regular activities. Data about perceived usefulness of the technological scaffolding interventions and traces about the events of the actual use of the technological interventions and micro-level processes of self-regulated learning were collected and analyzed. The analysis of the data showed that when directly asked about their own perspectives, the participants tended to rely on their organizational context in their planning phase. The results also indicate that the participants did consider the social context of their organization when planning their learning goals, but it was not found as the most influential factor. The analysis of the trace data revealed a moderate balance between reliance on both social and organizational contexts. The analysis of the theorized contingencies via trace data showed that the participants’ usefulness perceptions of the proposed interventions for their self-regulated processes, poorly matched with their actual learning actions. [ABSTRACT FROM AUTHOR]

This study examined the extent to which instructional conditions influence the prediction of academic success in nine undergraduate courses offered in a blended learning model (n = 4134). The study illustrates the differences in predictive power and significant predictors between course-specific models and generalized predictive models. The results suggest that it is imperative for learning analytics research to account for the diverse ways technology is adopted and applied in course-specific contexts. The differences in technology use, especially those related to whether and how learners use the learning management system, require consideration before the log-data can be merged to create a generalized model for predicting academic success. A lack of attention to instructional conditions can lead to an over or under estimation of the effects of LMS features on students' academic success. These findings have broader implications for institutions seeking generalized and portable models for identifying students at risk of academic failure. [ABSTRACT FROM AUTHOR]

This paper describes a study that looked at the effects of different technology-use profiles on educational experience within communities of inquiry, and how they are related to the students' levels of cognitive presence in asynchronous online discussions. Through clustering of students (N = 81) in a graduate distance education engineering course, we identified six different profiles: 1) task-focused users, 2) content-focused no-users, 3) no-users, 4) highly intensive users, 5) content-focused intensive users, and 6) socially-focused intensive users. Identified profiles significantly differ in terms of their use of learning platform and their levels of cognitive presence, with large effect sizes of 0.54 and 0.19 multivariate η 2 , respectively. Given that several profiles are associated with higher levels of cognitive presence, our results suggest multiple ways for students to be successful within communities of inquiry. Our results also emphasize a need for a different instructional support and pedagogical interventions for different technology-use profiles. [ABSTRACT FROM AUTHOR]

Contemporary literature on online and distance education almost unequivocally argues for the importance of interactions in online learning settings. Nevertheless, the relationship between different types of interactions and learning outcomes is rather complex. Analyzing 204 offerings of 29 courses, over the period of six years, this study aimed at expanding the current understanding of the nature of this relationship. Specifically, with the use of trace data about interactions and utilizing the multilevel linear mixed modeling techniques, the study examined whether frequency and duration of student–student, student–instructor, student–system, and student–content interactions had an effect of learning outcomes, measured as final course grades. The findings show that the time spent on student–system interactions had a consistent and positive effect on the learning outcome, while the quantity of student–content interactions was negatively associated with the final course grades. The study also showed the importance of the educational level and the context of individual courses for the interaction types supported. Our findings further confirmed the potential of the use of trace data and learning analytics for studying learning and teaching in online settings. However, further research should account for various qualitative aspects of the interactions used while learning, different pedagogical/media features, as well as for the course design and delivery conditions in order to better explain the association between interaction types and the learning achievement. Finally, the results might imply the need for the development of the institutional and program-level strategies for learning and teaching that would promote effective pedagogical approaches to designing and guiding interactions in online and distance learning settings. [ABSTRACT FROM AUTHOR]

Background: The surge of online platforms has generated interest in how specialized platforms support formal and informal learning in various disciplinary domains. Knowledge is still limited regarding how undergraduate students navigate and use platforms to learn. Objectives: This study explores computer and software engineering students' learning practices, wherein online platforms are used as resources for both curricular learning activities and students' interest‐driven learning. Methods: The learning practices of 73 students were examined using a mixed‐methods design and a conceptualization of practices accounting for the context and purpose of their enactment. The dataset includes students' self‐reports on domain‐specific learning activities, three‐month‐long web‐browsing history of multiple platforms and stimulated‐recall interviews. The data were analysed through latent class and thematic analyses. Results and Conclusion: Five distinct patterns were found in the use of online platforms. These patterns show that different types of platforms were used purposely and in combination during curricular and interest‐driven activities aimed at learning software development. Moreover, students' purposes were driven by both the need to progress in their learning activities and the development of their interest in software development. The findings highlight the complexity of students' learning with online platforms, which develop quite extensively beyond curricular boundaries. Implications: The findings stress the need to recognize that undergraduate students' learning practices involve multiple online platforms and activities beyond the formal curriculum and that these play a key role in developing students' interests in learning software development. Moreover, our findings indicate the importance of taking into account the way students' learning practices unfold within platforms and how these relate to domain‐specific practices. Lay Description: What is already known about this topic?: Online platforms are mostly used by teaching staff in undergraduate education to support student learning during formal curricular tasks.Research knowledge is limited mostly to specific features of social networking platforms, such as Facebook Groups or instant messaging technologies.Students informally use interest‐driven activities and both social and domain‐specific platforms to support their learning.Research on students' interest in using massive open online courses suggested that students' learning is motivated by academic success, prospective economic opportunities, and personal interests. What this paper adds?: Students combine various types of online platforms purposely (e.g., search engines or Q&A sites), many of which are domain‐specific (e.g., Stack Overflow).Students use online platforms informally during work on curricular tasks to, for example, collaborate, solve problems, and understand concepts or methods.Domain‐specific (e.g., software engineering) platforms are used during other study‐related leisure and entrepreneurial activities.Online platforms play a key role in how students develop their interests in learning. Through them, students connect not only with other students but also with professionals, companies, and information about them. Implications of study findings for practitioners: Curriculum developers and faculty should consider preparing students to use disparate types of online platforms beyond what is included in the curriculum.Curriculum developers should also consider other activities beyond the classroom that are study‐related and meaningful for students and their learning. [ABSTRACT FROM AUTHOR]

The analysis of data collected from the interaction of users with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new research field, learning analytics, and its closely related discipline, educational data mining. This paper first introduces the field of learning analytics and outlines the lessons learned from well-known case studies in the research literature. The paper then identifies the critical topics that require immediate research attention for learning analytics to make a sustainable impact on the research and practice of learning and teaching. The paper concludes by discussing a growing set of issues that if unaddressed, could impede the future maturation of the field. The paper stresses that learning analytics are about learning. As such, the computational aspects of learning analytics must be well integrated within the existing educational research. [ABSTRACT FROM AUTHOR]

This paper describes a study that looked at the effects of different teaching presence approaches in communities of inquiry, and ways in which student–student online discussions with high levels of cognitive presence can be designed. Specifically, this paper proposes that high-levels of cognitive presence can be facilitated in online courses, based on the community of inquiry model, by building upon existing research in i) self-regulated learning through externally-facilitated regulation scaffolding and ii) computer-supported collaborative learning through role assignment. We conducted a quasi-experimental study in a fully-online course ( N = 82) using six offerings of the course. After performing a quantitative content analysis of online discussion transcripts, a multilevel linear modeling analysis showed the significant positive effects of both externally-facilitated regulation scaffolding and role assignment on the level of cognitive presence. Specifically, the results showed that externally-facilitated regulation scaffolding had a higher effect on cognitive presence than extrinsically induced motivation through grades. The results showed the effectiveness of role assignment to facilitate a high-level of cognitive presence. More importantly, the results showed a significant effect of the interaction between externally-facilitated regulation scaffolding and role assignment on cognitive presence. The paper concludes with a discussion of practical and theoretical implications. [ABSTRACT FROM AUTHOR]

This article presents results from an investigation of the association between student academic performance and social ties. Based on social capital and networked learning research, we hypothesized that (a) students’ social capital accumulated through their course progression is positively associated with their academic performance and (b) students with more social capital have significantly higher academic performance (operationalized as grade point average). Both hypotheses were supported by results of an empirical study that analyzed 10 years of student course enrolment records (N = 505) in a master’s degree program offered through distance education at a Canadian university. These results are consistent with previous studies that looked at social networks built through student interaction in classrooms or computer-mediated communication environments. The significance of this research lies in the simplicity of the method used to establish student social networks from existing course registration records readily available through an institution’s student information system. Direct implications of this research are that (a) study plans for students should consider investment in building new social ties in each course during degree programs and (b) readily available data about cross-class networks can be used in software systems supporting study planning. [ABSTRACT FROM PUBLISHER]

Abstract: Representing and reasoning over different forms of preferences is of crucial importance to many different fields, especially where numerical comparisons need to be made between critical options. Focusing on the well-known Analytical Hierarchical Process (AHP) method, we propose a two-layered framework for addressing different kinds of conditional preferences which include partial information over preferences and preferences of a lexicographic kind. The proposed formal two-layered framework, called CS-AHP, provides the means for representing and reasoning over conditional preferences. The framework can also effectively order decision outcomes based on conditional preferences in a way that is consistent with well-formed preferences. Finally, the framework provides an estimation of the potential number of violations and inconsistencies within the preferences. We provide and report extensive performance analysis for the proposed framework from three different perspectives, namely time-complexity, simulated decision making scenarios, and handling cyclic and partially defined preferences. [Copyright &y& Elsevier]

Source code plagiarism is an easy to do task, but very difficult to detect without proper tool support. Various source code similarity detection systems have been developed to help detect source code plagiarism. Those systems need to recognize a number of lexical and structural source code modifications. For example, by some structural modifications (e.g. modification of control structures, modification of data structures or structural redesign of source code) the source code can be changed in such a way that it almost looks genuine. Most of the existing source code similarity detection systems can be confused when these structural modifications have been applied to the original source code. To be considered effective, a source code similarity detection system must address these issues. To address them, we designed and developed the source code similarity system for plagiarism detection. To demonstrate that the proposed system has the desired effectiveness, we performed a well-known conformism test. The proposed system showed promising results as compared with the JPlag system in detecting source code similarity when various lexical or structural modifications are applied to plagiarized code. As a confirmation of these results, an independent samples t-test revealed that there was a statistically significant difference between average values of F-measures for the test sets that we used and for the experiments that we have done in the practically usable range of cut-off threshold values of 35–70%. [ABSTRACT FROM AUTHOR]

Self-regulated learning processes have a potential to enhance the motivation of knowledge workers to take part in learning and reflection about learning, and thus contribute to the resolution of an important research challenge in workplace learning. An equally important research challenge for the successful completion of each step of a self-regulatory process is to enable learners to be aware of the characteristics of their organizationally-embedded learning context. In this paper, we describe how a combination of pedagogy and Semantic Web-based technologies can be utilized to address the above two challenges. Specifically, we demonstrate the proposed solution through the Learn-B tool that leverages ontologies to support self-regulation in organizational learning. [ABSTRACT FROM AUTHOR]

Semantic web technologies have been applied to many aspects of learning content authoring including semantic annotation, semantic search, dynamic assembly, and personalization of learning content. At the same time, social networking services have started to play an important role in the authoring process by supporting authors' collaborative activities. Whether semantic web technologies and social networking improved the authoring process and to what extent they make authors' life easier, however, remains an open question that we try to address in this paper. We report on the results of an empirical study based on the experiments that we conducted with the prototype of a novel document architecture called SDArch. Semantic web technologies and social networking are two pillars of SDArch, thus potential benefits of SDArch naturally extend to them. Results of the study show that the utilization of SDArch in authoring improves user' performances compared to the authoring with conventional tools. In addition, the users' satisfaction collected from their subjective feedback was also highly positive. [ABSTRACT FROM AUTHOR]

The article comments on the complexities involved in the process of software development. Current trends in the use and development of information technology raise various dimensions of complexities. As a solution to constant increase of complexity, model driven engineering (MDE) has emerged as one of the most prominent approaches. Domain specific modeling languages in MDE can enable different roles in software engineering process.

Abstract: In this paper, we propose an extension of the description logic (DL) in order to support part–part relations among concepts in knowledge representation. The proposed extension introduces a conjunction of a pair of role inclusion axioms (RIAs) and defines quasi simple roles. In our proposal, we define the syntax, semantics and knowledge base for the extension of the DL. We also present a sound and complete tableau algorithm for the DL extension. Finally, we give an analysis of the expressiveness of the proposed extension of the DL for modeling part–part relations and compare it with the state of the art in the area. [ABSTRACT FROM AUTHOR]

Today's technology-enhanced learning practices cater to students and teachers who use many different learning tools and environments and are used to a paradigm of interaction derived from open, ubiquitous, and socially oriented services. In this context, a crucial issue for education systems in general, and for Intelligent Learning Environments (ILEs) in particular, is related to the ability of leveraging these new paradigms for creating, maintaining and sharing the knowledge that these systems embed. This will enable ILEs to benefit from shared information from disparate systems, which is related to learning content and student activities, so that the overall complexity of system development and maintenance would be reduced while at the same time improving the capability of personalization, context-awareness, and interaction. In this article, we investigate how the Social Semantic Web can be leveraged for enabling and easing this process. We first analyze each module of a typical ILE, showing how it can benefit from the Social Semantic Web paradigm and then proceed to investigate how this new paradigm can be leveraged for increasing interactivity level of ILEs. [ABSTRACT FROM AUTHOR]

An introduction to series of articles discussed within the publication is presented, which include the article "Engineering of Framework-Specific Modeling Languages," by Michal Antkiewicz et al, and the article "A Flexible Infrastructure for Multilevel Language Engineering," by Colin Atkinson et al.

The article focuses on the use of opportunistic software development in helping computer students learn skills needed in integrating and testing software that they did not design. It mentions that colleges are starting to promote design-based education where students build concrete and realistic solutions when writing computer code. It examines tools used in rapid prototyping of computer code, including spreadsheets, Web browser scripting, declarative programming mashups, and programming libraries. It comments on the use of creativity support principles in opportunistic software development.

The article offers information about the Adaptable Multi-Interface Communicator (AMICO) infrastructure that supports prototyping from open source software (OSS) components. It offers a unified view of various communication interfaces that implements a common space for interconnecting these interfaces. It also enables flexible system configuration by deriving variables from the transformation script language, XSLT. Diagrams containing the AMICO integration infrastructure concepts, XSLT fragment transformation, configurations of the AMICO infrastructure, and others are also included.

This paper presents an ontology-based framework aimed at explicit representation of context-specific metadata derived from the actual usage of learning objects and learning designs. The core part of the proposed framework is a learning object context ontology, that leverages a range of other kinds of learning ontologies (e.g., user modeling ontology, domain ontology, and learning design ontology) to capture the information about the real usage of a learning object inside a learning design. We also present some learner-centered and teacher-centered scenarios enabled by the proposed framework in order to illustrate the benefits the framework offers to these key participants of any learning process. Finally, we demonstrate how two present educational tools (i.e. TANGRAM and LOCO-Analyst) correspond to the proposed architecture. [ABSTRACT FROM AUTHOR]

The paper proposes a framework for building ontology-aware learning object (LO) content. Previously ontologies were exclusively employed for enriching LOs' metadata. Although such an approach is useful, as it improves retrieval of relevant LOs from LO repositories, it does not enable one to reuse components of a LO, nor to incorporate an explicit specification of domain semantics into the LO content. We propose the use of domain ontologies to annotate LO content as well as content structure ontologies to enable direct access to LOs' components. That way, the same LO can be used in different ways and by different users, that is, it can be repurposed. In order to show the benefits of our proposal we discuss its application in adaptive learning systems. We also explore Semantic Web technologies and tools that are needed to support the presented approach. [ABSTRACT FROM AUTHOR]

This paper presents an eXtensible Stylesheet Language Transformation (XSLT)-based approach for automatic generation of the Web Ontology Language (OWL) from a UML model. Similar solutions that treat this problem are mostly partial since they do not use full metamodeling potentials. Although they emphasize the notion of the use UML for ontology development and propose necessary transformations into Semantic Web languages (e.g., RDF Schema, DAML, DAML+OIL), their UML models must be further refined using ontology-specialized tool. None of these approaches enables instance modeling and generation of OWL ontologies. In our efforts to make ontological and software engineering techniques closer, we have firstly defined ontology metamodeling architecture using Model Driven Architecture (MDA) concepts. This architecture consists of the Ontology Definition Metamodel defined using Meta Object Facility (MOF) and based on the OWL, as well as the related Ontology UML Profile (OUP). A transformation, that we present here, extends this metamodeling architecture and transforms an ontology from its OUP definition (i.e., XML Metadata Interchange – XMI) into the OWL description. Accordingly, we illustrate how an OUP-developed ontology can be shared with ontological engineering tools (i.e., Protégé). [ABSTRACT FROM AUTHOR]

Abstract: The paper presents the Petri net ontology that enables sharing Petri nets on the Semantic Web. Previous work on formal methods for representing Petri nets mainly defines tool-specific descriptions or formats for model interchange. However, such efforts do not provide a suitable description for using Petri nets on the Semantic Web. This paper uses the Petri net UML model as a starting point for implementing the ontology. Resulting Petri net models are represented on the Semantic Web using XML-based ontology languages, RDF and OWL. We implemented a Petri net tool, P3, which can be used as a knowledge acquisition tool based on the Petri net ontology. [Copyright &y& Elsevier]

This paper proposes an ontology mapping-based framewrowrk that allows searching for learning resources using multiple ontologies. The present applications of ontologies in e-learning use various ontologies (eg, domain, curriculum, context), but they do not give a solution on how to interoperate e-learning systems based on different ontologies. The proposed solution uses a mapping ontology that is a part of a recent Semantic Web initiative, the Simple Knowledge Organisation System. On top of that, we develop and implement two search algorithms. Finally, we evaluated the solution by developing a system that helps students search for relevant learning resource using a local context (ie, course curriculum) ontology. [ABSTRACT FROM AUTHOR]

The paper describes an ontology-based framework for bridging learning design and learning object content. In present solutions, researchers have proposed conceptual models and developed tools for both of those subjects, but without detailed discussions of how they can be used together. In this paper we advocate the use of ontologies to explicitly specify all learning designs, learning objects, and the relations between them, and show how this use of ontologies can result in more effective (semi-)automatic tools and services that increase the level of reusability. We first define a three-part conceptual model that introduces an intermediate level between learning design and learning objects called the learning object context. We then use ontologies to facilitate the representation of these concepts: LOCO is a new ontology based on IMS-LD, ALOCoM is an existing ontology for learning objects, and LOCO-Cite is a new ontology for the learning object contextual model. We conclude by showing the applicability of the proposed framework in a use case study. [ABSTRACT FROM AUTHOR]

Abstract: Development of an intelligent system requires not only profound understanding of the problem under study, but also employment of different knowledge representation techniques and tools often based on a variety of paradigms and technological platforms. In this context automation of knowledge sharing between different systems becomes increasingly important. One solution might be to extend a knowledge modeling tool by implementing a set of new classes or functions for importing other knowledge formats (using, e.g. Java, C++, etc.). But, this can be a rather difficult and time consuming task. Since XML is now widely accepted as knowledge representation syntax, we believe that a more suitable solution would be to use eXtensible Stylesheet Language Transformation (XSLT) a W3C standard for transforming XML documents. A special advantage of this approach is that even though an XSLT is written independently of any programming language, it can be executed by a program written in almost any up-to-date programming language. We experiment on an XSLT-based infrastructure for sharing knowledge between three knowledge modeling and acquisition tools that use different conceptual models for knowledge representation in order to evaluate cons and pros of the proposed XSLT approach. Two of these tools, JessGUI and JavaDON are ongoing efforts of the GOOD OLD AI research group to develop interoperable development tools for building intelligent systems, while the third one is Protégé-2000, a broadly accepted ontology development tool. [Copyright &y& Elsevier]

The paper describes JessGUI, a graphical user interface developed on top of the Jess expert system shell. The central idea of the JessGUI project was to make building, revising, updating, and testing Jess-based expert systems easier, more flexible, and more user friendly. There are many other expert system building tools providing a rich and comfortable integrated development environment to expert system builders. However, they are all either commercial or proprietary products. Jess and JessGUI are open-source freeware, and yet they are well suited for building even complex expert system applications, both stand-alone and Web-based ones. An important feature of JessGUI is its capability of saving knowledge bases in XML format (in addition to the original Jess format), thus making them potentially easy to interoperate with other knowledge bases on the Internet. Jess and JessGUI are also used as practical knowledge engineering tools to support both introductory and advanced university courses on expert systems. The paper presents design details of JessGUI, explains its links with the underlying Jess knowledge representation and reasoning tools, and shows examples of using JessGUI in expert system development. It also discusses some of the current efforts in extending Jess/JessGUI in order to provide intelligent features originally not supported in Jess. [Copyright &y& Elsevier]

Multimodal learning analytics (MMLA) offers the potential to provide evidence‐based insights into complex learning phenomena such as collaborative learning. Yet, few MMLA applications have closed the learning analytics loop by being evaluated in real‐world educational settings. This study evaluates the effectiveness of an MMLA solution in enhancing feedback and reflection within a complex and highly dynamic collaborative learning environment. A two‐year longitudinal study was conducted with 399 students and 17 teachers, utilising an MMLA system in reflective debriefings in the context of healthcare education. We analysed the survey data of 74 students and 11 teachers regarding their perceptions of the MMLA system. We applied the Evaluation Framework for Learning Analytics, augmented by complexity, accuracy and trust measures, to assess both teachers' and students' perspectives. The findings illustrated that teachers and students both had generally positive perceptions of the MMLA solution. Teachers found the MMLA solution helpful in facilitating feedback provision and reflection during debriefing sessions. Similarly, students found the MMLA solution effective in providing clarity on the data collected, stimulating reflection on their learning behaviours, and prompting considerations for adaptation in their learning behaviours. However, the complexity of the MMLA solution and the need for qualitative measures of communication emerged as areas for improvement. Additionally, the study highlighted the importance of data accuracy, transparency, and privacy protection to maintain user trust. The findings provide valuable contributions to advancing our understanding of the use of MMLA in supporting feedback and reflection practices in intricate collaborative learning while identifying avenues for further research and improvement. We also provided several insights and practical recommendations for successful MMLA implementation in authentic learning contexts. Practitioner notes What is currently known about this topic Multimodal learning analytics (MMLA) seeks to generate data‐informed insights about learners' metacognitive and emotional states as well as their learning behaviours, by utilising intricate physical and physiological signals. MMLA has not only pioneered novel data analytic methods but also aspired to complete the learning analytics loop by crafting innovative, tangible solutions that relay these insights to the concerned stakeholders. A prominent direction within MMLA research has been the formulation of tools to support feedback and reflection in collaborative learning scenarios, given MMLA's capacity to discern intricate and dynamic learning behaviours. What this paper adds Teachers' and students' positive perceptions of an MMLA implementation in stimulating considerations of adaptations in their pedagogical practices and learning behaviours, respectively. Empirical evidence supporting the potential of MMLA in assisting teachers to facilitate students' reflective practices during intricate collaborative learning scenarios. The importance of addressing issues related to design complexity, interpretability for users with disabilities, aggregated data representation, and concerns related to trust for building a practical MMLA solution in real learning settings. Implications for practice and/or policy The MMLA solution can provide teachers with a comprehensive view of student performance, illuminate areas for improvement, and confirm learning scenario outcomes. The MMLA solution can stimulate students' reflections on their learning behaviours and promote considerations of adaptation in their learning behaviours. Providing clear explanations and guidance on how to interpret analytics, as well as addressing concerns related to data completeness and representation, are essential to maximising utility. What is currently known about this topic Multimodal learning analytics (MMLA) seeks to generate data‐informed insights about learners' metacognitive and emotional states as well as their learning behaviours, by utilising intricate physical and physiological signals. MMLA has not only pioneered novel data analytic methods but also aspired to complete the learning analytics loop by crafting innovative, tangible solutions that relay these insights to the concerned stakeholders. A prominent direction within MMLA research has been the formulation of tools to support feedback and reflection in collaborative learning scenarios, given MMLA's capacity to discern intricate and dynamic learning behaviours. What this paper adds Teachers' and students' positive perceptions of an MMLA implementation in stimulating considerations of adaptations in their pedagogical practices and learning behaviours, respectively. Empirical evidence supporting the potential of MMLA in assisting teachers to facilitate students' reflective practices during intricate collaborative learning scenarios. The importance of addressing issues related to design complexity, interpretability for users with disabilities, aggregated data representation, and concerns related to trust for building a practical MMLA solution in real learning settings. Implications for practice and/or policy The MMLA solution can provide teachers with a comprehensive view of student performance, illuminate areas for improvement, and confirm learning scenario outcomes. The MMLA solution can stimulate students' reflections on their learning behaviours and promote considerations of adaptation in their learning behaviours. Providing clear explanations and guidance on how to interpret analytics, as well as addressing concerns related to data completeness and representation, are essential to maximising utility. [ABSTRACT FROM AUTHOR]

Background: Problem‐solving is a multidimensional and dynamic process that requires and interlinks cognitive, metacognitive, and affective dimensions of learning. However, current approaches practiced in computing education research (CER) are not sufficient to capture information beyond the basic programming process data (i.e., IDE‐log data). Therefore, how cognition and affect intertwine and unfold over time in programming problem‐solving activities are rarely investigated. Objectives: In this study, we examined how the theory‐informed measures from multimodal data that we have selected as proxies for cognitive and affective dimensions of learning, are associated with student performance, and in comparison, to prior‐knowledge. Methods: A high‐frequency temporal data was collected with a camera, an electroencephalogram, and an eye‐tracker from 40 computer science students (bachelor and master studies) in the context of a code‐debugging activity. To study the cognitive processes associated with learning we focused on cognitive load theory (CLT) and the human information processing model. In addition, we complemented CLT with the model of affective dynamics in learning to avoid the machine reductionism perspective. Results: Our findings demonstrated that attention, convergent thinking, and frustration were positively correlated with students' successful code‐debugging (i.e., performance), and frequently manifested by high performing participants. Cognitive load, memory load, and boredom were negatively correlated with students' performance, and typically manifested by low performing participants. Implications: Extending the context of analysis in reference to student cognitive processes and affective states, affords educators not just to identify lower performers, but also to understand the potential reasons behind their performance, making our method an important contribution in the confluence of CER and the learning technology communities. In addition, the insights extracted from our analyses allow us to discuss potential avenues for improving learning design and the design of interactive learning systems to support the development of students' problem‐solving skills. Lay Description: What is currently known about the subject matter: ● Problem‐solving interlinks cognitive, affective and metacognitive dimensions of learning. ● In digital settings instructors struggle to distinguish cognitive‐affective states of students. ● Learning technologies require interventions based on cognitive‐affective states of students. What the paper adds: ● When and what cognitive‐affective states are triggered during solving a programming problem. ● What and how measures as proxies for cognitive‐affective states are associated with performance. ● Emphasizes the benefits of augmenting IDE‐log data with sensor data in the context of learning design. Implications of study findings for practitioners: ● Methodology guidelines to identify and model variability in humans' natural behaviours. ● Importance of content and instructions personalization for student engagement and learning. ● Identification of multimodal data proxies for flexible and adaptive multimodal interfaces. [ABSTRACT FROM AUTHOR]

Self‐regulated learning (SRL) is an essential skill to achieve one's learning goals. This is particularly true for online learning environments (OLEs) where the support system is often limited compared to a traditional classroom setting. Likewise, existing research has found that learners often struggle to adapt their behaviour to the self‐regulatory demands of OLEs. Even so, existing SRL analysis tools have limited utility for real‐time or individualised support of a learner's SRL strategy during a study session. Accordingly, we explore a reinforcement learning based approach to learning optimal SRL strategies for a specific learning task. Specifically, we utilise the sequences of SRL processes acted by 44 participants, and their assessment scores for a prescribed learning task, in a purpose‐built OLE to develop a long short‐term memory (LSTM) network based reward function. This is used to train a reinforcement learning agent to find the optimal sequence of SRL processes for the learning task. Our findings indicate that the developed agents were able to effectively select SRL processes so as to maximise a prescribed learning goal as measured by predicted assessment score and predicted knowledge gains. The contributions of this work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualised SRL focused scaffolding. Practitioner notesWhat is already known about this topic Learners often fail to adequately adapt their behavior to the self‐regulatory demands of e‐Learning environments.In order to promote effective Self‐regulated learning (SRL) capabilities, researchers and educators need tools that are able to analyze and diagnose a learner's SRL strategy use.Current methods for SRL analysis are more often descriptive as opposed to prescriptive and have limited utility for real‐time analysis or support of a learner's SRL behavior.What this paper adds This paper proposes the use of Reinforcement Learning for prescriptive analytics of SRL. We train a Reinforcement Learning agent on sequences of SRL processes acted by learners in order to learn the optimal SRL strategy for a given learning task.Implications for practice and/or policy Our work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualized SRL focused scaffolding.The implications of our work can aid in course design by predicting the self‐regulatory load imposed by a given task.The ability to model SRL strategies using Reinforcement Learning can be extended to simulate or test SRL theories. [ABSTRACT FROM AUTHOR]

Scaffolds that support self‐regulated learning (SRL) have been found to improve learning outcomes. The effects of scaffolds can differ depending on how learners use them and how specific scaffolds might influence learning processes differently. Personalized scaffolds have been proposed to be more beneficial for learning due to their adaptivity to learning progress and individualized content to learning needs. The present study investigated finer‐grained effects of how personalized scaffolds driven by a rule‐based artificial intelligence system influenced SRL processes, especially how students learned with them. Using a pre‐post experimental design, we investigated personalized scaffolds based on university students' real‐time learning processes in a technologically enhanced learning environment. Students in the experimental group (n = 30) received personalized scaffolds, while the control group (n = 29) learned without scaffolds. All students completed a 45‐minute learning task with trace data recorded. Findings indicated scaffold effects on students' subsequent learning behaviour. Additionally, only scaffold interaction correlated to essay performance and suggests that the increase in frequencies of SRL activities alone does not contribute directly to learning outcomes. As guidelines for real‐time SRL support are lacking, this study provides valuable insights to enhance SRL support with adaptive learning technologies. Practitioner notesWhat is already known about this topic Self‐regulated learning scaffolds, especially adaptive scaffolds, improve learning.Personalized scaffolds have effects on self‐regulated learning activities.Past research focused on aggregated effects of scaffolds.What this paper adds Investigates how students learn with personalized scaffolds in terms of frequencies of learning activities and scaffold interaction.Takes a closer look at which learning activities and when the effects of personalized scaffolds occur.Examines how finer‐grained effects of personalized scaffolds correspond to learning outcomes.Implications for practice and/or policy Personalized scaffold effects vary across learning, and future research should consider finer‐grained investigations of SRL support in order to better understand their influence on learning.The number of personalized scaffolds provided should be reconsidered in the future as students only use some of the support provided, especially when task demands increase.Personalized scaffold interaction is linked to improvement in task performance, so future research should also focus on students' appropriate use of self‐regulated learning support. [ABSTRACT FROM AUTHOR]

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs‐based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer‐reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state‐of‐the‐art models (eg, GPT‐3/4), embracing the initiative of open‐sourcing models/systems, and adopting a human‐centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models. Practitioner notesWhat is currently known about this topic Generating and analysing text‐based content are time‐consuming and laborious tasks.Large language models are capable of efficiently analysing an unprecedented amount of textual content and completing complex natural language processing and generation tasks.Large language models have been increasingly used to develop educational technologies that aim to automate the generation and analysis of textual content, such as automated question generation and essay scoring.What this paper adds A comprehensive list of different educational tasks that could potentially benefit from LLMs‐based innovations through automation.A structured assessment of the practicality and ethicality of existing LLMs‐based innovations from seven important aspects using established frameworks.Three recommendations that could potentially support future studies to develop LLMs‐based innovations that are practical and ethical to implement in authentic educational contexts.Implications for practice and/or policy Updating existing innovations with state‐of‐the‐art models may further reduce the amount of manual effort required for adapting existing models to different educational tasks.The reporting standards of empirical research that aims to develop educational technologies using large language models need to be improved.Adopting a human‐centred approach throughout the developmental process could contribute to resolving the practical and ethical challenges of large language models in education. [ABSTRACT FROM AUTHOR]

Researchers have demonstrated that dialogue‐based intelligent tutoring systems (ITS) can be effective in assisting students in learning. However, little research has attempted to explore the necessity of equipping dialogue‐based ITS with one of the most important capabilities of human tutors, that is, maintaining polite interactions with students, which is essential to provide students with a pleasant learning experience. In this study, we examined the role of politeness by analysing a large‐scale real‐world dataset consisting of over 14K online human–human tutorial dialogues. Specifically, we employed linguistic theories of politeness to characterise the politeness levels of tutor–student‐generated utterances, investigated the correlation between the politeness levels of tutors' utterances and students' problem‐solving performance and quantified the power of politeness in predicting students' problem‐solving performance by applying Gradient Tree Boosting. The study results showed that: (i) in the effective tutorial sessions (ie, sessions in which students successfully solved problems), tutors tended to be very polite at the start of a tutorial session and become more direct to guide students as the session progressed; (ii) students with better performance in solving problems tended to be more polite at the beginning and the end of a tutorial session than their counterparts who failed to solve problems; (iii) the correlation between tutors' polite expressions and students' performance was not evident in non‐instructional communication; and (iv) politeness alone cannot adequately reveal students' problem‐solving performance, and thus other factors (eg, sentiment contained in utterances) should also be taken into account. Practitioner notesWhat is already known about this topic Human–human tutoring is acknowledged as an effective instructional method.Polite expression can help strengthen the relationship between tutors and students.Polite expression can promote students' learning achievements in many educational contexts.What this paper adds By considering the students' prior progress on a problem‐based learning task, we demonstrated the extent to which tutors and students display politeness in tutoring dialogues.Tutors' polite expressions might not correlate with students' problem‐solving performance in online human–human tutoring dialogues.Politeness alone was insufficient to predict the students' performance.Implications for practice Tutors might consider using words with positive sentiment values to express politeness to students with prior progress, which might encourage those students to make a further effort.The polite strategy of expressing indirect requests could help tutors mitigate the sense of directness, but this strategy should be carefully used in delivering instructional hints, especially for students without prior progress.To better assist students without prior progress, tutors might consider using more direct expression to explicitly guide students. [ABSTRACT FROM AUTHOR]