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1.
Artificial intelligence (AI) has generated a plethora of new opportunities, potential and challenges for understanding and supporting learning. In this paper, we position human and AI collaboration for socially shared regulation (SSRL) in learning. Particularly, this paper reflects on the intersection of human and AI collaboration in SSRL research, which presents an exciting prospect for advancing our understanding and support of learning regulation. Our aim is to operationalize this human-AI collaboration by introducing a novel trigger concept and a hybrid human-AI shared regulation in learning (HASRL) model. Through empirical examples that present AI affordances for SSRL research, we demonstrate how humans and AI can synergistically work together to improve learning regulation. We argue that the integration of human and AI strengths via hybrid intelligence is critical to unlocking a new era in learning sciences research. Our proposed frameworks present an opportunity for empirical evidence and innovative designs that articulate the potential for human-AI collaboration in facilitating effective SSRL in teaching and learning.

Practitioner notes

What is already known about this topic
  • For collaborative learning to succeed, socially shared regulation has been acknowledged as a key factor.
  • Artificial intelligence (AI) is a powerful and potentially disruptive technology that can reveal new insights to support learning.
  • It is questionable whether traditional theories of how people learn are useful in the age of AI.
What this paper adds
  • Introduces a trigger concept and a hybrid Human-AI Shared Regulation in Learning (HASRL) model to offer insights into how the human-AI collaboration could occur to operationalize SSRL research.
  • Demonstrates the potential use of AI to advance research and practice on socially shared regulation of learning.
  • Provides clear suggestions for future human-AI collaboration in learning and teaching aiming at enhancing human learning and regulatory skills.
Implications for practice and/or policy
  • Educational technology developers could utilize our proposed framework to better align technological and theoretical aspects for their design of adaptive support that can facilitate students' socially shared regulation of learning.
  • Researchers and practitioners could benefit from methodological development incorporating human-AI collaboration for capturing, processing and analysing multimodal data to examine and support learning regulation.
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2.
This study presents the outcomes of a semi-systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35 MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory-driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control–value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory-driven MMLA research and how this acceleration can extend or even create new theoretical knowledge.

Practitioner notes

What is already known about this topic
  • Multimodal learning analytics (MMLA) is an emerging field of research with inherent connections to advanced computational analyses of social phenomena.
  • MMLA can help us monitor learning activity at the micro-level and model cognitive, affective and social factors associated with learning using data from both physical and digital spaces.
  • MMLA provide new opportunities to support students' learning.
What this paper adds
  • Some MMLA works use theory, but, overall, the role of theory is currently limited.
  • The three theories dominating MMLA research are embodied cognition, control–value theory of achievement emotions and cognitive load theory.
  • Most of the theory-driven MMLA papers use theory ‘as is’ and do not consider the analytical and synthetic role of theory or aim to contribute to it.
Implications for practice and/or policy
  • If the ultimate goal of MMLA, and AI in Education in general, research is to understand and support human learning, these studies should be expected to align their findings (or not) with established relevant theories.
  • MMLA research is mature enough to contribute to learning theory, and more research should aim to do so.
  • MMLA researchers and practitioners, including technology designers, developers, educators and policy-makers, can use this review as an overview of the current state of theory-driven MMLA.
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3.
The anthropomorphic characteristics of artificial intelligence (AI) can provide a positive environment for self-regulated learning (SRL). The factors affecting adolescents' SRL through AI technologies remain unclear. Limited AI and disciplinary knowledge may affect the students' motivations, as explained by self-determination theory (SDT). In this study, we examine the mediating effects of needs satisfaction in SDT on the relationship between students' previous technical (AI) and disciplinary (English) knowledge and SRL, using an AI conversational chatbot. Data were collected from 323 9th Grade students through a questionnaire and a test. The students completed an AI basic unit and then learned English with a conversational chatbot for 5 days. Confidence intervals were calculated to investigate the mediating effects. We found that students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot, and that satisfying the need for autonomy and competence mediated the relationships between both knowledge (AI and English) and SRL, but relatedness did not. The self-directed nature of SRL requires heavy cognitive learning and satisfying the need for autonomy and competence may more effectively engage young children in this type of learning. The findings also revealed that current chatbot technologies may not benefit students with relatively lower levels of English proficiency. We suggest that teachers can use conversational chatbots for knowledge consolidation purposes, but not in SRL explorations.

Practitioner notes

What is already known about this topic
  • Artificial intelligence (AI) technologies can potentially support students' self-regulated learning (SRL) of disciplinary knowledge through chatbots.
  • Needs satisfaction in Self-determination theory (SDT) can explain the directive process required for SRL.
  • Technical and disciplinary knowledge would affect SRL with technologies.
What this paper adds
  • This study examines the mediating effects of needs satisfaction in SDT on the relationship between students' previous AI (technical) and English (disciplinary) knowledge and SRL, using an AI conversational chatbot.
  • Students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot.
  • Autonomy and competence were mediators, but relatedness was not.
Implications for practice and/or policy
  • Teachers should use chatbots for knowledge consolidation rather than exploration.
  • Teachers should support students' competence and autonomy, as these were found to be the factors that directly predicted SRL.
  • School leaders and teacher educators should include the mediating effects of needs satisfaction in professional development programmes for digital education.
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4.
Artificial intelligence (AI) is increasingly integrating into our society. University education needs to maintain its relevance in an AI-mediated world, but the higher education sector is only beginning to engage deeply with the implications of AI within society. We define AI according to a relational epistemology, where, in the context of a particular interaction, a computational artefact provides a judgement about an optimal course of action and that this judgement cannot be traced. Therefore, by definition, AI must always act as a ‘black box’. Rather than seeking to explain ‘black boxes’, we argue that a pedagogy for an AI-mediated world involves learning to work with opaque, partial and ambiguous situations, which reflect the entangled relationships between people and technologies. Such a pedagogy asks learners locate AI as socially bounded, where AI is always understood within the contexts of its use. We outline two particular approaches to achieve this: (a) orienting students to quality standards that surround AIs, what might be called the tacit and explicit ‘rules of the game’; and (b) providing meaningful interactions with AI systems.

Practitioner notes

What is already known about this topic
  • Artificial intelligence (AI) is conceptualised in many different ways but is rarely defined in the higher education literature.
  • Experts have outlined a range of graduate capabilities for working in a world of AI such as teamwork or ethical thinking.
  • The higher education literature outlines an imperative need to respond to AI, as underlined by recent commentary on ChatGPT.
What this paper adds
  • A definition of an AI that is relational: A particular interaction where a computational artefact provides a judgement about an optimal course of action, which cannot be easily traced.
  • Focusing on working with AI black boxes rather than trying to see inside the technology.
  • Describing a pedagogy for an AI-mediated world that promotes working in complex situations with partial and indeterminate information.
Implications for practice and/or policy
  • Focusing on quality standards helps learners understand the social regulating boundaries around AI.
  • Promoting learner interactions with AI as part of a sociotechnical ensemble helps build evaluative judgement in weighting AI's contribution to work.
  • Asking learners to work with AI systems prompts understanding of the evaluative, ethical and practical necessities of working with a black box.
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5.
Predictors of academic success at university are of great interest to educators, researchers and policymakers. With more students studying online, it is important to understand whether traditional predictors of academic outcomes in face-to-face settings are relevant to online learning. This study modelled self-regulatory and demographic predictors of subject grades in 84 online and 80 face-to-face undergraduate students. Predictors were effort regulation, grade goal, academic self-efficacy, performance self-efficacy, age, sex, socio-economic status (SES) and first-in-family status. A multi-group path analysis indicated that the models were significantly different across learning modalities. For face-to-face students, none of the model variables significantly predicted grades. For online students, only performance self-efficacy significantly predicted grades (small effect). Findings suggest that learner characteristics may not function in the same way across learning modes. Further factor analytic and hierarchical research is needed to determine whether self-regulatory predictors of academic success continue to be relevant to modern student cohorts.

Practitioner Notes

What is already known about this topic
  • Self-regulatory and demographic variables are important predictors of university outcomes like grades.
  • It is unclear whether the relationships between predictor variables and outcomes are the same across learning modalities, as research findings are mixed.
What this paper adds
  • Models predicting university students' grades by demographic and self-regulatory predictors differed significantly between face-to-face and online learning modalities.
  • Performance self-efficacy significantly predicted grades for online students.
  • No self-regulatory variables significantly predicted grades for face-to-face students, and no demographic variables significantly predicted grades in either cohort.
  • Overall, traditional predictors of grades showed no/small unique effects in both cohorts.
Implications for practice and/or policy
  • The learner characteristics that predict success may not be the same across learning modalities.
  • Approaches to enhancing success in face-to-face settings are not automatically applicable to online settings.
  • Self-regulatory variables may not predict university outcomes as strongly as previously believed, and more research is needed.
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6.
Capturing evidence for dynamic changes in self-regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected from n = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control, n = 48), (2) perform poorly and received intervention (treatment, n = 95) and (3) perform well (not flagged, n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments.

Practitioner notes

What is already known about this topic
  • Self-regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success.
  • SRL is a dynamic, temporal process that leads to purposeful student engagement.
  • Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed.
What this paper adds
  • A Markov process for measuring dynamic SRL processes using log data.
  • Evidence that dynamic, interaction-dominant aspects of SRL predict student achievement.
  • Evidence that SRL processes can be meaningfully impacted through educational intervention.
Implications for theory and practice
  • Complexity approaches inform theory and measurement of dynamic SRL processes.
  • Static representations of dynamic SRL processes are promising learning analytics metrics.
  • Engineered features of LMS usage are valuable contributions to AI models.
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7.
This paper suggests that artificial intelligence in education (AIEd) can be fruitfully analysed as ‘policies frozen in silicon’. This means that they exist as both materialised and proposed problematisations (problem representations with corresponding solutions). As a theoretical and analytical response, this paper puts forward a heuristic lens that can provide insights into how AI technologies (or advocated AI technologies) function as proposed solutions to certain problematisations based on various imaginaries about how education and learning are best performed or supported. The combined reading of imaginaries and problematisations can thereby aid in our understanding of why and how visions of learning and education are framed in relation to AIEd developments. The overall ambition is to advance theoretical and analytical approaches towards an educational system which is (anticipated as) increasingly permeated by AI systems—systems that also support and implement, more or less, invisible models, standards and assessments of learning, as well as more grand visions of (technology-augmented) education in society.

Practitioner notes

What is already known about this topic

  • Artificial intelligence in education (AIEd) is repeatedly presented as a solution for a range of educational ‘problems’.
  • This means that such ‘solutions’ must also frame certain aspects as ‘problems’.
  • Such problems and ‘solutions’ (problematisations) also exist within certain imaginaries of the present times and of the future, where these problematisations are presented as particularly significant and acute, and promoting specific anticipations of learning and ideals of education.

What this paper adds

  • An exposition of problematisations in educational settings.
  • An exposition of educational imaginaries.
  • A heuristic lens for understanding the ‘present’ and ‘future’ in a particular imaginary as entangled in, and dependent on, a certain ‘past’.

Implications for practice and/or policy

  • The approach presented in this paper provides a heuristic lens for examining how AI technologies (or advocated AI technologies) function as proposed solutions to problematisations based on imaginaries about how education and learning are best performed or supported.
  • This aids our understanding of how and why certain visions of learning and education are framed in relation to AIEd developments (real or imagined).
  • It also advances theoretical and analytical approaches towards an educational system, which is (anticipated as) increasingly permeated by AI systems—systems that also support and implement, more or less, invisible models, standards and assessments of learning, as well as more grand visions of (technology-augmented) education in society.
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8.
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9.
Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace-based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace-based approach to study SSRL, there remains a paucity of evidence on how trace-based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace-based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace-based data could be utilised to predict learning outcomes in collaborative learning.

Practitioner notes

What is already known about this topic
  • Socially shared regulation has been recognised as an essential aspect of collaborative learning success.
  • It is challenging to make the processes of learning regulation ‘visible’ to better understand and support student learning, especially in dynamic collaborative settings.
  • Multimodal learning analytics are showing promise for being a powerful tool to reveal new insights into the temporal and sequential aspects of regulation in collaborative learning.
What this paper adds
  • Utilising multimodal big data analytics to reveal the regulatory patterns of shared physiological arousal events (SPAEs) and regulatory activities in collaborative learning.
  • Providing evidence of using multimodal data including physiological signals to indicate trigger events in socially shared regulation.
  • Examining the differences of regulatory patterns between successful and less successful collaborative learning sessions.
  • Demonstrating the potential use of artificial intelligence (AI) techniques to predict collaborative learning success by examining regulatory patterns.
Implications for practice and/or policy
  • Our findings offer insights into how students regulate their learning during collaborative learning, which can be used to design adaptive supports that can foster students' learning regulation.
  • This study could encourage researchers and practitioners to consider the methodological development incorporating advanced techniques such as AI machine learning for capturing, processing and analysing multimodal data to examine and support learning regulation.
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10.
To test the suitability of an automatic system for emotional management in the classroom following the control-value theory of achievement emotions (CVT) framework, the performance of an emotional expression recognition software of our creation is evaluated in an online synchronous context. Sixty students from the Faculty of Education at the University of Alicante participated in 16 educational activities recording close-ups of their faces and completing the AEQ emotional self-report, as well as detailed reports from the subsequent review of their videos. In addition, they completed the VCQ-36 test to measure their volitional competencies and relate their influence on their emotional response. The results indicate a high coherence between the emotional expressions detected by the automatic system and the detailed emotional self-reports, but insufficient precision to meet the CVT requirements. On the other hand, both the AEQ test results and the emotion expression recognition software suggest students' preference for participative activities as opposed to passive ones. Meanwhile, statistical analysis results indicate that volitional competencies seem to influence the emotional response of students in the educational context, although the AI system does not show sufficient sensitivity in this field. Implications and limitations of this study for future work are discussed.

Practitioner notes

What is already known about this topic

  • Student motivation and involvement in the learning process are highly related to appropriate emotional regulation, which can be associated with particular educational activities, strategies and methodologies.
  • Deep learning technology based on convolutional neural networks feeds automatic systems focused on facial expression recognition from image analysis.

What this paper adds

  • There is high coherence between the emotional expressions detected by the AI system and the students' emotional self-reports, but the AI system provides just emotional valences, insufficient to meet the CVT framework.
  • Both emotional self-reports and the emotion recognition software suggest students' preference for active educational activities as opposed to passive ones.
  • Volitional competencies seem to influence the emotional response of students in the educational context.

Implications for practice and/or policy

  • It is possible to use automatic systems to effectively monitor the emotional response of students in the learning process.
  • Only if sensitivity improved, a real-time, easy-to-interpret emotional expression recognition software interface could be implemented to assist teachers with the emotional management of their classes within the CVT framework, maximizing their motivation and engagement.
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11.
Formative assessment is considered to be helpful in students' learning support and teaching design. Following Aufschnaiter's and Alonzo's framework, formative assessment practices of teachers can be subdivided into three practices: eliciting evidence, interpreting evidence and responding. Since students' conceptions are judged to be important for meaningful learning across disciplines, teachers are required to assess their students' conceptions. The focus of this article lies on the discussion of learning analytics for supporting the assessment of students' conceptions in class. The existing and potential contributions of learning analytics are discussed related to the named formative assessment framework in order to enhance the teachers' options to consider individual students' conceptions. We refer to findings from biology and computer science education on existing assessment tools and identify limitations and potentials with respect to the assessment of students' conceptions.

Practitioner notes

What is already known about this topic
  • Students' conceptions are considered to be important for learning processes, but interpreting evidence for learning with respect to students' conceptions is challenging for teachers.
  • Assessment tools have been developed in different educational domains for teaching practice.
  • Techniques from artificial intelligence and machine learning have been applied for automated assessment of specific aspects of learning.
What does the paper add
  • Findings on existing assessment tools from two educational domains are summarised and limitations with respect to assessment of students' conceptions are identified.
  • Relevent data that needs to be analysed for insights into students' conceptions is identified from an educational perspective.
  • Potential contributions of learning analytics to support the challenging task to elicit students' conceptions are discussed.
Implications for practice and/or policy
  • Learning analytics can enhance the eliciting of students' conceptions.
  • Based on the analysis of existing works, further exploration and developments of analysis techniques for unstructured text and multimodal data are desirable to support the eliciting of students' conceptions.
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12.
The promise of using immersive technologies in learning has increasingly been attracting researchers' and practitioners' attention. However, relevant empirical works are usually conducted in fully controlled Virtual Reality (VR) laboratories, as opposed to conventional settings. This quasi-experimental study compares the effectiveness of video learning resources to that of stereoscopic 360° VR, as supplements to the traditional instructional approach. The potential of such methods was examined in high school settings, in the context of the ‘Life and Evolution’ module, with participants (n = 70) divided equally into control and experimental groups. As a point of reference (control condition), we considered the adoption of Video Learning Resources, as students are more acquainted with this instructional method. In the intervention approach (experimental condition), students adopted the use of low-end mobile-VR (VeeR Mini VR Goggles). The key findings indicate differences in the learning motivation, confidence and satisfaction, but no statistically significant difference was identified regarding the factual or conceptual knowledge gains. The study offers insights on the potential of the investigated technologies in the subject of secondary school Biology and further provides implications for theory and practice.

Practitioner notes

What is already known about this topic
  • Researchers' interest over the potential of Virtual Reality on different STEM disciplines is increasing consistently.
  • An increasing number of efforts can be identified discussing the integration of multimedia learning resources in the secondary school context.
  • Empirical studies on the subject of Biology are focusing on students' academic performance and achievement but not on learning motivation and satisfaction.
What this paper adds
  • This quasi-experimental study comparatively examines academic performance, with the focus being on learning motivation and satisfaction, across different modalities (stereoscopic 360° Virtual Reality applications-VR, Video Learning Recourses-VLR).
  • The findings demonstrate that both instructional methods are sufficient in enhancing students' knowledge acquisition and academic performance.
  • The adoption of stereoscopic 360° VR influences students' learning motivation and impacts long-term memory retention.
Implications for practice and policy
  • Educators are advised to consider the systematic adoption of “immersive” multimedia tools to enhance the subject of Biology as they can greatly encourage scientific inquiry.
  • Instructional designers are advised to adopt open educational resources aligned to the curriculum of the local context.
  • Educational researchers are advised to integrate stereoscopic 360°-VR solutions in the conventional classroom settings.
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13.
Given the increased need for broadening participation in computing, there must be a focus not just on providing culturally relevant content but also on building accessible and inclusive computational tools. Most efforts to design culturally responsive computational tools redesign surface features, often through making nominal changes to add cultural meaning, yet the deeper structural design remains largely intact. We take a critical perspective towards novice programming environments to elucidate how the underlying structure privileges particular epistemologies and cultures. In this paper, we examine how the cultural practice of storytelling is supported and/or inhibited within novice programming tools. We draw upon the experiences of 38 Native American youth, who worked in teams to create place-based, interactive stories and games for their community. Findings offer insights to the embedded cultural biases that exist in the structures of computational tools. We discuss insights for how to address cultural biases and promote deeper integration of cultural practices in future designs of culturally responsive computational tools.

Practitioner Notes

What is already known about this topic?
  • Culturally responsive computing connects computing content heritage and vernacular cultural practices.
  • “Black boxing,” or lack of transparency in how it works, in computational tools makes it difficult for novices to enter computing cultures.
  • Design tools are embedded with particular ways of being, knowing, valuing and doing.
What this paper adds?
  • Thirty-eight novice learners’ computational designs were shaped by the ways in which a computational tool privileged particular knowledge systems.
  • Storytelling, as a critical cultural practice, especially in Indigenous cultures, is heavily constrained by the design structure of computational tools.
  • Computational tools are cultural artifacts with deeply embedded epistemological, ontological and axiological biases, which directly frame what learners can do with these tools.
Implications for practice
  • Collaborative, community-based design processes could mitigate the cultural biases that persist in computational tools.
  • Transparency in computation tools in critical to broadening participation in computing cultures.
  • Culturally responsive design of computational tools at the structural level is required to build inclusive computing cultures.
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14.
The COVID-19 pandemic has posed a significant challenge to higher education and forced academic institutions across the globe to abruptly shift to remote teaching. Because of the emergent transition, higher education institutions continuously face difficulties in creating satisfactory online learning experiences that adhere to the new norms. This study investigates the transition to online learning during Covid-19 to identify factors that influenced students' satisfaction with the online learning environment. Adopting a mixed-method design, we find that students' experience with online learning can be negatively affected by information overload, and perceived technical skill requirements, and describe qualitative evidence that suggest a lack of social interactions, class format, and ambiguous communication also affected perceived learning. This study suggests that to digitalize higher education successfully, institutions need to redesign students' learning experience systematically and re-evaluate traditional pedagogical approaches in the online context.

Practitioner notes

What is already known about this topic
  • University transitions to online learning during the Covid-19 pandemic were undertaken by faculty and students who had little online learning experience.
  • The transition to online learning was often described as having a negative influence on students' learning experience and mental health.
  • Varieties of cognitive load are known predictors of effective online learning experiences and satisfaction.
What this paper adds
  • Information overload and perceptions of technical abilities are demonstrated to predict students' difficulty and satisfaction with online learning.
  • Students express negative attitudes towards factors that influence information overload, technical factors, and asynchronous course formats.
  • Communication quantity was not found to be a significant factor in predicting either perceived difficulty or negative attitudes.
Implications for practice and/or policy
  • We identify ways that educators in higher education can improve their online offerings and implementations during future disruptions.
  • We offer insights into student experience concerning online learning environments during an abrupt transition.
  • We identify design factors that contribute to effective online delivery, educators in higher education can improve students' learning experiences during difficult periods and abrupt transitions to online learning.
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15.
This article reports on a trace-based assessment of approaches to learning used by middle school aged children who interacted with NASA Mars Mission science, technology, engineering and mathematics (STEM) games in Whyville, an online game environment with 8 million registered young learners. The learning objectives of two games included awareness and knowledge of NASA missions, developing knowledge and skills of measurement and scaling, applying measurement for planetary comparisons in the solar system. Trace data from 1361 interactions were analysed with nonparametric multidimensional scaling methods, which permitted visual examination and statistical validation, and provided an example and proof of concept for the multidimensional scaling approach to analysis of time-based behavioural data from a game or simulation. Differences in approach to learning were found illustrating the potential value of the methodology to curriculum and game-based learning designers as well as other creators of online STEM content for pre-college youth. The theoretical framework of the method and analysis makes use of the Epistemic Network Analysis toolkit as a post hoc data exploration platform, and the discussion centres on issues of semantic interpretation of interaction end-states and the application of evidence centred design in post hoc analysis.

Practitioner notes

What is already known about this topic
  • Educational game play has been demonstrated to positively affect learning performance and learning persistence.
  • Trace-based assessment from digital learning environments can focus on learning outcomes and processes drawn from user behaviour and contextual data.
  • Existing approaches used in learning analytics do not (fully) meet criteria commonly used in psychometrics or for different forms of validity in assessment, even though some consider learning analytics a form of assessment in the broadest sense.
  • Frameworks of knowledge representation in trace-based research often include concepts from cognitive psychology, education and cognitive science.
What this paper adds
  • To assess skills-in-action, stronger connections of learning analytics with educational measurement can include parametric and nonparametric statistics integrated with theory-driven modelling and semantic network analysis approaches widening the basis for inferences, validity, meaning and understanding from digital traces.
  • An expanded methodological foundation is offered for analysis in which nonparametric multidimensional scaling, multimodal analysis, epistemic network analysis and evidence-centred design are combined.
Implications for practice and policy
  • The new foundations are suggested as a principled, theory-driven, embedded data collection and analysis framework that provides structure for reverse engineering of semantics as well as pre-planning frameworks that support creative freedom in the processes of creation of digital learning environments.
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16.
Interactive apps are commonly used to support the acquisition of foundational skills. Yet little is known about how pedagogical features of such apps affect learning outcomes, attainment and motivation—particularly when deployed in lower-income contexts, where educational gains are most needed. In this study, we analyse which app features are most effective in supporting the acquisition of foundational literacy and numeracy skills. We compare five apps developed for the Global Learning XPRIZE and deployed to 2041 out-of-school children in 172 remote Tanzanian villages. A total of 41 non-expert participants each provided 165 comparative judgements of the five apps from the competition, across 15 pedagogical features. Analysis and modelling of these 6765 comparisons indicate that the apps created by the joint winners of the XPRIZE, who produced the greatest learning outcomes over the 15-month field trial, shared six pedagogical features—autonomous learning, motor skills, task structure, engagement, language demand and personalisation. Results demonstrate that this combination of features is effective at supporting learning of foundational skills and has a positive impact on educational outcomes. To maximise learning potential in environments with both limited resources and deployment opportunities, developers should focus attention on this combination of features, especially for out-of-school children in low- and middle-income countries.

Practitioner notes

What is already known about this topic
  • Interactive apps are becoming common to support foundational learning for children both in and out of school settings.
  • The Global Learning XPRIZE competition demonstrates that learning apps can facilitate learning improvements in out-of-school children living in sub-Saharan Africa.
  • To understand which app features are most important in supporting learning in these contexts, we need to establish which pedagogical features were shared by the winning apps.
What this paper adds
  • Effective learning of foundational skills can be achieved with a range of pedagogical features.
  • To maximise learning, apps should focus on combining elements of autonomous learning, motor skills, task structure, engagement, language demand and personalisation.
  • Free Play is not a key pedagogical feature to facilitate learning within this context.
Implications for practice and/or policy
  • When developing learning apps with primary-aged, out-of-school children in low-income contexts, app developers should try to incorporate the six key features associated with improving learning outcomes.
  • Governments, school leaders and parents should use these findings to inform their decisions when choosing an appropriate learning app for children.
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17.
Game-based assessment (GBA), a specific application of games for learning, has been recognized as an alternative form of assessment. While there is a substantive body of literature that supports the educational benefits of GBA, limited work investigates the validity and generalizability of such systems. In this paper, we describe applications of learning analytics methods to provide evidence for psychometric qualities of a digital GBA called Shadowspect, particularly to what extent Shadowspect is a robust assessment tool for middle school students' spatial reasoning skills. Our findings indicate that Shadowspect is a valid assessment for spatial reasoning skills, and it has comparable precision for both male and female students. In addition, students' enjoyment of the game is positively related to their overall competency as measured by the game regardless of the level of their existing spatial reasoning skills.

Practitioner notes

What is already known about this topic:
  • Digital games can be a powerful context to support and assess student learning.
  • Games as assessments need to meet certain psychometric qualities such as validity and generalizability.
  • Learning analytics provide useful ways to establish assessment models for educational games, as well as to investigate their psychometric qualities.
What this paper adds:
  • How a digital game can be coupled with learning analytics practices to assess spatial reasoning skills.
  • How to evaluate psychometric qualities of game-based assessment using learning analytics techniques.
  • Investigation of validity and generalizability of game-based assessment for spatial reasoning skills and the interplay of the game-based assessment with enjoyment.
Implications for practice and/or policy:
  • Game-based assessments that incorporate learning analytics can be used as an alternative to pencil-and-paper tests to measure cognitive skills such as spatial reasoning.
  • More training and assessment of spatial reasoning embedded in games can motivate students who might not be on the STEM tracks, thus broadening participation in STEM.
  • Game-based learning and assessment researchers should consider possible factors that affect how certain populations of students enjoy educational games, so it does not further marginalize specific student populations.
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18.
The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.

Practitioner notes

What is already known about this topic
  • Research on privacy and data protection in learning analytics has become a recognised challenge that hinders the further expansion of learning analytics.
  • Proposals to counter the privacy and data protection issues in learning analytics are blurry; there is a lack of a summary of previously proposed solutions.
What this study contributes
  • Establishment of what privacy and data protection issues exist at different phases of the learning analytics cycle.
  • Identification of how different stakeholders view privacy, similarities and differences, and what factors influence their views.
  • Evaluation and comparison of previously proposed solutions that attempt to address privacy and data protection in learning analytics.
Implications for practice and/or policy
  • Privacy and data protection issues need to be viewed in the context of the entire cycle of learning analytics.
  • Stakeholder views on privacy and data protection in learning analytics have commonalities across contexts and differences that can arise within the same context. Before implementing learning analytics, targeted research should be conducted with stakeholders.
  • Solutions that attempt to address privacy and data protection issues in learning analytics should be put into practice as far as possible to better test their usefulness.
  相似文献   

19.
Advancements in artificial intelligence are rapidly increasing. The new-generation large language models, such as ChatGPT and GPT-4, bear the potential to transform educational approaches, such as peer-feedback. To investigate peer-feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross-disciplinary framework that aims to facilitate the development of NLP-based adaptive measures for supporting peer-feedback processes in digital learning environments. To conceptualize this process, we introduce a peer-feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer-feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer-feedback process model to exemplify a range of NLP-based adaptive support measures. We also discuss the current challenges and suggest directions for future cross-disciplinary research on the effectiveness and other dimensions of NLP-based adaptive support for peer-feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer-feedback in digital learning environments.

Practitioner notes

What is already known about this topic
  • There is considerable research in educational science on peer-feedback processes.
  • Natural language processing facilitates the analysis of students' textual data.
  • There is a lack of systematic orientation regarding which NLP techniques can be applied to which data to effectively support the peer-feedback process.
What this paper adds
  • A comprehensive overview model that describes the relevant activities and products in the peer-feedback process.
  • A terminological and procedural scheme for designing NLP-based adaptive support measures.
  • An application of this scheme to the peer-feedback process results in exemplifying the use cases of how NLP may be employed to support each learner activity during peer-feedback.
Implications for practice and/or policy
  • To boost the effectiveness of their peer-feedback scenarios, instructors and instructional designers should identify relevant leverage points, corresponding support measures, adaptation targets and automation goals based on theory and empirical findings.
  • Management and IT departments of higher education institutions should strive to provide digital tools based on modern NLP models and integrate them into the respective learning management systems; those tools should help in translating the automation goals requested by their instructors into prediction targets, take relevant data as input and allow for evaluating the predictions.
  相似文献   

20.
Research on teachers’ use of social media has typically assumed that it is a) driven by a need for professional learning and b) best understood in terms of individual motivations. In this study, we use a dataset of nearly 600,000 tweets posted to one or more of 48 Regional Educational Twitter Hashtags associated with 44 U.S. states. To explore the influence of local contextual factors on hashtag- and account-level activity in these hashtags, we use an analytic approach heretofore uncommon in social media-focussed education research: generalised linear and multilevel modelling. At the hashtag level, higher numbers of teachers within a state, proportions of students receiving subsidised meals, student-to-teacher ratios, and amounts of state spending per child are associated with more activity within a regional hashtag; by contrast, more left-leaning state governments and citizenries are associated with less activity. At the account level, more experienced accounts and accounts in more right-leaning states contribute more tweets to these hashtags. These findings reinforce established understandings of Twitter as a site for teacher learning; however, they also underline the importance of acknowledging other important purposes of teachers’ Twitter use, including receiving emotional support and engaging in activism. Practitioner notes What is already known about this topic
  • Many teachers use Twitter (and other social media platforms) for professional purposes.
  • Teachers have identified professional learning—among other purposes—as motivating their use of Twitter.
  • Regional Educational Twitter Hashtags are diverse learning spaces for teachers and other education stakeholders.
What this paper adds
  • Local context and policy factors help influence teachers’ use of Twitter.
  • Teachers may turn to Twitter because of a lack of emotional or political support—not just a lack of material support or professional development opportunities.
  • Individual and idiosyncratic factors remain important in explaining teachers’ engagement with social media.
Implications for practice and/or policy
  • Informal spaces like social media may supplement formal support mechanisms for teachers.
  • Teachers’ use of social media may help administrators and policymakers identify existing gaps to be repaired in those formal support mechanisms.
  • Support for teachers should be conceived holistically and include emotional and political support.
  相似文献   

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