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1.
Understanding students' privacy concerns is an essential first step toward effective privacy-enhancing practices in learning analytics (LA). In this study, we develop and validate a model to explore the students' privacy concerns (SPICE) regarding LA practice in higher education. The SPICE model considers privacy concerns as a central construct between two antecedents—perceived privacy risk and perceived privacy control, and two outcomes—trusting beliefs and non-self-disclosure behaviours. To validate the model, data through an online survey were collected, and 132 students from three Swedish universities participated in the study. Partial least square results show that the model accounts for high variance in privacy concerns, trusting beliefs, and non-self-disclosure behaviours. They also illustrate that students' perceived privacy risk is a firm predictor of their privacy concerns. The students' privacy concerns and perceived privacy risk were found to affect their non-self-disclosure behaviours. Finally, the results show that the students' perceptions of privacy control and privacy risks determine their trusting beliefs. The study results contribute to understand the relationships between students' privacy concerns, trust and non-self-disclosure behaviours in the LA context. A set of relevant implications for LA systems' design and privacy-enhancing practices' development in higher education is offered.

Practitioner notes

What is already known about this topic
  • Addressing students' privacy is critical for large-scale learning analytics (LA) implementation.
  • Understanding students' privacy concerns is an essential first step to developing effective privacy-enhancing practices in LA.
  • Several conceptual, not empirically validated frameworks focus on ethics and privacy in LA.
What this paper adds
  • The paper offers a validated model to explore the nature of students' privacy concerns in LA in higher education.
  • It provides an enhanced theoretical understanding of the relationship between privacy concerns, trust and self-disclosure behaviour in the LA context of higher education.
  • It offers a set of relevant implications for LA researchers and practitioners.
Implications for practice and/or policy
  • Students' perceptions of privacy risks and privacy control are antecedents of students' privacy concerns, trust in the higher education institution and the willingness to share personal information.
  • Enhancing students' perceptions of privacy control and reducing perceptions of privacy risks are essential for LA adoption and success.
  • Contextual factors that may influence students' privacy concerns should be considered.
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2.
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3.
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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4.
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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5.
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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6.
This conceptual study uses dynamic systems theory (DST) and phenomenology as lenses to examine data privacy implications surrounding wearable devices that incorporate stakeholder, contextual and technical factors. Wearable devices can impact people's behaviour and sense of self, and DST and phenomenology provide complementary approaches for emphasizing the subjective experiences of individuals that occur with the use of wearable data. Privacy is approached through phenomenology as an individual's lived bodily experience and DST emphasizes the self-regulation and feedback loops of individuals and their uses of wearable data. The data collection, analysis and communication of wearable data to support learning systems alongside privacy implications for each are examined. The IoT, cloud computing, metadata and algorithms are discussed as they relate to wearable data, pointing out privacy risks and strategies to minimize harm.

Practitioner notes

What is already known about this topic

  • Data privacy is a complex topic and is approached through different perspectives, influencing the degree of an individual's data autonomy.
  • Wearable technology is increasing in the consumer market and offers great potential to learning environments.

What this paper adds

  • Extends extant literature on dynamic systems theory and phenomenology, contributing these perspectives to educational research in the context of student data privacy and wearable technologies.
  • Provides a framework to understand the complex and contingent ways that privacy can be understood in the collection, analysis, and communication of wearable data to support learning.

Implications for practice and/or policy

  • Higher education faculty and educational policymakers should consider various interactions in systems and among systems of how wearable data collection may be analysed, communicated and stored, potentially exposing students to privacy harms.
  • Multiple actors in learning systems must engage in continuous and evolving feedback loops around data security, consent, ownership and control to determine who has access to student data, how it is used and for what purposes.
  • The EU's General Data Protection and Regulation offers one of the most comprehensive frameworks for higher education institutions and faculty around the world to follow for protecting student data privacy.
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7.
Maintaining students' privacy in higher education, an integral aspect of learning design and technology integration, is not only a matter of policy and law but also a matter of design ethics. Similar to faculty educators, learning designers in higher education play a vital role in maintaining students' privacy by designing learning experiences that rely on online technology integration. Like other professional designers, they need to care for the humans they design for by not producing designs that infringe on their privacy, thus, not causing harm. Recognizing that widely used instructional design models are silent on the topic and do not address ethical considerations such as privacy, we focus this paper on how design ethics can be leveraged by learning designers in higher education in a practical manner, illustrated through authentic examples. We highlight where the ethical responsibility of learning designers comes into the foreground when maintaining students' privacy and well-being, especially in online settings. We outline an existing ethical decision-making framework and show how learning designers can use it as a call to action to protect the students they design for, strengthening their ethical design capacity.

Practitioner notes

What is already known about this topic
  • Existing codes of ethical standards from well-known learning design organizations call upon learning designers to protect students' privacy without clear guidance on how to do so.
  • Design ethics within learning design is often discussed in abstract ways with principles that are difficult to apply.
  • Most, if not all, design models that learning design professionals have learned are either silent on design ethics and/or do not consider ethics as a valid dimension, thus, making design ethics mostly excluded from learning design graduate programs.
  • Practical means for engaging in ethical design practice are scarce in the field.
What this paper adds
  • A call for learning designers in higher education to maintain and protect students' privacy and well-being, strengthening their ethical design capacity.
  • A demonstration of how to use a practical ethical decision-making framework as a designerly tool in designing for learning to maintain and protect students' privacy and well-being.
  • Authentic examples—in the form of vignettes—of ethical dilemmas/issues that learning designers in higher education could face, focused on students' privacy.
  • Methods—using a practical ethical decision-making framework—for learning design professionals in higher education, grounded in the philosophy of designers as the guarantors of designs, to be employed to detect situations where students' privacy and best interests are at risk.
  • A demonstration of how learning designers could make stellar design decisions in service to the students they design for and not to the priorities of other design stakeholders.
Implications for practice and/or policy
  • Higher education programs/institutions that prepare/employ learning designers ought to treat the topics of the designer's responsibility and design ethics more explicitly and practically as one of the means to maintain and protect students' privacy, in addition to law and policies.
  • Learning designers in higher education ought to hold a powerful position in their professional practice to maintain and protect students' privacy and well-being, as an important aspect of their ethical design responsibilities.
  • Learning designers in higher education ought to adopt a design thinking mindset in order to protect students' privacy by (1) challenging ideas and assumptions regarding technology integration in general and (2) detecting what is known in User Experience (UX) design as “dark patterns” in online course design.
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8.
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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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.
This study analyses the potential of a learning analytics (LA) based formative assessment to construct personalised teaching sequences in Mathematics for 5th-grade primary school students. A total of 127 students from Spanish public schools participated in the study. The quasi-experimental study was conducted over the course of six sessions, in which both control and experimental groups participated in a teaching sequence based on mathematical problems. In each session, both groups used audience response systems to record their responses to mathematical tasks about fractions. After each session, students from the control group were given generic homework on fractions—the same activities for all the participants—while students from the experimental group were given a personalised set of activities. The provision of personalised homework was based on the students' errors detected from the use of the LA-based formative assessment. After the intervention, the results indicate a higher student level of understanding of the concept of fractions in the experimental group compared to the control group. Related to motivational dimensions, results indicated that instruction using audience response systems has a positive effect compared to regular mathematics classes.

Practitioner notes

What is already known about this topic
  • Developing an understanding of fractions is one of the most challenging concepts in elementary mathematics and a solid predictor of future achievements in mathematics.
  • Learning analytics (LA) has the potential to provide quality, functional data for assessing and supporting learners' difficulties.
  • Audience response systems (ARS) are one of the most practical ways to collect data for LA in classroom environments.
  • There is a scarcity of field research implementations on LA mediated by ARS in real contexts of elementary school classrooms.
What this paper adds
  • Empirical evidence about how LA-based formative assessments can enable personalised homework to support student understanding of fractions.
  • Personalised homework based on an LA-based formative assessment improves the students' comprehension of fractions.
  • Using ARS for the teaching of fractions has a positive effect in terms of student motivation.
Implications for practice and/or policy
  • Teachers should be given LA/ARS tools that allow them to quickly provide students with personalised mathematical instruction.
  • Researchers should continue exploring these potentially beneficial educational implementations in other areas.
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11.
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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12.
Learning analytics is a fast-growing discipline. Institutions and countries alike are racing to harness the power of using data to support students, teachers and stakeholders. Research in the field has proven that predicting and supporting underachieving students is worthwhile. Nonetheless, challenges remain unresolved, for example, lack of generalizability, portability and failure to advance our understanding of students' behaviour. Recently, interest has grown in modelling individual or within-person behaviour, that is, understanding the person-specific changes. This study applies a novel method that combines within-person with between-person variance to better understand how changes unfolding at the individual level can explain students' final grades. By modelling the within-person variance, we directly model where the process takes place, that is the student. Our study finds that combining within- and between-person variance offers a better explanatory power and a better guidance of the variables that could be targeted for intervention at the personal and group levels. Furthermore, using within-person variance opens the door for person-specific idiographic models that work on individual student data and offer students support based on their own insights.

Practitioner notes

What is already known about this topic
  • Predicting students' performance has commonly been implemented using cross-sectional data at the group level.
  • Predictive models help predict and explain student performance in individual courses but are hard to generalize.
  • Heterogeneity has been a major factor in hindering cross-course or context generalization.
What this paper adds
  • Intra-individual (within-person) variations can be modelled using repeated measures data.
  • Hybrid between–within-person models offer more explanatory and predictive power of students' performance.
  • Intra-individual variations do not mirror interindividual variations, and thus, generalization is not warranted.
  • Regularity is a robust predictor of student performance at both the individual and the group levels.
Implications for practice
  • The study offers a method for teachers to better understand and predict students' performance.
  • The study offers a method of identifying what works on a group or personal level.
  • Intervention at the personal level can be more effective when using within-person predictors and at the group level when using between-person predictors.
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13.
Technology-based, open-ended learning environments (OELEs) can capture detailed information of students' interactions as they work through a task or solve a problem embedded in the environment. This information, in the form of log data, has the potential to provide important insights about the practices adopted by students for scientific inquiry and problem solving. How to parse and analyse the log data to reveal evidence of multifaceted constructs like inquiry and problem solving holds the key to making interactive learning environments useful for assessing students' higher-order competencies. In this paper, we present a systematic review of studies that used log data generated in OELEs to describe, model and assess scientific inquiry and problem solving. We identify and analyse 70 conference proceedings and journal papers published between 2012 and 2021. Our results reveal large variations in OELE and task characteristics, approaches used to extract features from log data and interpretation models used to link features to target constructs. While the educational data mining and learning analytics communities have made progress in leveraging log data to model inquiry and problem solving, multiple barriers still exist to hamper the production of representative, reproducible and generalizable results. Based on the trends identified, we lay out a set of recommendations pertaining to key aspects of the workflow that we believe will help the field develop more systematic approaches to designing and using OELEs for studying how students engage in inquiry and problem-solving practices.

Practitioner notes

What is already known about this topic
  • Research has shown that technology-based, open-ended learning environments (OELEs) that collect users' interaction data are potentially useful tools for engaging students in practice-based STEM learning.
  • More work is needed to identify generalizable principles of how to design OELE tasks to support student learning and how to analyse the log data to assess student performance.
What this paper adds
  • We identified multiple barriers to the production of sufficiently generalizable and robust results to inform practice, with respect to: (1) the design characteristics of the OELE-based tasks, (2) the target competencies measured, (3) the approaches and techniques used to extract features from log files and (4) the models used to link features to the competencies.
  • Based on this analysis, we can provide a series of specific recommendations to inform future research and facilitate the generalizability and interpretability of results:
    • Making the data available in open-access repositories, similar to the PISA tasks, for easy access and sharing.
    • Defining target practices more precisely to better align task design with target practices and to facilitate between-study comparisons.
    • More systematic evaluation of OELE and task designs to improve the psychometric properties of OELE-based measurement tasks and analysis processes.
    • Focusing more on internal and external validation of both feature generation processes and statistical models, for example with data from different samples or by systematically varying the analysis methods.
Implications for practice and/or policy
  • Using the framework of evidence-centered assessment design, we have identified relevant criteria for organizing and evaluating the diverse body of empirical studies on the topic and that policy makers and practitioners can use for their own further examinations.
  • This paper identifies promising research and development areas on the measurement and assessment of higher-order constructs with process data from OELE-based tasks that government agencies and foundations can support.
  • Researchers, technologists and assessment designers might find useful the insights and recommendations for how OELEs can enhance science assessment through thoughtful integration of learning theories, task design and data mining techniques.
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14.
Preparing data-literate citizens and supporting future generations to effectively work with data is challenging. Engaging students in Knowledge Building (KB) may be a promising way to respond to this challenge because it requires students to reflect on and direct their inquiry with the support of data. Informed by previous studies, this research explored how an analytics-supported reflective assessment (AsRA)-enhanced KB design influenced 6th graders' KB and data science practices in a science education setting. One intact class with 56 students participated in this study. The analysis of students' Knowledge Forum discourse showed the positive influences of the AsRA-enhanced KB design on students' development of KB and data science practices. Further analysis of different-performing groups revealed that the AsRA-enhanced KB design was accessible to all performing groups. These findings have important implications for teachers and researchers who aim to develop students' KB and data science practices, and general high-level collaborative inquiry skills.

Practitioner notes

What is already known about this topic
  • Data use becomes increasingly important in the K-12 educational context.
  • Little is known about how to scaffold students to develop data science practices.
  • Knowledge Building (KB) and learning analytics-supported reflective assessment (AsRA) show premises in developing these practices.
What this paper adds
  • AsRA-enhanced KB can help students improve KB and data science practices over time.
  • AsRA-enhanced KB design benefits students of different-performing groups.
  • AsRA-enhanced KB is accessible to elementary school students in science education.
Implications for practice and/or policy
  • Developing a collaborative and reflective culture helps students engage in collaborative inquiry.
  • Pedagogical approaches and analytic tools can be developed to support students' data-driven decision-making in inquiry learning.
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15.
An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.

Practitioner notes

What is already known about this topic

  • ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
  • Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
  • Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.

What this paper adds

  • An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
  • A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
  • An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.

Implications for practice and/or policy

  • Causal models can help us to explicitly specify educational theories in a testable format.
  • It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
  • Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.
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16.
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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17.
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.
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18.
Postsecondary institutions have a legal responsibility to ensure that students have access to a safe learning environment. While institutions adopt policies and hire administrators to protect students from harm, many are underprepared to support students when these harmful incidents happen online. This is of increased concern now that online aggression is pervasive across universities worldwide. While faculty, administrators and students agree that online aggression is a significant issue and that institutions ought to provide prevention and response services, there is concern that these efforts might violate privacy norms. We used the theory of privacy as contextual integrity (CI) to explore the tensions that postsecondary students and staff perceive regarding student privacy when responding to incidents of online aggression. To do so, we conducted focus groups with undergraduate students and student affairs administrators from a Historically Black College and University (HBCU) in the Mid-Atlantic USA. Our analysis surfaced three considerations that inform students' and staff's decision to report an incident of online aggression: their closeness to the person making the post, their perception of the online post content as a real threat and their knowledge of an authority figure who could help resolve the situation. We used CI theory to explain how these considerations can inform institutional policy, practice and future research.

Practitioner notes

What is already known about this topic

  • Online aggression is a pervasive issue at postsecondary institutions worldwide that can contribute to psychological, academic and developmental issues.
  • Postsecondary students and staff are unsure of how to respond to incidents of online aggression.
  • There is a gap in policies and procedures for responding to online aggression at postsecondary institutions.

What this paper adds

  • A novel use of Nissenbaum's (2010) theory of contextual integrity to understand students' and staff's perceptions of privacy.
  • Students' and staff's decisions to intervene or report an online aggression incident are determined by their relationship to the perpetrator, the severity of the social media post and their knowledge of who to tell on campus.
  • Students and staff are reluctant to inform the police out of fear of violence against the perpetrator.

Implications for practice and/or policy

  • Raise awareness about responding to online aggression incidents.
  • Implement online bystander intervention training programs to increase awareness and self-efficacy to intervene in unclear situations.
  • Develop clear policies regarding online aggression, as well as a trustworthy procedure for how to respond.
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19.
As universities moved to remotely taught courses during the COVID-19 pandemic, the importance of maintaining academic integrity in online environments intensified. In response, this study explores instructors' perceptions about the role of online proctoring as a tool for their courses with the intent of enhancing the understanding of online proctoring's usefulness in ensuring academic integrity and the factors that may be swaying instructors' adoption decisions. An online survey was completed by 158 instructors at a variety of higher education institutions with 118 responding to an open-ended question that allowed respondents to share any additional thoughts about or experiences with using online proctoring. A thematic review of the qualitative comments illustrates the multifaceted impact of online proctoring on instructors and students. Results identified instructors' perceived benefits and challenges of online proctoring to them, their students and the learning process. In addition, instructors voiced numerous legal, ethical and social concerns about the use of online proctoring, including concerns related to students' privacy. Despite these concerns, some instructors identified strong use cases for online proctoring while others provided alternative strategies for ensuring academic integrity in online courses. As institutions consider the role of online proctoring in ensuring academic integrity, a holistic approach that balances instructional design best practices, student-friendly policies and proctoring tools is recommended to serve the complex needs and concerns of instructors, students and their institutions.

Practitioner notes

What is already known about this topic

  • Prior research findings are mixed as to whether proctoring is valuable for ensuring academic integrity in online courses.
  • Studies investigating grade performance in proctored versus unproctored exam settings have conflicting results; however, studies have found that students completing proctored formative exams perform better on summative exams than students completing non-proctored formative exams.

What this paper adds

  • Qualitative data were collected to provide an overview of instructors' perceptions about and experiences with online proctoring.
  • Analysis suggests that online proctoring is beneficial to some instructors, students and the overall learning process. At the same time, its use is also concerning to other instructors and students. Among the issues raised by instructors are concerns for student privacy, increases in student test anxiety and discriminatory proctoring practices.

Implications for practice and/or policy

  • Institutions must be proactive in ensuring that the use of online proctoring aligns with their institutional values and the changing legal landscape.
  • Institutional policies should strive to find a balance between ensuring academic integrity and promoting a positive experience for students and instructors. Since there are strong use cases for online proctoring, these policies should include flexibility whenever possible.
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20.
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.
  相似文献   

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