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1.
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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2.
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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3.
Participation in educational activities is an important prerequisite for academic success, yet often proves to be particularly challenging in digital settings. Therefore, this study set out to increase participation in an online proctored formative statistics exam by digital nudging. We exploited targeted nudges based on the Fogg Behaviour Model, highlighting the relevance of acknowledging differences in motivation and ability in allocating nudges to elicit target behaviour. First, we assessed whether pre-existing levels of motivation and perceived ability to participate are effective in identifying different propensities of responsiveness to plain untailored nudges. Next, we evaluated whether tailoring nudges to students' motivation and perceived ability levels increases target behaviour by means of a randomized field experiment in which 579 first-year university students received 6 consecutive emails over the course of three weeks to nudge behaviour regarding successful participation in the online exam. First, the results point out that motivation explains differences in engagement as indicated by student responsiveness and participation, whereas the perceived ability to participate does not. Second, the results from the randomized field experiment indicate that tailored nudging did not improve observed engagement. Implications for the potential of providing motivational information to improve participation in online educational activities are discussed, as are alternatives for capturing perceived ability more effectively.

Practitioner notes

What is already known about this topic
  • Participation in educational activities is an important prerequisite for academic success, yet often proves to be particularly challenging in digital settings.
  • Students' internal barriers to online participation and persistence in higher education are lack of motivation and perceived ability.
  • Nudging interventions tackle students' behavioural barriers, and are particularly effective when guided by a theory of behaviour change, and when targeting students who suffer most from those barriers.
What this paper adds
  • This study examines whether the Fogg Behaviour Model is suited to guide a nudging intervention with the aim to increase student engagement in online higher education.
  • This study examines whether students with different levels of motivation and perceived ability vary in their online behaviour in response to nudges.
  • This study experimentally evaluates whether targeted nudges—targeted at students' motivation and perceived ability—are more effective than plain (not-targeted) nudges.
Implications for practice and/or policy
  • The results indicate the importance of motivation for performing nudged behaviours regarding successful participation in an online educational activity.
  • The results do not provide evidence for the role of perceived digital ability, yet do show prior performance on a similar educational activity can effectively distinguish between students' responsiveness.
  • Targeted nudges were not more effective than plain nudges, but the potential of other motivational nudges and how to increase perceived performance are discussed.
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4.
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5.
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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6.
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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7.
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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8.
A significant body of the literature has documented the potential of Augmented Reality (AR) in education, but little is known about the effects of AR-supported instruction in tertiary-level Medical Education (ME). This quasi-experimental study compares a traditional instructional approach with supplementary online lecture materials using digital handout notes with a control group (n = 30) and an educational AR application with an experimental group (n = 30) to investigate any possible added-value and gauge the impact of each approach on students' academic performance and training satisfaction. This study's findings indicate considerable differences in both academic performance and training satisfaction between the two groups. The participants in the experimental group performed significantly better than their counterparts, an outcome which is also reflected in their level of training satisfaction through interacting and viewing 3D multimedia content. This study contributes by providing guidelines on how an AR-supported intervention can be integrated into ME and provides empirical evidence on the benefits that such an approach can have on students' academic performance and knowledge acquisition.

Practitioner notes

What is already known about this topic
  • Several studies have applied various Augmented Reality (AR) applications across different learning disciplines.
  • The effects of AR on students' perceptions and achievements in higher education contexts is well-documented.
  • Despite the increasing use of AR-instruction in Medical Education (ME), there has been no explicit focus on AR's effects on students' academic performance and satisfaction.
What this paper adds
  • This quasi-experimental study compares the academic performance and training satisfaction of students in an experimental group (AR) and a control group (handout notes).
  • This study provides instructional insights into, and recommendations that may help students achieve better academic performance in AR-supported ME courses.
  • The experimental group reported greater training satisfaction than their counterparts.
Implications for practice and policy
  • Students who followed the AR-supported instruction achieved better academic performance that those in the control group.
  • AR-supported interventions encourage active learning and lead to significant performance improvement.
  • The experimental group outperformed the control group in academic performance and training satisfaction measurements, despite the lower experimental group's lower pre-test performance scores.
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9.
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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10.
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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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.
With the widespread use of learning analytics (LA), ethical concerns about fairness have been raised. Research shows that LA models may be biased against students of certain demographic subgroups. Although fairness has gained significant attention in the broader machine learning (ML) community in the last decade, it is only recently that attention has been paid to fairness in LA. Furthermore, the decision on which unfairness mitigation algorithm or metric to use in a particular context remains largely unknown. On this premise, we performed a comparative evaluation of some selected unfairness mitigation algorithms regarded in the fair ML community to have shown promising results. Using a 3-year program dropout data from an Australian university, we comparatively evaluated how the unfairness mitigation algorithms contribute to ethical LA by testing for some hypotheses across fairness and performance metrics. Interestingly, our results show how data bias does not always necessarily result in predictive bias. Perhaps not surprisingly, our test for fairness-utility tradeoff shows how ensuring fairness does not always lead to drop in utility. Indeed, our results show that ensuring fairness might lead to enhanced utility under specific circumstances. Our findings may to some extent, guide fairness algorithm and metric selection for a given context.

Practitioner notes

What is already known about this topic
  • LA is increasingly being used to leverage actionable insights about students and drive student success.
  • LA models have been found to make discriminatory decisions against certain student demographic subgroups—therefore, raising ethical concerns.
  • Fairness in education is nascent. Only a few works have examined fairness in LA and consequently followed up with ensuring fair LA models.
What this paper adds
  • A juxtaposition of unfairness mitigation algorithms across the entire LA pipeline showing how they compare and how each of them contributes to fair LA.
  • Ensuring ethical LA does not always lead to a dip in performance. Sometimes, it actually improves performance as well.
  • Fairness in LA has only focused on some form of outcome equality, however equality of outcome may be possible only when the playing field is levelled.
Implications for practice and/or policy
  • Based on desired notion of fairness and which segment of the LA pipeline is accessible, a fairness-minded decision maker may be able to decide which algorithm to use in order to achieve their ethical goals.
  • LA practitioners can carefully aim for more ethical LA models without trading significant utility by selecting algorithms that find the right balance between the two objectives.
  • Fairness enhancing technologies should be cautiously used as guides—not final decision makers. Human domain experts must be kept in the loop to handle the dynamics of transcending fair LA beyond equality to equitable LA.
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13.
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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14.
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.
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15.
This paper contributes to the scarce literature on factors affecting EdTech use in households. These factors were considered through exploratory mixed-methods analyses of cross-sectional data on Kenyan girls and caregivers, captured during the COVID-19 pandemic. Quantitative analysis of the child dataset (n = 544) suggested the importance of both structural factors—such as technology hardware availability—and non-structural factors—including caregiver permission. Findings were supported by a thematic analysis of interview data from girls' caregivers (n = 58), which emphasised the role they play in girls' use of EdTech. Interviews also highlighted numerous caregiver concerns with EdTech, related to the relevance and rigour of educational content, the possibility of children accessing age-inappropriate material and child health (especially eyesight). Policy makers could alleviate these concerns by providing guidance on EdTech use and clearly signalling their approval of verified initiatives.

Practitioner notes

What is already known about this topic
  • EdTech can benefit girls' education, yet there are various barriers to it being used.
  • Existing research shows clearly that EdTech use can be impeded by structural factors (eg, hardware ownership).
  • However, we find insufficient empirical evidence on the role of non-structural or behavioural factors.
What this paper adds
  • This paper addresses this gap, using a mixed-methods approach to explore the influence of 33 different measures (including non-structural factors) that could affect the number of hours girls spend using EdTech at home.
  • Findings from a quantitative sample of girls (n = 544) and a qualitative sample of girls' caregivers (n = 58) highlighted the importance of non-structural factors, especially caregiver permission.
  • The variable most strongly associated with girls' EdTech usage in our selected quantitative model concerned whether this was sanctioned by their caregivers.
  • Our qualitative data suggested why caregiver permission to use EdTech might be withheld: caregivers emphasised perceived concerns about the risks and rigour of EdTech.
Implications for practice and/or policy
  • Our findings suggest the viability of policy interventions that provide EdTech guidance to caregivers.
  • Caregivers uncertain about EdTech could be reassured of the appropriateness of verified initiatives, while those already convinced might be aided in their attempts to support EdTech learning.
  • Such guidance could provide a low-cost means of further exploiting the benefits that household EdTech learning can provide.
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16.
Online peer assessment (OPA) has been increasingly adopted to develop students' higher-order thinking (HOT). However, there has not been a synthesis of research findings on its effects. To fill this gap, 17 papers (published from 2000 to 2022) that reported either a comparison between a group using OPA (n = 7; k = 22) and a control group or a pre–post comparison (n = 10; k = 17) were reviewed in this meta-analysis. The overall effect of OPA on HOT was significant (g = 0.76). Furthermore, OPA exerted more significant effects on convergent HOT (eg, critical thinking, reasoning and reflective thinking; g = 0.97) than on divergent HOT (eg, creativity and problem-solving; g = 0.38). Reciprocal roles and anonymity were found to positively moderate the impacts of OPA on HOT, although their moderating effects were not statistically significant because of small sample size of studies in the analysis. The results of the meta-analysis reinforce the arguments for regarding OPA as a powerful learning tool to facilitate students' HOT development and reveal important factors that should be considered when adopting OPA to enhance students' HOT.

Practitioner notes

What is already known about this topic
  • Online peer assessment (OPA) has significant positive impacts on learning achievement.
  • OPA has been regarded as a potential approach to cultivating students' higher-order thinking (HOT) but has not been proved by meta-analysis.
  • OPA should be carefully designed to maximise its effectiveness on learning.
What this paper adds
  • OPA has been proved to significantly positively influence students' HOT via meta-analysis.
  • OPA exerted more significant effects on convergent HOT than on divergent HOT.
  • The potential of reciprocal roles and anonymity for moderating the impacts of OPA on HOT should not be underestimated.
Implications for practice and/or policy
  • OPA could be a wise choice for practitioners when they help students to achieve a balanced development of HOT dispositions and skills.
  • Students' divergent HOT can be encouraged in their uptake of peer feedback and by allowing them autonomy in deciding assessment criteria.
  • OPA with design elements of reciprocal roles and anonymity has great potential to promote students' HOT.
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17.
While gamification and game-based learning have both been demonstrated to have a host of educational benefits for university students, many university educators do not routinely use these approaches in their teaching. Therefore, this systematic review, conducted using the PRISMA guidelines, sought to identify the primary drivers and barriers to the use of gamification and game-based learning by university educators. A search of multiple databases (Web of Science, Scopus and EBSCO (Business Source Complete; ERIC; Library, Information Science & Technology Abstracts)) identified 1330 articles, with 1096 retained after duplicates were removed. Seventeen articles (11 quantitative, two mixed-methods and four qualitative) were included in the systematic review. The primary drivers described by the educators that positively influenced their gamification and game-based learning usage were their beliefs that it encourages student interactions and collaborative learning; provides fun and improves engagement; and can easily be used by students. Alternatively, the university educators' major barriers included a lack of time to develop gamification approaches, lack of proven benefits and classroom setting issues. Many of these and other less commonly reported drivers and barriers can be categorised as attitudinal, design-related or administrative in nature. Such categorisations may assist university educators, teaching support staff and administrators in better understanding the primary factors influencing the utilisation of gamification and game-based learning and develop more effective strategies to overcome these barriers to its successful implementation.

Practitioner notes

What is already known about this topic

  • Gamification and game-based learning may have many benefits for university students.
  • The majority of university educators do not routinely use gamification and game-based learning in their teaching.

What this paper adds

  • University educators' major drivers that positively influence the use of gamification and game-based learning include their perceptions that it encourages student interactions and collaborative learning, provides fun and improves engagement and can easily be used by students.
  • University educators' major barriers that negatively influence the use of gamification and game-based learning include their perceptions of a lack of time to develop gamification approaches, lack of proven benefits and classroom setting issues.
  • These drivers and barriers may be classified as attitudinal, design-related and administrative, with these categories providing a useful way for universities to develop strategies to better support educators who wish to use these approaches in their teaching.

Implications for practice and policy

  • Attitudinal factors such as university educators' intention to use gamification and game-based learning are influenced by a host of their perceptions including attitude, perceived usefulness and ease of use.
  • A range of design-related and administrative barriers may need to be overcome to increase the use of gamification and game-based learning in the university sector.
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18.
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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19.
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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20.
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.
  相似文献   

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