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1.
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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2.
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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3.
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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4.
The promise of using immersive technologies in learning has increasingly been attracting researchers' and practitioners' attention. However, relevant empirical works are usually conducted in fully controlled Virtual Reality (VR) laboratories, as opposed to conventional settings. This quasi-experimental study compares the effectiveness of video learning resources to that of stereoscopic 360° VR, as supplements to the traditional instructional approach. The potential of such methods was examined in high school settings, in the context of the ‘Life and Evolution’ module, with participants (n = 70) divided equally into control and experimental groups. As a point of reference (control condition), we considered the adoption of Video Learning Resources, as students are more acquainted with this instructional method. In the intervention approach (experimental condition), students adopted the use of low-end mobile-VR (VeeR Mini VR Goggles). The key findings indicate differences in the learning motivation, confidence and satisfaction, but no statistically significant difference was identified regarding the factual or conceptual knowledge gains. The study offers insights on the potential of the investigated technologies in the subject of secondary school Biology and further provides implications for theory and practice.

Practitioner notes

What is already known about this topic
  • Researchers' interest over the potential of Virtual Reality on different STEM disciplines is increasing consistently.
  • An increasing number of efforts can be identified discussing the integration of multimedia learning resources in the secondary school context.
  • Empirical studies on the subject of Biology are focusing on students' academic performance and achievement but not on learning motivation and satisfaction.
What this paper adds
  • This quasi-experimental study comparatively examines academic performance, with the focus being on learning motivation and satisfaction, across different modalities (stereoscopic 360° Virtual Reality applications-VR, Video Learning Recourses-VLR).
  • The findings demonstrate that both instructional methods are sufficient in enhancing students' knowledge acquisition and academic performance.
  • The adoption of stereoscopic 360° VR influences students' learning motivation and impacts long-term memory retention.
Implications for practice and policy
  • Educators are advised to consider the systematic adoption of “immersive” multimedia tools to enhance the subject of Biology as they can greatly encourage scientific inquiry.
  • Instructional designers are advised to adopt open educational resources aligned to the curriculum of the local context.
  • Educational researchers are advised to integrate stereoscopic 360°-VR solutions in the conventional classroom settings.
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5.
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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6.
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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7.
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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8.
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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9.
Prior research has shown that game-based learning tools, such as DragonBox 12+, support algebraic understanding and that students' in-game progress positively predicts their later performance. Using data from 253 seventh-graders (12–13 years old) who played DragonBox as a part of technology intervention, we examined (a) the relations between students' progress within DragonBox and their algebraic knowledge and general mathematics achievement, (b) the moderating effects of students' prior performance on these relations and (c) the potential factors associated with students' in-game progress. Among students with higher prior algebraic knowledge, higher in-game progress was related to higher algebraic knowledge after the intervention. Higher in-game progress was also associated with higher end-of-year mathematics achievement, and this association was stronger among students with lower prior mathematics achievement. Students' demographic characteristics, prior knowledge and prior achievement did not significantly predict in-game progress beyond the number of intervention sessions students completed. These findings advance research on how, for whom and in what contexts game-based interventions, such as DragonBox, support mathematical learning and have implications for practice using game-based technologies to supplement instruction.

Practitioner notes

What is already known about this topic
  • DragonBox 12+ may support students' understanding of algebra but the findings are mixed.
  • Students who solve more problems within math games tend to show higher performance after gameplay.
  • Students' engagement with mathematics is often related to their prior math performance.
What this paper adds
  • For students with higher prior algebraic knowledge, solving more problems in DragonBox 12+ is related to higher algebraic performance after gameplay.
  • Students who make more in-game progress also have higher mathematics achievement, especially for students with lower prior achievement.
  • Students who spend more time playing DragonBox 12+ make more in-game progress; their demographic, prior knowledge and prior achievement are not related to in-game progress.
Implications for practice and/or policy
  • DragonBox 12+ can be beneficial as a supplement to algebra instruction for students with some understanding of algebra.
  • DragonBox 12+ can engage students with mathematics across achievement levels.
  • Dedicating time and encouraging students to play DragonBox 12+ may help them make more in-game progress, and in turn, support math learning.
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10.
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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11.
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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12.
The technology acceptance model (TAM) uses perceived usefulness and perceived ease of use to predict the intention to use a technology which is important when deciding to invest in a technology. Its extension for e-learning (the general extended technology acceptance model for e-learning; GETAMEL) adds subjective norm to predict the intention to use. Technology acceptance is typically measured after the technology has been used for at least three months. This study aims to identify whether a minimal amount of exposure to the technology using video demonstrations is sufficient to predict the intention to use it three months later. In two studies—one using TAM and one using GETAMEL—we showed students of different cohorts (94 and 111 participants, respectively) video demonstrations of four digital technologies (classroom response system, classroom chat, e-lectures, mobile virtual reality). We then measured technology acceptance immediately after the demonstration and after three months of technology use. Using partial least squares modelling, we found that perceived usefulness significantly predicted the intention to use three months later. In GETAMEL, perceived usefulness significantly predicted the intention to use for three of the four learning technologies, while subjective norm only predicted the intention to use for mobile virtual reality. We conclude that video demonstrations can provide valuable insight for decision-makers and educators on whether students will use a technology before investing in it.

Practitioner notes

What is already known about this topic
  • The technology acceptance model helps decision-makers to determine whether students and teachers will adopt a new technology.
  • Technology acceptance is typically measured after users have used the technology for three to twelve months.
  • Perceived usefulness is a strong predictor of intention to use the technology.
  • The predictive power of perceived ease of use for the intention to use varies from insignificant to strong.
What this paper adds
  • For the four digital learning technologies (classroom chat, classroom response system, e-lectures and mobile virtual reality), we measure technology acceptance after a video demonstration and again after three months of usage.
  • Using structural equation modelling, we are able to predict intention to use after three months, with perceived usefulness measured after the video demonstration.
  • We replicate these findings with a second study using the general extended technology acceptance model.
Implications for practice and/or policy
  • Short video demonstrations can provide information for educators to predict whether students will use a technology.
  • Early impressions of perceived usefulness are very important and valuable to predict whether students will use a technology.
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13.
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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14.
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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15.
Video is a widely used medium in teacher training for situating student teachers in classroom scenarios. Although the emerging technology of virtual reality (VR) provides similar, and arguably more powerful, capabilities for immersing teachers in lifelike situations, its benefits and risks relative to video formats have received little attention in the research to date. The current study used a randomized pretest–posttest experimental design to examine the influence of a video- versus VR-based task on changing situational interest and self-efficacy in classroom management. Results from 49 student teachers revealed that the VR simulation led to higher increments in self-reported triggered interest and self-efficacy in classroom management, but also invoked higher extraneous cognitive load than a video viewing task. We discussed the implications of these results for pre-service teacher education and the design of VR environments for professional training purposes.

Practitioner notes

What is already known about this topic
  • Video is a popular teacher training medium given its ability to display classroom situations.
  • Virtual reality (VR) also immerses users in lifelike situations and has gained popularity in recent years.
  • Situational interest and self-efficacy in classroom management is vital for student teachers' professional development.
What this paper adds
  • VR outperforms video in promoting student teachers' triggered interest in classroom management.
  • Student teachers felt more efficacious in classroom management after participating in VR.
  • VR also invoked higher extraneous cognitive load than the video.
Implications for practice and/or policy
  • VR provides an authentic teacher training environment for classroom management.
  • The design of the VR training environment needs to ensure a low extraneous cognitive load.
  相似文献   

16.
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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17.

This study investigates the possibility of utilizing online learning data to design face-to-face activities in a flipped classroom. We focus on heterogeneous group formation for effective collaborative learning. Fifty-three undergraduate students (18 males, 35 females) participated in this study, and 8 students (3 males, 5 females) among them joined post-study interviews. For this study, a total of 6 student characteristics were used: three demographic characteristics obtained from a simple survey and three academic characteristics captured from online learning data. We define three demographic group heterogeneity variables and three academic group heterogeneity variables, where each variable is calculated using the corresponding student characteristic. In this way, each heterogeneity variables represents a degree of diversity within the group. Then, a two-stage hierarchical regression analysis was conducted to identify the significant group heterogeneity variables that influence face-to-face group achievement. The results show that the academic group heterogeneity variables, which were derived from the online learning data, accounted for a significant proportion of the variance in the group achievement when the demographic group heterogeneity variables were controlled. The interviews also reveal that the academic group heterogeneity indeed affected group interaction and learning outcome. These findings highlight that online learning data can be utilized to obtain relevant information for effective face-to-face activity design in a flipped classroom. Based on the results, we discuss the advantages of this data utilization approach and other implications for face-to-face activity design.

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18.
Game-based learning environments hold significant promise for facilitating learning experiences that are both effective and engaging. To support individualised learning and support proactive scaffolding when students are struggling, game-based learning environments should be able to accurately predict student knowledge at early points in students' gameplay. Student knowledge is traditionally assessed prior to and after each student interacts with the learning environment with conventional methods, such as multiple choice content knowledge assessments. While previous student modelling approaches have leveraged machine learning to automatically infer students' knowledge, there is limited work that incorporates the fine-grained content from each question in these types of tests into student models that predict student performance at early junctures in gameplay episodes. This work investigates a predictive student modelling approach that leverages the natural language text of the post-gameplay content knowledge questions and the text of the possible answer choices for early prediction of fine-grained individual student performance in game-based learning environments. With data from a study involving 66 undergraduate students from a large public university interacting with a game-based learning environment for microbiology, Crystal Island , we investigate the accuracy and early prediction capacity of student models that use a combination of gameplay features extracted from student log files as well as distributed representations of post-test content assessment questions. The results demonstrate that by incorporating knowledge about assessment questions, early prediction models are able to outperform competing baselines that only use student game trace data with no question-related information. Furthermore, this approach achieves high generalisation, including predicting the performance of students on unseen questions.

Practitioner notes

What is already known about this topic
  • A distinctive characteristic of game-based learning environments is their capacity to enable fine-grained student assessment.
  • Adaptive game-based learning environments offer individualisation based on specific student needs and should be able to assess student competencies using early prediction models of those competencies.
  • Word embedding approaches from the field of natural language processing show great promise in the ability to encode semantic information that can be leveraged by predictive student models.
What this paper adds
  • Investigates word embeddings of assessment question content for reliable early prediction of student performance.
  • Demonstrates the efficacy of distributed word embeddings of assessment questions when used by early prediction models compared to models that use either no assessment information or discrete representations of the questions.
  • Demonstrates the efficacy and generalisability of word embeddings of assessment questions for predicting the performance of both new students on existing questions and existing students on new questions.
Implications for practice and/or policy
  • Word embeddings of assessment questions can enhance early prediction models of student knowledge, which can drive adaptive feedback to students who interact with game-based learning environments.
  • Practitioners should determine if new assessment questions will be developed for their game-based learning environment, and if so, consider using our student modelling framework that incorporates early prediction models pretrained with existing student responses to previous assessment questions and is generalisable to the new assessment questions by leveraging distributed word embedding techniques.
  • Researchers should consider the most appropriate way to encode the assessment questions in ways that early prediction models are able to infer relationships between the questions and gameplay behaviour to make accurate predictions of student competencies.
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19.
Online professional development programmes have a long tradition in adult education. However, in early childhood education, such programmes are only just beginning to be developed. Before online professional development courses can be made inclusive, accessible and widely available to early childhood educators, they must meet a few basic requirements. The present study provides insights into essential aspects that need to be considered when setting up online professional development programmes, a field which is still in its infancy in Austria. The results of a representative survey (n = 317) demonstrated that early childhood educators' digital competencies are highly variable and cannot be taken for granted. The survey results also stress the need to provide educators with functional digital devices appropriate to their work environment. Early childhood educators' interest in online professional development programmes is very high, in recognition of the advantages afforded by flexible participation options independent of users' time constraints or location. The development of attractive, compelling and accessible online professional development courses can contribute to current professionalisation efforts in the field of early childhood education.

Practitioner notes

What is already known about this topic
  • Online professional development has already a very long tradition in the field of adult vocational training but not in Early Childhood Education.
  • The possibility of attending online professional development programmes independent of a participant's time constraints or location is viewed as a particularly beneficial advantage.
  • Online professional development programmes positively affect the professionalisation of educators. For this reason, they must increasingly be integrated into the field of early childhood education.
What this paper adds
  • In the field of early childhood education in German-speaking regions, online professional development training courses are only beginning to be developed.
  • This paper examines the challenges and barriers for early childhood educators associated with implementing online professional development programmes for early childhood educators.
  • The survey was conducted during the COVID-19 pandemic and includes current data on the ongoing digitalisation boom.
Implications for practise and/or policy
  • Online professional development courses should not be understood as competition for conventional face-to-face programmes. Instead, they act as a useful supplement.
  • Media competencies are a fundamental prerequisite for everyday professional life—early childhood educators need functional media devices, stable internet connections, and support structures in IT and computer literacy.
  • Early childhood educators require effective instruction in using online professional development programmes to expand online professional development programmes in their field. They must also address compelling topics in early childhood education relevant to educators' practise.
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20.
The influence of homework experiences on students’ academic grades was studied with 223 college students. Students’ self-efficacy for learning and perceived responsibility beliefs were included as mediating variables in this research. The students’ homework influenced their achievement indirectly via these two self-regulatory beliefs as well as directly. Self-efficacy for learning, although moderately correlated with perceptions of responsibility, predicted course grades more strongly than the latter variable. No gender differences were found for any of the variables, a finding that extends prior research based on high school girls. Educational implications about the importance of students’ homework completion and its relationship to college students’ development of self-regulation and positive self-efficacy beliefs is discussed from a social cognitive perspective.  相似文献   

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