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Background: Undergraduates' collective epistemic agency is critical for their productive collaborative inquiry and knowledge building (KB). However, fostering undergraduates' collective epistemic agency is challenging. Studies have demonstrated the potential of computer‐supported collaborative inquiry approaches, such as KB—the focus of this study, and reflective assessment to foster collective epistemic agency. Objectives: This study used a quasi‐experimental design to examine the impact of reflective assessment on undergraduates' collective epistemic agency and the mechanisms of this impact. Methods: An experimental class, comprising 40 undergraduates, engaged in KB inquiry enhanced by analytics‐assisted reflective assessment, while a comparison class, comprising 41 undergraduates, engaged in portfolio‐supported reflective assessment in KB inquiry. Classroom observations, audio recordings of group discussions and reflections, and student artefacts were collected in both conditions. Results and Conclusions: The experimental class demonstrated significantly higher levels of collective epistemic agency than the comparison class, as indicated by their collective knowledge advancement and epistemic and metacognitive actions. Qualitative thematic analysis revealed that reflective assessment enhanced the undergraduates' collective epistemic agency through their collective monitoring and regulation of inquiry and ideas, negotiation and synthesis of the community's ideas, conceptualization of collective theories and identification of collective inquiry trajectories with an internal reflective structure. Major Takeaways: Higher‐level metacognitive actions are critical for productive collaborative inquiry and higher‐level epistemic agency that does not often occur naturally and should be promoted by appropriate scaffolding strategies. Students need data from learning analytics and accompanying prompt sheets to help them develop the internal metacognitive structure for agency and learning. Lay Description: What is currently known about this topic?: Undergraduates' collective epistemic agency (CEA) is critical for productive KB.Analytics‐assisted reflective assessment (AARA) are promising in fostering CEA. What does this paper add?: AARA has positive influences on students' CEAAARA helps students develop CEA through the following mechanisms.AARA supports students' collective regulation and negotiation of collective ideas.AARA enhances conceptualization and deepening of collective trajectories with an internal reflective structure. Implications for practice/or policy: Higher‐level metacognitive actions are critical for collaborative inquiry and CEAAnalytics‐assisted RA can enhance students' higher‐level metacognitive actions.Students should be provided with learning data and appropriate scaffolding strategies.These data and strategies can develop students' internal metacognitive structure crucial for agency and learning. [ABSTRACT FROM AUTHOR]

Background: The use of e‐exams in higher education is increasing. However, the role of student diversity in the acceptance of e‐exams is an under‐researched topic. In the current study, we considered student diversity in terms of three sociodemographic characteristics (age, gender, and second language) and three dispositional student characteristics (computer anxiety, test anxiety, and technology openness). Objectives: The main objective of this study was to investigate the relationship between student diversity and acceptance of e‐exams. Methods: Our research combined cross‐sectional analyses (N = 1639) with data from a natural experiment on the introduction of e‐exams versus the established paper‐pencil exams (N = 626) and used both self‐report and institutional data. Sociodemographic and dispositional characteristics were indirectly related to pre‐exam acceptance via expectancy variables from the Technology Acceptance Model framework. Results and Conclusions: Comparisons of post‐exam acceptance showed that practical experience with the e‐exam led to a significant increase in e‐exam acceptance, and that students with low openness toward technology particularly benefited from this effect. Students' exam performance (i.e., grades) was unrelated to the exam format or their pre‐exam acceptance of the e‐exam format, and this was true across students' sociodemographic and dispositional characteristics. Takeaway: Student diversity plays a role in e‐exam acceptance, but its influence is mitigated by first‐hand experience with e‐exams. The practical implications for higher education institutions aiming to implement e‐exams are discussed. Lay Description: What is already known about this topic: The use of e‐exams in higher education is increasingThe role of student diversity for e‐exam acceptance is unclearTechnology acceptance is predicted by expectancies towards new system What this paper adds: We investigated students' sociodemographic and dispositional diversityDiversity predicted e‐exam acceptance via the expectanciesIn a natural experiment, first‐hand experience increased e‐exam acceptanceNo difference between performance in e‐exams and paper‐pencil‐exams found Implications of the study findings for practitioners: Higher education institutions implementing e‐exams should consider diversitySupport for older students and students with low technology openness neededNo student groups systematically disadvantaged by e‐exam implementationPractice rooms can increase experience with new system before exam [ABSTRACT FROM AUTHOR]

This paper reflects on the importance of 'time spent' in understanding the international student experience. Short‐term mobility programmes (involving stays of between 1 week and 2 months) attracting less privileged students, such as the relatively new Turing Scheme in the United Kingdom, have been hailed as a potential 'solution' to the fact that, traditionally, wealthier individuals have been far more likely to engage in study abroad. However, we do not yet know how short‐term and longer duration programmes compare in terms of the value they confer to students (in relation to their experiences and outcomes). How likely is it that short‐term mobility at undergraduate level is as valuable, according to different measures, as mobility lasting 6 months to several years (as with degree mobility)? This paper reviews some of the evidence to date on shorter duration mobility, addressing how value in international study is constructed and conferred and how this relates to 'time spent'. The paper concludes by arguing that the picture is mixed: although short‐term mobility will be beneficial to students, those engaging in longer term exchanges (usually more privileged students) are likely to derive greater benefits. [ABSTRACT FROM AUTHOR]

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 notesWhat 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. [ABSTRACT FROM AUTHOR]

Technologies and teaching practices can provide a rich log data, which enables learning analytics (LA) to bring new insights into the learning process for ultimately enhancing student success. This type of data has been used to discover student online learning patterns, relationships between online learning behaviors and assessment performance. Previous studies have provided empirical evidence that not all log variables were significantly associated with student academic achievement and the relationships varied across courses. Therefore, this study employs a systematic review with meta‐analysis method to provide a comprehensive review of the log variables that have an impact on student academic achievement. We searched six databases and reviewed 88 relevant empirical studies published from 2010 to 2021 for an in‐depth analysis. The results show different types of log variables and the learning contexts investigated in the reviewed studies. We also included four moderating factors to do moderator analyses. A further significance test was performed to test the difference of effect size among different types of log variables. Limitations and future research expectations are provided subsequently. Practitioner notesWhat is already known about this topic Significant relationship between active engagement in online courses and academic achievement was identified in a number of previous studies.Researchers have reviewed the literature to examine different aspects of applying LA to gain insights for monitoring student learning in digital environments (eg, data sources, data analysis techniques).What this paper adds Presents a new perspective of the log variables, which provides a reliable quantitative conclusion of log variables in predicting student academic achievement.Conducted subgroup analysis, examined four potential moderating variables and identified their moderating effect on several log variables such as regularity of study interval, number of online sessions, time‐on‐task, starting late and late submission.Compared the effect of generic and course‐specific, basic and elaborated log variables, and found significant difference between the basic and elaborated.Implications for practice and/or policy A depth of understanding of these log variables may enable researchers to build robust prediction models.It can guide the instructors to timely adjust teaching strategies according to their online learning behaviors. [ABSTRACT FROM AUTHOR]

As universities around the world have begun to use learning management systems (LMSs), more learning data have become available to gain deeper insights into students' learning processes and make data‐driven decisions to improve student learning. With the availability of rich data extracted from the LMS, researchers have turned much of their attention to learning analytics (LA) applications using educational data mining techniques. Numerous LA models have been proposed to predict student achievement in university courses. To design predictive LA models, researchers often follow a data‐driven approach that prioritizes prediction accuracy while sacrificing theoretical links to learning theory and its pedagogical implications. In this study, we argue that instead of complex variables (e.g., event logs, clickstream data, timestamps of learning activities), data extracted from online formative assessments should be the starting point for building predictive LA models. Using the LMS data from multiple offerings of an asynchronous undergraduate course, we analysed the utility of online formative assessments in predicting students' final course performance. Our findings showed that the features extracted from online formative assessments (e.g., completion, timestamps and scores) served as strong and significant predictors of students' final course performance. Scores from online formative assessments were consistently the strongest predictor of student performance across the three sections of the course. The number of clicks in the LMS and the time difference between first access and due dates of formative assessments were also significant predictors. Overall, our findings emphasize the need for online formative assessments to build predictive LA models informed by theory and learning design. Practitioner notesWhat is already known about this topic Higher education institutions often use learning analytics for the early identification of low‐performing students or students at risk of dropping out.Most predictive models in learning analytics rely on immutable student characteristics (e.g., gender, race and socioeconomic status) and complex variables extracted from log data within a learning management system.Prioritizing prediction accuracy without theory orientation often yields "black‐box" models that fail to inform educators on what remedies need to be taken to improve student learning.What this paper adds Predictive models in learning analytics should consider learning theory, pedagogy and learning design to identify key predictors of student learning.Online formative assessments can be a starting point for building predictive models that are not only accurate but also provide educators with actionable insights on how student learning can be improved.Time‐related and score‐related features extracted from online formative assessments are particularly useful for predicting students' course performance.Implications for practice and/or policy This study provides strong evidence for using online formative assessments as the foundation for predictive models in learning analytics.Student data from online formative assessments can help educators provide students with feedback while informing future formative assessment cycles.Higher education institutions should avoid the hype around complex data from learning management systems and instead rely on effective learning tools such as online formative assessments to revolutionize the use of learning analytics. [ABSTRACT FROM AUTHOR]

Background: The global COVID‐19 pandemic turned the adoption of on‐line assessment in the institutions for higher education from possibility to necessity. Thus, in the end of Fall 20/21 semester Tel Aviv University (TAU)—the largest university in Israel—designed and implemented a scalable procedure for administering proctored remote examinations. This procedure is applicable to different kinds of examinations in diverse disciplines and to disparate degree levels. Objectives: This study aims to deepen the knowledge with respect to the design and adoption of remote proctoring at an institutional level Methods: First, based on lessons learned from the first semester of COVID‐19, we describe the development and implementation of an institution‐wide protocol for conducting on‐line proctored assessment. We show the large‐scale applicability of the protocol for administering examinations via Moodle with remote proctoring via Zoom. The accurately designed procedures that included careful consideration of all parties involved—academic staff members, students, administrative staff and proctors—enabled TAU to successfully assess the learning outcomes of its 25,000 students, while maintaining validity, compatibility and reliability of the assessment. Second, we report combined data from surveys of 4380 students and 188 faculty‐members (18% and 12% response rate respectively), conducted at TAU following these examinations. Results and Conclusions: With over 90% of the students experiencing at least one, and 80% of faculty‐members administering at least one proctored remote examination, these heterogeneous sources of data allow us to investigate a unique and complementary perspective of the process. Some of the significant findings consist of the over‐all perception of the students' integrity by both groups surveyed; the discrepancy in the groups' perspectives of the adequate form of learning‐assessment—with the instructors preferring proctored examinations and the students—formative‐assessment; and the influence of the pedagogical challenges on the perception of validity that outweighed other factors, for example, the pandemic‐related stress, the on‐line assessment challenges and other. Lay Description: What is already known about this topic: Proctoring exams remotely is challenging, due to technical, ethical and other concerns.Proctoring can be conducted remotely by a human, record and review later technology, automated algorithms to detect atypical behaviour.Different methods for remote proctoring in higher education have been implemented on small scale. What this paper adds: Review of studies examining various facets of remote proctoring before and during COVID‐19.A protocol for remote proctoring in a large‐scale university, based on the first post COVID‐19 semester.The perspectives of students and faculty members regarding testing with remote proctoring. Implications for future practice and/or policy: The protocol can assist higher education institutions in developing their own guidelines.The implementation should take into consideration ethical norms and constitutional limitations.Unlike automated process using algorithms, remote human proctoring is not widely opposed by students.Further research comparing remotely proctored exam performance with the f2f proctoring is needed. [ABSTRACT FROM AUTHOR]

The advancement of technologies has promoted the increasing popularity and integration of mobile technologies in science education in the past decade. These trends have led to an increased interest among scholars to understand the effects of mobile technologies in science education and whether those effects differ depending on how mobile technologies are used in learning and teaching (eg, student‐led, teacher‐led, collaborative). In this study, we performed a meta‐analysis of 34 studies that directly examined the effects of users' pedagogical role on K‐16 students' achievement in science when engaging in mobile learning (ML). The analysis of the 34 studies yielded an overall significant main effect of ML on K‐16 science learning outcomes. We applied the mixed‐effects model with moderator variables and found that users' pedagogical role significantly moderated the ML effects as a whole. Collaborative and student‐led uses had a statistically significant impact on student science learning, whereas teacher‐led use did not. Findings from this meta‐analysis are consistent with prior research, providing synthesized research‐based evidence of the effects of ML on science learning that holds implications for both mobile curriculum design and mobile technology use. Practitioner notesWhat is already known about this topicMobile technology has been increasingly adopted in science learning with great potential to support learning and teaching.Prior meta‐analysis has suggested multiple moderators in measuring the mobile learning effect.Prior empirical studies examined the effect of mobile users' pedagogical role in the specific subject domains (eg, physics) and grade levels (eg, high school).What this paper addsThis meta‐analysis is among the first to examine the moderator effect of mobile users' pedagogical roles on ML in K‐16 science education.This study found that mobile technology use is associated with significant science learning outcomes across 34 studies.This study found that the effect of mobile learning was moderated by mobile users' pedagogical roles (ie, who initiated the use). Collaborative use between teachers and students tends to be the most effective. Teacher‐led use tends to be the least effective.Implications for practiceThis study suggests that we should consider who leads the use of mobile technology when integrating mobile learning in science education.In particular, educators should encourage collaborative and student‐led mobile use for learning and instruction. [ABSTRACT FROM AUTHOR]

Situational engagement plays a critical role in promoting students' academic performance. In a smart classroom environment, this study collected longitudinal real‐time data for 105 college students at a university in central China to investigate the relationship among situational engagement, personal characteristics and learning environment perceptions. Hierarchical linear modelling showed that environmental perception and students' personal factors have different effects on situational engagement. Specifically, (1) social support perceptions, autonomous motivation and controlled motivation have a significant impact on behavioural engagement; (2) perceptions of social and media support, autonomous motivation and controlled motivation have a significant impact on shallow cognitive engagement; and (3) perceptions of teacher and social support, self‐efficacy and autonomous motivation significantly predict deep cognitive and emotional engagement. This study suggests that the effect of the perception regarding advanced technology‐supported learning environments on students' situational engagement is limited, and instructors should pay more attention to improving students' perceptions of teacher and social support, self‐efficacy and autonomous motivation to promote students' situational deep cognitive engagement in smart classrooms. Practitioner notesWhat is already known about this topic Compared with overall engagement, situational engagement fluctuates and changes with time and context.Situational engagement is a product of environmental and personal factors.Few studies have focused on the nature of situational engagement and how environmental and personal factors influence situational engagement in smart classrooms.What the paper adds This study contributes to the existing literature by investigating the critical factors that predict situational engagement, using the experience sampling method in a smart classroom at a Chinese university.Environmental perception, self‐efficacy and students' motivation factors have different effects on situational engagement in a smart classroom.Perceptions of teacher and social support, self‐efficacy and autonomous motivation significantly predict deep cognitive and emotional engagement, while perceptions of media support only have a significant impact on shallow cognitive engagement.Personal factors (controlled and autonomous motivation) moderate the relationship between environmental perception factors and situational engagement.Implications for practice and/or policy Rather than only providing external technology‐rich conditions, instructors should focus more on improving students' perceptions of teacher and social support, self‐efficacy and autonomous motivation in the smart classroom environment.Instructors should promote students' perception of teacher support and their autonomous motivation to enhance their deep cognitive engagement. [ABSTRACT FROM AUTHOR]

This article uses the example of Germany to elaborate on the historical development, the current practice, and recent debates on employability. Employability as an outspoken aim of higher education was only established in recent decades due to the introduction of Polytechnics, the expansion of higher education, and the Bologna‐Process. Despite the diversity of the higher education sector (e.g., universities; universities of applied science), the assumption of equality means that degrees from any institution are of equal value according to the level of study. As in other countries, German higher education institutions have established measures to improve employability such as changes in the curricula or the establishment of career services. However, challenges remain to measure the employability and the employability outcome of HE. The article concludes that the identification of specific subject‐related and transferable skills and competences and their transferability to the labour market remains an ongoing task to increase the employability of graduates in Germany. [ABSTRACT FROM AUTHOR]

During the COVID‐19 crisis, digital informal learning is important for students' academic engagement. Although scholars have highlighted the importance of students' digital competence in improving digital informal learning (DIL), the mediating role of DIL between digital competence and academic engagement has remained ambiguous. The purpose of this study is to investigate the relationship between students' digital competence and their academic engagement with the mediating role of DIL in the higher education context. This study used a descriptive correlational design, and the data were analyzed using structural equation modelling (SEM). The study sample included 308 students from Shiraz University, Iran. The results showed that digital competence positively and significantly correlated with students' DIL and their academic engagement. Furthermore, DIL, as the mediator variable, was found to mediate the relationship between students' digital competence and their academic engagement. Since higher education institutions have a key role in improving students' academic engagement, particularly in the COVID‐19 pandemic, academic administrators should pay more attention to students' digital competencies and provide them with efficient and user‐friendly DIL platforms that can increase their academic engagement. Lay Description: What is already known about this topic: One of the challenges of online learning environments is students' engagement especially in the COVID‐19 crisis.The role of digital informal learning (DIL) in the students' engagement is ambiguous.Little work has been done to support the relationship between digital competence, DIL and academic engagement. What this paper adds: The structural equation model of academic engagement, digital competence, and DIL was examined.There is a significant relationship between digital competence and DIL of students.The engagement of students will increase both with the use of digital competencies and DIL. Implications for practice and/or policy: Administrators should provide efficient DIL platforms to increase students' academic engagement.Educators should consider students' digital competencies to increase their academic engagement. [ABSTRACT FROM AUTHOR]