Search

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 notesWhat is already known about this topicResearchers' 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 addsThis 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 policyEducators 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. [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]

Gamification is typically implemented digitally. However, digital gamification is not always possible because of limited resources and logistical problems. It is thus necessary to explore low‐threshold gamification methods that can be easily adopted in classrooms. One such method is non‐digital gamification using physical game design elements. Currently, the literature provides a limited understanding of the design and effects of non‐digital gamification. It is also unclear whether mixed gamification (ie, a combination of digital and non‐digital gamification) is better than purely non‐digital gamification. We explored these topics using an explanatory sequential mixed‐methods design to compare the effectiveness of non‐digital gamification in a face‐to‐face course and mixed gamification in a blended course in terms of student learning achievement, cognitive engagement and course satisfaction. Two secondary grade classes were randomly assigned to the mixed (N = 52) or non‐digital (N = 52) gamification conditions. Contrary to our expectations, the quantitative results showed that the two approaches improved the students' learning achievement to the same extent. Although the students in the non‐digital gamification condition reported significantly higher‐course satisfaction than those in the mixed condition, the improvement in student cognitive engagement was significantly greater in the mixed gamification condition than in the non‐digital condition. Through focus group interviews, we analysed the students' learning experiences and synthesised the factors that influenced student cognitive engagement and course satisfaction. Practitioner notesWhat is already known about this topic Gamification is increasingly being used to increase student engagement and enhance learning.Gamification studies have mostly focused on digital modes, which often require gamified digital platforms or applications.Non‐digital gamification can be an effective method to foster learning, but the design and evaluation of non‐digital gamification is at an early stage.What this paper adds We designed non‐digital and mixed (ie, a combination of digital and non‐digital) gamification programmes and compared their effects using an explanatory sequential mixed‐methods design.Both gamification approaches led to a similar improvement in learning achievement.While non‐digital gamification fostered students' course satisfaction, mixed gamification was more effective in improving students' cognitive engagement.Implications for practice and/or policy Non‐digital gamification can be an effective low‐threshold solution for instructors. It is worth further improving the non‐digital gamification approach in school settings as it is cost‐effective and can be transferred between classrooms.The benefits of mixed gamification should not be ignored because of its affordances that improve students' cognitive engagement. [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]

Identifying students facing difficulties and providing them with timely support is one of the educator's key responsibilities. Yet, this task is becoming increasingly challenging as the complexity of physical learning spaces grows, along with the emergence of novel educational technologies and classroom designs. There has been substantial research and development work focused on identifying student social behaviours in digital platforms (eg, the learning management system) as predictors of academic progression. However, little work has investigated such relationships in physical learning spaces. This study explores the potential of using wearable trackers for the early detection of low‐progress students based on their social and spatial (socio‐spatial) behaviours at the school. Positioning data from 98 primary school students and six teachers were automatically captured over a period of eight weeks. Fourteen socio‐spatial behavioural features were extracted and processed using a set of machine learning classifiers to model students' learning progression. Results illustrate the potential of prospectively identifying low‐progress students from these features and the importance of adapting classroom learning analytics to differences in pedagogical designs. Practitioner notesWhat is already known about this topicLearning analytics research on predicting students' academic progression is emerging in both digital and physical learning spaces.Students' social behaviours in learning activities is a key factor in predicting their academic progression.Emerging sensing technologies can provide opportunities to study students' real‐time social behaviours in physical learning spaces.What this paper addsFourteen progression‐related socio‐spatial behavioural features are extracted from students' physical (x‐y) positioning traces.Predictive learning analytics that achieved 81% accuracy in prospectively identifying low‐progress students from their real‐time socio‐spatial behaviours.Empirical evidence to support the need for classroom learning analytics to have instructional sensitivity (ie, be calibrated according to the learning design).Implications for practice and/or policySensing technologies and machine learning algorithms can be used to capture and generate valuable insights about higher‐order learning constructs (eg, performance and collaboration) from students' physical positioning traces in classrooms.Researchers and practitioners should be cautious with generalised classification algorithms and predictive learning analytics that do not account for the pedagogical differences between different subjects or learning designs.Researchers and practitioners should consider the potentially unforeseen ethical issues that can emerge in using sensing technologies and predictive learning analytics in authentic, physical classroom settings. [ABSTRACT FROM AUTHOR]