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Describes a study that seeks to confirm several key factors in computer-based versus paper-based assessment. Based on earlier research, the factors considered in this study of undergraduates include content familiarity; computer familiarity; competitiveness; and gender. Reports results of analysis of variance that showed the computer-based test group outperformed the paper-based test group. (Author/LRW)

There is little debate regarding the importance of student feedback for improving the learning process. However, there remain significant workload barriers for instructors that impede their capacity to provide timely and meaningful feedback. The increasing role technology is playing in the education space may provide novel solutions to this impediment. As students interact with the various learning technologies in their course of study, they create digital traces that can be captured and analysed. These digital traces form the new kind of data that are frequently used in learning analytics to develop actionable recommendations that can support student learning. This paper explores the use of such analytics to address the challenges impeding the capacity of instructors to provide personalised feedback at scale. The case study reported in the paper showed how the approach was associated with a positive impact on student perception of feedback quality and on academic achievement. The study was conducted with first year undergraduate engineering students enrolled in a computer systems course with a blended learning design across three consecutive years (N[subscript 2013] = 290, N[subscript 2014] = 316 and N[subscript 2015] = 415).

Learning Analytics and Multimodal Learning Analytics are changing the way of analysing the learning process while students interact with an educational content. This paper presents a systematic literature review aimed at describing practices in recent Multimodal Learning Analytics and Learning Analytics research literature in order to identify tools and strategies useful for the assessment of the progress and behaviour of children under 6 years old in respect of their learning. The purpose is to provide guidance for Multimodal Learning Analytics research with children under 6 years old to assess their engagement in a task, their emotions, attention, understanding and achievement of a goal. The current state of knowledge on Multimodal Learning Analytics research suggests how performance analytics, face and speech recognition systems, eye tracking, Kinect analytics and wristbands could be used with children. The results show the complexity of collecting data using non-invasive methodologies with children under 6 years old. Ethical implications related to multimodal data from audio, visual, biometric and quantitative measures of child behaviour are discussed.

The article focuses on cognitive workload as a key factor which influence paper-based and computer-based assessment of the academic performance of university undergraduates. Cognitive workload has been defined as the interaction between demands of a task that an individual experiences and a person's ability to cope with theses demand. Cognitive workload can be relatively easily measured using subjective, self-report measures. One of the most widely used measures of cognitive workloads is NASA-TLX. It assesses workload on a number of dimensions and has been shown to be more reliable than other subjective workload scales.

Research suggests that robotics can provide an engaging learning experience for learners with special educational needs. However, further work is required to explore the impact of robots within the classroom, particularly for learners with intellectual disabilities (ID). This paper seeks to further explore the potential effects of robots on such learners through examining engagement and goal achievement within teaching sessions. Eleven participants with ID were recruited from two countries to take part in the study using an ABAB design where the participants acted as their own controls. An appropriate learning goal for each participant was selected by the teacher and equivalent control sessions designed seeking to achieve the same learning goal but without the robot. Engagement, using eye-gaze, learning goal achievement with and without help and goals not achieved provided the outcome measures from the sessions. This study found no significant difference between the robot and the control sessions for any of the outcome measures utilized suggesting robots are as effective as teaching tools as traditional methods. Through an increased sample size and a rigorously applied experimental protocol, this study provides new data and methodological considerations for further work based on the techniques applied in this study.

Standardized, large-scale assessment of educational outcomes has become a global phenomenon over the past three decades (Smith, 2016). A key challenge facing assessment designers is that standard formats may be inaccessible or may create barriers to student performance. Schwanke, Smith, and Edyburn's (Schwanke, T. D., 2001) A3 model describes how advocates have reacted to structural barriers by providing accommodations and, ultimately, accessibility. This paper synthesizes and evaluates three studies that attempted to improve accessibility in assessments for students who struggle with print reading through audio presentation of assessment items. Cross-study implications for policy and practice are considered.

This paper reports the effects of game technology on student learning in mathematics as investigated in two data sets collected from slightly different subjects. In the first, 41 second graders (7 or 8 years old) from two classes used either a technology-based game or a paper-based game for 5 weeks. For the next 13 weeks, both classes used a technology-based game either two times per week, or more than three times per week. A quasi-experimental control-group design with repeated measures analysis of variance and analysis of covariance was employed to explore performance differences between groups. The second study examined student learning in relation to characteristics such as their game performance, attitudes toward the game and toward mathematics, and gender and ethnicity. During a 4-month period, 50 second grade students from three classes played a technology-based game under conditions that varied depending on their teacher's direction. Multiple regression was used to determine the relationship between students' arithmetic scores and learner characteristics. Descriptive analyses by ability level, gender and ethnicity, and interview data about attitudes toward the technology game were also analyzed. Results from the two studies revealed that using a technology-based game in the classroom was beneficial to students of all ability levels in learning arithmetic skills. (Contains 11 tables and 2 figures.)

This paper reports on a study that investigated whether a flipped classroom approach enhanced perceptions of levels of engagement and skill performance among students enrolled in a Blackboard course at a Saudi university. Fifty-eight participants were divided into control and experimental groups, which were taught using a traditional and a flipped approach respectively. To determine the effect of the approach on participants' perceived levels of engagement and skill performance, questionnaires were administered and student performance was examined in terms of quantitative descriptive analysis. The results revealed that participants in the experimental group were more active and engaged compared with those in the control group. In terms of classroom engagement specifically, participants in the experimental group exhibited greater behavioral and emotional engagement. Through the flipped approach, participants were able to study course content at home first, thereby preparing themselves to participate in relevant class activities, pose questions and engage in problem solving with peers. In addition, unlike in a traditional lecture, the instructor was able to move freely through the classroom, providing direct assistance to participants on a case-by-case basis.

This paper presents an exploratory study that investigates the effects of online learning on distance education students in an open university context. Two hypotheses are posited: (1) a direct relationship exists between students involvement in online learning and distance learning outcomes, and (2) an indirect relationship exists between these two domains via the mediating variable of an institutional Transactional Presence (TP), that is, a student's sense of the availability of and connectedness with an educational institution. Student learning achievement, satisfaction and intent-to-persist are used as indicators of outcomes of distance learning. The analysis of data garnered from 285 distance students reveals multifaceted relationships between students engagement in online learning, their perceptions of institutional TP and the three learning outcome variables. Besides the findings, the paper will discuss factors affecting students engagement in online learning in relation to different requirements, that is, optional or compulsory online use as well as the level of the course (undergraduate or postgraduate).

In dealing with numerous challenges, higher education instructors need to adapt their pedagogical practices to present students with meaningful, engaged learning experiences that are likely to promote student success and adequately prepare students for the world we live in. As part of this pedagogical transformation instructors also need to consider the potential of digital technologies to assist flexible pedagogies, as well as the role that students can play as partners in transforming the learning process (C. Evans, D. Muijs, & M. Tomlinson. "Engaged student learning: high-impact strategies to enhance student achievement." York: Higher Education Academy, 2015, p. 9). In this paper the author reflects on her particular engaged scholarship approach and the important role that her students--as collaborators in the pedagogical transformation process--have played in the creation of meaningful technology-enhanced learning experiences. She describes the evolution of her action inquiry approach over more than a decade and uses one particular project to highlight the value that student voice can contribute to pedagogical transformation. She also underscores the value of a "design for partnership" approach that can be incorporated as an underlying pedagogical approach to facilitate the creation of meaningful learning experiences in a technology-enhanced teaching and learning environment.

This paper seeks to explain why some individuals sink further into states of flow than others, and what effects flow has in the context of a virtual education environment. Our findings--gathered from both questionnaire and behavioural data--reveal that flow states are elicited by the e-learners' senses of controlling the virtual education environment, their attention centred on the learning activity, and their feelings of physically being in such an environment. We bring evidence about three benefits from flow states: they facilitate e-learner's positive emotions, they enhance e-learners' academic performance, and they contribute to students' effective continuance in e-learning.

In this paper, an inquiry-based ubiquitous gaming approach was proposed. The objective of the study was to enhance students' performances in in-field learning activities. To show the advantages of the approach, an experiment was carried out to assess the effects of it on students' learning achievement, motivation, critical thinking, and problem solving. Furthermore, the students' behavioral patterns were investigated via content and sequential analysis methods. The experimental outcomes show that the approach promoted students' performances of learning achievement and intrinsic motivation; moreover, the students' perceptions of their problem solving and critical thinking were significantly promoted as well. The learning behavior analysis further show that the designated approach stimulated the participants to actively engage in field observation, comparison, and data searching in the context ware in-field learning activity.

Teachers often use in-class questions to examine the level of understanding of their students, while these also enable students to reorganize their acquired knowledge. However, previous studies have shown that students may resist being questioned because of negative emotions. Therefore, this paper proposes the idea of eliciting positive emotions by showing students funny images to reward them for providing correct answers. Three studies were conducted with different courses, using either a video lecture or classroom lecture. The results of Study 1 indicated that 22 learners who watched 10 financial literacy video segments and were rewarded with funny images tended to have more positive emotions, an upward trend in their attention levels, and greater test scores as compared to the other 22 learners watching the same videos who received no rewards. Studies 2 and 3 were conducted in the data structure and computer networks courses, respectively. With regard to affective states, the 52 students in the two courses gained more learning confidence in classes that providing the amusing stimuli as compared to those without such rewards. However, only students in the computer networks course had higher test scores on their mid-term exams when answering rewarded questions compared to when they answered those questions without rewards, although this was not found with the students in the data structure course. The findings suggest that rewarding students with amusing stimuli can enhance students' affective states, even though this instructional strategy does not lead to higher learning performance all the time.

Computerised testing and diagnostics are critical challenges within an e-learning environment, where the learners can assess their learning performance through tests. However, a test result based on only a single score is insufficient information to provide a full picture of learning performance. In addition, because test results implicitly include information about the underlying subject concepts and their relationships to each other, this paper proposes a more effective method for analysing test results by providing a concept map (CM) to facilitate learners' understanding of their learning performance. An innovative approach, not explored in previous studies, is proposed to automatically construct a personalised CM. A CM-smart extraction and explicit diagnosis (CM-SEED) learning system has been developed to diagnose learning barriers and misconceptions and to supply relevant suggestions and guidance for remedial learning. This study examined 90 students from two classes at a university and assigned one class to be the experimental group and another class to be the control group. The results indicated that the students who used the CM-SEED learning system had superior perceptions regarding their learning; furthermore, they accomplished superior learning achievement that displayed statistical significance. Consequently, the study concluded that CM extraction in a test-based diagnostic environment can lead learners to enhanced learning performance.

This dataset contains participant data related to the use of badging (achievement) feedback in pedagogical design. Two sections each of web-based graphic design and web design undergraduate courses were offered at the University of Central Florida. A badging system for achievements was included in one section of each. Performance, engagement and behavior-related data were collected. The dataset comprises complete data from 44 undergraduate students. This paper includes a justification for data collection, methodology and preexisting usage of the data. It also discusses limitations of the dataset.

This paper presents a case study on the adoption and the impact of new modules in a learning analytics dashboard supporting the dialogue between student advisors and students when advising on a study plan for the next academic semester in Escuela Superior Politecnica del Litoral, a higher education institute in Ecuador. The impact and the adoption of the new dashboard modules were assessed using a mixed‐methods approach. The quantitative approach builds on data of 172 advisors in 34 programs and 4481 advising sessions in 2019 (post) and 4747 advising sessions in 2018 (pre) to assess the adoption and use of the dashboard, the level of support experienced by the advisors, the impact of the new dashboard modules on the difference between the advised study plan and the plan students register for, and students' academic achievement. The qualitative approach with observations of 14 staged advising dialogues and semi‐structured interviews with eight advisors was used to assess how the dashboard was used and to get deeper understanding of the perceived usefulness and impact of the dashboard. The results show that an institution‐wide deployment of dashboard modules tailored to the needs of the advisors can be achieved and can increase the level of support perceived by the advisors and significantly decrease the gap between the suggested study plans in advising dialogues and the study plans that students actually register for. On the short‐term, however, no significant changes in academic achievement were observed. Practitioner NotesWhat is already known about this topic? Academic advising can positively impact retention, academic achievement and study completion.Learning analytics dashboards are promising pieces of educational technology for academic advising as they can trigger reflection and sense‐making of educational data.Evaluation of learning analytics dashboards is often still immature and not well‐connected to the actual goals of the dashboards. Large‐scale evaluations looking at impact of dashboards are even scarcer.What this paper adds? This paper adds, to the scarce scientific evidence on academic advising dashboards, a large‐scale case study on a dashboard supporting the advisor student dialogue during the composition of well‐balanced study plans.The paper presents research evidence of the impact of the dashboard on the support advisors experience, the study plans suggested by the advisors and the ones actually registered by the students and students' academic achievement. Evidence is based on a quantitative analysis, using data of 172 advisors from 34 programs representing more than 9000 advising dialogues, and a qualitative analysis using observations and interviews.Implications for practice and/or policy Dashboards to support academic advising dialogues can be realized institution‐wide at scale. Training of student advisors supports a large scale deployment.Well‐designed dashboards that focus on addressing needs of advisors increase the level of support that advisor experience when advising students.Dashboard accommodating the simulation of study plans and the workload associated with them, succeed in decreasing the variance in suggested plans between advisors and reduce the gap between the study plans that advisors suggest to student and the study plans that students actually register for. Short‐term impact on academic achievement was not observed. [ABSTRACT FROM AUTHOR]

In this paper, we describe an in situ study that examined the diverse design features and effects of three pre-algebra games for middle school children who have either challenges with learning or different language backgrounds. Data were collected through in-field observation, artifact analysis, school performance report and knowledge test during 15 one-hour gaming sessions over a period of 3 weeks. A list of key design features related to creating engagement and learning for students with special learning needs emerged from the data. The findings and discussions will inform educational designers and practitioners on successful educational game design and implementation for learners with diverse characteristics and needs. (Contains 5 tables and 3 figures.)

This paper has focused on the productive use of information and communications technology (ICT) by university students and its influence on academic performance. The objective is to determine whether a comparison of successful and non-successful students (in relation to academic achievements) versus the variables studied (linked to the process of ICT incorporation) can help us to establish contextual and behavioral patterns associated with good academic performance. This differential analysis is carried out by making basic statistical comparisons (student's t-test). The data analysis is completed by applying receiver operating characteristic curves, a methodology of new application in the area of education. The sample was composed of 1194 students enrolled in 40 different subjects at various universities in two countries, Mexico and Spain. Students gave a very positive appraisal of the value of ICTs to support the teaching process, but they express an intermediate level of satisfaction with the teaching conditions in subjects, which are using the support of these technologies. Moreover, there is a clear relation between high scores in the variables associated with the processes of incorporation of ICT (satisfaction with teaching, higher levels of competence in ICT, less training needs in ICT, more favorable attitudes toward their integration in training processes) and achieving higher academic qualifications. (Contains 8 tables and 2 figures.)

While there is little doubt that technology is, in many ways, central to the college student experience today, the expectations of students and colleges vis-a-vis technology may not always be the same. This research explores the role of technology in the lives of today's college students, particularly within the institutional context of the university, and asks the question: What role does technology play in a freshman college student's participation in the academic Discourse of her institution? This paper focuses on the Discourse analysis of one student, Nichole, a focal participant in a semester-long, qualitative study of 34 undergraduate students at a university in the northeastern United States. Nichole's general dislike of technology places her in opposition to assumptions about the "typical" Net Gen student; results indicated that the ways in which Nichole used technology in her personal life conflicted with her participation in the pro-technology academic Discourse of her institution. For Nichole, the disconnect between the institutional expectations for technology use and her personal technology practices resulted in the need to negotiate another layer of complexity on the path to academic success.

As the number of courses offered online increases rapidly, it is important for teachers and institutions to identify specific learner characteristics of successful online students. This paper reports on a study that compared an online group of freshmen computer science majors with an equivalent on-campus group to find if their individual learning styles play a role in the selection of course delivery mode (online or face to face) and in their academic achievement. No significant statistical differences were detected in learning styles and learning performance between the two groups. Implications for teaching practice and design of learning activities that resulted from this study are discussed.

This paper reports on research investigating student experiences of learning through face-to-face and online discussions in a political science course in a large Australian university. Using methodologies from relational research into university student learning, the study investigates associations between key aspects of student learning focusing on conceptions of what students learn, approaches to learning, and learning outcomes. The main hypothesis tested here was that there are qualitative differences in the student conceptions of learning through discussions and their approaches to face-to-face and online discussions. Furthermore, it was expected to find that these differences were reflected in the learning outcomes, that is, student approaches would be linked to more complete conceptions of learning and to better academic performance. More tentatively, the existence of causal relationships between these aspects of learning and academic performance was also explored. Data on students' conceptions and approaches was collected through closed-ended questionnaires and final mark was used as an indicator of the quality of learning (academic performance). Our analysis identified variations in the quality of conceptions and student approaches also revealing strong associations between what students thought their learning is about, the way they approached their learning, and academic performance in both face-to-face and online contexts. Implications of these findings for research and practice are elaborated.

There is an increase use of wireless technologies in education all over the world. In fact, wireless technologies such as laptop computers, palmtop computers and mobile phones are revolutionizing education and transforming the traditional classroom-based learning and teaching into "anytime" and "anywhere" education. This paper investigates the use of wireless technologies in education with particular reference to the potential of learning new technical English language words using Short Message Service (SMS) text messaging. The system, developed by the authors, called mobile learning tool (MOLT), has been tested with 45 1st-year undergraduate students. The knowledge of students before and after the experiment has been measured. Our results show that students enjoyed and learned new words with the help of their mobile phones. We believe that using the MOLT system as an educational tool will contribute to the success of students.

While there is agreement that participation in online asynchronous discussions can enhance student learning, it has also been identified that there is a need to investigate the impact of participation in online discussions on student course performance. This paper presents a case study based on an undergraduate engineering management unit employing a formally assessed online discussion area. It was observed that while many students read a significant number of discussion postings, generally, the posting of new and reply messages occurred at the minimum level required to qualify for the assignment marks. Based on correlation and multiple regression analysis, it was observed that two variables were significantly related to a student's final unit mark--prior academic ability and the number of new postings made to the online discussion. Each new posting contributed three times as much to the final unit mark as its nominal assessment value, suggesting that the work in preparing their new discussion postings assisted students in the completion of a range of assessable tasks for the unit. The number of postings read was not significantly correlated with the final unit mark, suggesting that passive lurking in this online discussion did not significantly contribute to student learning outcomes.

With Big Data revolution, the education sector is being reshaped. The current data‐driven education system provides many opportunities to utilize the enormous amount of collected data about students' activities and performance for personalized education, adapting teaching methods, and decision making. On the other hand, such benefits come at a cost to privacy. For example, the identification of a student's poor performance across multiple courses. While several works have been conducted on quantifying the re‐identification risks of individuals in released datasets, they assume an adversary's prior knowledge about target individuals. Most of them do not utilize all the available information in the datasets. For example, event‐level information that associates multiple records to the same individual and correlation between attributes. In this work, we propose a method using a Markov Model (MM) to quantify re‐identification risks using all available information in the data under a more realistic threat model that assumes different levels of an adversary's knowledge about the target individual, ranging from any one of the attributes to all given attributes. Moreover, we propose a workflow for efficiently calculating MM risk which is highly scalable to large number of attributes. Experimental results from real education datasets show the efficacy of our model for re‐identification risk. Practitioner notesWhat is already known about this topic? There has been a number of works/research conducted on privacy risk quantification in datasets and in the Web.Majority of them have strong assumption about adversary's prior knowledge of target individual(s).Most of them do not utilize all the available information in the datasets, eg, event‐level or duplicate records and correlation between attributes.What this paper adds? This paper proposes a new re‐identification risk quantification model using Markov models. Our model addresses the shortcomings of existing works, eg, strong assumption about adversary's knowledge, unexplainable model, and utilizing available information in the datasets. Specifically, our proposed model not only focuses on the uniqueness of data points in the datasets (as most of the other existing methods), but also takes into account uniformity and correlation characteristics of these data points.Re‐identification risk quantification is computationally expensive and is not scalable to large datasets with increasing number of attributes. This paper introduces a workflow for data custodians to use to efficiently evaluate the worst‐case re‐identification risk in their datasets before releasing.It presents extensive experimental evaluation results of the proposed model for quantifying re‐identification risks on several real education datasets.Implications for practice and/or policy? Empirical results on real education datasets validate the significance and efficacy of the proposed model for re‐identification risk quantification compared to existing approaches.Our model can be used by the data custodians as a tool to evaluate the worst‐case risk of a dataset. It empowers data custodians to make informed decisions on appropriate actions to mitigate these risks (eg, data perturbation) before sharing or releasing their datasets to third parties. A typical use case would be one where the data custodians is an online course/program provider, which collects data about students' engagement with their courses and would like to share it with third parties for them to run learning analytics that would provide value‐added benefits back to the data custodian.We specifically study the privacy risk quantification for education data; however, our model is applicable to any tabular data release. [ABSTRACT FROM AUTHOR]

Describes the use of Keller's Personalized System for Instruction with the addition of videotapes, interactive videodisc, and computer-delivered tutorials in an undergraduate mathematics course. Modifications of Keller's plan for British universities are discussed, examination results are studied, and the quality of the learning experience is considered. (19 references) (LRW)

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]

Academic procrastination refers to individuals' unnecessary postponement of their coursework and is harmful for academic performance. When situated in self‐placed and remote learning environments, students' tendency to procrastinate increases. Therefore, understanding why students procrastinate and identifying who is more likely to delay unnecessarily in online learning environments is an important area to study. The goal of this study was to respond to this call by examining the structural relations between conscientiousness, prior online learning experience, achievement emotions and academic procrastination in online learning environments using structural equation modelling. In particular, two main facets of conscientiousness—proactive and inhibitive—were examined in order to understand which facet was more responsible for procrastination, how each facet was related to procrastination and to determine which facet of conscientiousness should be the primary target for intervention in future experimental research. A total of 746 students from 49 secondary and postsecondary schools participated in the current study. The results showed that the proactive aspect of conscientiousness was negatively related to academic procrastination through the pathway of enjoyment. The inhibitive aspect of conscientiousness was negatively related to academic procrastination through the pathways of negative emotions. Although prior online learning experience did not have a significant and direct association with academic procrastination, it was indirectly related to students' tendency to put off coursework through the pathways of enjoyment and negative emotions. Of all the paths, negative emotions had the strongest associations with academic procrastination in online learning environments. The findings of indirect effects suggest that students with low conscientiousness, the inhibitive aspect in particular, and few prior online learning experiences are more likely to procrastinate in online classes possibly owing to the suboptimal emotional experiences aroused during their learning processes. Practitioner notesWhat is already known about this topicConscientiousness has a direct association with academic procrastination in in‐person classes.Prior online learning experience has direct associations with students' adaptive regulatory processes in online classes.Achievement emotion is an integral part of online learning.What this paper addsConscientiousness has a direct association with academic procrastination in online learning environments.Prior online learning experience has no direct associations with academic procrastination in online learning environments.Conscientiousness indirectly relates to academic procrastination through achievement emotions in online learning environments.Prior online learning experience indirectly relates to academic procrastination through achievement emotions in online learning environments.Implications for practice and/or policyInstructors are suggested to pay attention to students with low conscientiousness and few prior online learning experiences since they are more likely to procrastinate in online learning environments.Students' tendency to procrastinate in online learning environments is strongly associated with their emotional experience. [ABSTRACT FROM AUTHOR]

Virtual contexts play a crucial role in assisting students' learning. Researchers have taken advantage of the potential of immersive virtual reality (VR) for situating students in inaccessible places, and for engaging them in learning activities. Meanwhile, several previous studies have reported that, in VR‐based learning contexts, students' perception of immersion and learning motivation could be low owing to the lack of clear objectives and problem‐solving scenarios. On the other hand, digital game‐based learning is capable of enhancing students' engagement in a task. In this study, we designed a learning approach (namely RPG‐VR) by means of integrating a role‐playing game with VR technology to enhance students' science learning. Furthermore, a quasi‐experiment was conducted to evaluate the students' learning effectiveness via, respectively, providing the RPG‐VR learning approach and the conventional VR (called C‐VR) learning approach for the experimental and control groups. The experimental results indicated that this approach significantly enhanced the students' perceptions of immersion, self‐efficacy and extrinsic learning motivation, in comparison with the C‐VR learning approach. Specifically, students who learned with the RPG‐VR learning approach perceived more immersion in the engagement and the total immersion stages. This study contributes to existing knowledge of VR technology by providing that a digital role‐playing game could reinforce the main characteristics of VR‐based learning environments and enhance students' concentration on a learning task. However, no significant difference was found between the two groups in terms of promoting students' learning achievements, implying that more instructional assistance is needed to improve students' knowledge gains in VR‐based learning environments. Practitioner notesWhat is already known about this topic Virtual contexts play a crucial role in promoting students' learning performances.Virtual reality has been adopted for situating students in inaccessible places to promote their learning.A DGBL context provides students with opportunities for learning that emphasize immersion in practice, resulting in concept acquisition and skill development.What this paper adds An RPG‐VR learning approach was designed by means of integrating a role‐playing game with VR technology to enhance students' science learning.A virtual reality learning system with a role‐playing game was developed based on the proposed approach.This approach significantly enhanced the students' perceptions of self‐efficacy, extrinsic learning motivation and immersion, especially in the engagement and the total immersion stages.A digital role‐playing game could reinforce the main characteristics of VR‐based learning environments.Implications for practice and/or policy The RPG‐VR learning approach could be a notable reference for further research on VR‐based learning environments.More instructional assistance is needed to improve students' knowledge gains in VR‐based learning environments. [ABSTRACT FROM AUTHOR]

Digital data are transforming higher education (HE) to be more student‐focused and metrics‐centred. In the UK, capturing detailed data about students has become a government priority, with an emphasis on using student data to measure, compare and assess university performance. The purpose of this paper is to examine the governmental and commercial drivers of current large‐scale technological efforts to collect and analyse student data in UK HE. The result is an expanding data infrastructure which includes large‐scale and longitudinal datasets, learning analytics services, student apps, data dashboards and digital learning platforms powered by artificial intelligence (AI). Education data scientists have built positive pedagogic cases for student data analysis, learning analytics and AI. The politicization and commercialization of the wider HE data infrastructure is translating them into performance metrics in an increasingly market‐driven sector, raising the need for policy frameworks for ethical, pedagogically valuable uses of student data in HE. Practitioner NotesWhat is already known about this topic Learning analytics, education data science and artificial intelligence are opening up new ways of collecting and analysing student data in higher education.UK government policies emphasize the use of student data for improvements to teaching and learning.What this paper adds A conceptual framework from "infrastructure studies" demonstrates how political objectives and commercial aims are fused to HE data systems, with data infrastructure becoming a key tool of government reform.A critical infrastructure analysis shows that student data processing technologies are being developed and deployed to measure university performance through student data.Implications for practice and/or policy Educators and managers in universities need to prepare robust institutional frameworks to govern their use of student data.Learning analytics practitioners, data scientists, learning scientists and social science researchers need to collaborate with the policy community and education technology developers on new policy frameworks to challenge narrow uses of student data as performance metrics. [ABSTRACT FROM AUTHOR]

Formative assessment is essential for improving teaching and learning, and AI and visualization techniques provide great potential for its design and delivery. Using NLP, cognitive diagnostic and visualization techniques designed to analyse and present students' monthly exam data, we developed an AI‐enabled visual report tool comprising six modules and conducted an empirical study of its effectiveness in a high school biology classroom. A total of 125 students in a ninth‐grade biology course were assigned to a treatment group (n = 63) receiving AI‐enabled visual reports as the intervention and a control group (n = 62) receiving overall oral feedback from the teacher. We present the main statistical results of the within‐subjects design and the between‐subjects design respectively, to better capture the main findings. Repeated measures ANOVA revealed a significant interaction effect of intervention and time on learning achievement, and the paired‐sample Wilcoxon test indicated that the treatment group had experienced increasing learning anxiety (Cohen's d = 0.203, p = 0.046) and self‐efficacy (Cohen's d = 1.793, p = 0.000) over time. Moreover, we conducted a series of non‐parametric tests to compare the effects of AI‐enabled visual reports and teacher feedback, but found no significant differences except for an increased self‐efficacy (Cohen's d = 0.312, p = 0.046). Additionally, we had the students in the treatment group rate their favourable modules in the AI‐enabled visual report and provide evaluative feedback. The study results provide important insights into the design and implementation of effective formative assessment supported by artificial AI and visualization techniques. Practitioner notesWhat is already known about this topic Formative assessment is essential for improving teaching and learning.Traditional formative assessment tools lack accurate data‐oriented assessment and usability.AI and visualization techniques have great potential for formative assessment.What this paper adds This study designs and implements an AI‐enabled visual report tool that generates data‐driven, user‐friendly reports.The AI‐enabled visual report can not only enhance students' learning achievement and self‐regulated learning over time but also increase their test anxiety.The AI‐enabled visual report has a comparable effect with teacher feedback but leads to increased self‐efficacy.Implications for practice and/or policy We recommend using the AI‐enabled visual report in large‐size classes for its overall positive effects on both learning achievement and self‐regulated learning.We recommend using the AI‐enabled visual report over teacher feedback for its capacity to enhance students' self‐efficacy.We recommend prioritizing the modules of Performance Ranking, Personal Mastery and Knowledge Alert when designing the AI‐enabled visual report. [ABSTRACT FROM AUTHOR]

An important educational goal for the next generation is the ability to think computationally. Cultivating K‐12 students' computational thinking (CT) requires the capacity building of teachers through teacher development programs. However, if students' learning outcomes were not assessed, it is not known whether the enhancement of teacher capacity could impact on students' learning. This study aimed to examine how a teacher development program changed teachers' content knowledge and collaborative engagement, which in turn influenced their students' learning achievement. The participants were 81 teachers and 3226 students from primary schools. We employed multi‐level modelling to study the relationships between teacher‐level variables and student achievement assessed by a CT concepts test. The teachers' content knowledge and collaborative engagement were significantly enhanced through the program, which in turn had a significant impact on student achievement. Teachers' years of teaching experience, but not their gender or whether they had majored in computer science, could also affect student achievement. The results indicate that a teacher development program can play a significant role in enhancing the teachers' CT content knowledge and collaborative engagement, which can then have a positive effect on student learning. Practitioner notes: What is already known about this topic The cultivation of students' computational thinking (CT) in K‐12 education requires the capacity building of teachers.A teacher development program can improve teachers' knowledge and attitudes related to CT.Whether the enhancement of teacher capacity can in turn affect students' learning achievement in CT is less studied. What this paper adds We found that a teacher development program could significantly enhance teachers' knowledge in CT and their collaborative engagement in the program.Using multilevel modelling, we found further that the enhancement of teachers' knowledge and collaborative engagement could affect students' understanding of CT concepts positively.Teachers' years of teaching experience, but not their gender or whether they had majored in computer science, could also influence student achievement. Implications for practice and/or policy In designing similar teacher development programs for teachers, collaborative engagement opportunities should be provided as it can have a positive impact on student achievement.Although the majority of teachers do not have a background in computer science, a teacher development program can play a significant role in enhancing their knowledge in CT, no matter they had majored in computer science or not.The significance of the program is not limited to the development of teachers, as the enhancements of teachers' knowledge and collaborative engagement through the program can in turn have a positive impact on students' understanding of CT concepts. [ABSTRACT FROM AUTHOR]

Affect can significantly influence education/learning. Thus, understanding a learner's affect throughout the learning process is crucial for understanding motivation. In conventional education/learning research, learner motivation can be known through postevent self-reported questionnaires. With the advance of affective computing technology, researchers are able to objectively identify and measure a learner's affective status during the entire learning process in a real-time manner, and then they are able to understand the interrelationship between emotion, motivation and learning performance. There are over 100 papers in the ScienceDirect database with the keywords 'affective computing in education' or 'affective computing in learning,' which reveals that this emerging technology has been applied to education/learning. This study intends to categorize and summarize those measurements so as to realize their applicability, feasibility and trends. Finally, some challenges and suggestions are then raised for helping educational researchers when applying affective computing technology. [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]

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]

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]

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]

There is little debate regarding the importance of student feedback for improving the learning process. However, there remain significant workload barriers for instructors that impede their capacity to provide timely and meaningful feedback. The increasing role technology is playing in the education space may provide novel solutions to this impediment. As students interact with the various learning technologies in their course of study, they create digital traces that can be captured and analysed. These digital traces form the new kind of data that are frequently used in learning analytics to develop actionable recommendations that can support student learning. This paper explores the use of such analytics to address the challenges impeding the capacity of instructors to provide personalised feedback at scale. The case study reported in the paper showed how the approach was associated with a positive impact on student perception of feedback quality and on academic achievement. The study was conducted with first year undergraduate engineering students enrolled in a computer systems course with a blended learning design across three consecutive years (N2013 = 290, N2014 = 316 and N2015 = 415). [ABSTRACT FROM AUTHOR]

Researchers have demonstrated that dialogue‐based intelligent tutoring systems (ITS) can be effective in assisting students in learning. However, little research has attempted to explore the necessity of equipping dialogue‐based ITS with one of the most important capabilities of human tutors, that is, maintaining polite interactions with students, which is essential to provide students with a pleasant learning experience. In this study, we examined the role of politeness by analysing a large‐scale real‐world dataset consisting of over 14K online human–human tutorial dialogues. Specifically, we employed linguistic theories of politeness to characterise the politeness levels of tutor–student‐generated utterances, investigated the correlation between the politeness levels of tutors' utterances and students' problem‐solving performance and quantified the power of politeness in predicting students' problem‐solving performance by applying Gradient Tree Boosting. The study results showed that: (i) in the effective tutorial sessions (ie, sessions in which students successfully solved problems), tutors tended to be very polite at the start of a tutorial session and become more direct to guide students as the session progressed; (ii) students with better performance in solving problems tended to be more polite at the beginning and the end of a tutorial session than their counterparts who failed to solve problems; (iii) the correlation between tutors' polite expressions and students' performance was not evident in non‐instructional communication; and (iv) politeness alone cannot adequately reveal students' problem‐solving performance, and thus other factors (eg, sentiment contained in utterances) should also be taken into account. Practitioner notesWhat is already known about this topic Human–human tutoring is acknowledged as an effective instructional method.Polite expression can help strengthen the relationship between tutors and students.Polite expression can promote students' learning achievements in many educational contexts.What this paper adds By considering the students' prior progress on a problem‐based learning task, we demonstrated the extent to which tutors and students display politeness in tutoring dialogues.Tutors' polite expressions might not correlate with students' problem‐solving performance in online human–human tutoring dialogues.Politeness alone was insufficient to predict the students' performance.Implications for practice Tutors might consider using words with positive sentiment values to express politeness to students with prior progress, which might encourage those students to make a further effort.The polite strategy of expressing indirect requests could help tutors mitigate the sense of directness, but this strategy should be carefully used in delivering instructional hints, especially for students without prior progress.To better assist students without prior progress, tutors might consider using more direct expression to explicitly guide students. [ABSTRACT FROM AUTHOR]

Supporting productive disciplinary engagement (PDE) in low‐achieving students is an important but challenging goal in education. This study used a knowledge‐building inquiry approach augmented by reflective assessment to facilitate low‐achieving students' PDE. A quasi‐experimental design method was employed to examine the effects of reflective assessment in supporting low‐achieving students' PDE. The experimental class of tenth graders (n = 20) conducted inquiries in the visual arts in a knowledge‐building design augmented by reflective assessment, while the comparison class of tenth graders (n = 14) conducted inquiries in a regular knowledge‐building design without reflective assessment. This study lasted approximately 4 months and the primary data source was the online discourse. A comparative analysis of the knowledge‐building discourse characteristics and the sequential patterns of the discourse moves revealed higher cognitive, emotional and epistemic engagement in the experimental class compared with the comparison class. Epistemic network analysis showed that reflective assessment facilitated low‐achieving students' collective reflection, monitoring and regulation, as shown by their metacognitive discourse moves. Their metacognition further helped them to achieve higher levels of cognitive, emotional and epistemic engagement than the comparison class. This study provides insights into the connections between inquiry, engagement and assessment. Moreover, it has implications for designing technology‐supported collaborative inquiry environments to support low‐achieving students' engagement and higher‐level skills. Practitioner notesWhat is already known about this topic Supporting low‐achieving students' productive disciplinary engagement (PDE) is an important but challenging goal in education and limited research has focused on it.Knowledge‐building inquiry design augmented by reflective assessment is promising in supporting low‐achieving students' PDE.Little research has examined the effects of reflective assessment‐augmented knowledge building inquiry on low‐achieving students' PDE and the mechanism through which reflective assessment supports their PDE.What this paper adds Reflective assessment‐augmented knowledge‐building inquiry is effective in supporting low‐achieving students' PDE.Portfolio‐supported Reflective assessment in Knowledge Forum facilitated low‐achieving students' enactment of collective reflection, monitoring and regulation, and these metacognitive processes further helped low‐achieving students to achieve higher levels of cognitive, emotional and epistemic engagement.This study provides insights into the connections between inquiry, engagement, and assessment.Implications for practice and/or policy It is crucial to create a collaborative and reflective community culture to help low‐achieving students gradually gain agency.It is critical to help low‐achieving students to develop a productive belief that idea improvement and reflection is a continuous and iterative process.The effectiveness of reflective assessment requires face‐to‐face knowledge‐building discussion and collaborative reflection opportunities scaffolded by assessment tools and analytics that encourage low‐achieving students to engage in meta‐level discussion. [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]

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 notesWhat is already known about this topicPredicting 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 addsIntra‐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 practiceThe 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. [ABSTRACT FROM AUTHOR]

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 notesWhat 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. [ABSTRACT FROM AUTHOR]

Concerns about noncompletion and the quality of the 1st-year student experience have been linked to recent changes in higher education such as modularisation, increased class sizes, greater diversity in the student intake and reduced resources. Improving formative assessment and feedback processes is seen as one way of addressing academic failure, of enhancing the learning experience and students’ chances of success in the early years of study. This paper argues that if this is to happen, a broader perspective on the purposes of formative assessment and feedback is required, one that links these processes to the development of learner self-regulation. It then shows, through two case studies drawn from the Re-engineering Assessment Practices project, how information and communication technology might support formative assessment processes and the development of self-regulation in large 1st-year classes. Finally, the paper presents a set of principles for the effective design and evaluation of formative assessment and feedback processes. [ABSTRACT FROM AUTHOR]

This response to the previous paper on information and communication technology and attainment levels of primary school students as reported by the British Educational Communications and Technology Agency (BECTa) is critical of the author's approach to the reports. Emphasizes that they were preliminary reports, commissioned by the government. (LRW)

Goal setting is an important component in successful teaching and learning, but relatively little is known about its impact on course persistence and achievement in massive open online courses. Using an experimental design and employing a variety of data including student writings, content‐related assignment attempts, and quantitative achievement in the courses, we compared the outcomes of two groups of learners who were given different writing prompts at the beginning of their course. While no overall effects of writing prompt type on the dependent variables were observed, highly statistically significant differences were found when goal writings were more closely examined and compared via qualitative coding. When learners' written responses to prompts contained either learning or performance goals, those participants both achieved more and engaged in learning longer than participants whose written responses did not fall into either of these categories. Practitioner notesWhat is already known about this topic Goals are related to students' behaviors and performance.Performance goals' influences on learning have inconsistent results, while learning goals are considered beneficial.What this paper adds The effects of conscious goal setting in massive open online courses (MOOCs) may be different from traditional learning contexts.Having either learning and performance goals results in better persistence and performance than not having these goals.Implications for practice and/or policy More interventions should be designed to help MOOC learners set and commit to their goals.Use MOOC learner's learning and performance goals to promote learning and persistence. [ABSTRACT FROM AUTHOR]

In dealing with numerous challenges, higher education instructors need to adapt their pedagogical practices to present students with meaningful, engaged learning experiences that are likely to promote student success and adequately prepare students for the world we live in. As part of this pedagogical transformation instructors also need to consider the potential of digital technologies to assist flexible pedagogies, as well as the role that students can play as partners in transforming the learning process (C. Evans, D. Muijs, & M. Tomlinson. Engaged student learning: high-impact strategies to enhance student achievement. York: Higher Education Academy, , p. 9). In this paper the author reflects on her particular engaged scholarship approach and the important role that her students-as collaborators in the pedagogical transformation process-have played in the creation of meaningful technology-enhanced learning experiences. She describes the evolution of her action inquiry approach over more than a decade and uses one particular project to highlight the value that student voice can contribute to pedagogical transformation. She also underscores the value of a 'design for partnership' approach that can be incorporated as an underlying pedagogical approach to facilitate the creation of meaningful learning experiences in a technology-enhanced teaching and learning environment. [ABSTRACT FROM AUTHOR]

Learning Analytics and Multimodal Learning Analytics are changing the way of analysing the learning process while students interact with an educational content. This paper presents a systematic literature review aimed at describing practices in recent Multimodal Learning Analytics and Learning Analytics research literature in order to identify tools and strategies useful for the assessment of the progress and behaviour of children under 6 years old in respect of their learning. The purpose is to provide guidance for Multimodal Learning Analytics research with children under 6 years old to assess their engagement in a task, their emotions, attention, understanding and achievement of a goal. The current state of knowledge on Multimodal Learning Analytics research suggests how performance analytics, face and speech recognition systems, eye tracking, Kinect analytics and wristbands could be used with children. The results show the complexity of collecting data using non‐invasive methodologies with children under 6 years old. Ethical implications related to multimodal data from audio, visual, biometric and quantitative measures of child behaviour are discussed. [ABSTRACT FROM AUTHOR]