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Formative assessment is considered to be helpful in students' learning support and teaching design. Following Aufschnaiter's and Alonzo's framework, formative assessment practices of teachers can be subdivided into three practices: eliciting evidence, interpreting evidence and responding. Since students' conceptions are judged to be important for meaningful learning across disciplines, teachers are required to assess their students' conceptions. The focus of this article lies on the discussion of learning analytics for supporting the assessment of students' conceptions in class. The existing and potential contributions of learning analytics are discussed related to the named formative assessment framework in order to enhance the teachers' options to consider individual students' conceptions. We refer to findings from biology and computer science education on existing assessment tools and identify limitations and potentials with respect to the assessment of students' conceptions. Practitioner notesWhat is already known about this topic Students' conceptions are considered to be important for learning processes, but interpreting evidence for learning with respect to students' conceptions is challenging for teachers.Assessment tools have been developed in different educational domains for teaching practice.Techniques from artificial intelligence and machine learning have been applied for automated assessment of specific aspects of learning.What does the paper add Findings on existing assessment tools from two educational domains are summarised and limitations with respect to assessment of students' conceptions are identified.Relevent data that needs to be analysed for insights into students' conceptions is identified from an educational perspective.Potential contributions of learning analytics to support the challenging task to elicit students' conceptions are discussed.Implications for practice and/or policy Learning analytics can enhance the eliciting of students' conceptions.Based on the analysis of existing works, further exploration and developments of analysis techniques for unstructured text and multimodal data are desirable to support the eliciting of students' conceptions. [ABSTRACT FROM AUTHOR]

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

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

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

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

This 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]

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]

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]

Abstract: 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. [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]

Digital media have unique potential to amplify student voice in both high school teacher preparation and academic development for college faculty. This paper applies narrative analysis to participant descriptions of how three uses of digital media amplify student voice in the context of a single higher education consortium: (1) high school students and prospective high school teachers use email to engage in dialogue as part of a larger project within the consortium's secondary teacher preparation program, (2) undergraduate student consultants use visual mapping technology as a classroom observation tool through one of the consortium's academic development programs for college faculty and (3) graduate and undergraduate students, faculty and staff engaged in pedagogical partnerships within and beyond this consortium use on an online platform to publish reflective essays on their collaborative work. Amplifying student voice through these uses of digital media has the following actual effects: it creates the possibility for exchanges between students and teachers across space and time; it supports the development of reflective practice; and it encourages the embrace of a partnership approach to teaching and learning. [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]

This paper explores a 'pedagogy first' approach to technology enhanced learning developed by Sheffield Hallam University (SHU) as a method to encourage use of, and experimentation with, technology within teaching practice and to promote the mainstreaming of innovative practice. Through a consultative approach where all staff members were invited to contribute, SHU has created a Teaching Approaches Menu that reflects practice at the institution and can be used to explore teaching practice and appropriate supporting technology either by individuals or as part of a facilitated discussion. Background to the project is provided, along with the design philosophy and approach, including a brief review of other frameworks. The Teaching Approaches Menu is introduced, its development outlined, and some initial feedback is presented. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper examines the effect of a podcasting task on the examination performance of several hundred first-year chemistry undergraduate students. Educational researchers have established that a deep approach to learning that promotes active understanding of meaning can lead to better student outcomes, higher grades and superior retention of knowledge over time. We attempted to promote such an approach by setting a task that involved student collaboration, contextualisation of content, and communication through new media, specifically creative podcasting. Examination results were used as a source of empirical evidence of changes in understanding and retention that occurred for students who completed this task. In comparing results across 2 years on similar questions related to the podcasting topics, we found a statistically significant improvement after introduction of the podcasting task on the questions related to one of the topics on which students had actively created their own podcasts ('acids & bases'), but not on the other ('oxidisation and reduction'). Improved learning outcomes in the form of better understanding and retention over time in at least one case suggest that under some circumstances creative podcasting may indeed help to promote a deep learning approach. [ABSTRACT FROM AUTHOR]