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Background: Mindfulness practices enhance executive function skills and academic achievement, spurring interest in integrating mindfulness interventions into education. Embedding mindfulness practice into a digital math game may provide a low‐cost, scalable way to induce mindfulness and boost game‐based learning, yet this approach remains unexplored. Objectives: We investigated the learning benefits of integrating mindfulness exercises in a digital math learning game and examined how students' trait mindfulness might moderate the outcomes. Methods: Two classroom studies were conducted with 404 5th and 6th grade students from six public schools in the U.S. (nStudy 1 = 227, nStudy 2 = 177). The two randomized controlled experiments assigned students to one of the three conditions: passive control (playing the digital learning game Decimal Point), story‐enriched active control, or mindfulness‐enriched condition. Trait mindfulness, learning gains, and in‐game problem‐solving (including problem‐solving duration, error count and correctness after reminder) were assessed. Study 2 included a manipulation check to better understand the effects of the mindfulness intervention. Results: Findings showed no significant differences in learning gains, problem‐solving duration or error count among the conditions. Students' trait mindfulness did not moderate these outcomes. Mindfulness reminders in the mindfulness‐enriched game led to more correct answers after errors than jokes in the story‐enriched game. Study 2 revealed that we failed to induce higher state mindfulness through the mindfulness inductions. Conclusions: Mindfulness prompts could be especially beneficial for students experiencing frustration during gameplay, warranting more exploration for digital game‐based instruction. We highlight barriers and future directions for fostering mindfulness through computer‐based instruction in classrooms. Lay Description: What is currently known about this topic?: Researchers focus on digital games as productive learning environments for math, with potential for higher learning gains compared with traditional methods. However, results are mixed, and not all games lead to improved math outcomes.Executive function (EF) skills are crucial for math learning, and mindfulness‐based interventions show promise in enhancing EF skills in school‐aged students.Embedding mindfulness practice into a digital math game may provide a low‐cost, scalable way to induce mindfulness and, in turn, boost EF skills and game‐based learning, yet this approach remains unexplored What does this paper add?: This paper presents the results of two randomized control trials investigating the feasibility and benefits of incorporating mindfulness exercises into a digital math game designed for middle school students. Benefits are compared to those of a story‐enriched and regular version of the same digital math learning game.The paper also explores variations in the effects of the mindfulness‐enriched game based on students' initial trait mindfulness levels.We observe that listening to mindfulness inductions at the beginning of game sessions do not induce mindfulness, and therefore does not benefit math learning.We find that mindfulness prompts received after recurrent errors can be beneficial for students' problem solving. Implications of study findings for practitioners: Our study provides important information on how digital learning game designers should attempt to induce mindfulness in a digital game to promote learning.Digital learning game designers should consider incorporating mindfulness exercises into their games strategically. Presenting mindfulness inductions in audio format at the beginning of game sessions may not induce mindfulness or benefit math learning. Instead, designers should focus on integrating mindfulness prompts at moments when students encounter frustration within the learning game.Beyond embedding audio mindfulness exercises in the game, learning designers should also consider alternative technological and game features to induce mindfulness within a learning game. [ABSTRACT FROM AUTHOR]

Background: Teachers often rely on the use of open‐ended questions to assess students' conceptual understanding of assigned content. Particularly in the context of mathematics; teachers use these types of questions to gain insight into the processes and strategies adopted by students in solving mathematical problems beyond what is possible through more close‐ended problem types. While these types of problems are valuable to teachers, the variation in student responses to these questions makes it difficult, and time‐consuming, to evaluate and provide directed feedback. It is a well‐studied concept that feedback, both in terms of a numeric score but more importantly in the form of teacher‐authored comments, can help guide students as to how to improve, leading to increased learning. It is for this reason that teachers need better support not only for assessing students' work but also in providing meaningful and directed feedback to students. Objectives: In this paper, we seek to develop, evaluate, and examine machine learning models that support automated open response assessment and feedback. Methods: We build upon the prior research in the automatic assessment of student responses to open‐ended problems and introduce a novel approach that leverages student log data combined with machine learning and natural language processing methods. Utilizing sentence‐level semantic representations of student responses to open‐ended questions, we propose a collaborative filtering‐based approach to both predict student scores as well as recommend appropriate feedback messages for teachers to send to their students. Results and Conclusion: We find that our method outperforms previously published benchmarks across three different metrics for the task of predicting student performance. Through an error analysis, we identify several areas where future works may be able to improve upon our approach. Lay Description: What is already known about this topic: Open‐ended questions are used by teachers in the domain of mathematics to assess their students understanding but automated support for these types of questions are limited in online learning platforms. Recent advancements in areas of machine learning and natural language processing have led to promising results in a range of domains and applications. What this paper adds: Emulating how teachers identify similar student answers can be used to build tools that support teachers in assessing and providing feedback to student open‐ended work. Implications for practice: Developing better automated supports can increase the amount of direct feedback students receive to guide their learning. [ABSTRACT FROM AUTHOR]

Background: Deep learning methods, where models do not use explicit features and instead rely on implicit features estimated during model training, suffer from an explainability problem. In text classification, saliency maps that reflect the importance of words in prediction are one approach toward explainability. However, little is known about whether the salient words agree with those identified by humans as important. Objectives: The current study examines in‐line annotations from human annotators and saliency map annotations from a deep learning model (ELECTRA transformer) to understand how well both humans and machines provide evidence for their assigned label. Methods: Data were responses to test items across a mix of United States subjects, states, and grades. Humans were trained to annotate responses to justify a crisis alert label and two model interpretability methods (LIME, Integrated Gradients) were used to obtain engine annotations. Human inter‐annotator agreement and engine agreement with the human annotators were computed and compared. Results and Conclusions: Human annotators agreed with one another at similar rates to those observed in the literature on similar tasks. The annotations derived using the integrated gradients (IG) agreed with human annotators at higher rates than LIME on most metrics; however, both methods underperformed relative to the human annotators. Implications: Saliency map‐based engine annotations show promise as a form of explanation, but do not reach human annotation agreement levels. Future work should examine the appropriate unit for annotation (e.g., word, sentence), other gradient based methods, and approaches for mapping the continuous saliency values to Boolean annotations. Lay Description: What is known about subject matter: Explainability of deep learning models is complicated by their large size, design, and complexity.Explainability methods in deep learning exist but have not been rigorously tested.Saliency maps, as a form of explainability, can be used to generate engine annotations.Human annotations can serve as basis for comparison. What paper adds to knowledge: How well human annotations work in one domain (crisis paper identification).How well two engine annotation methods match human annotations. Implications of study findings: Human annotations have reasonable reliability.Engine annotations have lower reliability than humans.Model‐based engine annotations perform better than model‐agnostic engine annotations. [ABSTRACT FROM AUTHOR]

Background: Social annotation (SA) allows users to collaboratively highlight important texts, make comments and discuss with each other on the same online document. This would not only accelerate and deepen learners' cognitive understanding of information, but also help build a sense of rapport, which is critical especially because of the worldwide shift from face‐to‐face class to remote education as a response to the COVID‐19 pandemic. Objective: To provide a systematic review of empirical SA studies, so that current development as well as issues in SA practices and research are identified. Methods: A total of 32 studies were identified and bibliometrical, instructional, and methodological analysis were conducted. Results and Conclusions: The United States has published the most SA research and technology‐related journals are most receptive of SA research; one‐shot quantitative designs with a sample size between 30 and 100 have been adopted most often; there is a lack of theoretical support for SA studies; higher education settings have been more frequently researched than other educational levels; SA technological features and activities have focused more on student uses and outcomes than on those of instructors; self‐designed technologies were more preferred than commercial ones; both cognitive and affective outcomes were emphasized and nearly all studies reported positive findings. Implications: Future SA studies may conduct blended designs with larger sample sizes that is grounded upon solid theoretical frameworks; more customized and affordable SA technologies that support both students and teachers should be developed. Learning analytics and emotional design may be capitalized more to meet the demand of remote education during the pandemic. Lay Description: What is Already Known About this Topic: The behaviour of annotating often helps learners obtain deeper understanding and retain longer memorization of learning content.Social annotation, which capitalizes the advantage of digital tools and collaboration, allows learners to annotate on the same document, and view each other's annotations to ignite discussion and maximize their individual learning.Social annotation has been widely used in educational settings amongst all ages of learners. What this Paper Adds: Provides a systematic review of empirical social annotation studies between 2000 and 2020, highlighting the analysis of bibliometrics, research designs, and learning outcomes and effects.Proposes a conceptual framework (the WIRE model) to guide the design and assessment of social annotation activities.Identifies challenges and gaps in existing research designs as well as instructional practices. Implications for Practice: Instructors can use the results to locate desirable social annotation technologies and design effective activities.Researchers may utilize the trend and research gaps to design future social annotation studies.Programmers can use WIRE to make an initial assessment of social annotation tool designs, and develop more customized products for formal education. [ABSTRACT FROM AUTHOR]

Background Study: Technology‐supported learning environments, act as significant observational and enabling indicators for evaluating and encouraging the digital revolution of education, are of vital importance in current educational research. Keeping track of the dynamics of technology‐supported learning environment research allows for the enrichment of theoretical studies and a prompt innovation of talent‐training environments. Objectives: The present study carried out a bibliometric analysis of the core collection of WoS database over the last two decades. Method: This study is a bibliometric research. We applied CiteSpace and VOSviewer for co‐occurrence and evolution analysis. In addition, we extracted the fundamental ideas and thoughts through reading and analysis. Results: First, the study found that technology‐supported learning environment research is in a growth phase, with core journals such as Interactive Learning Environments or Computers & Education. The findings show that a core research team comprised of such scholars as Hwang Gwo‐Jen, Lester James C. and Wong Lung‐Hsiang. Key nations of publishing and research strength are from China, the United States, the United Kingdom and the Netherlands. Second, the hot topics in research on technology‐supported learning environments were virtual learning environments, technology‐enabled learning environments and interactive learning environments. Nine study specifics were derived from these three topics such as moulding function, generating scenario, stimulating sensation, effect verification, design idea, research reflection, inquiry and research, virtual community, and mixed environment. Lastly, the frontier evolution offers a pattern of development from enhancing performance to changing mode to incorporating experience. Conclusion: Looking ahead, our research recommendations for the field of technology‐supported learning environments include a multifaceted approach. We should aim to enhance research designs and methodologies, develop contemporary guiding theories and strive for a balanced representation across various educational domains, expanding our scope to encompass all academic disciplines. In terms of practical application, it is imperative to focus on the design and implementation of technology‐supported learning environments from three critical perspectives: "change‐demand," "student‐learning" and "online‐offline." These approaches will collectively ensure that technology‐supported learning environments are both innovative and responsive to the diverse needs of the educational landscape. Lay Description: What is already known about this topic: Technology‐supported learning environments are currently a focal and hot research topic in the field of education.Although numerous studies have been conducted on the learning environment of technical support, there has yet to be a systematic and comprehensive review of the related research. What this paper adds: This study provides a comprehensive review of research on the technology‐supported learning environments over the past two decades.This study primarily presents an overview of research on the technology‐supported learning environments over the past two decades, focusing on main literature, research strength, hot topics and frontier evolution. Implications for practice and/or policy: Researchers need to further advance research on technology‐supported learning environments by enriching research designs and methods, constructing up‐to‐date guiding theories and achieving a balance across different educational domains while extending the reach to all disciplines.Educational practitioners need to pay attention to the design and implementation of technology‐supported learning environments from the three viewpoints of "change‐demand," "student‐learning" and "online‐offline." [ABSTRACT FROM AUTHOR]

Background: Computer‐based assessment allows for the monitoring of reader behaviour. The identification of patterns in this behaviour can provide insights that may be useful in informing educational interventions. Objectives: Our study aims to explore what different patterns of reading activity exist, and investigates their interpretation and consistency across different task sets (units), countries, and languages. Three patterns were expected: on‐task, exploring and disengaged. Methods: Using log data from the PISA 2012 digital reading assessment (9226 students from seven countries), we conducted hierarchical cluster analyses with typical process indicators of digital reading assessments. We identified different patterns and explored whether they remained consistent across different units. To validate the interpretation of the identified patterns, we examined their relationship to performance and student characteristics (gender, socio‐economic status, print reading skills). Results and Conclusions: The results indicate a small number of transnational clusters, with unit‐specific differences. Cluster interpretation is supported by associations with student characteristics—for example, students with low print reading skills were more likely to show a disengaged pattern than proficient readers. Exploring behaviour tended to be exhibited only once across the three units: It occurred in the first unit for proficient readers and in later units for less skilled readers. Major Takeaways: Behavioural patterns can be identified in digital reading tasks that may prove useful for educational monitoring and intervention. Although task situations are designed to evoke certain behaviours, the interpretation of observed behavioural patterns requires validation based on task requirements, assessment context and relationships to other available information. Lay Description: What is already known about the subject matter?: Students differ in how they read, comprehend and use digital information.Indicators from process data provide insight about how students engage with digital reading tasks. What does this paper add to the subject matter?: Based on multiple process indicators, a small number of clusters indicating different behavioural activity can be distinguished.These clusters can be described as on‐task, passive, hasty, exploring, disengaged, persistent and lost interest.A meaningful interpretation of the clusters must consider the requirements of the underlying tasks. Implications for practice and/or policy: Knowledge of how students engage with digital resources may provide useful feedback for teachers to guide students' learning or intervene when they struggle.Educational monitoring: The high comparability of country‐specific results suggests an invariant set of solution strategies in the digital reading assessment. [ABSTRACT FROM AUTHOR]

Background: Valuable safeguards against fast‐spreading conjectures about learning in times of fear and uncertainty are evidence‐based approaches to the assessment of the impact of sudden and unforeseen disruptions on learning practices. The present research focused on physics learning in such times because conceptual and computational literacy in physics is critical to the development of a scientifically and technologically literate society. Objectives: The present research aimed (a) to offer an objective assessment of whether performance differences in a physics course of the general education curriculum existed between the face‐to‐face medium (familiar mode of instruction) and the online medium (unfamiliar mode of instruction) for both male and female students, and then (b) to develop a response to the evidence collected to ensure a quality education for all parties involved. The research intended to fill two critical gaps in the extant literature: mixed findings concerning students' performance in the face‐to‐face and online mediums as well as scarce coverage of specific domains of knowledge that are critical to STEM learners. Methods: Students' performance was examined as a function of the type of assessment (formative and summative), instructional mode (online and face‐to‐face), and gender. An understudied student population of STEM students of Middle Eastern descent without prior formal exposure to online instruction was targeted. Results and Conclusions: In both formative and summative assessments, male students performed better online than face‐to‐face, whereas the performance of female students was either higher online or equivalent between instructional mediums. The evidence collected suggested that consideration be given to remedies that foster academic success in the face‐to‐face instructional medium, particularly for male students. Takeaways: An evidence‐based approach to learning dismantled emotion‐driven expectations regarding the impact of the online medium on physics learning, and encouraged new perspectives about instruction. Lay Description: What is already known?: Ongoing debates on the impact of the pandemic on students' learning shape decisions regarding future reliance on the online medium for instruction. What this paper adds: The extant literature is mixed concerning performance in the face‐to‐face and online mediums, including particular domains of knowledge critical to STEM learning.This study focused on physics learning in an understudied population of STEM students who were unaccustomed to online instruction.Performance was higher online than face‐to‐face, but gender differences emerged. Implications for practice: Lessons learned focused on the flexibility of online learning and on the need to develop materials to enhance learning in males enrolled in face‐to‐face or hybrid/blended classes. [ABSTRACT FROM AUTHOR]

Background: Providing students with worked out problem solutions is a beneficial instructional technique in STEM disciplines, and studying examples that have been worked out incorrectly may be especially helpful for reducing misconceptions in students with low prior content knowledge. However, past results are inconclusive and the effects of incorrect worked examples alone or in combination with correct examples remains unclear. Objectives: We aim to address whether studying incorrect examples alone or in combination with correct examples can support the reduction of students' fraction misconceptions, operationalized as errors made with high confidence. Methods: After incorrectly solving a sampling problem, 130 students in 4th through 11th grade in the U.S. were randomly assigned to a condition in an online problem set focused on fraction equivalence. Students studied either single‐type worked examples (i.e., correct or incorrect; n = 49) or combination‐type worked examples (correct and incorrect; n = 41) or engaged in a problem‐solving control (n = 50). Results: Studying a combination of correct and incorrect worked examples was as effective as the problem‐solving control with feedback at improving fraction equivalence knowledge and reducing the rate of high‐confidence errors. Students in both the combination condition and the problem‐solving with feedback condition outperformed those who studied either correct or incorrect worked examples alone. Conclusions: Results support the inclusion of a combination of correct and incorrect worked examples when teaching students with low prior content knowledge. Studying a combination of example types within an online tutor helps to reduce misconceptions about fractions, a topic students commonly struggle with. A problem‐solving task with corrective feedback worked equally well. Lay Description: What is already known about this topic: Studying worked examples of mathematical solutions improves student learning.Incorrect examples, either alone or in combination with correct examples, are particularly effective for improving learning for students with low prior knowledge in the target content.Comparing correct and incorrect examples is also effective at improving problem‐solving but this effect may be specific to those with high prior knowledge.Still unknown are the impacts of these different combinations of examples on misconceptions in particular. What this paper adds: We show that studying a combination of correct and incorrect examples reduces math misconceptions.Problem‐solving with corrective feedback within an online tutor also has this effect. Implications for practice and/or policy: Using a combination of example types will help reduce students' misconceptions about fractions. [ABSTRACT FROM AUTHOR]

Background: Students' tendencies to seek feedback are associated with improved learning. Yet, how soon this association becomes robust enough to make predictions about learning is not fully understood. Such knowledge has strong implications for early identification of students at‐risk for underachievement via digital learning platforms. Objectives: We sought to understand how early in the academic year students' end‐of‐year learning outcomes could be predicted by their performance and feedback‐seeking behaviours within a digital learning platform. We analysed data collected at different time points in the academic year and across different cohorts of students within the context of high school advanced placement (AP) Statistics courses. Methods: High school students enrolled in AP Statistics spanning three academic years between 2017 and 2020 (N = 726; Mage = 16.72 years) completed 3 or 4 homework assignments, each 2 and 3 months apart. Results and conclusions: Across the three cohorts, and even as early as the first assignment, a model consisting of demographic variables (gender, race/ethnicity, parental education), assignment performance, and interaction with the digital score report explained significant variation in students' final course grades (R2 = 0.314–0.412) and AP exam scores (κ = 0.583–0.689). Students' assignment performance was positively associated with end‐of‐year learning outcomes. Students who more frequently checked their digital score reports tended to receive better learning outcomes, though not consistently across cohorts. Implications: These findings further an understanding of how students' early performance and feedback‐seeking behaviours within a digital learning platform predict end‐of‐year learning outcomes. Lay Description: What is already known about this topic: Students' early academic performance predicts end‐of‐year success.Students' self‐regulated learning behaviours are another predictor of academic success.Clickstream data have been used as an indicator of the application of self‐regulated learning strategies, particularly behaviours associated with seeking feedback.Indicators of feedback‐seeking behaviours reflected via clickstream data have been shown to predict student learning.Yet, how soon associations between early performance and such indicators of self‐regulated learning as feedback‐seeking are robust across different groups of learners, different learning circumstances, and different types of learning outcomes has yet to be more fully understood. What this paper adds: We investigated whether performance and feedback‐seeking behaviours predict end‐of‐year learning outcomes at different points during the academic year across multiple cohorts of learners (2017–2018, 2018–2019, and 2019–2020) and using two different learning outcomes (class grades, AP exam scores).One cohort was affected by COVID‐19 during the 2019–2020 academic year, allowing us to examine whether differences emerge amidst unprecedented circumstances.We used assignment scores and online feedback‐seeking to predict end‐of‐year learning outcomes.Assignment scores were positively associated with end‐of‐year grades.First assignment performance predicted AP exam scores.Use of feedback (an indication of self‐regulated learning behaviours) tended to predict learning outcomes, though not consistently across cohorts. Implications for practice and/or policy: The findings from this study could improve understanding of how digital learning platform measurements predict end‐of‐year learning outcomes though also highlight the importance of context differences.Such findings hold implications for early identification of at‐risk students and appreciation for differences between learners and contexts.These findings also contribute to a growing understanding of the impact of the COVID‐19 pandemic on student engagement and learning. [ABSTRACT FROM AUTHOR]

Background: In fact, most schools around the world are not well equipped to have discussions and keep current on the expansion of artificial intelligence (AI) in many aspects of society and economy. They either ignore this conversation, or simply criticize technology, but these resistances are not stopping wide spread of various types of AI projects in schools, mainly driven by corporations, and fueled by incentives that might not match well with long term educational objectives of student success, diversity, equity, and inclusivity. Objectives: The overall purpose is to address the rising gap between the ultrafast development of AI and the meticulous technological application of education, and to suggest the important bridge of building technological leadership in teacher preparation to get ready for the grow of AI in education. Methods: The contextual review examines the field of AI development in education through three lenses: conceptual context, practice context, and research context. Results and Conclusions: The paper provides educators and policy makers an overall background of the phenomenon of AI in education. The study has revealed there is an urgent need for research and development in teacher preparation as well as in the philosophy of technology in education to bridge the gap between AI and education. Lay Description: The initiative addresses the rising gap between the ultrafast development of artificial intelligence and the meticulous technological application of education.To get ready for AIED, the project advises educators, technologists, and policy makers to focus on educational leadership, and philosophy of technology in education to overcome the ethical bottleneck hindering AI progress at the philosophical level.The study highlights the critical need for more research on learning and classroom management, teacher preparation programs, school administration, educational philosophy, and educational policy. [ABSTRACT FROM AUTHOR]

Background: The sudden growth in online instruction due to COVID‐19 restrictions has given renewed urgency to questions about remote learning that have remained unresolved. Web‐based assessment software provides instructors an array of options for varying testing parameters, but the pedagogical impacts of some of these variations has yet to be examined. Objectives: The current study examines whether question order affects student performance on online college math assessments. Drawing on the literature of testing and math anxiety, we hypothesized that difficult questions near the beginning of an assessment would have a destructive effect on student confidence, which would in turn have a deleterious effect on their performance. Methods: We employed an observational 'big data' methodology, analysing 23,468 final exams completed by students in 10 different courses over eight semesters at a Math Emporium in a large technical university in the eastern United States. Students were freshmen and sophomores enrolled in non‐engineering math courses. We regressed the final score on the difficulty level of the first and second questions, controlling for several other factors. Results and Conclusions: We found that several factors—day of the week, amount of time before the deadline, number of minutes spent on the exam—have more of an impact on score than question order. This pattern was consistent across sexes. Takeaways: Our findings contradict some previous studies, which have found that difficult early questions degrade student performance, and that this affect is more pronounced in females. This work enriches our understanding of how students respond to online assessment. Lay Description: What is already known about this topic: Previous studies have had mixed results about the effect of question difficulty ordering on student performance.Furthermore, few studies have examined this issue for computer assessments. What this paper adds: We employ a novel methodology with a uniquely large data set.Our findings indicate that difficulty ordering has no effect on student performance. Implications for practice: Math instructors can use the random ordering feature of computer assessment generating tools because question order does not affect performance. [ABSTRACT FROM AUTHOR]

Background: Immersive virtual reality (VR) is increasingly used in organizational training interventions. However, few studies have systematically investigated VR compared to standard training methods in actual organizational contexts. Objectives: The focus of this study was to evaluate the effectiveness of a VR simulation for training professionals in the biotech industry. Aligning training needs to unique media affordances, the study designed an immersive story‐based VR simulation for training customer‐facing employees on a new product and tested it in an international biotech company. Methods: The system was evaluated by comparing its effectiveness to a traditional video presentation with the same content in a randomized between subjects experiment. The sample consisted of 95 employees across three locations: Brazil, Denmark, and USA. Results: The VR simulation group performed better than the video presentation group on the outcomes of conceptual knowledge (d = 0.41) spatial knowledge (d = 0.61), transfer intentions (d = 0.57), enjoyment (d = 1.74), self‐efficacy (d = 0.68), perceived learning (d = 0.89), personal value (d = 0.83), and organizational value (d = 0.82), but no significant difference was found for factual knowledge (d = −0.10). Implications: Results suggest that VR simulations can be effective across cultures in organizational training interventions. VR is specifically effective when the goals of the training are to increase conceptual and spatial understanding as well as enjoyment, and self‐efficacy, but not factual knowledge. Furthermore, employees report higher levels of perceived learning, personal and organizational value and transfer intentions after VR training compared to standard video‐based training. Lay Description: What is currently known about the subject matter: Immersive virtual reality (VR) is increasingly used in organizational training interventions. This interest has especially surged amid recent global challenges, and the sudden need for companies and employees to adapt to the new remote working conditions.However, few studies have systematically investigated VR compared to standard training methods in actual organizational contexts that include a real‐world scenario and a representative testing sample.Applied research that assesses the outcomes of VR based training in realistic learning and training settings is needed. What their paper adds: We propose and test an interdisciplinary approach that links unique features of VR with training needs and multidimensional outcome measures in an organizational training context within the biotech industry in Brazil, Denmark and USA.The findings suggest that a specifically designed VR tool is superior to a comparable video presentation for the outcomes of spatial and conceptual knowledge acquisition and transfer intentions, but not factual knowledge acquisition.The VR group also reported significantly higher personal value, organizational value, self‐efficacy, perceived learning, and enjoyment compared to the video group. Implications of study findings for practitioners: The results support the value of using VR simulations in organizational training, specifically related to affective outcomes as well as spatial and conceptual knowledge and transfer intentions.The study suggests that it is not the medium of VR, but rather the alignment between the training goals with the instructional methods and the unique affordances of VR that will contribute to better or worse learning outcomes.The article describes how to evaluate the effectiveness of VR tools in applied contexts, suggesting that we have to evaluate the effectiveness of tested content with respect to different outcome variables, which coincide with the training outcomes of a particular organizational context.The project as a whole, can be used as a blueprint for future applied research efforts, exemplifying how to bridge gaps between small‐medium‐enterprises, international corporate companies and academic institutions. [ABSTRACT FROM AUTHOR]

Background: Process data, data generated by a user's interaction with a web‐based application, is an emerging tool in educational research. The current study explores using process data as a measure of implementation fidelity to a randomized control trial (RCT) of the Read It Again Mobile (RIA‐M) curricular supplement. Objectives: To determine the extent to which teachers implemented RIA‐M and to assess the utility of using process data in the assessment of fidelity. Methods: The RCT involved 30 pre‐kindergarten classrooms with a sample of n = 216 students. RIA‐M provides a curricular supplement which teachers may incorporate into classroom instruction and is delivered via a tablet computer. Pre and post literacy assessments are used to determine treatment effect. Process data, produced from teacher interactions with the tablet, and classroom observations are used to assess fidelity. Results and Conclusions: Our findings indicate no difference between treatment and control students in the RCT. Yet, we find that process data provides unique fidelity information concerning treatment exposure, adherence, and quality of program delivery. Specifically, process data indicated that teachers did not demonstrate the same level of fidelity that was captured in classroom observations. This finding provides some evidence for the absence of an intervention effect. Major Takeaways: The current study improves our understanding of how web‐based interventions may be assessed for implementation fidelity using process data. Further, process data offers a potentially reliable and scalable measure of fidelity for other web‐based educational interventions. Lay Description: What we know about the topic: Mobile technology‐based interventions are on the rise in many educational settings.Fidelity to educational interventions can be difficult to reliably assess.Teachers may be resistant to implementing technology in classrooms. What this paper adds: Examines a pilot study of a mobile technology‐based curricular intervention for teachers designed to improve early reading and literacy.Examines a new method for assessing fidelity of mobile technology‐based interventions.Compares the new method to traditional methods of assessing fidelity to educational interventions. Implications for practice and/or policy: The presence of technology in the classroom does not assure that the technology will be used effectively.Mobile technology‐based interventions may require additional monitoring and coaching to assure use as intended.Process data, generated in the use of technology‐based interventions, may provide a unique way to measure fidelity to the intervention. [ABSTRACT FROM AUTHOR]

The response process of problem‐solving items contains rich information about respondents' behaviours and cognitive process in the digital tasks, while the information extraction is a big challenge. The aim of the study is to use a data‐driven approach to explore the latent states and state transitions underlying problem‐solving process to reflect test‐takers' behavioural patterns, and to investigate how these states and state transitions could be associated with test‐takers' performance. We employed the Hidden Markov Modelling approach to identify test takers' hidden states during the problem‐solving process and compared the frequency of states and/or state transitions between different performance groups. We conducted comparable studies in two problem‐solving items with a focus on the US sample that was collected in PIAAC 2012, and examined the correlation between those frequencies from two items. Latent states and transitions between them underlying the problem‐solving process were identified and found significantly different by performance groups. The groups with correct responses in both items were found more engaged in tasks and more often to use efficient tools to solve problems, while the group with incorrect responses was found more likely to use shorter action sequences and exhibit hesitative behaviours. Consistent behavioural patterns were identified across items. This study demonstrates the value of data‐driven based HMM approach to better understand respondents' behavioural patterns and cognitive transmissions underneath the observable action sequences in complex problem‐solving tasks. Lay Description: What is already known about this topic?: Process data of interactive problem‐solving tasks contains rich information about test‐takers.To extract underlying information from process data is a big challenge. ​ What this paper adds?: The hidden Markov model helps identify latent states underlying problem‐solving process.Different performance groups displayed different behaviour characteristics.A consistent behavioural pattern was observed across problem‐solving items. ​ Implications for practitioners: The use of HMM holds promise in better visualize and understand behaviour patterns in response process.HMM is potential in understanding cognitive transmissions underneath the observable action sequences. ​ [ABSTRACT FROM AUTHOR]

The widespread use of technology and the Internet have changed many of language learners' everyday practices, including literacies. While there have been many studies with the focus on language learners' digital literacies, few, however, have explored language learners' digital information literacy and online reading practices with the use of social bookmarking tools, especially in a community college setting. We address this gap by investigating community‐college language learners' digital literacies when social bookmarking with the focus on digital information and online reading practices from an ecological perspective. In this qualitative multiple‐case study, the focal participants were five English learners, students in an English as a Second Language writing course in a community college in the northeastern United States. Data collection included interviews, observations, and researchers' e‐journals. Thorough within‐ and cross‐case analysis of data shows that language learners searched for digital texts and evaluated them based on relevance, reliability, interest, language, and importance for them and their learning community in the social bookmarking tool. The participants struggled with the number of results in search engines, keywords, and evaluation of digital texts for relevance and reliability. We show the need for more instruction, support, and guidance of language learners' digital information literacy practices as well as the benefits of providing students with opportunities to read digital texts. Our suggestions for future research include investigating the role of multimodality and other factors that influence language learners' evaluation practices when they look for and read information online. Lay Description: What is currently known about this topic: Digital literacies are essential for students' success in the future.Language learners often face challenges when developing digital literacies in the target language.The use of various Web 2.0 tools, such as social networking sites and games, may be beneficial for language learners' development of literacies.More research on language learners' digital information literacy and reading practices online using social bookmarking tools is needed, especially in the school‐ and community‐college contexts. What this paper adds: Our participants are language learners in a community‐college context.Language learners searched for digital texts and evaluated them based on relevance, reliability, interest, language, and importance for them and their learning community in the social bookmarking tool.Language learners struggled with the number of results in search engines, keywords, and evaluation of digital texts for relevance and reliability.Language learners engaged in a number of diverse practices when reading online. The implications of study findings for practitioners: Language learners need more instruction and support when developing digital information literacy.We offer guiding questions and suggestions that might help educators and administrators at the planning stage of their language courses and/or activities with digital information literacy practices. [ABSTRACT FROM AUTHOR]