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1.
Educational applications (apps) are ubiquitous within children's learning environments and emerging evidence has demonstrated their efficacy. However, it remains unclear what the active ingredients (ie, mechanisms), or combination of ingredients, of successful maths apps are. The current study developed a new, open-access, three-step framework for assessing the educational value of maths apps, comprised of type of app, mathematical content and app design features. When applied to a selection of available maths apps previously evaluated with children in the first 3 years of school (the final sample included 23 apps), results showed that practice-based apps were the most common app type tested (n = 15). Basic number skills, such as number representation and relationships, were the most common area of mathematics targeted by apps (n = 21). A follow-up qualitative comparative analysis showed observed learning outcomes with maths apps were enhanced when apps combined the following: a scaffolded and personalised learning journey (programmatic levelling) and explanations of why answers were right or wrong (explanatory feedback), as well as praise, such as ‘Great job!’ (motivational feedback). This novel evidence stresses the significance of feedback and levelling design features that teaching practitioners and other stakeholders should consider when deciding which apps to use with young children. Directions for future research are discussed.

Practitioner notes

What is already known about this topic
  • Educational apps have been shown to support maths attainment in the first 3 years of school.
  • Several existing frameworks have attempted to assess the educational value of some of these maths apps.
  • Emerging experimental evidence also demonstrates the benefits of specific app design features, including feedback and levelling.
What this paper adds
  • Practice-based maths apps are the most common type of app previously evaluated with young children.
  • These evaluated maths apps have mostly focused on basic number skills.
  • The combination of explanatory and motivational feedback, with programmatic levelling (either dynamic or static), was a necessary condition for enhancing learning outcomes with maths apps.
Implications for practice and policy
  • The inclusion of feedback and levelling in maths apps should be considered by app developers when designing apps, and by educational practitioners and parents when deciding which apps to use with their children.
  • Further consideration is also needed for the development of educational apps that include a broad range of maths skills.
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2.
In the present paper, we assess whether website rating systems are useful for selecting educational apps for preschool age children. We selected the 10 highest scoring and 10 lowest scoring apps for 2–4-year-olds from two widely used websites (Good App Guide; Common Sense Media). Apps rated highly by the two websites had a higher educational potential as assessed by a validated questionnaire for evaluating the educational potential of apps and were more likely to include a learning goal and feedback compared to low scoring apps. However, high scoring apps scored on average just 9/20 for indicators of educational potential, and both high and low scoring apps had poor language quality as determined by psycholinguistic and construction type analyses. We argue that website rating systems should also include quality of feedback, adjustable content, social interactions, storyline and a more fine-grained analysis of language in their assessments.

Practitioner notes

What is already known about this topic
  • Appropriately designed apps for preschool age children have the potential to teach early school readiness skills.
  • Selecting high quality educational apps for preschool age children is challenging.
  • The children's app marketplace is currently unregulated.
What this paper adds
  • We assess whether two leading app rating websites are useful for selecting educational apps for preschool age children.
  • Children's apps rated highly by two app website rating systems had a higher educational potential than low rated apps as measured by a research informed app evaluation tool.
  • In-depth analysis of the language in apps shows that highly rated children's apps on app rating websites may not enrich a child's early language environment.
Implications for practice and/or policy
  • Children's app rating website assessments should include potential for feedback, language, adjustable content, storyline and social interactions.
  • Policy should be implemented for app ratings in the app stores or on website app rating systems.
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3.
Interactive apps are commonly used to support the acquisition of foundational skills. Yet little is known about how pedagogical features of such apps affect learning outcomes, attainment and motivation—particularly when deployed in lower-income contexts, where educational gains are most needed. In this study, we analyse which app features are most effective in supporting the acquisition of foundational literacy and numeracy skills. We compare five apps developed for the Global Learning XPRIZE and deployed to 2041 out-of-school children in 172 remote Tanzanian villages. A total of 41 non-expert participants each provided 165 comparative judgements of the five apps from the competition, across 15 pedagogical features. Analysis and modelling of these 6765 comparisons indicate that the apps created by the joint winners of the XPRIZE, who produced the greatest learning outcomes over the 15-month field trial, shared six pedagogical features—autonomous learning, motor skills, task structure, engagement, language demand and personalisation. Results demonstrate that this combination of features is effective at supporting learning of foundational skills and has a positive impact on educational outcomes. To maximise learning potential in environments with both limited resources and deployment opportunities, developers should focus attention on this combination of features, especially for out-of-school children in low- and middle-income countries.

Practitioner notes

What is already known about this topic
  • Interactive apps are becoming common to support foundational learning for children both in and out of school settings.
  • The Global Learning XPRIZE competition demonstrates that learning apps can facilitate learning improvements in out-of-school children living in sub-Saharan Africa.
  • To understand which app features are most important in supporting learning in these contexts, we need to establish which pedagogical features were shared by the winning apps.
What this paper adds
  • Effective learning of foundational skills can be achieved with a range of pedagogical features.
  • To maximise learning, apps should focus on combining elements of autonomous learning, motor skills, task structure, engagement, language demand and personalisation.
  • Free Play is not a key pedagogical feature to facilitate learning within this context.
Implications for practice and/or policy
  • When developing learning apps with primary-aged, out-of-school children in low-income contexts, app developers should try to incorporate the six key features associated with improving learning outcomes.
  • Governments, school leaders and parents should use these findings to inform their decisions when choosing an appropriate learning app for children.
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4.
Educational applications (apps) offer opportunities for designing learning activities children enjoy and benefit from. We redesigned a typical mobile learning activity to make it more enjoyable and useful for children. Relying on the technology acceptance model, we investigated whether and how implementing this activity in an app can increase children's intention to use. During the 27-day study, children (N = 103, 9–14 years) used the app to memorize one-sentence learning plans each day. Children used three different app-based learning activities throughout the study. In two standard activities, children reread or reassembled the words of the plan. In the redesigned activity, children represented the meaning of the plan with emojis. Children repeatedly reported on their attitude towards each activity. Subsequently, children reported perceived enjoyment and intention to use the app. Results showed children found the emoji activity most enjoyable, and enjoyment of the emoji activity contributed uniquely towards intention to use. Additionally, children's enjoyment of the app mediated their intention to use the app in the future. Overall, the study suggests that children's enjoyment of an app is crucial in predicting their subsequent intention to use, and it provides a concrete example of how emojis can be used to boost enjoyment.

Practitioner notes

What is already known about this topic
  • Educational applications provide children with unrestricted access to mobile learning resources.
  • Positive attitudes towards educational applications predict behavioural intention to use these applications, at least in young adults.
  • There is a need for more research examining the relevance of enjoyable learning activities in fostering children's sustained usage of an educational application.
What this paper adds
  • Positive attitude towards the use of emojis during learning activities uniquely contributed to children's behavioural intention to use the application.
  • Perceived enjoyment predicted behavioural intention to use the application.
  • Perceived enjoyment mediated the effect of attitude towards using learning activities on the behavioural intention to use the mobile educational application.
Implications for practice and/or policy
  • These findings highlight the importance of enjoyment for children's' acceptance of educational applications.
  • Enjoyable learning activities are necessary to ensure sustained usage of educational applications.
  • The paper provides a concrete example of how emojis can be used to boost enjoyment of a typical mobile learning activity.
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5.
While gamification and game-based learning have both been demonstrated to have a host of educational benefits for university students, many university educators do not routinely use these approaches in their teaching. Therefore, this systematic review, conducted using the PRISMA guidelines, sought to identify the primary drivers and barriers to the use of gamification and game-based learning by university educators. A search of multiple databases (Web of Science, Scopus and EBSCO (Business Source Complete; ERIC; Library, Information Science & Technology Abstracts)) identified 1330 articles, with 1096 retained after duplicates were removed. Seventeen articles (11 quantitative, two mixed-methods and four qualitative) were included in the systematic review. The primary drivers described by the educators that positively influenced their gamification and game-based learning usage were their beliefs that it encourages student interactions and collaborative learning; provides fun and improves engagement; and can easily be used by students. Alternatively, the university educators' major barriers included a lack of time to develop gamification approaches, lack of proven benefits and classroom setting issues. Many of these and other less commonly reported drivers and barriers can be categorised as attitudinal, design-related or administrative in nature. Such categorisations may assist university educators, teaching support staff and administrators in better understanding the primary factors influencing the utilisation of gamification and game-based learning and develop more effective strategies to overcome these barriers to its successful implementation.

Practitioner notes

What is already known about this topic

  • Gamification and game-based learning may have many benefits for university students.
  • The majority of university educators do not routinely use gamification and game-based learning in their teaching.

What this paper adds

  • University educators' major drivers that positively influence the use of gamification and game-based learning include their perceptions that it encourages student interactions and collaborative learning, provides fun and improves engagement and can easily be used by students.
  • University educators' major barriers that negatively influence the use of gamification and game-based learning include their perceptions of a lack of time to develop gamification approaches, lack of proven benefits and classroom setting issues.
  • These drivers and barriers may be classified as attitudinal, design-related and administrative, with these categories providing a useful way for universities to develop strategies to better support educators who wish to use these approaches in their teaching.

Implications for practice and policy

  • Attitudinal factors such as university educators' intention to use gamification and game-based learning are influenced by a host of their perceptions including attitude, perceived usefulness and ease of use.
  • A range of design-related and administrative barriers may need to be overcome to increase the use of gamification and game-based learning in the university sector.
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6.
Video is a widely used medium in teacher training for situating student teachers in classroom scenarios. Although the emerging technology of virtual reality (VR) provides similar, and arguably more powerful, capabilities for immersing teachers in lifelike situations, its benefits and risks relative to video formats have received little attention in the research to date. The current study used a randomized pretest–posttest experimental design to examine the influence of a video- versus VR-based task on changing situational interest and self-efficacy in classroom management. Results from 49 student teachers revealed that the VR simulation led to higher increments in self-reported triggered interest and self-efficacy in classroom management, but also invoked higher extraneous cognitive load than a video viewing task. We discussed the implications of these results for pre-service teacher education and the design of VR environments for professional training purposes.

Practitioner notes

What is already known about this topic
  • Video is a popular teacher training medium given its ability to display classroom situations.
  • Virtual reality (VR) also immerses users in lifelike situations and has gained popularity in recent years.
  • Situational interest and self-efficacy in classroom management is vital for student teachers' professional development.
What this paper adds
  • VR outperforms video in promoting student teachers' triggered interest in classroom management.
  • Student teachers felt more efficacious in classroom management after participating in VR.
  • VR also invoked higher extraneous cognitive load than the video.
Implications for practice and/or policy
  • VR provides an authentic teacher training environment for classroom management.
  • The design of the VR training environment needs to ensure a low extraneous cognitive load.
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7.
Game-based assessment (GBA), a specific application of games for learning, has been recognized as an alternative form of assessment. While there is a substantive body of literature that supports the educational benefits of GBA, limited work investigates the validity and generalizability of such systems. In this paper, we describe applications of learning analytics methods to provide evidence for psychometric qualities of a digital GBA called Shadowspect, particularly to what extent Shadowspect is a robust assessment tool for middle school students' spatial reasoning skills. Our findings indicate that Shadowspect is a valid assessment for spatial reasoning skills, and it has comparable precision for both male and female students. In addition, students' enjoyment of the game is positively related to their overall competency as measured by the game regardless of the level of their existing spatial reasoning skills.

Practitioner notes

What is already known about this topic:
  • Digital games can be a powerful context to support and assess student learning.
  • Games as assessments need to meet certain psychometric qualities such as validity and generalizability.
  • Learning analytics provide useful ways to establish assessment models for educational games, as well as to investigate their psychometric qualities.
What this paper adds:
  • How a digital game can be coupled with learning analytics practices to assess spatial reasoning skills.
  • How to evaluate psychometric qualities of game-based assessment using learning analytics techniques.
  • Investigation of validity and generalizability of game-based assessment for spatial reasoning skills and the interplay of the game-based assessment with enjoyment.
Implications for practice and/or policy:
  • Game-based assessments that incorporate learning analytics can be used as an alternative to pencil-and-paper tests to measure cognitive skills such as spatial reasoning.
  • More training and assessment of spatial reasoning embedded in games can motivate students who might not be on the STEM tracks, thus broadening participation in STEM.
  • Game-based learning and assessment researchers should consider possible factors that affect how certain populations of students enjoy educational games, so it does not further marginalize specific student populations.
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8.
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9.
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 notes

What 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.
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10.
Preparing data-literate citizens and supporting future generations to effectively work with data is challenging. Engaging students in Knowledge Building (KB) may be a promising way to respond to this challenge because it requires students to reflect on and direct their inquiry with the support of data. Informed by previous studies, this research explored how an analytics-supported reflective assessment (AsRA)-enhanced KB design influenced 6th graders' KB and data science practices in a science education setting. One intact class with 56 students participated in this study. The analysis of students' Knowledge Forum discourse showed the positive influences of the AsRA-enhanced KB design on students' development of KB and data science practices. Further analysis of different-performing groups revealed that the AsRA-enhanced KB design was accessible to all performing groups. These findings have important implications for teachers and researchers who aim to develop students' KB and data science practices, and general high-level collaborative inquiry skills.

Practitioner notes

What is already known about this topic
  • Data use becomes increasingly important in the K-12 educational context.
  • Little is known about how to scaffold students to develop data science practices.
  • Knowledge Building (KB) and learning analytics-supported reflective assessment (AsRA) show premises in developing these practices.
What this paper adds
  • AsRA-enhanced KB can help students improve KB and data science practices over time.
  • AsRA-enhanced KB design benefits students of different-performing groups.
  • AsRA-enhanced KB is accessible to elementary school students in science education.
Implications for practice and/or policy
  • Developing a collaborative and reflective culture helps students engage in collaborative inquiry.
  • Pedagogical approaches and analytic tools can be developed to support students' data-driven decision-making in inquiry learning.
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11.
The COVID-19 pandemic has posed a significant challenge to higher education and forced academic institutions across the globe to abruptly shift to remote teaching. Because of the emergent transition, higher education institutions continuously face difficulties in creating satisfactory online learning experiences that adhere to the new norms. This study investigates the transition to online learning during Covid-19 to identify factors that influenced students' satisfaction with the online learning environment. Adopting a mixed-method design, we find that students' experience with online learning can be negatively affected by information overload, and perceived technical skill requirements, and describe qualitative evidence that suggest a lack of social interactions, class format, and ambiguous communication also affected perceived learning. This study suggests that to digitalize higher education successfully, institutions need to redesign students' learning experience systematically and re-evaluate traditional pedagogical approaches in the online context.

Practitioner notes

What is already known about this topic
  • University transitions to online learning during the Covid-19 pandemic were undertaken by faculty and students who had little online learning experience.
  • The transition to online learning was often described as having a negative influence on students' learning experience and mental health.
  • Varieties of cognitive load are known predictors of effective online learning experiences and satisfaction.
What this paper adds
  • Information overload and perceptions of technical abilities are demonstrated to predict students' difficulty and satisfaction with online learning.
  • Students express negative attitudes towards factors that influence information overload, technical factors, and asynchronous course formats.
  • Communication quantity was not found to be a significant factor in predicting either perceived difficulty or negative attitudes.
Implications for practice and/or policy
  • We identify ways that educators in higher education can improve their online offerings and implementations during future disruptions.
  • We offer insights into student experience concerning online learning environments during an abrupt transition.
  • We identify design factors that contribute to effective online delivery, educators in higher education can improve students' learning experiences during difficult periods and abrupt transitions to online learning.
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12.
Digital literacy games can be beneficial for children with reading difficulties as a supplement to classroom instruction and an important feature of these games are the instructional supports, such as feedback. To be effective, feedback needs to build on prior instruction and match a learner's level of prior knowledge. However, there is limited research around the relationship between prior knowledge, instruction and feedback in the context of learning games. This paper presents an empirical study exploring the influence of prior knowledge on response to feedback, in two conditions: with or without instruction. Thirty-six primary children (age 8–11) with reading difficulties participated: each child was assessed for their prior knowledge of two suffix types—noun and adjective suffixes. They subsequently received additional instruction for one suffix type and then played two rounds of a literacy game—one round for each suffix type. Our analysis shows that prior knowledge predicted initial success rates and performance after a verbal hint differently, depending on whether instruction was provided. These results are discussed with regards to learning game feedback design and the impact on different types of knowledge involved in gameplay, as well as other game design elements that might support knowledge building during gameplay.

Practitioner notes

What is already known about this topic
  • Instructional supports, such as elaborative feedback, are a key feature of learning games.
  • To be effective, feedback needs to build on prior instruction and match a learner's level of prior knowledge.
  • Prior knowledge is an important moderator to consider in the context of elaborative feedback.
What this paper adds
  • Providing additional instruction (eg, pre-training) may act as a knowledge enhancer building on children's existing disciplinary expertise, whereas the inclusion of elaborative feedback (eg, a hint) could be seen as a knowledge equaliser enabling children regardless of their prior knowledge to use the pre-training within their gameplay.
  • Highlights the importance of children's preferred learning strategies within the design of pre-training and feedback to ensure children are able to use the instructional support provided within the game.
  • Possible implications for pre-training and feedback design within literacy games, as well as highlighting areas for further research.
Implications for practice and/or policy
  • Pre-training for literacy games should highlight key features of the learning content and explicitly make connections with the target learning objective as well as elaborative feedback.
  • Pre-training should be combined with different types of in-game feedback for different types of learners (eg, level of prior knowledge) or depending on the type of knowledge that designers want to build (eg, metalinguistic vs. epilinguistic).
  • Modality, content and timing of the feedback should be considered carefully to match the specific needs of the intended target audience and the interaction between them given the primary goal of the game.
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13.
This article reports on a trace-based assessment of approaches to learning used by middle school aged children who interacted with NASA Mars Mission science, technology, engineering and mathematics (STEM) games in Whyville, an online game environment with 8 million registered young learners. The learning objectives of two games included awareness and knowledge of NASA missions, developing knowledge and skills of measurement and scaling, applying measurement for planetary comparisons in the solar system. Trace data from 1361 interactions were analysed with nonparametric multidimensional scaling methods, which permitted visual examination and statistical validation, and provided an example and proof of concept for the multidimensional scaling approach to analysis of time-based behavioural data from a game or simulation. Differences in approach to learning were found illustrating the potential value of the methodology to curriculum and game-based learning designers as well as other creators of online STEM content for pre-college youth. The theoretical framework of the method and analysis makes use of the Epistemic Network Analysis toolkit as a post hoc data exploration platform, and the discussion centres on issues of semantic interpretation of interaction end-states and the application of evidence centred design in post hoc analysis.

Practitioner notes

What is already known about this topic
  • Educational game play has been demonstrated to positively affect learning performance and learning persistence.
  • Trace-based assessment from digital learning environments can focus on learning outcomes and processes drawn from user behaviour and contextual data.
  • Existing approaches used in learning analytics do not (fully) meet criteria commonly used in psychometrics or for different forms of validity in assessment, even though some consider learning analytics a form of assessment in the broadest sense.
  • Frameworks of knowledge representation in trace-based research often include concepts from cognitive psychology, education and cognitive science.
What this paper adds
  • To assess skills-in-action, stronger connections of learning analytics with educational measurement can include parametric and nonparametric statistics integrated with theory-driven modelling and semantic network analysis approaches widening the basis for inferences, validity, meaning and understanding from digital traces.
  • An expanded methodological foundation is offered for analysis in which nonparametric multidimensional scaling, multimodal analysis, epistemic network analysis and evidence-centred design are combined.
Implications for practice and policy
  • The new foundations are suggested as a principled, theory-driven, embedded data collection and analysis framework that provides structure for reverse engineering of semantics as well as pre-planning frameworks that support creative freedom in the processes of creation of digital learning environments.
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14.
This paper discusses a three-level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities.

Practitioner notes

What is already known about this topic
  • Numerous learning theories exist with significant cross-over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning.
  • Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified.
  • Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices.
What this paper adds
  • A three-level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education.
  • A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels.
  • The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels.
  • Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity.
Implications for practice and policy
  • Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems.
  • Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.
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15.
Well-designed computer or app-based instruction has a number of potential benefits (eg increasing accessibility and feasibility of high-quality instruction, reducing time and resources required for training expert delivery, saving instructional time). However, variation in implementation can still affect outcomes when using educational technology. Research generally suggests that without follow-up support after training, implementation of educational interventions is often poor and outcomes reduced. However, the extent to which this is the case when the core element of an intervention is computer or app-delivered is not yet clear. This study investigated the effects of providing ongoing implementation support for Headsprout Early Reading (HER, an early reading programme accessible via a computer or an app), to determine whether such support leads to better outcomes. Twenty-two primary schools (269 learners) participated in a cluster-randomised controlled trial. Eleven schools received initial training followed by ongoing support across the school year, whereas the other 11 schools received initial training and technical support only. Pre- and post-measures of reading skills were conducted using the York Assessment of Reading for Comprehension. We found no effect of implementation support on outcomes, and no effect of implementation support on delivery of the core element of HER. However, there were some effects of implementation support on the implementation of other HER elements relating to the responsiveness of educators to learners' learning within HER. These findings have implications for providing access to high quality online instruction in early reading skills at scale, with minimal training. More broadly, the current study suggests that well-designed computer or app-based instruction can yield positive outcomes with minimal implementation support and training. However, further research is required to ensure the interplay between learners' app-based learning and teacher intervention functions as intended to provide additional support for those who need it.

Practitioner notes

What is already known about this topic

  • Well-designed computer or app-based instruction has a number of potential benefits (eg increasing accessibility and feasibility of high-quality instruction, reducing time and resources required for training expert delivery, saving instructional time).
  • Implementation can still affect outcomes when using educational technology, and without follow-up support after training, implementation of educational interventions is often poor and outcomes reduced.
  • The extent to which this is the case when the core element of an intervention is computer or app-delivered is not yet clear.

What this paper adds

  • We found that providing implementation support for teachers and teaching assistants delivering Headsprout Early Reading (HER; an early reading programme accessible via a computer or an app) did not affect the reading outcomes of learners.
  • We also found the implementation support did not affect delivery of the core, app-delivered element of the programme.
  • However, there were notable differences in implementation of other aspects of the programme, particularly in relation to the role of the teacher or educational practitioner in managing the interplay between the app-based learning and teacher intervention for learners who require further support.

Implications for practice and policy

  • These findings have implications for providing access to high quality instruction in early reading skills at scale, with minimal training.
  • More broadly, the current study suggests that well-designed computer or app-based instruction can yield positive outcomes with minimal implementation support and training.
  • However, the findings of this study identify some potential risk of an over-reliance on technology to facilitate the learning of all learners accessing the programme.
  • Further research is required to ensure the interplay between learners' app-based learning and teacher intervention functions as intended to provide additional support for those who need it.
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16.
Artificial intelligence (AI) has generated a plethora of new opportunities, potential and challenges for understanding and supporting learning. In this paper, we position human and AI collaboration for socially shared regulation (SSRL) in learning. Particularly, this paper reflects on the intersection of human and AI collaboration in SSRL research, which presents an exciting prospect for advancing our understanding and support of learning regulation. Our aim is to operationalize this human-AI collaboration by introducing a novel trigger concept and a hybrid human-AI shared regulation in learning (HASRL) model. Through empirical examples that present AI affordances for SSRL research, we demonstrate how humans and AI can synergistically work together to improve learning regulation. We argue that the integration of human and AI strengths via hybrid intelligence is critical to unlocking a new era in learning sciences research. Our proposed frameworks present an opportunity for empirical evidence and innovative designs that articulate the potential for human-AI collaboration in facilitating effective SSRL in teaching and learning.

Practitioner notes

What is already known about this topic
  • For collaborative learning to succeed, socially shared regulation has been acknowledged as a key factor.
  • Artificial intelligence (AI) is a powerful and potentially disruptive technology that can reveal new insights to support learning.
  • It is questionable whether traditional theories of how people learn are useful in the age of AI.
What this paper adds
  • Introduces a trigger concept and a hybrid Human-AI Shared Regulation in Learning (HASRL) model to offer insights into how the human-AI collaboration could occur to operationalize SSRL research.
  • Demonstrates the potential use of AI to advance research and practice on socially shared regulation of learning.
  • Provides clear suggestions for future human-AI collaboration in learning and teaching aiming at enhancing human learning and regulatory skills.
Implications for practice and/or policy
  • Educational technology developers could utilize our proposed framework to better align technological and theoretical aspects for their design of adaptive support that can facilitate students' socially shared regulation of learning.
  • Researchers and practitioners could benefit from methodological development incorporating human-AI collaboration for capturing, processing and analysing multimodal data to examine and support learning regulation.
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17.
Predictors of academic success at university are of great interest to educators, researchers and policymakers. With more students studying online, it is important to understand whether traditional predictors of academic outcomes in face-to-face settings are relevant to online learning. This study modelled self-regulatory and demographic predictors of subject grades in 84 online and 80 face-to-face undergraduate students. Predictors were effort regulation, grade goal, academic self-efficacy, performance self-efficacy, age, sex, socio-economic status (SES) and first-in-family status. A multi-group path analysis indicated that the models were significantly different across learning modalities. For face-to-face students, none of the model variables significantly predicted grades. For online students, only performance self-efficacy significantly predicted grades (small effect). Findings suggest that learner characteristics may not function in the same way across learning modes. Further factor analytic and hierarchical research is needed to determine whether self-regulatory predictors of academic success continue to be relevant to modern student cohorts.

Practitioner Notes

What is already known about this topic
  • Self-regulatory and demographic variables are important predictors of university outcomes like grades.
  • It is unclear whether the relationships between predictor variables and outcomes are the same across learning modalities, as research findings are mixed.
What this paper adds
  • Models predicting university students' grades by demographic and self-regulatory predictors differed significantly between face-to-face and online learning modalities.
  • Performance self-efficacy significantly predicted grades for online students.
  • No self-regulatory variables significantly predicted grades for face-to-face students, and no demographic variables significantly predicted grades in either cohort.
  • Overall, traditional predictors of grades showed no/small unique effects in both cohorts.
Implications for practice and/or policy
  • The learner characteristics that predict success may not be the same across learning modalities.
  • Approaches to enhancing success in face-to-face settings are not automatically applicable to online settings.
  • Self-regulatory variables may not predict university outcomes as strongly as previously believed, and more research is needed.
  相似文献   

18.
This study presents the outcomes of a semi-systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35 MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory-driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control–value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory-driven MMLA research and how this acceleration can extend or even create new theoretical knowledge.

Practitioner notes

What is already known about this topic
  • Multimodal learning analytics (MMLA) is an emerging field of research with inherent connections to advanced computational analyses of social phenomena.
  • MMLA can help us monitor learning activity at the micro-level and model cognitive, affective and social factors associated with learning using data from both physical and digital spaces.
  • MMLA provide new opportunities to support students' learning.
What this paper adds
  • Some MMLA works use theory, but, overall, the role of theory is currently limited.
  • The three theories dominating MMLA research are embodied cognition, control–value theory of achievement emotions and cognitive load theory.
  • Most of the theory-driven MMLA papers use theory ‘as is’ and do not consider the analytical and synthetic role of theory or aim to contribute to it.
Implications for practice and/or policy
  • If the ultimate goal of MMLA, and AI in Education in general, research is to understand and support human learning, these studies should be expected to align their findings (or not) with established relevant theories.
  • MMLA research is mature enough to contribute to learning theory, and more research should aim to do so.
  • MMLA researchers and practitioners, including technology designers, developers, educators and policy-makers, can use this review as an overview of the current state of theory-driven MMLA.
  相似文献   

19.
Maintaining students' privacy in higher education, an integral aspect of learning design and technology integration, is not only a matter of policy and law but also a matter of design ethics. Similar to faculty educators, learning designers in higher education play a vital role in maintaining students' privacy by designing learning experiences that rely on online technology integration. Like other professional designers, they need to care for the humans they design for by not producing designs that infringe on their privacy, thus, not causing harm. Recognizing that widely used instructional design models are silent on the topic and do not address ethical considerations such as privacy, we focus this paper on how design ethics can be leveraged by learning designers in higher education in a practical manner, illustrated through authentic examples. We highlight where the ethical responsibility of learning designers comes into the foreground when maintaining students' privacy and well-being, especially in online settings. We outline an existing ethical decision-making framework and show how learning designers can use it as a call to action to protect the students they design for, strengthening their ethical design capacity.

Practitioner notes

What is already known about this topic
  • Existing codes of ethical standards from well-known learning design organizations call upon learning designers to protect students' privacy without clear guidance on how to do so.
  • Design ethics within learning design is often discussed in abstract ways with principles that are difficult to apply.
  • Most, if not all, design models that learning design professionals have learned are either silent on design ethics and/or do not consider ethics as a valid dimension, thus, making design ethics mostly excluded from learning design graduate programs.
  • Practical means for engaging in ethical design practice are scarce in the field.
What this paper adds
  • A call for learning designers in higher education to maintain and protect students' privacy and well-being, strengthening their ethical design capacity.
  • A demonstration of how to use a practical ethical decision-making framework as a designerly tool in designing for learning to maintain and protect students' privacy and well-being.
  • Authentic examples—in the form of vignettes—of ethical dilemmas/issues that learning designers in higher education could face, focused on students' privacy.
  • Methods—using a practical ethical decision-making framework—for learning design professionals in higher education, grounded in the philosophy of designers as the guarantors of designs, to be employed to detect situations where students' privacy and best interests are at risk.
  • A demonstration of how learning designers could make stellar design decisions in service to the students they design for and not to the priorities of other design stakeholders.
Implications for practice and/or policy
  • Higher education programs/institutions that prepare/employ learning designers ought to treat the topics of the designer's responsibility and design ethics more explicitly and practically as one of the means to maintain and protect students' privacy, in addition to law and policies.
  • Learning designers in higher education ought to hold a powerful position in their professional practice to maintain and protect students' privacy and well-being, as an important aspect of their ethical design responsibilities.
  • Learning designers in higher education ought to adopt a design thinking mindset in order to protect students' privacy by (1) challenging ideas and assumptions regarding technology integration in general and (2) detecting what is known in User Experience (UX) design as “dark patterns” in online course design.
  相似文献   

20.
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 notes

What 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.
  相似文献   

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