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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.
The present study assessed the effectiveness of the ECRIMO educational application designed to build first-grade level spelling skills. We tested whether using the app to teach spelling would be as effective as providing the same training using traditional paper exercises. The effect of integrating gamification into mobile learning apps, which has been little studied in the context of young children, is also investigated. A pretest/training/posttest design was implemented with 311 first-graders divided in four groups: no training, paper training, the ECRIMO app with gamification features, and the ECRIMO app without gamification. Spelling, reading and phonological awareness abilities was measured at both pretest and posttest. The training was conducted over a 7-week period (4.40 hours in total). The experimental design allowed us to answer three questions: (1) Is spelling training effective regardless of the medium used? (2) Is training through the app as efficient as paper-based training? (3) Does gamification impact students' learning performance? Mixed-model analyses revealed (1) a positive effect on the training outcome depended on the initial spelling ability of participants, (2) a comparable efficiency between autonomous training using the ECRIMO app on tablets and the same training provided by teachers using paper exercises and (3) a marginally positive effect of gamification that is greater for the weakest students. The present study proposes an original and pertinent experimental design to test the relevance of educational applications. The design features of learning apps can impact students' learning differently depending on their initial level. A critical step should be verifying that using online apps for training is at least as effective as the same training using paper exercises.

Practitioner notes

What is already known about this topic
  • A significant number of children experience difficulties in reading and spelling from the first years of learning.
  • The use of new technologies to support classroom teaching is rapidly developing as a topic of interest for educational professionals and researchers.
  • Evaluations of new technologies developed to enhance literacy skills suggest that many factors can vary their effectiveness.
  • The effectiveness of a digital educational application can be enhanced or undermined by design choices, such as gamification.
What this paper adds
  • Spelling training with the app ECRIMO seems effective for first year students, especially those with the lowest and middle level.
  • Comparable effects of both the tablet-based and paper equivalent training on participants' spelling were found.
  • The use of gamification in ECRIMO could be more suitable for the weakest students.
Implications for practice and/or policy
  • Educational technologies should be evidence-based and should be evaluated with both a passive and an active control group.
  • The design should be carefully considered and tested, as it may be advantageous for some students and disadvantageous for others.
  • The use of digital technology in education can be beneficial for classroom practice, when the activity can be carried out in total autonomy, leaving the teacher available for a group of pupils with specific needs.
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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.
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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6.
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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7.
Capturing evidence for dynamic changes in self-regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected from n = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control, n = 48), (2) perform poorly and received intervention (treatment, n = 95) and (3) perform well (not flagged, n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments.

Practitioner notes

What is already known about this topic
  • Self-regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success.
  • SRL is a dynamic, temporal process that leads to purposeful student engagement.
  • Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed.
What this paper adds
  • A Markov process for measuring dynamic SRL processes using log data.
  • Evidence that dynamic, interaction-dominant aspects of SRL predict student achievement.
  • Evidence that SRL processes can be meaningfully impacted through educational intervention.
Implications for theory and practice
  • Complexity approaches inform theory and measurement of dynamic SRL processes.
  • Static representations of dynamic SRL processes are promising learning analytics metrics.
  • Engineered features of LMS usage are valuable contributions to AI models.
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8.
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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9.
Recent years have seen a surge of calls for personalization of education. Automatised adaptivity in serious games has been advocated as a potential instantiation of such calls. Yet little is known about the extent to which personalised learning through automatised adaptivity poses an advantage for language learning over generalised teacher-led sequencing in digital, game-based learning environments. The goal of this paper is to address this question by comparing the learning outcomes in reading accuracy and fluency of didactic sequences designed by EFL teachers or by an adaptive algorithm. A total of 67 participants completed several proficiency and reading skills pretest and posttest and used the iRead system for 6 months. Results showed that all learners made progress in reading skills, but no significant differences were found between the two sequences in relation to the development of reading skills. It was also shown that adaptivity works best if it leads to increase in the number of games per feature. Results are discussed in the context of previous findings, and the role of adaptivity and sequencing is critically assessed.

Practitioner notes

What is already known about this topic?
  • Serious games have the potential to aid learning but empirical research is needed.
  • Findings about the efficiency of serious games are mixed.
  • Current and reviewed versions of the Simple View of Reading constitute a suitable framework to measure reading acquisition.
What this paper adds?
  • It contributes to the growing corpus of research on digital serious games.
  • It provides empirical evidence on the use of an adaptive system in formal education.
  • Comparing a teacher-led sequence to an algorithmic adaptive sequence on the same digital serious game has never been done before.
  • The paper shows the need to obtain both system-internal and system-external data in order to capture the impact of gameplay on the development of L2 reading skills.
Implications for practise and/or policy
  • It sheds some light on how certain game designs may actually help practise with different degrees of intervention by teachers.
  • It is interesting for teachers to use an adaptive sequence that they can check and intervene in if needed.
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10.
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.
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11.
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12.
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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13.
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.
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14.
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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15.
While interactive touchscreens are currently entering into educational practice, little is known about what this means for learning in early childhood and, in particular, how touchscreens shape action and communication. In this paper, we examine the interactions of 2-year-olds and their teachers in a multilingual preschool in Sweden. We analyse the communicative environment between the children, teachers and shared touchscreens and books in the context of reading. A mixed-methods analysis was used, taking a concept of action that includes both verbal, non-verbal utterances and digital touch. The analysis shows a reconfiguration to the interactional dynamic where children perform comparable amounts of actions in sessions with the touchscreen and book reading but less talk during the touchscreen sessions. However, while talking less, children display other types of communicative actions. We analyse the changing interactional dynamic that follows, its implications to learning and early childhood pedagogical practice and how interaction can be reconceptualised as cycles of communication and action in which educational scaffolding unfolds.

Practitioner notes

What is already known about this topic
  • Touchscreens are a significant part of children's lives and educational curricula.
  • There is considerable uncertainty on how touchscreens can be incorporated into early childhood education.
  • Little is known about how educational social interaction changes with touchscreens such as iPads.
What this paper adds
  • A mixed methods multimodal analysis of the changing actions and dynamics of iPads as compared with bookreading.
  • Children's patterns of communication change towards less talk and more bodily communication, while teachers’ actions remain somewhat similar.
  • Touch actions change the dynamics of interaction, can alter the pedagogical situation and bring a reconceptualisation towards a cyclical and embodied view of interaction.
Implications for practice and/or policy
  • New patterns of action may require a recalibration of educational practices.
  • Teachers need to attend to new sets of touch actions that children use to communicate and act with as displays of knowledge.
  • The use of touch screens should be seen as complementary to established practices of language and literacy training (such as book reading) rather than replacing them.
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16.
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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17.
Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace-based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace-based approach to study SSRL, there remains a paucity of evidence on how trace-based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace-based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace-based data could be utilised to predict learning outcomes in collaborative learning.

Practitioner notes

What is already known about this topic
  • Socially shared regulation has been recognised as an essential aspect of collaborative learning success.
  • It is challenging to make the processes of learning regulation ‘visible’ to better understand and support student learning, especially in dynamic collaborative settings.
  • Multimodal learning analytics are showing promise for being a powerful tool to reveal new insights into the temporal and sequential aspects of regulation in collaborative learning.
What this paper adds
  • Utilising multimodal big data analytics to reveal the regulatory patterns of shared physiological arousal events (SPAEs) and regulatory activities in collaborative learning.
  • Providing evidence of using multimodal data including physiological signals to indicate trigger events in socially shared regulation.
  • Examining the differences of regulatory patterns between successful and less successful collaborative learning sessions.
  • Demonstrating the potential use of artificial intelligence (AI) techniques to predict collaborative learning success by examining regulatory patterns.
Implications for practice and/or policy
  • Our findings offer insights into how students regulate their learning during collaborative learning, which can be used to design adaptive supports that can foster students' learning regulation.
  • This study could encourage researchers and practitioners to consider the methodological development incorporating advanced techniques such as AI machine learning for capturing, processing and analysing multimodal data to examine and support learning regulation.
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18.
The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.

Practitioner notes

What is already known about this topic
  • Research on privacy and data protection in learning analytics has become a recognised challenge that hinders the further expansion of learning analytics.
  • Proposals to counter the privacy and data protection issues in learning analytics are blurry; there is a lack of a summary of previously proposed solutions.
What this study contributes
  • Establishment of what privacy and data protection issues exist at different phases of the learning analytics cycle.
  • Identification of how different stakeholders view privacy, similarities and differences, and what factors influence their views.
  • Evaluation and comparison of previously proposed solutions that attempt to address privacy and data protection in learning analytics.
Implications for practice and/or policy
  • Privacy and data protection issues need to be viewed in the context of the entire cycle of learning analytics.
  • Stakeholder views on privacy and data protection in learning analytics have commonalities across contexts and differences that can arise within the same context. Before implementing learning analytics, targeted research should be conducted with stakeholders.
  • Solutions that attempt to address privacy and data protection issues in learning analytics should be put into practice as far as possible to better test their usefulness.
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19.
This study analyses the potential of a learning analytics (LA) based formative assessment to construct personalised teaching sequences in Mathematics for 5th-grade primary school students. A total of 127 students from Spanish public schools participated in the study. The quasi-experimental study was conducted over the course of six sessions, in which both control and experimental groups participated in a teaching sequence based on mathematical problems. In each session, both groups used audience response systems to record their responses to mathematical tasks about fractions. After each session, students from the control group were given generic homework on fractions—the same activities for all the participants—while students from the experimental group were given a personalised set of activities. The provision of personalised homework was based on the students' errors detected from the use of the LA-based formative assessment. After the intervention, the results indicate a higher student level of understanding of the concept of fractions in the experimental group compared to the control group. Related to motivational dimensions, results indicated that instruction using audience response systems has a positive effect compared to regular mathematics classes.

Practitioner notes

What is already known about this topic
  • Developing an understanding of fractions is one of the most challenging concepts in elementary mathematics and a solid predictor of future achievements in mathematics.
  • Learning analytics (LA) has the potential to provide quality, functional data for assessing and supporting learners' difficulties.
  • Audience response systems (ARS) are one of the most practical ways to collect data for LA in classroom environments.
  • There is a scarcity of field research implementations on LA mediated by ARS in real contexts of elementary school classrooms.
What this paper adds
  • Empirical evidence about how LA-based formative assessments can enable personalised homework to support student understanding of fractions.
  • Personalised homework based on an LA-based formative assessment improves the students' comprehension of fractions.
  • Using ARS for the teaching of fractions has a positive effect in terms of student motivation.
Implications for practice and/or policy
  • Teachers should be given LA/ARS tools that allow them to quickly provide students with personalised mathematical instruction.
  • Researchers should continue exploring these potentially beneficial educational implementations in other areas.
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20.
Technology-based, open-ended learning environments (OELEs) can capture detailed information of students' interactions as they work through a task or solve a problem embedded in the environment. This information, in the form of log data, has the potential to provide important insights about the practices adopted by students for scientific inquiry and problem solving. How to parse and analyse the log data to reveal evidence of multifaceted constructs like inquiry and problem solving holds the key to making interactive learning environments useful for assessing students' higher-order competencies. In this paper, we present a systematic review of studies that used log data generated in OELEs to describe, model and assess scientific inquiry and problem solving. We identify and analyse 70 conference proceedings and journal papers published between 2012 and 2021. Our results reveal large variations in OELE and task characteristics, approaches used to extract features from log data and interpretation models used to link features to target constructs. While the educational data mining and learning analytics communities have made progress in leveraging log data to model inquiry and problem solving, multiple barriers still exist to hamper the production of representative, reproducible and generalizable results. Based on the trends identified, we lay out a set of recommendations pertaining to key aspects of the workflow that we believe will help the field develop more systematic approaches to designing and using OELEs for studying how students engage in inquiry and problem-solving practices.

Practitioner notes

What is already known about this topic
  • Research has shown that technology-based, open-ended learning environments (OELEs) that collect users' interaction data are potentially useful tools for engaging students in practice-based STEM learning.
  • More work is needed to identify generalizable principles of how to design OELE tasks to support student learning and how to analyse the log data to assess student performance.
What this paper adds
  • We identified multiple barriers to the production of sufficiently generalizable and robust results to inform practice, with respect to: (1) the design characteristics of the OELE-based tasks, (2) the target competencies measured, (3) the approaches and techniques used to extract features from log files and (4) the models used to link features to the competencies.
  • Based on this analysis, we can provide a series of specific recommendations to inform future research and facilitate the generalizability and interpretability of results:
    • Making the data available in open-access repositories, similar to the PISA tasks, for easy access and sharing.
    • Defining target practices more precisely to better align task design with target practices and to facilitate between-study comparisons.
    • More systematic evaluation of OELE and task designs to improve the psychometric properties of OELE-based measurement tasks and analysis processes.
    • Focusing more on internal and external validation of both feature generation processes and statistical models, for example with data from different samples or by systematically varying the analysis methods.
Implications for practice and/or policy
  • Using the framework of evidence-centered assessment design, we have identified relevant criteria for organizing and evaluating the diverse body of empirical studies on the topic and that policy makers and practitioners can use for their own further examinations.
  • This paper identifies promising research and development areas on the measurement and assessment of higher-order constructs with process data from OELE-based tasks that government agencies and foundations can support.
  • Researchers, technologists and assessment designers might find useful the insights and recommendations for how OELEs can enhance science assessment through thoughtful integration of learning theories, task design and data mining techniques.
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