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1.
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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2.
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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3.
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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4.
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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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.
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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7.
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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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.
This conceptual study uses dynamic systems theory (DST) and phenomenology as lenses to examine data privacy implications surrounding wearable devices that incorporate stakeholder, contextual and technical factors. Wearable devices can impact people's behaviour and sense of self, and DST and phenomenology provide complementary approaches for emphasizing the subjective experiences of individuals that occur with the use of wearable data. Privacy is approached through phenomenology as an individual's lived bodily experience and DST emphasizes the self-regulation and feedback loops of individuals and their uses of wearable data. The data collection, analysis and communication of wearable data to support learning systems alongside privacy implications for each are examined. The IoT, cloud computing, metadata and algorithms are discussed as they relate to wearable data, pointing out privacy risks and strategies to minimize harm.

Practitioner notes

What is already known about this topic

  • Data privacy is a complex topic and is approached through different perspectives, influencing the degree of an individual's data autonomy.
  • Wearable technology is increasing in the consumer market and offers great potential to learning environments.

What this paper adds

  • Extends extant literature on dynamic systems theory and phenomenology, contributing these perspectives to educational research in the context of student data privacy and wearable technologies.
  • Provides a framework to understand the complex and contingent ways that privacy can be understood in the collection, analysis, and communication of wearable data to support learning.

Implications for practice and/or policy

  • Higher education faculty and educational policymakers should consider various interactions in systems and among systems of how wearable data collection may be analysed, communicated and stored, potentially exposing students to privacy harms.
  • Multiple actors in learning systems must engage in continuous and evolving feedback loops around data security, consent, ownership and control to determine who has access to student data, how it is used and for what purposes.
  • The EU's General Data Protection and Regulation offers one of the most comprehensive frameworks for higher education institutions and faculty around the world to follow for protecting student data privacy.
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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.
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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12.
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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13.
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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14.
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15.
An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.

Practitioner notes

What is already known about this topic

  • ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
  • Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
  • Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.

What this paper adds

  • An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
  • A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
  • An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.

Implications for practice and/or policy

  • Causal models can help us to explicitly specify educational theories in a testable format.
  • It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
  • Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.
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16.
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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17.
This paper suggests that artificial intelligence in education (AIEd) can be fruitfully analysed as ‘policies frozen in silicon’. This means that they exist as both materialised and proposed problematisations (problem representations with corresponding solutions). As a theoretical and analytical response, this paper puts forward a heuristic lens that can provide insights into how AI technologies (or advocated AI technologies) function as proposed solutions to certain problematisations based on various imaginaries about how education and learning are best performed or supported. The combined reading of imaginaries and problematisations can thereby aid in our understanding of why and how visions of learning and education are framed in relation to AIEd developments. The overall ambition is to advance theoretical and analytical approaches towards an educational system which is (anticipated as) increasingly permeated by AI systems—systems that also support and implement, more or less, invisible models, standards and assessments of learning, as well as more grand visions of (technology-augmented) education in society.

Practitioner notes

What is already known about this topic

  • Artificial intelligence in education (AIEd) is repeatedly presented as a solution for a range of educational ‘problems’.
  • This means that such ‘solutions’ must also frame certain aspects as ‘problems’.
  • Such problems and ‘solutions’ (problematisations) also exist within certain imaginaries of the present times and of the future, where these problematisations are presented as particularly significant and acute, and promoting specific anticipations of learning and ideals of education.

What this paper adds

  • An exposition of problematisations in educational settings.
  • An exposition of educational imaginaries.
  • A heuristic lens for understanding the ‘present’ and ‘future’ in a particular imaginary as entangled in, and dependent on, a certain ‘past’.

Implications for practice and/or policy

  • The approach presented in this paper provides a heuristic lens for examining how AI technologies (or advocated AI technologies) function as proposed solutions to problematisations based on imaginaries about how education and learning are best performed or supported.
  • This aids our understanding of how and why certain visions of learning and education are framed in relation to AIEd developments (real or imagined).
  • It also advances theoretical and analytical approaches towards an educational system, which is (anticipated as) increasingly permeated by AI systems—systems that also support and implement, more or less, invisible models, standards and assessments of learning, as well as more grand visions of (technology-augmented) education in society.
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18.
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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19.
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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20.
To test the suitability of an automatic system for emotional management in the classroom following the control-value theory of achievement emotions (CVT) framework, the performance of an emotional expression recognition software of our creation is evaluated in an online synchronous context. Sixty students from the Faculty of Education at the University of Alicante participated in 16 educational activities recording close-ups of their faces and completing the AEQ emotional self-report, as well as detailed reports from the subsequent review of their videos. In addition, they completed the VCQ-36 test to measure their volitional competencies and relate their influence on their emotional response. The results indicate a high coherence between the emotional expressions detected by the automatic system and the detailed emotional self-reports, but insufficient precision to meet the CVT requirements. On the other hand, both the AEQ test results and the emotion expression recognition software suggest students' preference for participative activities as opposed to passive ones. Meanwhile, statistical analysis results indicate that volitional competencies seem to influence the emotional response of students in the educational context, although the AI system does not show sufficient sensitivity in this field. Implications and limitations of this study for future work are discussed.

Practitioner notes

What is already known about this topic

  • Student motivation and involvement in the learning process are highly related to appropriate emotional regulation, which can be associated with particular educational activities, strategies and methodologies.
  • Deep learning technology based on convolutional neural networks feeds automatic systems focused on facial expression recognition from image analysis.

What this paper adds

  • There is high coherence between the emotional expressions detected by the AI system and the students' emotional self-reports, but the AI system provides just emotional valences, insufficient to meet the CVT framework.
  • Both emotional self-reports and the emotion recognition software suggest students' preference for active educational activities as opposed to passive ones.
  • Volitional competencies seem to influence the emotional response of students in the educational context.

Implications for practice and/or policy

  • It is possible to use automatic systems to effectively monitor the emotional response of students in the learning process.
  • Only if sensitivity improved, a real-time, easy-to-interpret emotional expression recognition software interface could be implemented to assist teachers with the emotional management of their classes within the CVT framework, maximizing their motivation and engagement.
  相似文献   

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